This post covers four commonly used measurement techniques that 9 times out of 10 work against the evolution of Reporting Squirrels into Analysis Ninjas.

I'll also admit that most of the times when I encounter them I might think slightly less of you (especially if you present the aggregate version to me rather, presenting the segmented view atleast gets you time to explain :)).

If I am being slightly tough minded here it is only because I am hugely upset by the fact that analytics on the web is deeply under leveraged, though the good lord knows we try and pump out KPI's by the minute.

One root cause of this under leveraging it our dashboards that are crammed full of metrics that use these four measurement techniques. The end results: Data pukeing and not insights revelation.

So who are the four amigos?

Each a technique that when used "as normal" actively hinder your ability to communicate effectively the insights that your data contains.

Only one caveat: I am not saying these techniques are evil. What I am saying is don't be "default" when using them, be smart (or don't).

Before we get going here's my definition of what a Key Performance Indicator is:

Measures that help you understand how you are doing against your objectives.

Note the stress on Measures. And Objectives. It it doesn't meet Both criteria its not a KPI.

With that out of the way lets understand why Averages, Percentages, Ratios and Compound Metrics are four usually disappointing measurement techniques.

#1. Averages

Raise your hand if you are average? Ok just Ray? No one else?

Raise your had if your visit on any website reflects an average visit? Just you Kristen?

No one is "average" and no user experience is "average". But Averages are everywhere because: 1) well they are everywhere, which feeds the cycle and 2) they are an easy way to aggregate (roll up) information so that others can see it more easily.

Sadly seeing it more easily does not mean we actually understand and can identify insights.

Take a look at the number above.

51 seconds.

Ok you know something.

Now what?

Are you any wiser? Do you know any better what to do next? Any brilliant insights?

No.

It is likely that the Average Time on Site number for your website has been essentially unchanged for a year (and yet, yes sirrie bob, it is still on your "Global Senior Website Management Health Dashboard"!).

Averages have an astonishing capacity to give your "average" data, they have a great capacity to lie, and they hinder decision making. [You are going to disagree, quite ok, please share feedback via comments.]

What can you do?

I have two recommendations for you to consider.

Uno. Segment the data.

Identify your most important / interesting segments for your business and report those along with the Overall averages.

You have more context. Social Media boo! Paid Search booer! Organic yea! Email yea! Etc Etc Etc. : )

While this is not the most optimal outcome, it will at the very minimum give your Decision Makers context within which to ask questions, to think more clearly (and mostly wonderfully ignore the overall average number).

So on your dashboards and email reports make sure that the Key Performance Indicators that use Averages as the measurement technique are shown segmented. It will prod questions. A good thing, as Martha would say.

Dos. Distributions baby, distributions!

If averages often (*not always*) stink then distributions rock.

They are a wonderful way to dissect what makes up the average and look at the numbers in a much more manageable way.

Here's how I like looking at time on site. . . .

So delightful.

I can understand the short visits (most!) and decide what to do (ignore 'em, focus hard core, etc).

I can see there is deep loyalty, about 30%, I can decide what these people like, what they don't like, where they come from, what else I can do. [Would you have imagined from the Average Time on Site that you have fanatics on your site who are spending more than 10% on each visit!!]

I can try to take care of the midriff, what is up with that any way.

See what I mean? The difference between the two: Reporting Squirrel vs. Analysis Ninja!

#2. Percentages.

Nothing, really nothing, is perhaps more ubiquitous in our world of Web Analytics than percentages.

You can't take a step without bumping into one.

Some percentages are ok, but very very rarely are they good at answering the "so what" or the "now what" questions.

The problem with percentages is that they gloss over what's really important and also tend to oversell or under sell the opportunity.

Let's compare two pictures. In the first one we just report conversion rates, see what you can understand in terms of insights fro this one. . . . .

Now try to answer the question: So What?

Any answers?

Yes some conversions are lower and others are higher? Anything else? Nope?

Ok try this one. . . .

Better right?

You get context. The raw numbers give you key context around performance.

[Update: I use this plugin to get raw conversion rate numbers into Google Analytics: Better Google Analytics Firefox Extension. I highly recommend it, you get the above and a bunch more really cool stuff. Must have for GA users.]

Also notice another thing, I'll touch on this in a bit as well. If you only report overall conversion rate (as we all do in our dashboards) your use of a percentage KPI is much worse. You get nothing.

By showing the various "segments" of conversions I am actually telling the story much better to the Sr. Management. What's working, what needs work.

Here's another constant problem with conversion rates. . . .

I am looking at a table of data (in any tool really) and it looks like there's something here.

Ok well I want to fix things. I want to know where I can improve bounce rates, so I sort. . . .

Data yes. Totally useless. I can't possibly waste my time with things that bring one visit.

So I re sort to see if I can find where its totally working for me. . . .

Strike three, again not very useful, just take a peek at the Visits column.

What I really want is not where the percents are high or low. I want to take action.

What I really really want is some way of identifying statistically significant data, where bounce rates are "meaningfully up" or "meaningfully down" so that I can take action confidently.

I can't do that in Google Analytics. Quite sad.

Some other tools like Coremetrics (by default) and WebTrends (in some places by default or with a external "plugin" you can buy from external consultants) will compute a %delta (difference between two numbers) and color it red or green.

That's not what I am taking about.

That is equally useless because that percentage difference make you take action where there is no significance in the two numbers. Don't fall for that.

It is truly a crying shame that the Google Analytics does not have something like the Google Website Optimizer does. . . .

. . . . a trigger for me to know when results are statistically significant, and by how much I should jump for joy or how many hairs I need to pull out of my hair in frustration. See those sweet colored bars in the middle? See the second column after that? Minorly orgasmic right?

Isn't it amazing that after 15 years web analytics tools are still not smart, even though they have so much data and computations. Ironic if you think about it.

What can you do?

I have three recommendations for you to consider.

Uno. Segment the data.

Wait, did I not say that already? : )

Do it.

Useless. . . .

Useful. . . .

Show opportunities, show failures, let the questions comes.

Dos. Always show raw numbers.

Often conversion rates mask the opportunity available.

Conversion rate from Live is 15% and conversion rates for Yahoo! are 3%.

Misleading.

We all know that Yahoo! has significantly more inventory than Live and even if you had all the money in the world you can't make use of that 15% conversion rate from Live.

Show raw Visits. It will look something like this:

See what difference that would make on a dashboard? No false alarms.

You overcome the limitation of just showing the percentage.

In the example above I am using Visits, because I want to show the HiPPO's where the constraints are (without them having to think, thus earning my Ninja credentials!). But I am most fond of using Outcomes when I pair up raw numbers (Orders, Average Order Value, Distribution of Time, Task Completion Rates, etc etc) because HiPPO's love Outcomes.

Tres. Don't use % delta! User Statistical Significance et al.

When you use percentages it is often very hard to discern what is important, what is attention worthy, what is noise and what is completely insignificant.

Be very aware of it and use sophisticated analysis to identify for your Sr. Management (and yourself!) what is worthy.

Use Statistical Significance, it truly is your BFF!

Use Statistical Control Limits, they help you identify when you should jump and when you should stay still (so vital!).

This is all truly sexy cool fun, trust me.

#3. Ratios.

Can I be honest with you?
[Ok so I can hear your sarcastic voice saying: "Why stop now?" ;)]

Ratios have a incredible capacity to make you look silly (or even "dumb").

I say that with love.

What's a ratio?

"The relative magnitudes of two quantities (usually expressed as a quotient)." (Wordnetweb, Princeton.)

That was easy. : )

In real life you have see ratio's expressed as 1.4 or as 4:2 or other such variations.

You are comparing two numbers with the desire to provide insights.

So let's say the ratio between new and returning visitors. Or the ratio of friend requests sent on Facebook to friend request received. Or the ratio of articles submitted on a tech support websites to the articles read. Or… make your own.

They abound in our life. But they come with challenges.

The first challenge to be careful of is that the two underlying numbers could shift dramatically without any impact on your ratio (then you my friend are in a, shall we say, pickle). . . .

I have put my "brilliant" excel skills to demonstrate that point. In your dashboard you'll how the ratio (all "green" for four months). Yet the fundamentals, which is really what your Sr. Management is trying to get at, have changed dramatically, perhaps worth an investigation, yet they'll get overlooked.

I hear you protesting all the way from Spain, "aw come one, you have got to be kidding me!". I kid you not.

Think of all the effort you have put into automating the dashboard and cramming all the data into it. Ahh… you've stuffed it with percentages and ratios to make it fit. And you've automated it to boot.

Casualty? Insights. Actionability.

The second problem with ratios is a nuance on the above. It is perhaps more insidious. It occurs when you compare two campaigns or sources or people or other such uniquely valuable things.

I see it manifested by a HiPPO / Consultant / Vendor Serviceman foisting upon you that 1.2 is a "good ratio".

Then you start measuring and people start gaming the system. Because you see 12/10 gets you that ratio as does 12,000/10,000. Yet they both get "rated" the same and that as you'll agree is dumb.

What can you do?

I have two recommendations for you to consider.

Uno. Resist just showing the ratio.

Throw in a raw number, throw in some other type of context and you are on your way to sharing something that will highlight a important facet, prod good questions.

Enough said.

Dos. Resist the temptation to set "golden" rules of thumb.

This is very hard to pull off, we all want to take the easy way out.

But doing this will mean you'll incent the wrong behavior, hinder any thought about what's actually good or bad.

You can a ratio as a KPI, but incent the underlying thing of value. For example Reach and not the ratio of Visits to Subscribers (!!).

#4. Compound Metrics (aka Calculated Metrics).

Here's a visual for you:

Its a compound metric. : )

A unrecognizable paste you produce after mixing a bunch of, perhaps perfectly good, things.

All kidding aside compound metrics are all around us. Most Government data tends to be compound metrics (is it a wonder that we understand nothing that the government does?).

A compound metric is a metric whose sub components are other metrics (or it is defined in terms of other computations).

Here's an example:

(% of New Visits) times (Average Page Views per Visit) equals, making something up here, Visit Depth Index.

What?

Yes what indeed.

The environments where compound metrics thrive are ones where things are really really hard to measure (so we react by adding and multiplying lots of things) or when confidence in our ability to drive action overtakes reality.

Honestly no matter what the outcome is here (or how much of a "god's gift to humanity" it is) how can you possibly do anything with this:

Website Awesomeness= (RT*G)+(T/Q)+((z^x)-(a/k)*100)

(If you don't know what those alphabets stand for just make something up.)

Compound metrics might be important, after all the Government users them, but they have two corrosive problems:

1) When you spit a number out, say 9 or 58 or 1346, no one,except you has any idea what it means (so a huge anti actionability bias) and worse

2) You have no way of knowing if it is good or bad or if you should do something. You can easily see how a raise in some numbers and fall in others could cause nothing to happen. Or all hell could break loose and yet you still get 9. Or 58. Or 1346.

What can you do?

I have three recommendations for you to consider.

Uno. Take them with a grain of salt (or a truck full of salt).

Really.

Regardless of if it comes from me or President Obama or [insert the name of your favorite religious deity here].

Stress test how you'll overcome the two challenges above. If your compound metric passes those tests you are all set.

Dos. Degrade to key "critical few" components.

Grinding RT and G and T and Q and z and x and a and k into a mush is the problem. Not RT or G or T or Q or z or x or a or k themselves.

Spend some time with your HiPPO's and Marketers and people who pay your salary. Try to understand what is the business really trying to solve for. Put the nose to the grind stone and so some hard work.

At the end of this process, as you decompose the individual components, what you'll realize is that all you need is RT and Q and G. Report them.

No not as a weird married "couple". As individuals.

Everyone will know what you are doing, you help the business and your dashboard focus, drive action.

Tres. Revisit and revalidate.

If you must use compound metrics please revisit them from time to time to see if they are adding value. Also check that they are adding value in all the applicable scenarios

If you are using weights, as many compound metrics tend to do, then please please stress test to ensure the weights are relevant to you. Also revalidate the weights over time to ensure you don't have to compensate for seasonality or other important business nuances.

End of story.

I'll close There are two schools of thought about Analtyics.

One is that math is easy so let's go add, subtract, multiply and divide because calculators, computers and data are easily available.

This is the "Reporting Squirrel" mental model, data above all else.

The other is that your entire existence is geared towards driving action. So think, stress test, be smart about the math you do. Computers and calculators are cheap but it does not excuse doing the things outlined above.

This is the "Analysis Ninja" mental model, insights above all else.

Good luck!

Ok now its your turn. What do You think of these four measurement techniques? Agree with my point of view? Why? Why not? Care to share your own bruises from the wonderful world of Web Analytics Key Performance Indicators? Got questions?

Please share your feedback. Thank you.

PS:
Couple other related posts you might find interesting:

• Eight Rules for Choosing Web Analytics Key Performance Indicators
• Be Awesome At Comparing KPI Trends Over Time
• Make Web Analytics Actionable: Focus On “What’s Changed”
• How To Excite People About Web Analytics: Five Tips.
• Consultants, Analysts: Present Impactful Analysis, Insightful Reports

Actively Avoid Insights: 4 Useful KPI Measurement Techniques is a post from: Occam's Razor by Avinash Kaushik

"Dear Avinash" is an occasional series where I share some of my answers that might benefit the greater ecosystem. I'll only share the questions that might be universal, and ones where the source would be impossible to identify (to preserve confidentiality).

The first one covered career advice for Stressed "Agency Analysts" and Search Robots messing up your life.

In this post we'll cover a topic near and dear to all our hearts, comparing trends of our Key Performance Indicators and two specific strategies you can use to drive action. One that focuses on presenting data, the second an approaching to analyzing trends.

Let's go. . . the question first. . . . .

We have an ongoing debate and I was wondering if you can shed some light on the issue. We make some reports that track month over month change and others with year over year change.

One argument is that if there is a holiday in a given month or new products come out in that month then month over month will give a distorted picture. (For many companies the same product types usually come out in the same time period each year.) So year over year is what we should look at

On the other hand there should (and usually is) always an increase in year over year. And year over year is less actionable. If I want to know what product/page types need more attention, last year’s types are long gone.

I have tried to find some metric that mixes Y/Y and M/M, but to no avail.

Do you have any thoughts on this matter?

I have often advised that the cheapest way to give context to your performance is to use comparisons to other time periods.

Here's me comparing performance of this blog over two years for the Visitors metric:

Overall happiness reigns, I think. [The blue line is '07 and red '08.]

And that in some sense is the catch.

The best that a comparison of historical trends can do is give you some initial context (yea or nay) and the next best thing is that it is a good way to raise initial set of questions. "Hmmm what happened over there?" "Why don't the peaks line up?" [Digg effect]

But, as is pointed out in the email question, it is the context around changes that makes things more valuable. The graph itself won't answer the questions "why is that up or down" or, perhaps more importantly, "is our current performance better than last year's".

Product sales for you might peak each Thanksgiving (in the US). But if Thanksgiving this year was $15 mil revenue and last year was $10 mil then is that good? More importantly, is that good enough?

This is where tribal knowledge comes into play. What is different about this year and last (or this month or last)? Have you doubled the team? You have free shipping this year? This year you spend a lot on AdWords? Or you just hired me to do consulting for you this year at $10 per hour? Etc etc.

Because of that it would be nearly impossible to come up with a perfect historical trend that will be "clean". So the first thing is to realize that and then not expect too much :), except that comparing trends is a good thing and that its purpose is to just raise questions.

Next I have two suggestions:

# 1: Collect the tribal knowledge and annotate the graph.

Rather than the graph above, I'll present this one (in this case to myself! :) . . . .

When I send out the above graph or present it in the meeting everyone will say "ah ha, we can discount that peak, oh we did not do as well at that point, and we need to do more of this thing over here". I.E. important actionable conversation.

So talk to your Marketers, Boss, Cleaning Lady, the dudette you replaced at half her salary! Get the tribal knowledge, paste it in.

One of these days my hope is that Web Analytics vendors will A] Make it easier for us to add the annotations and/or B] Mine other sources and automatically add context / tribal knowledge as Google Trends does today.

[And one of these days the term "avinash kaushik" will have enough search volume to show up on the top graph! Miraculously it does show up in the 2nd graph above - though it is quite likely that my friends at google are just drawing a "pity line" for me! :)]

But if you are presenting in excel (or powerpoint) then consider annotating your data as you go along. That will be fantastic at providing some immediate answers.

# 2: Segmentation to the rescue!

In aggregate trends can hide insights and hence "dirty" the data. If you want to compare "clean" trends then your best option is to compare different segments within your data. [In all scenarios segmentation rules!]

For example you could just look at Organic traffic trends. Or performance of email campaigns. Or everyone who comes to your site from Florida. Or number of people who see more than five pages. Or % of Direct (free!) traffic. Or…. You catch my drift.

The benefit of comparing segmented trends is that you are able to go from trying to figure out which of the 1,800 variables is causing a impact to having to investigate just a couple of variables. This means you'll understand cause and effect (what you did and what was the outcome) much faster.

Here is an example. This graph shows, for the same time period as above, the Visitors from Organic Search for the keyword "avinash kaushik". . . .

Now the cause and effect can be understood much faster and actionable insights delivered intelligently.

Let's say I did lots of things to drive SEO from Nov 2007 to May 2008. Well clearly it worked, I wrote a lot less content yet my traffic increased very nicely (even better YOY).

So I could summarize that paying Matt Cutts $1.6 million to help me with search engine optimization of my blog's URL stems was a genius investment! [QQ for Matt: Why is my Page Rank still 4? Tears. :]

Do the same for your business. No no no, not hire Matt, segment your trends when you compare them.

One of my other favorites was segmenting out Direct Traffic. That is so very cool because Direct traffic (non campaign, non search, etc) was usually less influenced by other things (acquisition related thing you do – places you spend money) and hence served as a great barometer for over health of the site.

Are you getting better at getting free traffic? Do people remember your site and just show up? Do you have enough engaging content (in case of pure content non-ecommerce sites) that people return again and again each month? Etc etc.

To me that is a "clean" segment / trend to look at. For your websites there will be others. Dig and poke.

Ok its your turn now.

What are your tips when it comes to comparing trends? What things fail spectacularly? What works really well? Do you dread these things or love 'em? Care to share your war stories? What is right about the approach above? What's wrong? We would love to have your thoughts.

Thanks.

PS:
Couple other related posts you might find interesting:

• Context Is King Baby! Go Get Your Own
• Five Rules for High Impact Web Analytics Dashboards
• Excellent Analytics Tip#2: Segment Absolutely Everything
• Excellent Analytics Tip#4: Make Your Analysis/Reports "Connectable"
• Consultants, Analysts: Present Impactful Analysis, Insightful Reports

"Dear Avinash": Be Awesome At Comparing KPI Trends Over Time is a post from: Occam's Razor by Avinash Kaushik

(Did you attend the summit? Please share your feedback via comments, or email.)

In this post I wanted to share reflections and insights that stood out for me from some of the 20 or so presentations.

Who? Matthew Banks, Lloyds TSB
What? The Power of Targeting

Why? We have all talked about testing (multivariate or A/B) and targeting (as in behavior targeting). We are all well educated on all that we can do in terms of testing pages and experience on our websites. What is delightful is that you can exponentially increase the lessons you can learn from testing / targeting by using the right “triggers”.

Typically you might hear that of splitting and placing traffic into each version of the test.  But there is more.

Most tools on the market now allow you to trigger tests by using any number of variables, as the slide shows. The beauty of triggers is that you can understand customer intent a little better and hence place them in relevant tests, but perhaps more importantly when you get the results of your tests you’ll understand them better.

For example rather than simply splitting your traffic, you can test on your search engine traffic by splitting your organic traffic and your paid traffic. Or you can trigger a test between new and returning visitors to see what works, or time zones or … you get the idea.

You can see how you’ll understand the outcome a lot better if you know who is going in. If you are testing then push your team and vendor to apply the triggers mindset.

__________________________________________________

Who? Giles Sutehall, BP
What? Managing multiples: – brands, plans & platforms at BP

Why? We have stressed the value of benchmarking often on this blog, it provides context to the metrics that you are reporting on your website. On the web everyone is “competing” with everyone else, others in unrelated businesses are setting standards that you are being measured against (whether you want to or not).

It was fascinating to see this slide from BP that showed that they practiced exactly this type of benchmarking. They are measuring their performance for key elements of customer experience and websites performance against the World Wildlife Fund, Nike, Honda, Alcoa, GlaxoSmithKline, America Express etc. Each company they choose faced atleast some similar challenges and was trying to use the web in similar ways to meet that challenge.

By measuring itself against not just its peer group (ExxonMobile, Shell, Chevron) BP is not only able to judge its performance against the best out there but at the same time it can also learn from companies / websites that might otherwise not be on its radar.

When you think of benchmarking your performance are you applying the same mindset? You should be, the benefits are too large to ignore.

(Data on the slide above is from 2003, current BP performance, in each category, is substantially higher! :) 

__________________________________________________

Who? Vincent Kermorgant, Nokia
What? Implementing Web Analytics the Nokia way

Why? Here is a common advice: Sit down with your decision makers and understand their business goals and life will be a bed of roses for you Mr. Marketing or Ms. Analyst. It is good advice. :)

The above slide from Vincent was great because it very nicely articulated exactly what you should do when you have that conversation.

I particularly liked the framing: ask for the strategic intent, then don’t let go, follow up with understanding / documenting the micro goals (that contribute to the macro goal success), don’t let them go yet, understand the Resources as well. Keep a copy of the above image / framing handy with you.

This was another wonderful slide in the presentation illustrating the concept of a “KPI card”. Vincent recommends creating a card for all the KPI’s that have been identified at the end of the goal discussion.

The card will document key factors of the KPI, such as the definitions, targets, identify actions to be taken and the owner of the KPI. This can serve as a handy guide for everyone in the organization, besides getting everyone on the same page. You can imagine how powerful this can be, imagine everyone in the organization carrying around with them a deck of these cards! :)

What do you all think of the concepts of complex triggers, expansive benchmarking, goal data gathering and KPI cards? Please share your feedback, and your own tips and tricks, via comments. Thanks.

(PS: My deepest thanks to the 18 people who made it to my London Calling dinner. The conversation was wonderful and the food was nice as well, what a great way to cap off the Emetrics summit! Thank you for taking the time from your busy schedules.)

[Like this post? For more posts like this please click here.]

Emetrics London Reflections: Triggers, Benchmarking, Identifying Goals & KPI Cards is a post from: Occam's Razor by Avinash Kaushik

I mean really use the tools. Ravage all the features. Exploit every possible button. Produce built-in visualization magic. Poke into the hidden crevices and discover exotic delights. Nourish yourself with the "info snacks" the tool's engineers and product managers cooked up.

This post is all about that.

When it comes to data analysis, you are usually more likely to see me share guidance on advanced segmentation or custom reports or advanced social metrics or controlled experiments or economic value or competitive intelligence or web analytics maturity or one of an infinite number of difficult, if hugely rewarding, things.

Not today.

Today is going to be about healing heartbreak. Ravaging data. Poking and prodding. Nourishing ourselves. And doing so with simple mouse clicks inside the standard tool interface (!) with the reports and features you can already access.

Here is a summary of the eight incredible recommendations in this post:

#1. Create a Customized Dashboard – Earn Love, Drive Change

#2. Leverage Custom Alerts – Let Data Kick Your Butt Into Action

#3. Use Table View Options (Comparison, Pivots, In-line Filters) – Faster Initial Insights

#4. In-Page Analytics – Re-imagine Traveling Through Data

#5. Perform Recency, Frequency & Pan Session Analysis: Fall in Love with People not Page Views

#6. Matched Query Type, Keyword Position, Day Parts: Sexier PPC Analytics

#7. Custom Report Filters, Tabs: Bring Deeper Relevance To Your Custom Reports

#8. Quit Google Analytics: Move Beyond Tool/Creativity Limitations

If you are an Analysis Ninja, focus on the mental model and approach used in each recommendation. If you are an Analysis Ninja in-the-making, close the door to your office/room – you are going to repeatedly squeal with delight.

Ready?

#1. Create a Customized Dashboard – Earn Love, Drive Change!

Who does not love dashboards? Humans love them. Aliens love them. HiPPOs adore them.

So why is it that we don't spend time creating customized ones for our stakeholders? After all, humans, aliens and HiPPOs have different needs.

Pledge to shift away from a one-size-fits-all data puke, and use your web analytics tool to create a customized dashboard.

One day, Google Analytics will default to be the Home tab when you log in, but until that blessed day arrives, just click on the Home icon in the orange top navigation. Then click on Dashboards, and what do you see? Oh yes! + New Dashboard. Click!

I love that phrase "Blank Canvas." So open. So full of possibilities. So much hope and wonder.

Now just because you can do anything does not mean you should. My process is to name the dashboard first. Seems odd, right? But by naming it, I am giving it a purpose; and a purpose requires asking questions and focusing. And great, relevant, dashboards spring from asking questions.

I named my dashboard: VP, Digital. It now has a specific audience and a purpose. Rather than data puking, I'm now forced to go talk to the VP of Digital and ask this question: "What are your business priorities for the next six months?" That will lead to: "And how will you know if we've successfully executed on priority x?" That will lead to: "Awesome, I know exactly which critical few Key Performance Indicators I'll be showing in our dashboard."

Boom!

Every element in the dashboard has a purpose and is tied to a business priority. She/he wants more Social traffic. You, the Ninja that you are, are showing all segments of traffic to give context (you rock!). She/he wants time on site, you have no idea why, but you add it (along with a sparkline that shows the trend – sweet!). It is a content site, so rather than silly things like page views you use Loyalty (more on this below) and you also show consumption of videos (events). Finally, you bring together Conversion Rate with the Goal Value delivered by the Social obsession.

Charming!

Pro Tip: Always, always, always let the Acquisition, Behavior and Outcomes framework be your guide. After you've created a dashboard, check to see that you have all three elements. If you don't, you are not showing the end-to-end picture. Without this you fail in your duty (and the data recipients will make poor decisions).

Create a customized dashboard for your Search team, one for your Display team, one for the folks doing onsite merchandizing, one for the nice lady that owns the ecommerce shopping cart and all the other key clusters of your audience. Give them hyper-relevant starting points, collections of "info snacks."

The cool bit is that in addition to standard widgets and simple tables, you can also bundle along your smarts into the dashboard and delight your users.

One way is to use the awesome built in inline Filters feature when you use the dashboard widgets, to show just the data that is relevant (did I already say less data puking? :).

In this case, I've done that by adding a filter to segment revenue to only show social value.

And it is not all social media, it is just the money made from the company's own social media efforts by using the right campaign parameter. I'm (secretly) trying to show the VP how much (or how little!) money our own efforts are generating. Smart widget, smart insights, smart decisions.

So go forth and multiply! Create a small cluster of hyper-relevant (secretly smart) dashboards!!

#2. Leverage Custom Alerts – Let Data Kick Your Butt Into Action!

Sometimes (actually frequently) it is not enough to rely on our own diligence in terms of remembering to log into SiteCatalyst and look at the right set of numbers (across a hundred reports!) to know what's up with the business. It is especially undesirable to be surprised about something awful happening to our digital existence.

We can't predict the unknown unknowns easily, but we can be magnificent at proactively identifying the known unknowns by leveraging the custom alerts feature in our web analytics tools. Here's a screenshot from Google Analytics:

These alerts will let you know if engagement on your website crosses certain thresholds or when the bounce rate spikes for traffic from Google or if there is a spike in conversions (praise the lord!). All things you know will happen, you just don't know when. Known unknowns.

With smart alerts set, you don't have to remember to check the data every eighteen seconds. An email, or a text message, will poke you into action. Your boss will be impressed at how you seem to always have your act together!

Here's one of my favorite custom alerts. I would like an alert when goal conversion rate for any day is greater than 25%. My normal is around 18%, so if it jumps up by that much I can get an alert and I can do deeper analysis to figure out what might have caused the spike.

You pick the period for comparison, your the necessary dimension and metric, add the condition, type a value and you're in business.

If you don't have at least five custom alerts set up, you can't call yourself an Analysis Ninja in training. At least not a serious one.

Five of my favorite alerts are in the second part of this blog post: Identify The Known Unknowns: Leverage Analytics Custom Alerts Here are more clever examples from the team at Google: Five Custom Alert Examples

Don't rely on yourself to remember to look for your site’s magic moments. Put yourself in position to be proactively informed when they happen.

#3. Use Table View Options – Faster Initial Insights!

Enough dancing around the outside of the tool. Let's rip off our clothes and jump into the cold inviting water!

It is very hard to quickly understand a lot of numbers when they are presented together. When you log into WebTrends or Google Analytics or CoreMetrics, you're lucky if the standard report does not contain five or seven metrics at the very least for every table row. Data puke!

Not only will you not see the forest, you'll be lucky to even see the trees.

My preferred path is to leverage the tool's built-in features for filtering/visualizing the data.

In Google Analytics there are a few super cute options. Click on the table like icon next to View. You can see five different ways to look at the data in any table: Percentage, Performance, Comparison, Term Cloud and Pivot. All exist to make your life easy.

My personal favorite is Comparison. This option takes the site average for a metric and compares the individual performance of every row to that average, and it visualizes the data for you.

For the top websites that refer traffic, I wanted to know quickly (without having to do the math) which source sends traffic that tends to see more than one page. AND I want to know contextual performance of every row with site average AND every other row. Hard? Nope. I simply choose Comparison. Then I choose Bounce Rate. And in two seconds…

Like every two-year-old child, I know that red is bad and green is good. GA is telling me is that Twitter (t.co) traffic bounces 14.59% more than site average. Ouch.

Scanning the rest of the table, remember I want contextual performance analysis, I can quickly see that I should love the GA blog, Linkedin and SEOmoz more and other folks a little less. :) But I am also now a lot more curious about Ycombinator. That is a lot of traffic. What post on YC did they come from? What content did they read here? Why might they not have cared for anything else? I can analyze and then identify an specific optimization/engagement strategy to reduce bounce rates.

You can literally do this for any metric in the standard tables in GA. Try to look at your top 25 campaigns and compare conversion rate. Or open the new search engine optimization reports in Google Analytics , for your Queries look at Impression and try Comparison for CTR.

Pretty cool. But that is not all.

I've always been partial to pivot tables in Microsoft Excel, hence it is not surprising that my second favorite view option in Google Analytics is Pivot.

Now I can create a lovely report, for example, to find "arbitrage" opportunities across search engines? Here's how you do it.

1. Go the keywords report (in Traffic Sources section). From View choose Pivot (as above).

2. Click on the box next to Pivot, type in Source, select it.

3. Click the box next to Pivot metrics and choose Visits (or whatever else you like, go crazy!).

4. Look at the performance. I typically look for anomalies. For which keywords do I get more traffic from Bing when compared to Google. Or Yahoo! compared to Ask, etc.

Every search engine's SEO algorithm is unique. For example I get twice the traffic for "digital marketing" from Bing than from Google. I use the data above to customize my SEO strategy for each search engine.

You can use pivot tables in pretty much every GA report.

In this case, I can more easily figure out which of my top pieces of content are delivering the micro-conversions that are valuable to me. I track these micro conversions as Events, here's my Pivot table:

Use your creativity when it comes to pivot tables and you'll be delighted at how wonderfully they help you answer hard questions.

One last bonus item when it comes to using tables in web analytics tools spectacularly: Use the in-line table filters. Just click on the link called advanced next to the magnifying glass on top of the table you are viewing (in any report).

Now, rather than looking at half a million rows and trying to find an answer, you can simply type in your question. In this case I only want the rows of data (keywords, campaigns, pages, products purchased, videos watched, whatever) only for those people who:

1. Saw more than 3 pages during their visit AND

2. Entered my website on the cluster of 900 pages about Aruba.

These people are of particular interest to me … I click Apply and, voilà, I have them cornered!

Using this strategy I can go to the standard table with hundreds of thousands of rows of data and quickly only look at data for my brand keywords or just for my email campaigns or just for people who visited more than 10 times or just for those who came via Yandex or just those that read a segmentation post or just those that donated or…. anything. And I can do it fast.

Why stare at a table, or worse just the top ten rows, wondering what to do? Speed up your time from data to information by using the Comparison view, Pivot tables and in-line Filters.

#4. In-Page Analytics – Re-imagine Traveling Through Data!

This is one of the hidden gems of Google Analytics, especially for traversing lots and lots of data in context of the web page itself. It is fantastic at communicating data, complex data, to people whose primary job is not data analysis.

The In-Page Analytics report takes all the data you would find in the Explorer and Navigation Summary reports (essentially all the links you have on a page and their performance) and shows it to you in an elegant visually appealing view.

There are two ways to get to this report.

1. Just go to Content > In-Page Analytics.

2. Go to Content > Site Content > Pages, then click on the URL you want (or use the in-line table filter mentioned above to find the URL), and click on In-Page at the top.

On top of the report you'll see the scorecard, or aggregate performance of the page via metrics like Pageviews, Unique Pageviews, Time on Page, Page Load Time (!) and Bounce Rate. Having the % of Total (grey text, small font below) provides great context.

Below that, in blue, green, red and orange I see the percentage of clicks on each link. I don't have to infer data in the table, it is all laid out for me nicely!

And note the orange bar at the bottom, it is particularly nice. It shows how many people click on links below the fold. The fold is defined by your browser size. As you resize the browser windows you'll see that number dynamically change. This data is extremely valuable for long pages, especially if you have valuable links below the fold. IF you're New York Times or Amazon, you want to know if people scroll!

This is so important if you are responsible for merchandizing. If you have a few different layouts of your web pages, this is a great way to know which links, promos, and annoying dancing banners are attracting the clicks.

But you don't have to watch clicks. Aren't clicks are the new HITS :).

You can click on the Viewing drop down (#1 below) and choose any goal. When you choose a goal, the display changes to show what percentage of people who click on a particular link go on to complete a goal in that same session!

In my case, below, 15% of the people who click and read the comments end up meeting my goal of going to Market Motive (and hopefully sign up for the Web Analytics Master Certification program!). But only 1.9% of the people who visit the Digital Marketing section of the blog do the same.

In this case you can also see that the links on the top are especially valuable for this goal. Only 9% of the people who ultimately went to Market Motive clicked on any links below the fold (and the fold here is pretty much the top of the blog post!). So I have to be particularly good at the information architecture on top of the page. Once they scroll, the chances for goal conversion go down dramatically.

I can do this type of "conversion click" analysis on any of my 8 goals. How awesome is that? With those insights, I can go and optimize my key pages for my individual business goals.

Imagine what you can do with your home page optimization if you know this. Now when everyone wants a link on the home page or the category pages you can show them which links your visitors are actually interested in and let data fight your political battles!

I rarely find anything really sexy (in an analysis context :) unless it comes with segmentation. You saw that in every single recommendation above. And my choice for this report is no different. You can segment like crazy.

When I use the In-Page Analytics report I don't want to look at all the traffic in one ugly bucket. I want to analyze groups of like type people, like type behavior. For example, I want to know how the behavior of search traffic is different from direct traffic. How hard is it? Three simple clicks…

1. I click on the Advanced Segments drop down and choose the standard segments (or one of my 50 custom segments).

2. I click on the In-Page tab to go to the report. (I was in the Pages report.)

3. I choose the metric I want. In this case I, selfishly, want to know if there is a difference the money I make (Goal Value) if Visitors from Search and Direct traffic click on the exact same link on the page.

4. Bam! Bam! Bam!

There is a substantive difference. When people come from search I make $142, on average, when they click on that link, but if they are direct I only make $58 (boo!).

Imagine what a gift this is when it comes to figuring out how to create the best landing pages. I know what the Search Traffic gravitate towards, I can now optimize their experience on the site rather than serving them random/generic links!

You can do this analysis for social media visits, for a particular keyword, for people who watch videos or download catalogs or, well, anything you can segment in Google Analytics (which is pretty much everything).

Forget tables. Be sexier. Let your site tell you what to do.

But there is one fly in the ointment.

The implementation of In-Page Analytics in GA is frustrating and silly. When you first go to see that report (if you are using Internet Explorer), you are going to see this insane warning:

If that box was not scary enough, the whole darn text is wrong. My ga.js (and most likely yours) loads from Google, and I have the snippet on my site. #aaaarrrrrhhhhh

In addition to the above you'll also see this at the very bottom of your browser window at the same time…

So, how do you make this report work?

It is supremely annoying that the Google Analytics team and front end does not make that clear.

But it is simple. Ignore the first error, and click the "Show all content" button on the second error. Magically, everything will work.

If you are using an older version of IE you might see this error:

Classic useless error. Don't click the default Yes – just click No and the report will work fine.

In Chrome, mercifully, it works fine with no errors.

While it is disappointing that the error shows up initially, the report itself, as you can see above, is quite valuable. I hope you'll give it a chance.

#5. Perform Recency, Frequency & Pan Session Analysis: Fall in Love with People not Page Views!

I'm a big fan of pan-session behavior. What happens across multiple visits by the same person? (And are there multiple visits at all in the first place?)

Having grown up in the traditional business intelligence and direct marketing world, I'm also a huge fan of RFM analysis .

In Google Analytics, you'll find them in the Audience Section under Behavior.

Here is a great example of the type of business-critical question you can answer with these reports. We are a photo-sharing website (think little sister of Flickr ). We make money on content consumption (via display ads) and premium subscriptions to the site. But we can only make money if other people come and upload their photos, and still others come to view those photos. Long-term success is achieved if our audience becomes loyal and we don't have to keep spending money on Google and MSN and Yahoo! renting traffic.

So, are they loyal? Check out the Frequency (count of visits) report. It shows how many people visited only once (42%) and how many 2 times and 3 times and… so on and so forth.

For this business the results are fantastic:

While a chunk of people come only once and never again, notice how bottom loaded the report is. 43% of the traffic comes to the site between 9 and 200 times in a month! That is loyalty! We can feel better about our marketing and engagement strategy.

How about for your site? Are you having one-night stands or building longer-term relationships with your audience?

Another nuance of loyalty is that you not only want people to come to the site multiple times, you want a shorter gap between two visits. You're looking for recency. This report show us how spectacularly we are doing for our photo site:

The vast majority of visitors visit the site every day! Analysis Ninjas know that the 83% number above includes new visitors to the site, so we should subtract that (why are web analytics tools so annoying some times!). But, it is still a huge number, and we should be happy.

How about for your site? Does the recency line up with, for example, the rate at which you publish new content/launch new products/execute new marketing campaigns?

Another facet of pan-session analysis is looking at the number of visits it takes to convert our visitors. Not everyone wants to marry you on the first date, right? (Yet almost all digital marketing and almost all landing pages are constructed as though this were the case. Sad.)

My favorite report to use to answer this question about customer behavior is the Path Length report in the new Multi-Channel Funnels section in Google Analytics.

In our case, around 23% of our conversions happen in the first visit, and then there is a long tail and then look…

OMG! 48% conversions that took 12+ visits to convert! We can specifically look at that segment of customers and figure out what combination of Google, Atlas, YouTube and Email Marketing (or whatever) it took to get that conversion!

We can use this data to create better experiences for our users. We can optimize the ads and marketing messages (across channels) it took to get these folks to come to our website multiple times, prior to conversions.

This is hard work. Most definitely senior Analysis Ninja work. But that is how you win big. When you skip this type of analytical effort, you doom your company to live on scraps. And really, who wants that?

#6. Matched Query Type, Keyword Position, Day Parts: Sexier PPC Analytics!

I've always been a bit miffed that most web analytics users are less than sophisticated when it comes to analyzing search/AdWords campaigns. So many companies spend so much money. Why not do some incredible analysis? Especially when our web analytics tools make it so easy.

My first example is a good representation of that.

Most people don't realize that when you view the keyword report in the AdWords section, you are looking at the key words/key phrases you bid on, not the queries that were typed by users into Google. If you base you AdWords success on just the keywords report, you might end up making substantially poor decisions.

For that reason, I love and adore the Matched Search Queries report (in the Advertising section). It shows what users typed into Google when your ad was served. The report is standard in Google Analytics.

All you have to do is click on the box next to Secondary dimension and type in Keyword. Now you are looking at both the word you'd bid on (right) and the word the user typed (left):

You can quickly see the differences between your bid and the matched query (#2 above). The next obvious step is to look at the performance and optimize your Match Type strategy based on the results.

In the screenshot above you can see that the keyword bid on was "calico critters toys." Those ads were matched to the user queries "little critters toys" and "calico critters cloverleaf manor." And there was a 9 points difference in the bounce rate (ouch!). Good to know. Go back, optimize your match types in AdWords and optimize your landing pages.

Fun right?

My second favorite? Keyword Positions report. Why? SEOs obsess about their rank on the search engine results page (SERP). That obsession is often valueless. But for your PPC campaigns? Obsession will deliver glory!

So why not analyze which position your ads show up in when it comes to AdWords?

A combination of your max bid, your quality score, match type will determine the position of your ad for every search query. Google Analytics will show you that information beautifully.

Here it is…

Just click on a keyword and the visualization on the right comes to life. Now you are better able to determine which position gets you the most clicks. Top 3 is better than Top 1 (the position your boss was obsessed about – "I WANT #1 RANK!!"), and neither can beat Side 1 (the cheaper position!).

Another lovely thing you can do with this report is look at the performance once those clicks (ok, people) land on your website. Just click on the down arrow and choose the metric you want, Bounce Rate in my case below:

You can see that every position has a bounce rate. Side 1 still has the best performance. You don't have to just use Bounce Rates. You can also use % New Visits, Time on Site and Pages/Visit as your metrics. The goal is still the same: find the position that delivers best performance.

If a position works optimally for you, then you can use AdWords Automated Rules to have your ads show up in particular positions.

You use your money wisely and get higher ROI. #winning

One small bonus tip: I love looking at the AdWords Day Parts report a couple of times a month. Most of the time, the data shows the normal trend, more clicks and conversions during the business day.

But every once in a while for certain keywords, or segments, I'll discover that the pattern is very different. For example, you can see below that the conversion rate actually peaks at midnight…

We did not know that people were searching for us late in the night, and they were highly qualified (!). Hence sadly our AdWords budget was capped at that time, we did not to "waste" money. Sad. Once we saw this data we loosened up the budget and picked up loads of extra conversions.

You'll discover other delights like this. In the view above I'm using the Compare Metric feature of Google Analytics. It is cleverly hidden in light gray text on white background on the top right of the main graph in every report. Just click on the drop down and choose the comparative metric you want.

If you spend money on AdWords, be smarter about the analysis you do. There is no better way into your boss's heart. If you spend money on other types of campaigns, I hope you'll find inspiration above to do interesting off-the-normal analysis.

#7. Custom Report Filters: Bring Deeper Relevance To Your Custom Reports!

It is hard to keep pace with all the changes that web analytics vendors make to their tools. I wanted to share two clever features in Custom Reports that make them even more super magnificent (and mandatory if you are a Ninja!).

The first one is the filters that are built right into the custom report you are creating.

I love custom reports because you don't have to data puke any more, you can just show the data that is needed. [Helpful post: Leverage Custom Reports For Better Insights]

Now you can focus even more by embedding the segments your leadership cares about right into the report!

Above is my awesome Visitor Acquisition Efficiency Analysis report (click link to get it). But if my leadership team is only interested in understanding how good the company is at acquiring mobile traffic, I can include a filter right into the report (see above) to just show mobile traffic.

And if they only care about USA (and why not?), I can limit my custom report to show just that. Why bug them with everything?

Now my custom report is not just relevant, it is hyper-personalized. I have shortened the distance between data and insights.

Your imagination is the limit in terms of the clever filters you can build into your custom reports.

Second tip on custom reports: Create micro-ecosystems.

I was not too pleased with the eight or ten standard mobile reports and their data views and all that. So, why not create my own custom report? Wait, not just a custom report but rather replace all the standard reports with my one Awesome Mobile Report? [Click to grab it!]

My primary strategy was to create three tabs. One for device drill downs and metrics, a second one for search performance, and a final one to understand performance of content:

Each tab has specific metrics relevant for just that dimensions (Device, Search, Page), and it is all in one place to give decision makers one go-to place for all their mobile performance needs.

Same outcome: Faster movement from data to insights.

You'll know you are an Analysis Ninja when you can replace 100% of your company's reporting needs with just five such micro-ecosystems. (Not 100% of the analysis needs, 100% of the reporting needs.) It is entirely possible, and think of how easy your life will be then…

And I have to tell you it is a tremendous amount of fun.

One final, surprising, way to do the data hustle with GA…

#8. Quit Google Analytics: Move Beyond Tool/Creativity Limitations!

Sometimes all the reports and features are simply not enough.

You can't understand why it is impossible to see Keywords in rows and a monthly count of Visits in columns. Weird, right?

You can't fathom why something so amazing and straightforward as tag clouds are so uncool and utterly useless in Google Analytics.

You are frustrated with the insane report/table formatting requirements by your business leaders. They want a particular font type, or your dashboard goes into the junk folder!

When you run up against the tool's limitations, weird implementations by tool vendor, or hard-to-please clients… quit the tool. Get the data out. Unleash your creativity.

It is, of course, possible to take data out of Google Analytics. The straightforward way is to simply use the Export button in the top nav.

The problem is the second image above. You can only download 500 rows easily, when you actually, in this case, have 122,397 rows of data. [And you all know how much I love mining the long tail by moving beyond the top ten rows of data! Not possible with 500 rows.]

Option one is simple, yet slightly painful: "Trick" GA into giving you all the data that you want to download.

Happy?

Now here's the caveat.

Using the method above it is possible to download all of the 122,397 rows of data. The challenge is that you might not have enough cache allocated to your browser. Or you don't have enough memory. Or you might have an older browser. Or one of so many things that will cause your browser, not the web analytics tool, to hang. It is just hard to get that much data rendered into a browser.

Of course where there is a problem, there is an incredible solution.

If you want to export all your data frequently just use the free Google Analytics API. It is pretty cool. [Tools like WebTrends and Adobe have APIs as well. WebTrends is free, for Adobe API pricing please call your Account Rep.]

If you want to have a quick naughty flirtation with the GA API, visit the Data Feed Query Explorer. If you enjoy that (and you will, because that is what naughty flirtation is all about) get more context about the Google Analytics Core Reporting API. End your journey devouring the handy dandy Dimensions & Metrics reference guide.

Now allow your inner geek to rejoice!

If, like a majority amongst us, you want to skip the flirting and jump to marriage, mosey over to the Google Analytics Application Gallery. Everything you can dream of is there. Data Warehouse integration? There. Business Intelligence? Got it. Campaign Management with a side of Email Marketing? Sure. Mobile Apps and Widgets and Gadgets? Absolutely!

It is pretty cool to use the API to integrate your offline phone call data with your Google Analytics data, understand the demographics, gender, income, etc. of people who come to your site, or overcome the sub-optimal standard GA Funnel report by using PadiTrack.

Going back to extracting data efficiently and making magic, three apps you'll find particularly useful are Excellent Analytics , Nextanalytics and GA Data Grabber.

Excellent is free (hurray!). Nextanalytics costs $199/year and GA Data Grabber costs $299/year. Both tools are full of pre-built dashboards, reports, cool visualizations and easy ways to collect data from tons of sites and pull it all nicely into one report. Both also contain loads and loads of automation capabilities. They allow you to shift from 90% data collection and 10% actual work, to 10% data collection 70% data analysis 20% social media time-wasting. What's not to love? :)

It may seem odd to spend money on a free tool. But not paying just one dollar a day to make your life better is most likely a Class 1 analytics crime. Don't commit crimes!

Regardless of if you use WebTrends or Google Analytics, the API allows you to do better reporting, smarter analysis (with offline data) and automate the mundane. Create a better life for yourself.

So that's it.

Eight simple ways you can hustle with data, convert skeptics, earn the love of your website visitors, and improve profitability of your web business. All without leaving the confines of standard reporting features already inside your tool (except that last tip).

I hope this post will accelerate your mastery of Google Analytics (or IBM or Yahoo! Web Analytics or Open Stats). And I hope it will mean less time spent wrestling data and more time taking action based on intelligent insights.

Good luck!

As always, it's your turn now.

Are the strategies outlined above already a part of your daily data hustle? Which recommendation surprised you the most? Which one do you think is most over-rated? If you are a GA power user, did I miss a feature or approach that you love a lot? From your experience, with any tool, do you have a tip to share with your peer readers?

It would be wonderful to hear from you. Please share your feedback, ideas and awesomeness via comments.

Thank you.

Google Analytics Tutorial: 8 Valuable Tips To Hustle With Data! is a post from: Occam's Razor by Avinash Kaushik

It is a lot easier to predict the present. So I'm not going to do that either.

Rather, I'm going to share a clump of realities/rules garnered from the present to help ready you for the predictable near future . Now here is the great part… if you follow these rules and act on these insights I believe you'll be significantly better prepared for the unpredictable future.

Awesome right?

Now here's another surprise: These rules/insights/mind shifts are not about data!

Here's important context (before we get into the rules for revolutionaries)…

The Fundamental Web Analytics Problem Is Not Data!

A huge part of the last few years for me have been about bringing more data, better strategies, more powerful tools, ever more impactful keynotes to people around the world.

One of my biggest learnings?

Most companies are astonishingly blasé about data and possibilities of measurement. Most web "analysts" remain glorified "data pukers" or glorified JavaScript taggers.

Why?

The problem, it turns out, is not data. The problem is only partly the data pukers or JavaScript taggers. The real problem is that our management teams lack imagination when it comes to the web, and our marketing executives continue to do TV on Twitter, catalogs on display ads, irrelevant shouting on search, etc.

That frustrating reality is the source of numerous problems for the company (and the web as a whole), but it also means Executives ask for unimaginative data. "Count Impressions, in real time!" "Show me Clicks and the count of Facebook Fans!" "My dashboard should have Page Views and Exit Rate!" Sad, unimaginative measurements of their sad, unimaginative campaigns.

If you are doing lame stuff, why try harder in an analytics context by asking for Economic Value or Visitor Loyalty or Conversation Rate or a thousand other super powerful and insightful metrics ?

The problem is not analytics or data (or your blood, sweat and tears). The problem is Marketing and lack of imagination in using the web/digital channels.

And here's the thing… if you are a "Web Analyst" in the broadest sense of that word, then this is your problem. Solve it or suffer the indignity of making decent money doing work that will have no impact on your organization. If you are a digital marketer then this absolutely is your problem. You're the massive, under-appreciated, hidden part.

In the last eighteen months or so, I've spent a lot of time trying to solve that problem. Get the senior-most people in the largest companies in the world to unlock their imaginations when it comes to their digital existence via impactful digital strategies. Convert them to be revolutionaries for their companies and customers.

I've discovered that if we can just get them to imagine a better existence, undertake serious risks, experiment with new better ideas, and spend money executing them… they will ask for more robust measurement! Because you need serious new good analysis to understand the impact of serious new good stuff!!

In the same spirit, if they don't do wonderful, beautiful, imaginative things, we people who play with data will continue to play a marginal, at best, role in most corporations in the world. Even if these unimaginative companies spend a ton of money on Omniture, IBM, WebTrends, Yandex Analytics and Google Analytics, we digital analysts will lead unimpactful puking tagging lives.

And no one deserves that.

In case you are reading this and you are the aforementioned Digital Marketer, then your life is sadder still. How good can it possibly feel to do unimaginative things that barely even worked on TV/radio/magazines/catalogs?

Whether you are the Marketer/CMO or the Web Analyst/Ninja, it is imperative that we unleash imagination.

Why doesn't everyone do that already?

I know that this sounds utterly simple but we, people and companies, don't always realize that the "rules" have changed. Our mental model has not shifted enough to the existing reality. This lack of internalizing the rules jeopardizes our current state to some extent, and our future to a significantly greater extent.

A lot of my work is making companies realize the implications of these rules on their company strategy and structures. You've probably seen these rules sneak into my blog posts. I want to share them below as a collection with the hope that it will motivate you to create a veritable primordial ooze from which new ideas (or indeed life) will spark for an imaginative digital existence.

7 Rules for Digital Marketing Revolutionaries!

These are my observations on changes already underway, changes that are dramatically affecting what marketing is and should be. You might have observed at least some of them in bits and pieces, but perhaps you have not considered them as a whole. Adapting to the implications will allow the creation of a more future-proof you.

#1 Customer expectations on the web are insane, will get super-insane.

We expect more.

High bounce rates show how horrible slow-loading websites are. Lack of loyalty shows simply re-publishing AP stories is useless. After 19 visits, www.bloomberg.com should create a home page around my interests, not their one-size-fits-all pimping to everyone. With an iPhone there is no friction between me being in your store or on your site (or, omg, getting a mobile geo-targeted coupon from your competitor for 5% off your price while I'm in your store!). There are 12 different alternatives to your site that provide free return shipping. Just because your site is B2B, you do not have the right to create a 1940s website and force visitors to type their name, precise GPS coordinates and underwear size to get a PDF that should have existed as a webpage in the first place (as HTML has been invented).

It is no longer acceptable to just meet past expectations. Alternatives to you are one click away, one Google search away, one tweeted recommendation away. Aim to meet super-insane customer expectations and you'll future-proof your business.

Oh and yes, I do get that this is hard. You have to rethink everything. Price of greatness, sadly.

#2 Multiplicity: Competencies, Campaigns, Systems, Everything.

This is something we are most unprepared for.

You can no longer be good at just one thing, or two. It is a 10-thing world now (and maybe a 20-thing world soon).

If you are a catalog company you have to be good at catalog marketing (as long as it continues to provide incremental revenue ), and you have to be good at NASCAR (as long as it provides incremental revenue), and you have to be good at Facebook, and you have to be good at email, and search, and YouTube and… a hundred other things. All while constantly optimizing your portfolio via controlled experiments .

You have to be good at sourcing your products and you have to be good at delivering them.

You have to be good at using clickstream and surveys and competitive intelligence and heuristic evaluations.

You have to be good on every device of every screen size in every country with a monetizable audience.

You have to be good at… many things all at the same time. For far too long we've been able to be successful by relying on our sheer strength on one thing. Catalog. Paid search. YouTube. Billboards. TV. With every passing day that strategy now ensures we are rejecting tons of revenue and tons of prospective customers.

It is hard to rewire the company's DNA to truly execute a multiplicity strategy. That's why you allocate 15% of your Marketing budget to getting good at multiplicity. All the time.

#3 One-trick ponies are going to be a liability.

This is a subset of the one above, but I wanted to call it out separately because I am madly, deeply convinced of its importance.

Increasingly, your people can't be one-trick ponies. Especially not people you consider stars.

If your Marketer is not savvy in basic finance and analytics and writing some html and creating mobile campaigns and tag clouds then you have a long term liability on your hands, and not an asset who is really, really, really, really good at writing copy for display campaigns.

The web demands immense agility and flexibility from every company. Having one-trick ponies can limit your capacity to think smart and move fast.

If you have an Analyst who is just good at Omniture and has never done an online usability study, and used Compete, and taken a whack at a rough digital P/L, then it is time to set them on a path to evolve, or get someone else.

If you have a Finance person for your web business who has never run campaigns on Facebook, and who doesn't understand the uniqueness of mobile applications, and a little bit about the insanity of ad exchanges then over time try to hire someone who does.

At one time, it was okay to be 100% good at one thing, and only one thing. But today companies with people who are 70% magnificent at one thing and have filled the remaining 30% with being good at everything in the periphery of their jobs will rule this world.

You want to change HR hiring practices now to nurture such 70/30 people inside your company, and to make that a mandatory condition for all new hires. Then you'll rule this world.

PS: Here's the raw brutal truth for you dear reader… no company is going to invest in you. The most precious Digital Folks are those who choose to invest in themselves, on their own time. Especially in the 30% area referenced above. Now you know.

#4 Attention is the most precious commodity.

We live in a hyper fragmented world with, quite literally, hundreds of TV channels, thousands of social connections and millions of websites. The single biggest gift any brand can get is attention. And not just the few seconds you get by showing 19 ads on one web page, or tweeting one relevant link in 1000, or showing the same ad for DirecTV six times while watching one 23 min program on Hulu, or showing up for a query for "flights to Sao Paulo" when you only offer flights to Europe, or… a million other ways.

Attention results from understanding the true strength of each channel and then engaging uniquely with your audience. Here's a good example. I bike ride a lot. I walk a lot. In general, I'm a big fan of exercise. I would follow Gatorade on Twitter with the exercise connection of that brand. But you know what they do on Twitter? They retweet other peoples tweets about them. The most lame thing you can imagine using Twitter for. (That is if they don't waste time with condescending tweets like "We've got your back xyz.")

How could Gatorade have my attention? With a Twitter stream about hydration. If their tweets supported their bio on Twitter: "Helping athletes get the most out of their bodies before, during and after activity." I could not find a single tweet of the 250 I reviewed that fell in that category. Why not try that? Why not go for grabbing my attention and then keeping it? Why not go from trying to have a Gatorade ad on every TV sports event in the hopes that I'm watching to doing that plus doing social media right and have a direct relationship with me?

Not one or the other. Both done exceptionally well. That's how you earn attention.

Or consider this example. Why do Priceline or Expedia mobile apps only do prices? Why do they not have a TripIt-like functionality built in? If they did, I would go having to remember which app to use to search for a hotel to having an app that is central to my life (and TripIt provides such value that it is) that I use all the time and that I will of course use when I have to think about booking any travel.

Get it? Attention. Via incredible daily utility.

One more example. With 55k RSS Subscribers and 110k Visits a month, this blog could make a few dollars with AdSense or Display ads or annoying interstitials offers. It could also make a few more dollars constantly pimping my two books in posts. Yet it does not. It simply gives you content (my goal: "incredible, relevant, of value"). You see, I don't want your AdSense clicks. I want your attention. And I know I can monetize that 100x all other things combined.

So what is your business shooting for online when it comes to digital marketing? What are you doing to earn, and keep, attention?

#5 Brand destruction is insanely efficient now. Beware!

United breaks guitars. Kenneth Cole goes too far with Egyptian protests. Gap logo. Bank of America everything. You can add 100 more examples in 100 seconds.

Those are big ones. But there are small ones too. I told 20 people that Nikon's site is slow and profoundly sub-optimal on mobile. (Guess what I had on hand when I saw their sexy ad on TV? A mobile device!) Now these 20 people will tell others. Small, silent brand destruction.

Yet so few companies have built organizational capabilities with this efficiency in mind. The distance between a story and an audience is six pixels (as my friend Mitch Joel might say). It is ever more important to live your values, walk the talk, deliver what you promise, not say stupid stuff, be real and accessible, and all those delightful things.

You see, the power that can so efficiently destroy your business, is also the power that can boost you to untold heights. And that's marketing money can't buy.

Oh, and you are right that people bought Kenneth Cole stuff even after the insensitive tweet because only a few people are on Social Media. The challenge is that everyone will be Social in ways they can't even imagine. Then we move from six pixels to two. Then what will you do?

Imagine a better future for your company.

PS: It is no longer optional for you to just create TV ads and not have the most brilliant, engaging and helpful mobile websites. In case you were wondering, the year of the mobile was two years go.

#6 Being good at the Long Tail matters just as much as the Head.

I've talked about the long tail on this blog, especially in context for Search. But the concept applies across all channels.

Here's a good example. You can spend all your money on the four standard channels on TV and get in front of 1000 people. But you can probably find 1000 people *relevant* to your brand and message by advertising on 28 *relevant* channels in the long tail (those after channel #14). Or the relevant 50. It is much harder to do, and much harder to explain to your boss who is still looking at GRPs, as GRPs for the long tail mostly don't exist. But if you do, you'll be more efficient, shout less, and deliver more value to your company and delight to your customers.

In every channel we have, Facebook or YouTube or Google or AOL or AdMob or pick your favorite, we have the capacity to shout at concentrations of irrelevant people, or show up for the dispersed hyper-relevant few. While I can't dissuade most Executives from the former, I try as hard as I can to help create strategies for the latter. I'm convinced it is the ability to do the latter that makes you future-proof.

Oh, and this is why Multiplicity matters (TV AND Catalog AND Mobile). This is why owing your own strong digital outpost (your own website) and being present in a space someone else owns (Facebook) matters. This is why having multiple trick ponies matters. They combine to get you really good at the Long Tail execution complexity and massive bottom-line benefit.

#7 Glory will come to the precious few who are willing to embarrass themselves.

We don't take risk and try things, imaginative (possibly glorious) things, because we believe the price of failure is so high. And it is in the real world. Consider creating a TV commercial or re-packaging a product or trying a new offer. First, it takes a very long time to actually try something (add longer plus infinity for risky things). Second, when you fail, you fail spectacularly. Heads roll. Companies get entrenched in what they know and end up constantly optimizing for what's always worked, meanwhile the world changes and these companies die, albeit slowly.

Now consider the web. You can have your most embarrassing idea for a redesign/new offer/product launch/whatever out there in one day. AND you can control for risk! You can only show the redesign to 1% of the site traffic. You can try the offer with just one affiliate or some Bing ads. You can launch the product to a selected group of opted-in customers (or only to people in New York). You can literally control for risk should everything blow up in your face. AND you can have analysis of your risk in almost real time to get an early read and in a few days with statistical significance!

And yet it is the rare company that is able to get over its mental model from the real (old) world and try imaginative things in the digital world where the rules are different and stacked in your favor. Yes, brand destruction is easy in our world, but we are not talking about destroying our brand. We are talking about taking controlled risks and optimization. What marketing program in the universe does not need that?

If you are an executive, encourage your company to check its old world thinking at the door. Consider rewarding people with new ideas. Allocate some of your aforementioned 15% budget to experimentation and testing. If you are a large company don't live without someone with strong Design of Experiments skills. Don't brush off Twitter or Google+ because you are B2B or A2K. Try. With 100% effort . Then do more of what works, or kill ruthlessly.

If you can't embarrass yourself, in controlled quantities, you can't become magnificent. and you can't future-proof your company.

Bonus: #8 Data is your friend.

You did not think I would miss this one did you? :)

This blog is about the joys of measurement and the transformative power of data. So I won't talk about it a lot more in this post.

Let me just say this… more of marketing is becoming algorithmically driven and a lot more decisions we make using reports today are being automated to be made faster, more efficiently, on our behalf. The ability to have a real analytical competency will mean the difference between winners and losers.

So do the 7 things above, but ensure you have a clearly articulated Digital Marketing & Measurement Model. Fill it with the best web metrics to measure success. If you partake in analysis, let that be at the intersection of custom reports and advanced segments.

Data + You = BFF = Business & Personal Success.

Eight simple rules for digital revolutionaries to follow in order to unlock the imagination of their companies and be massively successful in the future. Absorb them. Undertake the very hard task of slowly evolving your company to adapt to them. Monetize the opportunity presented, future-proof your company.

I wish you all the very best.

It's your turn now.

Do you agree with my learning that our primary problem is not web analytics/data but, rather, it is unimaginative web strategies? Have your own stories to share about brand destruction? Do you agree with the eight rules for revolutionaries above? Got your own?

Please share your feedback, ideas and awesomeness via comments.

Thank you.

The 2015 Digital Marketing Rule Book. Change or Perish. is a post from: Occam's Razor by Avinash Kaushik

This blogpost is a collection of tips I share with my friends who are just starting out. Each tip is a "simple" mistake that is easily avoided. My hope is that you'll skip them if you are aware of them, and move on to making more important valuable mistakes. :)

My plan is to wrap each tip with additional observations, context that will be of value even to those who have been at this game for a very long time.

Ready for a can of concentrated compressed energy?

Let's do this.

1. Never Compare Apples to Watermelons.

There are some things that are quite promising about this graph.

I love that the analyst is segmenting the data rather than showing the aggregate trend ("all data in aggregate is essentially crap" – me). I also like that the analyst is showing a six month trend.

But there is something fundamentally wrong about this analysis. Before you jump to my reveal below this graphic, can you guess what's wrong with this data? Try it?

Found the problem?

Four different segments are being compared (yea!), but they are calibrated wrong (boo!).

On the surface this is hard to detect.

The part that is clean is that there is very little overlap between Search Traffic and Referral Traffic. If you use Omniture's Site Catalyst or Google Analytics or whatever, they do a good job of collecting clean data into those two segments. But Mobile is a platform. That traffic (or conversions in this case) is most likely in both Referrals and Search. So it is unclear what to make of that orange stacked bar. Is that good? Is that bad? Additionally it is showing conversions already included in Search and Referral (double counting) and because you have no idea what it is, it is impossible to know what action to take. [The analyst recommended a higher investment in Mobile based on this graph!]

Ditto for Social Media. It is likely that the Social Media conversions are already included in Referrals and, of course, in Mobile. Making that green graph useless. [The analyst recommended a massive increase in investment in Social Media as well. An imprecise conclusion.]

Ensure that you always calibrate the "altitude" of your segments. Always.

So if you want to analyze Mobile performance then you want to compare Mobile and Desktop segments. Very easy to create. For bonus points you can analyze Mobile Search traffic performance with Mobile Non-Search traffic performance. You can analyze Mobile Search performance with Mobile Referring traffic information. Then compare those two to Desktop Search and Desktop Referring traffic. So on and so forth.

Nice clean segments that will help you find nice clean answers (as good or as stinky as they might turn out to be :).

For Social Media you can compare it to Search (with no other changes to that segment, use the Default in GA/SC/WT/YWA), and for Referring Traffic make sure you create a new segment where you take out Referrers such as Facebook.com, Twitter.com, plus.Google.com, Stumbleupon.com etc., etc. So you'll be comparing clean buckets of Social Media, Search and Referring Traffic with no social referrals included.

Nice clean segments that provide you nice clean answers.

Always pause and ask yourself: "Are my segments all at the right 'altitude?' Are they individually unpolluted by the other?"

Then go analyze and confidently make recommendations based on what you find.

2. Don't Alarm HiPPOs and Sr. Leaders Unnecessarily.

Creating graphs is easy, and I could fill five blog posts with all the nonsense one can accomplish by playing with the axes. Yes it is a pet peeve of mine.

What do you think is wrong with this commonly available graph?

Look at it carefully? Found it?

It artificially inflates the importance of a change in the metric might not be all that important. In this case for my data it is not statistically significant (more on that later in this post), but there is no way you would know that (or not know that) just from the data in front of you. Yet the scale used for the y-axis implies that something huge has happened.

I am going to go out on a limb…. unless you are performing surgery and the above graph is showing the heart rate or blood pressure, try and avoid being so melodramatic in your data presentation. It causes people to read things into the performance that they should most likely not read.

You don't always have to have the y-axis at zero. But over-dramatizing this 1.5 point difference is a waste of everyone's time. And you know what happened to the boy who cried wolf one too many times right?

Another important thing.

Label your x axis. Please.

What time period does this graph cover? The last x hours? The last y weeks? The last z months? Depending on what you choose the data is completely ignorable or deserving of insane additional analytical love. (Assuming of course that you fix the y-axis first.)

As the analyst you hold a lot of power in your hands when it comes to visualizing data. Use that power with caution, and great responsibility.

3. Calibrate Your Time Series Optimally.

I am positive that many of you, including my friends who are just getting started, will have taken this screen shot out of Google Analytics and included it in a dashboard or presentation of some kind.

Don't scroll down yet.

Look at it carefully…. what's wrong with it?

It is a chart that shows nine months of performance… by day! The "trend" is completely useless.

But because this is the default view in Google Analytics everyone uses is. [Arrrrrhhh!] The uselessness comes from the fact that when you look at individual days over such a long time period you are effectively hiding insights / important changes.

It is impossible to find anything of value above.

Let's switch to looking at the exact same time period but by week.

Much better right? No more puke of squiggly lines that mean nothing, show nothing. You can kind of sort of see some kind of trend above, especially towards the end of the graph (even this simple thing was essentially hidden before).

Here's the amazing thing… when looking at long time periods you can do better!

The best practice I recommend in Web Analytics 2.0 is that if you are looking at four weeks of data then you can look at the daily trend and still find interesting insights.

If you look at three months of data (one quarter) then you should switch from the day view to week view. The macro trends won't get masked/hidden in the daily noise.

If you look at time periods long than that then it is optimal to look at the monthly view of the data.

In our case this is what that would look like….

Sweet, right?

You can clearly see the dip from Jan to Feb. You can see the nice consistent dip through July. Then something magical happened (What! What! What!) that has traffic rising to record levels.

All of this was nearly impossible to see in the daily graph, and most of it was hard to see in the weekly graph.

Do remember this really important point: When you look at lots of data, nine months in this case, you are usually not looking for tactical bits, you are trying to find big hairy things… calibrate your time series accordingly.

And if you calibrate your segments optimally you can quickly start doing deep dive analysis looking for some answers. What happened post July? What caused the funk between March and July? Why did x or y or z not happen? All the right good questions that otherwise might have been hidden in plain sight.

Simple best practice. Use it.

4. Always, Always, Always Make Your Point Clearly! (Oh, and Colors Matter.)

Everyone of you will present decks with 95 slides. Or at least 55. : )

When you are doing that data regurgitation it is important to try to make life for the person at the other end (typically your boss, or worse your boss's boss) as easy as possible.

At some point in the data tsunami you unleash eyes glaze over and life becomes boring.

So try to… ok, what do you think the two colors in the below graph represent? Don't look at the legend.

Bonus, what do you think the data is telling you? Don't scroll, think for just five seconds.

I my first thought was how come only 29 percent of the organizations have more than one person! That is bad.

Wait. That did not make sense.

I went back to read the question. Then the graph. Then the legend. Then back to the question. Then the legend.

Problem one is that "red" denotes "good" in this case and "green" represents "bad."

Here's something very, very simple you should understand and slavishly follow: Red is bad and Green is good. Always. Don't try to be cute. People will instinctively think that. We have been patterned that way. So show "good" in green and "bad" in red. It will communicate your point faster.

Problem two, much worse, and perhaps only for me, was that it was harder than it should be to understand what this data. First stacked bar above: "Yes 71 percent of the organizations Yes, more than one person." Too many yesses.

And what is the 29 percent? If the question is how many people are directly responsible for improving conversion rates and 71 percent have more than one person, then 29 percent are those that have less than one person or no one? Or just less than one person? Unclear (and frustrating).

[Third bar above] And if 62 percent of the people said "Yes we have no one responsible for improving conversion rates," then what does the 38 percent in green mean? Is it: "No, No we have someone responsible for conversion rate improvement?"

This graph actually comes from a source I deeply respect, an organization with really great analysts. But I'm afraid I completely failed to grasp the point. Do you understand it?

Sometimes you just want to skip the graph.

I don't understand the data above so I'm going to make some numbers up, but would a table like the one below have worked much better to communicate the point?

Why do the graph at all?

Okay so sometimes the application of something humorous might not work (I do always try :). But the rest of the table? Effective?

And if you have data for the last two years then perhaps this table is even more valuable…

Much, much better with context. I love context dearly. Amazingly so does your boss.

Or perhaps if you want to show it to very senior executives then maybe the numbers themselves are less than useful. You could go with something like this…

Scroll back up.

Look at the graph.

Now look at the table above.

I'm riding a horse! No, not really. What do you think?

I love graphs as much as all of you. But above all, what I crave is simple and effective communication. I want to make the point as fast as I can so that we can begin the politics and hard work of taking action. That is after all what pays our salary right?

5. Statistical Significance is Your BFF.

Okay I gave this one away with the title. We all (novices and experts) make this mistake all the time.

We create a table like the one below. (Mercifully the segments are calibrated right, hurray!) We create a "heat map" in the table highlighting where the conversion rate is good. We declare Organic to be the winner, Direct is close behind. Then the other two. And we recommend doing more SEO.

What's the problem with that?

None of this data could be significant – as in the fact the numbers seem to be so different might not mean anything. [Looking at July...] It is entirely possible that it is completely immaterial that Direct is 34% and Email is 10%, or that Referral is 7%.

One simple fix (covered in more detail in this post: 4 Not Useful KPI Measurement Techniques ) is to share the raw numbers to see if the percentage is meaningful at all. For example all the data in the Direct row could represent conversions out of 10 visits and all the Referral data could be representing conversions from 1,000,000 visits each month.

The better, much, much better thing to do would be to compute statistical significance to identify which comparison sets we can be confident are different, and in which cases we simply don't have enough confidence.

I have something special for you. I've just uploaded a brand spanking new Statistical Significance Calculator to my old post on that topic. It does 1-tail and 2-tail tests and the even more beloved chi-square test. Download it. Adapt it for your use. Ecstasy will follow.

One of the most common complaints of our Sr. Leaders is that we engage in massive data puking (true!) and never help them identify with any degree of certainty if an action you are recommending will produce results. Well, this is our chance. If you check to see if the results you are seeing are statistically significant, then make recommendations of action knowing that that will produce results you want (all other things held constant).

Remember ecstasy awaits!

Update: Bonus: If you use Google Analytics the always wonderful Michael Whitaker has created something delightful (triggered by our discussion in comments below). A Z-Test calculation that you can embed directly into Google Analytics!

Here is a mini-tutorial on how to use this delightful feature:

1. Visit Michael's blog and drag the bookmarklet into your browser's bookmarks bar. Stats calculator for Google Analytics.

2. Go to any report in Google Analytics and switch to a Goal tab or the Ecommerce tab.

3. Click Z-Test bookmarklet in your bookmarks bar.

4. At the bottom of your GA report table you'll see a new button called Z-test.

5. Check the box next to two dimensions for whom you would like to check statistical significance (apply the Z-test).

6. Press the button at the bottom of the table, Z-test, and boom (!) you have your answer. Green is good, red (lower then 95%) means you need to collect more data before you decide.

The conversion rate between our two main PPC keywords is 1.33% and 1.94%. Is that data statistically significant? Should we go ahead and invest more in Calico Critters (if we are using fixed budgets or there is more inventory)? Let's check…

Why yes of course we can!

Twitter sends 5,546 Visits and has (on a non-ecommerce website) a Goal Conversion Rate of 5.27%. Facebook sadly only sends a fraction of traffic and has a lower conversion rate 4.71%. Stop spending money/time in Facebook based on this data? Deprioritize it at least? Let's check….

No! See how that saved your goat, you were just about to plunk down a million dollars into Twitter! :)

7. Celebrate your new found awesomeness!

This only, currently works for Ecommerce Conversion Rate and Goal Conversion Rate key performance indicators.

For computing significance ("are the two conversion rates different enough that you can confidently take action") on Ecommerce Conversion Rates you can use this with no thought. (Ok always apply some thought!) But for using it to compute significance for Goal Conversion Rate you should be a little more careful. Unlike Ecommerce Conversion Rate, it is possible for a person to have more than one unique Goal Conversion during a visit in Google Analytics. So when you apply the Z-test you'll be comparing "rotten apples to rotten apples," i.e. measuring the same way for all dimensions. In the most ideal scenario you would apply the Z-test to each goal by itself. I still believe it is of value to use the Z-test for Goal Conversion Rates, but be aware of the nuances.

One more important caveat. Z-test / statistical computations are most optimally applied to results of controlled experiments and not to observational data because in the latter there could be other, uncontrolled, variables at play. So this is not "pure" in some sense. But (as I mention below in comments) we are better off being aware of this purity and still using this test because the insight delivered is better than just "eyeballing" the number to figure out when to take action.

Many thanks to Michael for doing this. No more going to excel (at least for GA), we can be a little smarter quicker directly in our web analytics tools. Makes me wonder why web analytics vendors are so enamored with data puking and can't build all this stuff natively to make more of us Analysis Ninjas!

6. There is Such a Thing as Too Little Data!

A variation on the above "simple" mistake.

I know we all get excited about having data, especially if we are new at this. And we get our tables and charts together and we start reporting data and having a lot of fun.

This, dear reader, is very dangerous. You see there is such a thing as too little data.

You don't want to wait until you've collected millions of rows of data to make any decision, but the table on the left is nearly useless. Recommending doubling down on Facebook (as the Analyst did) this early in your evolution would be a profound mistake.

Things can change so much in just a few days (and they will for you!).

So you can't do anything with data like this?

Pretty close.

But what you can do is look at this report to see if places you've invested time in earning links from are sending you traffic (or not). Look for surprises, places you did not invest money, and see why they linked to you. You can get a tiny bit of understanding of your initial marketing strategy.

Do other useful things.

Look at your search keyword reports. Do you see a few people coming on keywords you SEOed the site for? Better still, go into Webmaster Tools and look to see if your site is well indexed. Look at the keywords for which your site is showing up in Google search results. Are they the ones you were expecting?

Even better… spend time with competitive intelligence tools like Compete / Trends for Websites, Insights for Search, Ad Planner and others to seek clues from your competitors and your industry ecosystem. At this stage you can learn a lot more from their data than your data!

We all tend to read too much into data sometimes. A good analyst knows when there's just not much there and volunteers her/his time on helping run a Task Completion Rate survey or creating new/better Inbound Marketing programs. Go get traffic!

7. Pie Charts Are Evil.

Okay maybe not evil. They are useful on rare occasions. See "Enchanting Analysis: Rule 2: Establish Macro Importance" in this post: Mate Custom Reports With Advanced Segments!

But most of the time they are an active hindrance to communicating anything of value.

Examples of horrible pie charts abound. But let me share this really simple one that I am sure you've seen or perhaps created yourself. :)

Take a moment to breathe it into your brain. What do you think?

The 3D effect does not help. Trust me on that.

This set of charts very cleverly hides any available insights because it makes your executive do these operations for every segment of understanding: Look left, find the interesting slice. Commit the color and number to memory. Go right. Find the color and segment and commit the new number to memory. Now subtract the first number from the second. Decide if the result is good or bad.

Repeat five more times.

Remember to remember only the interesting bits.

When the chart was created did you think you were going to torture your executive today? Would it be surprising then that everyone atom in this universe thinks "omg, numbers are so haaaarrrrd!"?

Why torture people who are so critical to your financial well being?

Just use a table (as we did in #4 above).

Much easier, right?

At the very least, you don't have to dart your eyes from left to right all the time and commit numbers to memory to understand what's happening.

And since you the Ninja-in-making are not being paid to just data puke, why even show things that might not be material?

Just go with this…

Would the discussion with your management team be much more focused now? And faster?

Oh and… you've already put so much effort into collecting and analyzing the data. Why not use your intelligence (and the statistical significance calculator) to filter data and just show what's most relevant?

It is easy to make things hard to understand. Working hard to make them easy to understand is what brings glory. Sustained glory.

So do that.

Okay it is your turn now.

What are the simple mistakes that you've learned to avoid? Would you recommend a different strategy to follow for one of the mistakes above? Got a better picture to submit? The mistake that most sets you off in the field of web analytics? How did you learn not to make these mistakes?

Please share your feedback, pictures, complaints, mistakes via comments.

Thanks!

Data Analysis 101: Seven Simple Mistakes That Limit Your Salary is a post from: Occam's Razor by Avinash Kaushik

This article guides you in understanding the value of the Digital Marketing & Measurement Model (notice the repeated emphasis on Marketing, not just Measurement), and how to create one for yourself. At the end you'll also find some additional examples to inspire you.

Let's go…

The root cause of failure in most digital marketing campaigns is not the lack of creativity in the banner ad or TV spot or the sexiness of the website. It is not even (often) the people involved. It is quite simply the lack of structured thinking about what the real purpose of the campaign is and a lack of an objective set of measures with which to identify success or failure.

I've developed the Digital Marketing & Measurement Model as a simple, structured, five step process to infuse this much needed thinking. Here is what each step in the process helps accomplish:

Step one is to force us to identify the business objectives upfront and set the broadest parameters for the work we are doing. Sr. Executives play a key role in this step.

Step two is to identify crisp goals for each business objective. Executives lead the discussion, you’ll play a contributing role.

Step three is to write down the key performance indicators. You’ll lead the work in this stop, in partnership with a “data person” if you have one.

Step four is to set the parameters for success upfront by identifying targets for each KPI. Organization leaders play a key role here, with input from Marketing and Finance.

Step five, finally, is to identify the segments of people / behavior / outcomes that we’ll analyze to understand why we succeed or failed.

Simple, right? It is harder than you might think, “soft” work always is. Before we go into each step in detail I want to share something extremely critical. The scope/breadth the model has to cover.

A complete, and competent, Digital Marketing & Measurement Model will focus on three key areas of your marketing, and in each answer the cluster of questions provided:

1. Acquisition.
How are you anticipating acquiring traffic for your website / YT video / whatever else you are creating? Did you cover all three components of successful acquisition: Earned, Owned, Paid media? How would you prioritize each? Where are you spending most of your efforts?

2. Behavior.

What is the behavior you are expecting when people arrive? What pages should they see? What videos should they watch? Should they visit repeatedly? Are there certain actions they should take? What is unique about your effort that ties to an optimal experience for a customer?

3. Outcomes.

What outcomes signify value delivered to the business bottom-line? A download? A phone call to your call center? A qualified online lead? Signing up for email promotions? People buying your product / services ? A 95% task completion rate? A 10 point lift in brand perception?

Simply put: Why are we undertaking this digital initiative?

My sincerest hope is that these questions will seed your discussions as you go through the five steps below. If your Digital Marketing & Measurement Model does not cover all three areas of your digital effort, then it is not complete. Please consider revisiting it. Don’t accept a mediocre model.

With that macro thought out of the way, let’s get going and look at a real example of the five step process to solidify this concept.

The business we are doing this for is a real estate company. I’ve picked a tough one because the main outcome is offline success. If they can create a good model then your job is much much easier!

Step 1: Identify the Business Objectives.

Ask this question: Why does your website/campaign exist? (Think of acquisition, behavior and outcomes.)

This is a difficult question to answer because it requires more thinking that you might anticipate. If you do it right at the end of step one you’ll have something that looks like this:



Identifying the business objectives mandates a discussion, multiple discussions, with the senior-most leaders in your company and working with them / sweet-talking their egos and hearts with gentle encouragement, to identify why the site / campaign / digital marketing invitiative exists.

Based on those discussions, in our case, we’ve identified three objectives: Create awareness, generate leads for the builders and highlight community events.

Here’s a great test. Your objectives should be DUMB:

Doable.
Understandable.
Manageable.
Beneficial.

If they are too out there, you'll never get anywhere. If they are too vague, nothing will get done. If they are too lame, they'll inspire no one. Go for real world, clear, executable and those that deliver value to the company (short term and long).

Are your objectives dumb?

Pro Tip: One way to ensure success is to forget that you are creating a set of videos or that you are building a site to host downloads of pdfs or that you are trying to mimic a campaign from Europe. Really, really, really think hard about why you are doing what you are doing. Get the answer from your executive/client.

Step 2: Identify Goals for each Objective.

Drilling down to identify website/campaign Goals requires critical thinking from both the Management, Marketers, and the Analysts – with Management in the leadership role.

My definition: Goals are specific strategies you'll leverage to accomplish the business objectives.

After going through some of the acquisition, behavior questions with stakeholders, here’s our model:



Clean. Has a clear direct line between Goal and each objective. Provides immense clarity.

To deliver on "Create Awareness," in this case, the site needs to support all the offline efforts along with having a relevant online traffic acquisition strategy.

"Generating Leads" comprises the twin goals of providing all kinds of information that will help potential home buyers to make their decision and to collect e-newsletter registrations as well as e-requests for an onsite tour of the model home by the builder.

Finally, "Highlight Events" is for prospective home buyers (visitors to our site). By making them happy with delightful events, at the construction site hopefully in model homes for sale, they can be converted into Net Promoters (to others) and Buyers (themselves).

These goals provide clarity, but they also contain large chunks of specific marching orders for what the Marketers and Analysts need to get done.

Pro Tip: This is super key: Macro + Micro Conversions! If the goals identified don’t cover all the jobs the site/campaign is doing then you might need to revisit your work.

Step 3: Identify the Key Performance Indicators.

Finally we get to deal with data!! I know you’ve been dying to get here. You’ll be the ideas leader here.

My definition: A key performance indicator (KPI) is a metric that helps you understand how you are doing against your objectives.

For each goal, sweat, and find the most hyper relevant KPI. This is what it will look like:



So amazing right?

I am sure your head is buzzing with all the possibilities for custom reports and things to report on, and how much clearer it is what you are supposed to do! Awesome, but hold your horses. We have two more steps to complete. Stay with me.

Pro Tip: Try to look for smart KPIs? Here’s specific guidance to help you…



Pick super awesome key performance indicators that truly reveal success or failure.

Step 4: Identify the Targets.

It is heartbreaking how few people complete this step. It is absolutely critical, in so many ways.

My definition: Targets are numerical values you’ve pre-determined as indicators of success or failure.

Why do you need targets? Consider this: You had an amazing campaign on YouTube. You got 1.2 million views. Is that great or awful? How do you decide? That is why you need targets!

Ok, so you also need them to plan your site / campaign / marketing initiative better. If you were responsible for getting 5 million visits in a month would you execute your campaign differently than if that number was 500k? Or if you were supposed to reach 1,000 CMO’s would you remember not to use Social Media as your primary acquisition strategy? That’s also why you need targets.

Targets can come from historical performance (how you did last time you / someone did something similar). They can come from other efforts (if my one hour long boring video can get 30k views in a week, should your two min peppy video get 1.2 million views?).

Seek people who are accountable (client, management, Finance), they will help you identify targets for each KPI.

Your Digital Marketing & Measurement Model will now look like this…



Now everyone knows what the company is shooting for. When you crack open Google Analytics, or other tools you’re using, you'll immediately jump with joy or weep when you see the KPI. You'll instantly know what is good and what is bad.

Pro Tip: If you have no targets then make something up. Use a number that if reached won’t embarrass you / your management / me. :) That is a good start; you can revise the number next month after you get the first blush of data. What’s important is that you never measure without having some sense of what good or bad performance looks like. The more experience you have, the better you’ll get at setting targets. Good targets.

Step 5: Identify valuable Segments for analysis.

This last part is one that is particularly meaningful to me because of its incredible value.

My definition: A group of people, their sources, onsite behavior, and outcomes.

When you log into Google Analytics or any other data source you are deluged by data and you could go in a million different directions.

Remember: We not only wanted focus, we wanted hyper-focus.

Take 10 more minutes from the key executives. Have a discussion with them about what the most important segments to focus on are for each goal.

Identify the sources of traffic, types of people desirable, their attributes, their behavior, business outcomes that they care about the most. And what customers to the site might want to accomplish. Balance for the company and the customers.

You’ll provide leadership here and if you did a great job then your DMMM will look something like this:



What groups of visitors were important? What visitor behavior is desirable? What a traffic source was Marketing focused on? Who are we trying to attract? What on our site is important – at least according to us? And more such questions are important to answer to get to the optimal segments.

Pro Tip: How to Identify Analytics Segments. Read. Act. Enough said.

Hallelujah, praise the lord you are done!

This was a lot of work, but I assure you that at this point you will thank God and your Cat that you worked this hard. You now have a structure that will guide your measurement efforts. The insights you derive will be of value because they are grounded in what’s important to the business and the leadership. And when you make recommendations based on data… guess what… action will be taken. Worth it, right?

Here's the sexiness: You now know what's important and where to start and what to focus on. Your boss/client knows what success or failure looks like and how to connect her/his business objectives to your data. Prioritized business focus for relevant data analysis!

You have the basis of a solid contract. Get the DMMM signed (preferably in blood!) so that all parties are clear on what everyone is supposed to be solving for.

Punch-line: Always, always, always work with the above "Marketing & Measurement contract" in hand.

Two Bonus Items.

Some of you might have noticed that I’d eliminated the Tour Conversions KPI in step five. That was simply to make the image in step five looks prettier. But worry not, with that KPI included our Digital Marketing & Measurement Model will have this beautiful final form…



Can we run the most fantastically actionable web analytics program in any company now? Yes we can!

One last gift for you.

When you create your own Digital Marketing & Measurement Model you don't have to use the format I've used above, you can add to it as you see fit.

I wanted to share with you a different format, and example. Below is the model for a retail e-commerce website with an online and offline presence:



I hope that the two examples in this blog post will help inspire you to use the Digital Marketing & Measurement Model as the foundation of your web analytics efforts.

I believe, with every fiber of my being, that this is will empower magnificent success.

Good luck.

Digital Marketing and Measurement Model is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/digital-marketing-and-measurement-model/feed/ 0 http://www.kaushik.net/avinash/google-analytics-visits-metric-change-implications-opportunities/ http://www.kaushik.net/avinash/google-analytics-visits-metric-change-implications-opportunities/#comments Mon, 29 Aug 2011 09:29:50 +0000 Avinash Kaushik http://www.kaushik.net/avinash/?p=4394

...  the rough path: Web Analytics 2.0 immersion > Adv KPI analysis techniques > Segmentation (Oh I love this so) > Actionable ...

Google Analytics Visits Metric: Change. Implications. Opportunities. is a post from: Occam's Razor by Avinash Kaushik

]]> Recently the Google Analytics team changed the way sessionization algorithm. In lay terms… how the start, duration and end of a Visit is computed.

A minor version of the butterfly effect occurred, one small change in a part of the system caused a few other smaller changes in other parts of the system. Some people freaked out. Others wondered what the fuss was all about. Still others wondered what they were going to eat for lunch. :)

If you were in the first two categories this blogpost (delightful video actually!) is for you.

Some of you know that I am the co-Founder of Market Motive, and also lead the Web Analytics Master Certification course (which is one of eight courses in Internet Marketing certification). The course is a structured program that teaches the gamut of web analytics. You start at the beginning (I know!) and evolve to an Analysis Ninja-hood.

Here's the rough path: Web Analytics 2.0 immersion > Adv KPI analysis techniques > Segmentation (Oh I love this so) > Actionable reports > Surveys > Testing > Multi-Channel analytics > Competitive Intelligence > Dissertation > (If you pass the rigorous evaluation) > Certification.

A key strategy for having the most kick butt course in the world is that it is astonishingly fresh. In addition to the structured curriculum we do weekly hour and half calls. Guess what the latest video in the Web Analytics Master Certification course was? Did you guess GA Visits changes? Ding, ding, ding!

I wanted to make an exception and share that video with all of you. It will hopefully give you an idea of the love and unique approach that Market Motive brings to teaching, along with sharing some very critical context and guidance on this important issue.

This important video covers:

00:00 – 02:14 Intro. Which metrics are impacted?

02:14 – 09:20 Context. How do web analytics tools currently compute visits? And if you use Omniture, WebTrends, CoreMetrics, Piwik, what important question should you ask your vendor.

09:20 – 12:45 Impact. Estimating how much the change impacts you! Details, and grab the custom report.

12:45 – 17:50 The change. Clear articulation of what the change actually was. The new sessionization algorithm.

17:50 – 21:48 The implications. With examples that impact on Average Page Views per Visit, Bounce Rates, % New Visits, % Returning Visits.

21:48 – 31:20 Multi-Channel Funnels. Impact, new sweet yummy implications, and digital media attribution analysis. The one report that will probably cause you to hyper-ventilate a little bit.

31:20 – 37:02 Closing thoughts. My four point guidance on the implications and opportunities.

There is one important thing the video does not cover. At the time Google Analytics switched over to the new sessionization algorithm, coincidently a bug also impacted GA users data. The bug, temporarily, made it seem as if the impact was crazy because of the sessionization change. This bug was identified, data was fixed, that "craziness" is gone. Hence I'm skipping covering that.

Okay are you raring to go? Sink your teeth? Jump into the knowledge pond?

Here's the video:





Full of surprises, right?

I hope the video shared the reports and data you need to figure out for yourself in what way this change impacts you, the context you need to figure out how to internalize the change and share concrete information with your management team (rather than the FUD so easily accessible via tweets, Plus, Facebook posts, etc). For some of you the changes might be larger than the range I've mentioned in the video. But consider this: You know have a MUCH better idea of how your customers behave. You would not want to be blind to that (and have no change in data!). I suspect not.

I particularly enjoyed sharing with you the multi-channel funnels part, and the implications on attribution analysis. Is it not amazing that without the monkey gyrations – JavaScript tag updates, variables capturing, praying for rain – other tools might put you through (*if* they do multi-channel unique visitor analysis), that you can get this amazing data?

Beyond what's in the video consider the Assisted Conversions report, Top Conversions Paths, Time Lag and Path Length details. Earning a load of money and happiness because, finally, you'll deliver big consistent impact to the company bottom-line is now reality.

In closing… Change is always hard to accept, especially when it comes with even the slightest impact on status quo. But if there has to be progress in life, then change is just the thing that puts us in a higher, more optimal orbit. It makes a better existence possible.

Go give the new data and reports a try. Thinking in a new way will require effort and brain power. But real happiness is worth it.

All the best!

As always, it is your turn now.

Did the video clarify how the visits algorithm works today? Did it help you internalize the change, and its value? Given my scenario of Google > Bing > Email > Mom > Conversion, would you have changed the GA sessionization algorithm in a different way? If you downloaded the custom report, how much did "zero visits" (multi-visit sessions) impact your company?

Please share your feedback on the video format, and the content of the video via comments.

Thanks.

PS: A small present for you. I've created a special page that collects every single thing I've written on this blog. All 443,192 words (as of today). The posts are categorized into 11 clusters, making it easy for you to find what you are looking for.

Access it here: Digital Marketing & Analytics Knowledge

Google Analytics Visits Metric: Change. Implications. Opportunities. is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/google-analytics-visits-metric-change-implications-opportunities/feed/ 81 http://www.kaushik.net/avinash/4384/ http://www.kaushik.net/avinash/4384/#comments Mon, 22 Aug 2011 07:23:59 +0000 Avinash Kaushik http://www.kaushik.net/avinash/?p=4384

...  you can give me?” ~ Web Metrics: “What is a KPI? How do I choose well? How to focus?”         + Standard Metrics ...  Perfection Triggers, Benchmarking, Identifying Goals & KPI Cards Five Rules for High Impact Web Analytics Dashboards Web Analytics: ...

is a post from: Occam's Razor by Avinash Kaushik

]]> Here you’ll find all my blog posts categorized into a structure that will hopefully make it easy for you to discover new content, find answers to your questions, or simply wallow in some excellent analytics narratives.

To assist with that process everything’s organized into these sections:

~ Digital Marketing: “What is amazing out there? How can my company become great?”

~ Digital Analytics: “Am I thinking right? How do I crush tough problems? Be data driven?”

~ Practical Analysis: “How do I become an Analysis Ninja?”
        + Strategic Analysis Articles
        + Tactical Analysis Articles

~ Qualitative Analysis: “What’s Voice of Customer? Why care about “the why?” Surveys?”

~ Competitive Intelligence Analysis: “What can I learn from my others / my industry?”

~ Excellent Analytics Tips Series: “What is the best possible advice advice you can give me?”

~ Web Metrics: “What is a KPI? How do I choose well? How to focus?”
        + Standard Metrics Revisited Series

~ Tools, Tools, Tools!: “How to pick? What’s the best out there? How can I be smart?”

~ Digital Careers: “How can I get a bigger salary? How do I learn? Find people?”

~ Compiled Knowledge: “Why does that happen? How can I make this work? I wonder…”

~ Grab bag: Life lessons, observations, advice, and more
        + Blogging Experience Articles
        + I. Me. Myself.
        + Book Articles
        + Interviews
        + Misc Articles

In each section the listing is from the latest article to the earliest. Let’s go and and have some fun!

Digital Marketing: “What is amazing out there? How can my company become great?”

11 Digital Marketing “Crimes Against Humanity”
Mobile Marketing and Analytics: Click-to-Call Mobile Ad Campaigns
Key To Your Digital Success: Web Analytics Measurement Model
Hyper Focus Your Digital Marketing: Ensure A Clear Line Of Sight To Net Income!
Online Marketing Still A Faith Based Initiative. Why? What’s The Fix?
Six Tips For Improving High Bounce / Low Conversion Web Pages
Redefining Innovation: Incremental, w/ Side Effects & Transformational
How To Excite People About Web Analytics: Five Tips
Lack Management Support or Buy-in? Embarrass Them!
Six Rules For Creating A Data Driven Boss!
The Promise & Challenge of Behavior Targeting (& Two Prerequisites)
Trinity: A Mindset & Strategic Approach
The 10 / 90 Rule for Magnificent Web Analytics Success

Digital Analytics: “Am I thinking right? How do I crush tough problems? Be data driven?”

10 Fundamental Web Analytics Truths: Embrace ‘Em & Win Big
Who Owns Web Analytics? A Framework For Critical Thinking
Barriers To An Effective Web Measurement Strategy [+ Solutions!]
Slay The Analytics Data Quality Dragon & Win Your HiPPO’s Love!
Web Data Quality: A 6 Step Process To Evolve Your Mental Model
Multichannel Analytics- Tracking Online Impact Of Offline Campaigns
Multichannel Analytics: Tracking Offline Conversions. 7 Best Practices
The Ultimate Web Analytics Data Reconciliation Checklist
Evolve Intelligently: Achieve Web Analytics Nirvana, Successfully
A Primer On Web Analytics Visitor Tracking Cookies
The “Action Dashboard” (An Alternative To Crappy Dashboards)
In Web Analytics Context Is King Baby! Go Get Your Own
Measuring Online Engagement: What Role Does Web Analytics Play?
“Engagement” Is Not A Metric, It’s An Excuse
History Is Overrated. (Atleast For Us, Atleast For Now.)
Is Conversion Rate Enough? It’s A Good Start, Now Do More!
Accuracy, Precision & Predictive Analytics
Multiplicity: Succeed Awesomely At Web Analytics 2.0!
Convert Data Skeptics: Document, Educate & Pick Your Poison
Rethink Web Analytics: Introducing Web Analytics 2.0
Data Mining And Predictive Analytics On Web Data Works? Nyet!
Offsite and Onsite Resourcing, Behavioral Targeting Variables
I Got No Ecommerce. How Do I Measure Success?
Web Analytics Data Sampling 411
Podcast: Being Data-Driven, Exploiting the Long Tail, Analyst Skills
Defining a “Master Metric”, + a Framework to Gain a Competitive Advantage in Web Analytics
Redefining Conventional Wisdom On “Enterprise Class” Web Analytics
Delibrate Your Data, Dig Into Your Data, Reimagine Content Reporting
Podcast: Google, Evangelism, Data Privacy, Analytics, Yahoo! & Getting The “Web”
Perfection: Perfection Is Dead, Long Live Perfection
Triggers, Benchmarking, Identifying Goals & KPI Cards
Five Rules for High Impact Web Analytics Dashboards
Web Analytics: A Puzzle or A Mystery?
Podcast: Measuring Rich Media (Ajax, Flash / Flex, RSS & Blogs)
Customer Personas, Customer Value, Customer Retention and Non-line Marketing
Web Analysis: In-house or Out-sourced or Something Else?
Five “Ecosystem” Challenges for Web Analytics Practitioners
Is Real-Time Analytics Really Relevant?
Seven Steps to Creating a Data Driven Decision Making Culture
Customer Lifetime Value ROI, Buzz Monitoring, Click Fraud
Measuring Success for a Support Website: A Point of View
Video: Google Tech Talks: Avinash Kaushik
Tips for Web Analytics Success for Small Businesses
Data Quality Sucks, Let’s Just Get Over It
Traditional Web Analytics is Dead

Practical Analysis: “How do I become an Analysis Ninja?”

Strategic Analysis Articles
The Difference Between Web Reporting And Web Analysis
Beginner’s Guide To Web Data Analysis: Ten Steps To Love & Success
Rebel! Refuse Report Requests. Only Answer Business Questions, FTW.
Best Web Analysis Tip: Eliminate Data & Eschew Fake Proxies
5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two
Web Analytics Segmentation: Do Or Die, There Is No Try!
Analyze This: 5 Rules For Awesome Impromptu Web Analysis
Web Analytics Success Measurement For Government Websites
Aggregation of Marginal Gains: Recession Busting Analytics!
Paid Search Analytics: Measuring Value of “Upper Funnel” Keywords
Videos: Actionable Web Analytics Tips
Consultants, Analysts: Present Impactful Analysis, Insightful Reports
Web Analytics Demystified
Six Data Visualizations That Rock!
Getting Started With Web Analytics: Step One – Glean Macro Insights
The Awesome Power of Visualization 2 -> Death and Taxes 2007
Stop Obsessing About Conversion Rate
The Awesome Power of Data Visualization
Path Analysis: A Good Use of Time?

Tactical Analysis Articles
Produce Actionable Insights: Mate Custom Reports With Adv Segments!
Three Amazing Web Data Analyses Techniques For Analysis Ninjas
3 Advanced Web Analytics Visitor Segments: Non-Flirts, Social, Long Tail
3 Awesome, Downloadable, Custom Web Analytics Reports
End of Dumb Tables in Web Analytics Tools! Hello: Weighted Sort
Identify The Known Unknowns: Leverage Analytics Custom Alerts
Leverage Custom Reports For Better Insights!
Awesome Analysis Tip: Move Beyond The Top Ten. Find Love (/Insights)
Google Analytics Becomes Intelligent. Hello Insights!
PPC / SEM Analytics: 5 Actionable Tips To Improve ROI
Google Analytics Maximized: Deeper Analysis, Higher ROI & You
Google Analytics Releases Advanced Segmentation: Now Be A Ninja!
Be Awesome At Comparing KPI Trends Over Time
The Best Web Analytics Report
Pick One, Just One Web Analytics Report, Go!
Make Web Analytics Actionable: Focus On “What’s Changed”
Kick Butt With Internal Site Search Analytics
Tips For Measuring Success Of Your Blog (365 Days In Numbers)
Google Analytics Is Re-Launched: Do These Five Things First
Are You Into Internal Site Search Analysis? You Should Be

Qualitative Analysis: “What’s Voice of Customer? Why care about “the why?” Surveys?”

Think Smart, Move Fast: Heuristic Evaluations Rock!
Experiment or Die. Five Reasons And Awesome Testing Ideas
4Q – The Best Online Survey For A Website, Yours Free!
Solving For The What & The Why: On-demand Webinar
Eight Tips For Choosing An Online Survey Provider
The Three Greatest Survey Questions Ever
Lab Usability Testing: What, Why, How Much
Build A Great Web Experimentation & Testing Program
Got Surveys? Recommendations from the Trenches
Experimentation and Testing: A Primer
Overview & Importance of Qualitative Metrics

Competitive Intelligence Analysis: “What can I learn from my others / my industry?”

The Definitive Guide To (8) Competitive Intelligence Data Sources!
Paris Hilton, Kim Kardashian & Telling Stories With Data
Google’s Search Based Keyword Tool: Monetize The Long Tail of Search
Google Insights for Search: Analyze the Database of Intentions
Google / DoubleClick Ad Planner: Amazing Demographic & Psychographic Analysis
Google Trends for Websites: Competitor Traffic Analysis
Podcast: Competitive Analysis Best Practices, Competency Judgement Model
Five Free “Advanced” Web Analytics Examples: Look Outside, Think Different
Competitive Intelligence Analysis: Metrics, Tips & Best Practices
Competitive Intelligence Analysis: Why, What & How to Choose

Excellent Analytics Tips Series: “What is the best possible advice advice you can give me?”

Tip #19: Identify Website Goal [Economic] Values
Tip #18: Make Love To Your Direct Traffic
Tip #17: Calculate Customer Lifetime Value
Tip #16: Brand Evangelists Index
Tip #15: Measure Latent Conversions & Visitor Behavior
Tip #14: Measuring Value of Ecommerce Sales Tools
Tip #13: Measure Macro AND Micro Conversions
Tip #12: Unsuspected Correlations Are Sweet!
Tip #11: Measure Effectiveness Of Your Web Pages
Tip #10: How Thick is Your Head and How Long is Your Tail?
Tip #9: Leverage Statistical Control Limits
Tip #8: Measure the Real Conversion Rate & “Opportunity Pie”
Tip #7: The Adorable Site Abandonment Rate Metric
Tip#6: Measure Days & Visits to Purchase
Tip#5: Conversion Rate Basics & Best Practices
Tip#4: Make Your Analysis/Reports “Connectable”
Tip#3: Turbocharge Your SEM/PPC Analysis
Tip#2: Segment Absolutely Everything
Tip#1: Statistical Significance

Web Metrics: “What is a KPI? How do I choose well? How to focus?”

Email Marketing: Campaign Analysis, Metrics, Best Practices
Your Web Metrics: Super Lame or Super Awesome?
Web Analytics 101: Definitions: Goals, Metrics, KPIs, Dimensions, Targets
Kill Useless Web Metrics: Apply The “Three Layers Of So What” Test
Search Engine Optimization (SEO) Metrics & Analytics
Social Media Analytics: Twitter: Quantitative & Qualitative Metrics
Brand Measurement: Analytics & Metrics for Branding Campaigns
Actively Avoid Insights: 4 Useful KPI Measurement Techniques
Six Web Metrics / Key Performance Indicators To Die For
Web Metrics Demystified
Blog Metrics: Six Recommendations For Measuring Your Success
Web Analytics Standards: 26 New Metrics Definitions
How To Measure Success of a Blog (120 Days in Numbers)
Standard Web Metrics Definitions from the Web Analytics Association
Thirty Days In Numbers (how to measure blog success)

Standard Metrics Revisited Series
#6: Daily, Weekly, Monthly Unique Visitors
#5 : Conversion / ROI Attribution
#4 : Time on Page & Time on Site
#3: Bounce Rate
#2: Top Exit Pages

Tools, Tools, Tools!: “How to pick? What’s the best out there? How can I be smart?”

Best Web Analytics 2.0 Tools: Quantitative, Qualitative, Life Saving!
Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions
Analytics Tools Comparison: Coradiant vs. Tealeaf
Negotiating A Web Analytics Vendor Contract? Check SLA’s
Web Analytics Vendor Tools Comparison (And One Challenge)
Find You Web Analytics Soul Mate (How To Run An Effective Tool Pilot)
Web Analytics Tools Comparison: A Recommendation
Web Analytics Tools: Does User Interface (UI) Matter?
Web Analytics Tool Selection: Three Questions to ask Yourself
Web Analytics Tool Selection: 10 Questions to ask Vendors
Web Analytics Technical Implementation Best Practices. (JavaScript Tags)
The Great Web Data Capture Debate: Web Logs or JavaScript Tags?
All Web Analytics Tools Should Be Free! Not.
How to Choose a Web Analytics Tool: A Radical Alternative
Five “Ecosystem” Challenges for Web Analytics Vendors

Digital Careers: “How can I get a bigger salary? How do I learn? Find people?”

Web Analytics Career Guide: From Zero To Hero In Five Steps!
The Market Motive Master Certification Manifesto: Web Analytics
I Wish I’d Known That. [Digital Analytics Edition.]
Five Sweet Web Analytics Resolutions To Kick It Up A Notch
Checklist for a Comprehensive Web Analytics Education
Analytics Career: Job Titles, Salaries, Technical & Business Roles
Interviewing Tip: Stress Test Critical Thinking. Please.
Hiring? What Works: Fresh blood or old hands? Experience or Novicity?
Market Motive: Internet Marketing Knowledge On Tap
How Should Web Analysts Spend Their Day?
Web Analytics Career Advice: Statistics, Business, IT & Mushrooms
Seven Skills to Look for in a Web Analytics Manager
Hiring a Senior Web Analyst? Here’s a Suggested Job Requisition / Description
Make a Great Vendor / Agency / Consulting Pitch – Win Big Contracts
Top Ten: Signs You Are A Great Analyst

Compiled Knowledge: “Why does that happen? How can I make this work? I wonder…”

Web Analytics: Frequently Asked Questions And Direct Answers
Dear Avinash: Web Metrics & Analytics Questions, Facebook Edition
Dear Avinash: Top Web Analytics Questions, Twitter Edition
Dear Avinash: Bounces, Abandonment, Visitor Ratios & Data Drops!
Google Analytics Help: Questions, Answers, Tips, Ideas, Suggestions
“Yes M’am, Yes You In The Back Row” [13 Web Analytics Questions]
Dear Avinash: Stressed Agency Analyst & Robots Are Out To Get Me!
Three Interviews, Six Interesting Web Analytics Questions

Grab bag: Life lessons, observations, advice, and more

Blogging Experience Articles
An Incredible Analytics Experience: 5 Years of Occam’s Razor
Benefits Of Blogging – A Practitioner’s Perspective
Web Analytics Blog: Reflections
Ten More Blogging Tips From A Novice Blogger
Top Ten Web Analytics Blogs
Top Ten Blogging Tips & Insights from a Novice Blogger

I. Me. Myself.
This I Believe [A Manifesto for Web Marketers & Analysts]
Life, Liberty And The Pursuit Of Happiness
Social Objects and doing the Stupid Passion Thing
The Burmese Satyagraha
My Friend John
Web Analytics Demystified: Revisited
10 Insights From 11 Months Of Working At Google
Next Stop, Wonderland
Redemption for the Indianapolis Colts at Super Bowl XLI
Nine Rules To Work / Live By
Three “Spire’s” of Great Leadership

Book Articles
Web Analytics 2.0 Book: In Stores Now!!
Web Analytics Books!
Web Analytics: An Hour A Day – Book Update
Five Thousand Times Two Translates Into Goodness
The People Of Web Analytics: An Hour A Day
Web Analytics: An Hour A Day
Me Talk Pretty One Day

Interviews
Ten Minutes With… Mike Moran, IBM
Ten Minutes With… Jason Burby, ZAAZ
Ten Minutes With… Brett Crosby, Google Analytics
Ten Minutes With… Dr. Stephen Turner
Ten Minutes With… Matt Belkin

Misc Articles
What Makes A Great Conference? Lessons From Shop.Org
Microsoft Gatineau: My Wish-list for the Web Analytics Application
Celebrating Two One Thousands
2007 Predictions: Web Analytics
Hello, My Name is Avinash. Vendors What is Unique About You?
Google AdWords plus Google Analytics: Market Manipulation and Possibility of Mischief?
1,200,157 to 9,744 in 3: Thank You All

is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/4384/feed/ 0 http://www.kaushik.net/avinash/sitemap/ http://www.kaushik.net/avinash/sitemap/#comments Mon, 22 Aug 2011 07:22:42 +0000 Avinash Kaushik http://www.kaushik.net/avinash/?page_id=4339

...  advice you can give me?" ~ Web Metrics: "What is a KPI? How do I choose well? How to focus?"         +  ...  Benchmarking, Identifying Goals & KPI Cards Five Rules for High Impact Web Analytics Dashboards Web Analytics: ...  Segmentation: Now Be A Ninja! Be Awesome At Comparing KPI Trends Over Time The Best Web Analytics Report Pick One, Just One Web ...

Knowledge is a post from: Occam's Razor by Avinash Kaushik

]]> Here you'll find all my blog posts categorized into a structure that will hopefully make it easy for you to discover new content, find answers to your questions, or simply wallow in some excellent analytics narratives.

To assist with that process everything's organized into these sections:

~ Digital Marketing: "What is amazing out there? How can my company become great?"

~ Digital Analytics: "Am I thinking right? How do I crush tough problems? Be data driven?"

~ Practical Analysis: "How do I become an Analysis Ninja?"
        + Strategic Analysis Articles
        + Tactical Analysis Articles

~ Qualitative Analysis: "What's Voice of Customer? Why care about "the why?" Surveys?"

~ Competitive Intelligence Analysis: "What can I learn from my others / my industry?"

~ Excellent Analytics Tips Series: "What is the best possible advice advice you can give me?"

~ Web Metrics: "What is a KPI? How do I choose well? How to focus?"
        + Standard Metrics Revisited Series

~ Tools, Tools, Tools!: "How to pick? What's the best out there? How can I be smart?"

~ Digital Careers: "How can I get a bigger salary? How do I learn? Find people?"

~ Compiled Knowledge: "Why does that happen? How can I make this work? I wonder…"

~ Grab bag: Life lessons, observations, advice, and more
        + Blogging Experience Articles
        + I. Me. Myself.
        + Book Articles
        + Interviews
        + Misc Articles

In each section the listing is from the latest article to the earliest. Let's go and and have some fun!

Digital Marketing: "What is amazing out there? How can my company become great?"

11 Digital Marketing “Crimes Against Humanity”

Mobile Marketing and Analytics: Click-to-Call Mobile Ad Campaigns

Key To Your Digital Success: Web Analytics Measurement Model

Hyper Focus Your Digital Marketing: Ensure A Clear Line Of Sight To Net Income!

Online Marketing Still A Faith Based Initiative. Why? What's The Fix?

Six Tips For Improving High Bounce / Low Conversion Web Pages

Redefining Innovation: Incremental, w/ Side Effects & Transformational

How To Excite People About Web Analytics: Five Tips

Lack Management Support or Buy-in? Embarrass Them!

Six Rules For Creating A Data Driven Boss!

The Promise & Challenge of Behavior Targeting (& Two Prerequisites)

Trinity: A Mindset & Strategic Approach

The 10 / 90 Rule for Magnificent Web Analytics Success

Digital Analytics: "Am I thinking right? How do I crush tough problems? Be data driven?"

Measuring Incrementality: Controlled Experiments to the Rescue!

10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big

Who Owns Web Analytics? A Framework For Critical Thinking

Barriers To An Effective Web Measurement Strategy [+ Solutions!]

Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love!

Web Data Quality: A 6 Step Process To Evolve Your Mental Model

Multichannel Analytics- Tracking Online Impact Of Offline Campaigns

Multichannel Analytics: Tracking Offline Conversions. 7 Best Practices

The Ultimate Web Analytics Data Reconciliation Checklist

Evolve Intelligently: Achieve Web Analytics Nirvana, Successfully

A Primer On Web Analytics Visitor Tracking Cookies

The "Action Dashboard" (An Alternative To Crappy Dashboards)

In Web Analytics Context Is King Baby! Go Get Your Own

Measuring Online Engagement: What Role Does Web Analytics Play?

"Engagement" Is Not A Metric, It's An Excuse

History Is Overrated. (Atleast For Us, Atleast For Now.)

Is Conversion Rate Enough? It's A Good Start, Now Do More!

Accuracy, Precision & Predictive Analytics

Multiplicity: Succeed Awesomely At Web Analytics 2.0!

Convert Data Skeptics: Document, Educate & Pick Your Poison

Rethink Web Analytics: Introducing Web Analytics 2.0

Data Mining And Predictive Analytics On Web Data Works? Nyet!

Offsite and Onsite Resourcing, Behavioral Targeting Variables

I Got No Ecommerce. How Do I Measure Success?

Web Analytics Data Sampling 411

Podcast: Being Data-Driven, Exploiting the Long Tail, Analyst Skills

Defining a "Master Metric", + a Framework to Gain a Competitive Advantage in Web Analytics

Redefining Conventional Wisdom On "Enterprise Class" Web Analytics

Delibrate Your Data, Dig Into Your Data, Reimagine Content Reporting

Podcast: Google, Evangelism, Data Privacy, Analytics, Yahoo! & Getting The "Web"

Perfection: Perfection Is Dead, Long Live Perfection

Triggers, Benchmarking, Identifying Goals & KPI Cards

Five Rules for High Impact Web Analytics Dashboards

Web Analytics: A Puzzle or A Mystery?

Podcast: Measuring Rich Media (Ajax, Flash / Flex, RSS & Blogs)

Customer Personas, Customer Value, Customer Retention and Non-line Marketing

Web Analysis: In-house or Out-sourced or Something Else?

Five "Ecosystem" Challenges for Web Analytics Practitioners

Is Real-Time Analytics Really Relevant?

Seven Steps to Creating a Data Driven Decision Making Culture

Customer Lifetime Value ROI, Buzz Monitoring, Click Fraud

Measuring Success for a Support Website: A Point of View

Video: Google Tech Talks: Avinash Kaushik

Tips for Web Analytics Success for Small Businesses

Data Quality Sucks, Let's Just Get Over It

Traditional Web Analytics is Dead

Practical Analysis: "How do I become an Analysis Ninja?"

Strategic Analysis Articles

The Difference Between Web Reporting And Web Analysis

Beginner's Guide To Web Data Analysis: Ten Steps To Love & Success

Rebel! Refuse Report Requests. Only Answer Business Questions, FTW.

Best Web Analysis Tip: Eliminate Data & Eschew Fake Proxies

5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two

Web Analytics Segmentation: Do Or Die, There Is No Try!

Analyze This: 5 Rules For Awesome Impromptu Web Analysis

Web Analytics Success Measurement For Government Websites

Aggregation of Marginal Gains: Recession Busting Analytics!

Paid Search Analytics: Measuring Value of "Upper Funnel" Keywords

Videos: Actionable Web Analytics Tips

Consultants, Analysts: Present Impactful Analysis, Insightful Reports

Web Analytics Demystified

Six Data Visualizations That Rock!

Getting Started With Web Analytics: Step One – Glean Macro Insights

The Awesome Power of Visualization 2 -> Death and Taxes 2007

Stop Obsessing About Conversion Rate

The Awesome Power of Data Visualization

Path Analysis: A Good Use of Time?

Tactical Analysis Articles

Produce Actionable Insights: Mate Custom Reports With Adv Segments!

Three Amazing Web Data Analyses Techniques For Analysis Ninjas

3 Advanced Web Analytics Visitor Segments: Non-Flirts, Social, Long Tail

3 Awesome, Downloadable, Custom Web Analytics Reports

End of Dumb Tables in Web Analytics Tools! Hello: Weighted Sort

Identify The Known Unknowns: Leverage Analytics Custom Alerts

Leverage Custom Reports For Better Insights!

Awesome Analysis Tip: Move Beyond The Top Ten. Find Love (/Insights)

Google Analytics Becomes Intelligent. Hello Insights!

PPC / SEM Analytics: 5 Actionable Tips To Improve ROI

Google Analytics Maximized: Deeper Analysis, Higher ROI & You

Google Analytics Releases Advanced Segmentation: Now Be A Ninja!

Be Awesome At Comparing KPI Trends Over Time

The Best Web Analytics Report

Pick One, Just One Web Analytics Report, Go!

Make Web Analytics Actionable: Focus On "What's Changed"

Kick Butt With Internal Site Search Analytics

Tips For Measuring Success Of Your Blog (365 Days In Numbers)

Google Analytics Is Re-Launched: Do These Five Things First

Are You Into Internal Site Search Analysis? You Should Be

Qualitative Analysis: "What's Voice of Customer? Why care about "the why?" Surveys?"

Think Smart, Move Fast: Heuristic Evaluations Rock!

Experiment or Die. Five Reasons And Awesome Testing Ideas

4Q – The Best Online Survey For A Website, Yours Free!

Solving For The What & The Why: On-demand Webinar

Eight Tips For Choosing An Online Survey Provider

The Three Greatest Survey Questions Ever

Lab Usability Testing: What, Why, How Much

Build A Great Web Experimentation & Testing Program

Got Surveys? Recommendations from the Trenches

Experimentation and Testing: A Primer

Overview & Importance of Qualitative Metrics

Competitive Intelligence Analysis: "What can I learn from my others / my industry?"

The Definitive Guide To (8) Competitive Intelligence Data Sources!

Paris Hilton, Kim Kardashian & Telling Stories With Data

Google's Search Based Keyword Tool: Monetize The Long Tail of Search

Google Insights for Search: Analyze the Database of Intentions

Google / DoubleClick Ad Planner: Amazing Demographic & Psychographic Analysis

Google Trends for Websites: Competitor Traffic Analysis

Podcast: Competitive Analysis Best Practices, Competency Judgement Model

Five Free "Advanced" Web Analytics Examples: Look Outside, Think Different

Competitive Intelligence Analysis: Metrics, Tips & Best Practices

Competitive Intelligence Analysis: Why, What & How to Choose

Excellent Analytics Tips Series: "What is the best possible advice advice you can give me?"

Tip #19: Identify Website Goal [Economic] Values

Tip #18: Make Love To Your Direct Traffic

Tip #17: Calculate Customer Lifetime Value

Tip #16: Brand Evangelists Index

Tip #15: Measure Latent Conversions & Visitor Behavior

Tip #14: Measuring Value of Ecommerce Sales Tools

Tip #13: Measure Macro AND Micro Conversions

Tip #12: Unsuspected Correlations Are Sweet!

Tip #11: Measure Effectiveness Of Your Web Pages

Tip #10: How Thick is Your Head and How Long is Your Tail?

Tip #9: Leverage Statistical Control Limits

Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"

Tip #7: The Adorable Site Abandonment Rate Metric

Tip#6: Measure Days & Visits to Purchase

Tip#5: Conversion Rate Basics & Best Practices

Tip#4: Make Your Analysis/Reports "Connectable"

Tip#3: Turbocharge Your SEM/PPC Analysis

Tip#2: Segment Absolutely Everything

Tip#1: Statistical Significance

Web Metrics: "What is a KPI? How do I choose well? How to focus?"

Best Social Media Metrics: Conversation, Amplification, Applause, Economic Value.

Google Analytics Visits Metric: Change. Implications. Opportunities.

Email Marketing: Campaign Analysis, Metrics, Best Practices

Your Web Metrics: Super Lame or Super Awesome?

Web Analytics 101: Definitions: Goals, Metrics, KPIs, Dimensions, Targets

Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test

Search Engine Optimization (SEO) Metrics & Analytics

Social Media Analytics: Twitter: Quantitative & Qualitative Metrics

Brand Measurement: Analytics & Metrics for Branding Campaigns

Actively Avoid Insights: 4 Useful KPI Measurement Techniques

Six Web Metrics / Key Performance Indicators To Die For

Web Metrics Demystified

Blog Metrics: Six Recommendations For Measuring Your Success

Web Analytics Standards: 26 New Metrics Definitions

How To Measure Success of a Blog (120 Days in Numbers)

Standard Web Metrics Definitions from the Web Analytics Association

Thirty Days In Numbers (how to measure blog success)

Standard Metrics Revisited Series

#6: Daily, Weekly, Monthly Unique Visitors

#5 : Conversion / ROI Attribution

#4 : Time on Page & Time on Site

#3: Bounce Rate

#2: Top Exit Pages

#1: Visitors

Tools, Tools, Tools!: "How to pick? What's the best out there? How can I be smart?"

Best Web Analytics 2.0 Tools: Quantitative, Qualitative, Life Saving!

Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions

Analytics Tools Comparison: Coradiant vs. Tealeaf

Negotiating A Web Analytics Vendor Contract? Check SLA's

Web Analytics Vendor Tools Comparison (And One Challenge)

Find You Web Analytics Soul Mate (How To Run An Effective Tool Pilot)

Web Analytics Tools Comparison: A Recommendation

Web Analytics Tools: Does User Interface (UI) Matter?

Web Analytics Tool Selection: Three Questions to ask Yourself

Web Analytics Tool Selection: 10 Questions to ask Vendors

Web Analytics Technical Implementation Best Practices. (JavaScript Tags)

The Great Web Data Capture Debate: Web Logs or JavaScript Tags?

All Web Analytics Tools Should Be Free! Not.

How to Choose a Web Analytics Tool: A Radical Alternative

Five "Ecosystem" Challenges for Web Analytics Vendors

Digital Careers: "How can I get a bigger salary? How do I learn? Find people?"

Web Analytics Career Guide: From Zero To Hero In Five Steps!

The Market Motive Master Certification Manifesto: Web Analytics

I Wish I'd Known That. [Digital Analytics Edition.]

Five Sweet Web Analytics Resolutions To Kick It Up A Notch

Checklist for a Comprehensive Web Analytics Education

Analytics Career: Job Titles, Salaries, Technical & Business Roles

Interviewing Tip: Stress Test Critical Thinking. Please.

Hiring? What Works: Fresh blood or old hands? Experience or Novicity?

Market Motive: Internet Marketing Knowledge On Tap

How Should Web Analysts Spend Their Day?

Web Analytics Career Advice: Statistics, Business, IT & Mushrooms

Seven Skills to Look for in a Web Analytics Manager

Hiring a Senior Web Analyst? Here's a Suggested Job Requisition / Description

Make a Great Vendor / Agency / Consulting Pitch – Win Big Contracts

Top Ten: Signs You Are A Great Analyst

Compiled Knowledge: "Why does that happen? How can I make this work? I wonder…"

Web Analytics: Frequently Asked Questions And Direct Answers

Dear Avinash: Web Metrics & Analytics Questions, Facebook Edition

Dear Avinash: Top Web Analytics Questions, Twitter Edition

Dear Avinash: Bounces, Abandonment, Visitor Ratios & Data Drops!

Google Analytics Help: Questions, Answers, Tips, Ideas, Suggestions

"Yes M'am, Yes You In The Back Row" [13 Web Analytics Questions]

Dear Avinash: Stressed Agency Analyst & Robots Are Out To Get Me!

Three Interviews, Six Interesting Web Analytics Questions

Grab bag: Life lessons, observations, advice, and more

Blogging Experience Articles

An Incredible Analytics Experience: 5 Years of Occam’s Razor

Benefits Of Blogging – A Practitioner's Perspective

Web Analytics Blog: Reflections

Ten More Blogging Tips From A Novice Blogger

Top Ten Web Analytics Blogs

Top Ten Blogging Tips & Insights from a Novice Blogger

I. Me. Myself.

This I Believe [A Manifesto for Web Marketers & Analysts]

Life, Liberty And The Pursuit Of Happiness

Social Objects and doing the Stupid Passion Thing

The Burmese Satyagraha

My Friend John

Web Analytics Demystified: Revisited

10 Insights From 11 Months Of Working At Google

Next Stop, Wonderland

Redemption for the Indianapolis Colts at Super Bowl XLI

Nine Rules To Work / Live By

Three "Spire's" of Great Leadership

Book Articles

Web Analytics 2.0 Book: In Stores Now!!

Web Analytics Books!

Web Analytics: An Hour A Day – Book Update

Five Thousand Times Two Translates Into Goodness

The People Of Web Analytics: An Hour A Day

Web Analytics: An Hour A Day

Me Talk Pretty One Day

Interviews

Ten Minutes With… Mike Moran, IBM

Ten Minutes With… Jason Burby, ZAAZ

Ten Minutes With… Brett Crosby, Google Analytics

Ten Minutes With… Dr. Stephen Turner

Ten Minutes With… Matt Belkin

Misc Articles

What Makes A Great Conference? Lessons From Shop.Org

Microsoft Gatineau: My Wish-list for the Web Analytics Application

Celebrating Two One Thousands

2007 Predictions: Web Analytics

Hello, My Name is Avinash. Vendors What is Unique About You?

Google AdWords plus Google Analytics: Market Manipulation and Possibility of Mischief?

1,200,157 to 9,744 in 3: Thank You All

Knowledge is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/sitemap/feed/ 0 http://www.kaushik.net/avinash/articles-multimedia/ http://www.kaushik.net/avinash/articles-multimedia/#comments Wed, 06 Jul 2011 21:15:13 +0000 joostdv http://www.kaushik.net/avinash/?p=4181

...  May 19, 2009     Useful Key Performance Indicators (KPIs) for Small Businesses – Avinash Kaushik. Google: Video: May 18, ...

Press, Videos, Podcasts is a post from: Occam's Razor by Avinash Kaushik

]]> A simple listing of videos, podcasts, articles, and interviews that you might find to be of value.

The Chronicle of Higher Education: Apr, 2011     College Rehab Their Web Sites for Major Payoffs.

Search Marketing Standard: Apr, 2011     The Future Of Marketing — Analytics Vs. Wisdom. Part 1, Part 2.

IT World Canada: Mar 09, 2011     Are you making sense of your social engagement?

Inc. Magazine: Feb 02, 2011     How to Reduce Your Website’s Bounce Rate

The Vancouver Sun: Jan 06, 2011     A 2011 digital marketing reboot

Inc. Magazine: Dec 31, 2010     11 Best Web Analytics Tools

Financial Post, Canada: Dec 10, 2010     ‘Most websites suck’

Web Analytics TV with Avinash Kaushik & Nick Mihailovski: 2009 – 10     Advanced Web Analytics Tips, Solutions & Advice.

Higher Ed Analytics : Oct 28, 2010     Higher Education Websites & Web Analytics.

Bruce Clay, Inc: Oct 12, 2010     Kick-Ass Web Analytics.

Mixergy: Sept 21, 2010     The “Minor Orgasm” Of Using Analytics Well.

TopRank Blog: Sept 14, 2010     Avinash Kaushik on Storytelling & Web Analytics.

Applied Arts Canada: Sept 01, 2010     Creativity In The Age of Web Analytics.

Interactiva – Spain: Aug 18, 2010     Tendencias en online branding: Web Analytics 2.0

            Alternative Link.

Six Pixels of Separation: Podcast: Jul 11, 2010     Mistakes We Have Made A Few – Featuring Avinash Kaushik.
    Download Podcast : MP3 (54 mins, 12 mb)

Marketing Obsessions with Kevin Lee: Jul 02, 2010     DidIt Interview Series.
            Videos:
            The Best Sources of Data For Marketers
            Social Media’s Impact on Analytics
            High-Velocity Testing
            CMO Mistakes

Financial Post – Canada: May 31, 2010     A dashboard for every business.

Search Insider: May 28, 2010     Searching For Answers.

The Wharton School of the University of Pennsylvania: Apr 18, 2010     Fast Forward: Perspectives on the changing face of marketing.
            Videos:
            What is the most important data to evaluate when it comes to online?
            How do you see offline campaigns correlating with online campaigns?
            What do you want retailers to learn in the next three years?
            How to leverage conversational (social) channels to influence people?
            What tests/experiments would you recommend to online retailers?

Destination CRM: Mar 30, 2010     Avinash Kaushik’s four tips for making your metrics matter.

MarkeZine Japan: Feb 09, 2010     「加速し続ける電車にいち早く乗った人が勝者になる」 ― 世界トップレベルのWeb Analystが語るオンラインマーケティングの真髄

Six Pixels of Separation: Podcast: Feb 08, 2010     Avinash Kaushik With His Hair On Fire.
    Download Podcast : MP3 (49 mins, 13 mb)

Duct Tape Marketing Podcast: Feb 04, 2010     Marketing podcast with Avinash Kaushik
    Download Podcast : MP3 (20 mins, 6 mb)

Media Post News : Dec 03, 2009     Keyword Campaigns Must Grow Longer Tails

Vancouver Sun : Oct 07, 2009     Successful Web strategy is in enticing visitors to stay awhile

MediaPost Search Insider : Oct 05, 2009     CPG, Search, & Circular Logic

WebmasterRadio.com: Press This: Podcast: Sep 15, 2009     Blog Analytics and Metrics.
    Download Podcast : MP3 (55 mins, 13 mb)

Six Pixels of Separation: Podcast: Aug 23, 2009     Web Analytics 2.0 With Avinash Kaushik.
    Download Podcast : MP3 (25 mins, 6 mb)

DMNews : Aug 04, 2009     The Social Media Alter Ego

Site Visibility: Podcast: July 29, 2009     Podcast Interview With World Leading Anayltics Authority Avinash Kaushik.
    Download Podcast : MP3 (40 mins, 36 mb)

MediaPost, Marketing Daily: July 09, 2009     Small Effort, Big Gains: Winning With Analytics

Small Business Newz: June 19, 2009     The Analytics of Customer Loyalty

Marketing Magazine: June 15, 2009     The Numbers Game

Web Marketing Today: Video: May 26, 2009     Innovative Uses for Twitter for Small Business – Avinash Kaushik.

LaVanguardia.es: May 21, 2009     Avinash Kaushik: “La web será más inteligente y se anticipará: nos envolverá”

Web Marketing Today: Video: May 19, 2009     Useful Key Performance Indicators (KPIs) for Small Businesses – Avinash Kaushik.

Google: Video: May 18, 2009     Little Changes, Large Results: 5 Things Marketers Can Do Now.

The Vancouver Sun : Apr 24, 2009     What’s working and what isn’t is right at your fingertips.

Six Pixels of Separation: Podcast: Apr 19, 2009     Orgasmic Digital Marketing with Avinash Kaushik.
    Download Podcast : MP3 (35 mins, 6 mb)

Advertising Age : Mar 30, 2008     The Right Data Makes Marketing More Effective.
        Click for the Archived Version

Info Brand Digital – Argentina : Mar 30, 2008     Google trajo a Avinash Kaushik, su gurú de Web Analytics.

Mequoda Daily : Mar 05, 2008     Rethinking Decision-Making in a 2.0 World.

Ad Age : Mar 03, 2008     Don’t Just Use Site to Sell Subscriptions.

Buzz Marketing For Technology: Podcast: Nov 18, 2008     Secrets of Web Analytics – a podcast with Avinash Kaushik.
    Download Podcast : MP3 (26 mins, 6 mb)

Inc Magazine : Oct 29, 2008     Improving Your Sense of Site. Your web analytics report is filled with clues on how to boost sales.

The Conversation Marketing Summit: Video: Oct 20, 2008     The Super Metrics: Data that Matters in Conversational Media.     [Bonus at the start: Why the "engagement" metric equals "truthiness"!]

Internet Retailer : Sept 25, 2008     Focusing on key web site metrics through analytics

ECommerce-Guide : Sept 18, 2008     Web Analytics Primer: Five Metrics Demystified

Intelligent Enterprise : Sept 17, 2008     Rethink Web Analytics For the ’2.0′ World

Information Week : Sept 17, 2008     5 Web Lessons From Google’s Analytics Guru

The Vancouver Sun : Sept 11, 2008     Google’s faith-based initiative

FutureNow Blog: Podcast: Sept 11, 2008     Avinash Kaushik & Bryan Eisenberg Discuss Testing.
    Download Podcast : MP3 (24 mins, 5 mb)

ClickZ : Enid Burns : Sept 03, 2008     Questions for Avinash Kaushik, Analytics Evangelist at Google

SES Conference Expo YouTube Channel: Video: Aug 22, 2008     Avinash’s Long Tail Terms, Bouncy Icebergs, and Analytics.

San Francisco Chronicle : Aug 20, 2008     Tools teach owners about Web site traffic

Fast Company: WorkFast.TV with Robert Scoble : Video: Aug 07, 2008     How Analytics Are Changing Business

Website Magazine : Aug 01, 2008     Understanding Consumer Behavior with Web Analytics

The Montreal Gazette : July 24, 2008     Hire a sales rep named Google: The key to selling on the Web is a few clicks away

Twinkle Netherlands: July 17, 2008     Avinash Kaushik:‘Webanalytics is sexy’     [Google Translate Version.]

Tampa Bay’s Media Talk: Video: July 17, 2008     Why Websites “Suck”?

Dutch Cowboys: Podcast: July 10, 2008     Kaushik: ‘Analytics is a 3 year old baby’     [Google Translate Version.]
    Download Podcast : MP3 (22 mins, 5 mb)

JMP Innovators’ Summit: Podcast: July 01, 2008     Web Analysis = Powerful Profitable Clues!
    Download Podcast : MP3 (23 mins, 5 mb)

Compete.Com : Cynthia Stephens     Interview with Analytics Evangelist Avinash Kaushik     Part One: June 24, 2008     Part Two: June 25, 2008

EL PAÍS S.L. Madrid: June 19, 2008     “La web da información más rica sobre la audiencia que la televisión”     [Google Translate Version.]

Burlington Free Press : June 18, 2008     Online Marketing Boot Camp Begins

Advertising Age – Ad Age : May 24, 2008     Google’s ‘Analytics Evangelist’ Explains Why Websites ‘Suck’

iCitizen 2008: Video: May 21, 2008     A Battle Cry for Qualitative Metrics

Web Marketing Today: Video: May 10, 2008     Rethinking Search: Keyphrases & Long Tail

DaveChaffey.com Internet Marketing: May 02, 2008     Avinash Kaushik – On web business optimisation

Infopresse Canada : Marie-Claude Ducas: Apr 18, 2008     Les humains avant les logiciels (Humans Before Software)     [French Article + English Videos]
    YouTube: Avinash Kaushik at Web Analytics Day: Clip 1, Clip 2, Clip 3, Clip 4.

e-consultancy : Richard Maven: Apr 01, 2008     Avinash Kaushik on Visitor Experience Optimisation

MediaPost Online Publishing Insider : Kevin Mannion: Mar 27, 2008     Who Are The People Behind The Numbers?

SESConferenceExpo: Video: Mar 17, 2008     Web Analytics at SES NY 2008 with Greg Jarboe & Avinash Kaushik

Computer World – Business Intelligence Feb 25, 2008
      Life after page views: Web analytics 2.0

ClickZ : Neil Mason
Jan 22, 2008 : Web Analytics: Insights From the Front Line, Part 1
Feb 5, 2008 : Web Analytics: Insights From the Front Line, Part 2

Google Inc.: Video: Dec 11, 2007     Internal Site Search Analysis & Visitor Intent

Six Pixels Of Separation: Mitch Joel : Podcast : Nov 18, 2007     Widgets, Analytics And The Fluid Web With Avinash Kaushik
      Download Podcast : MP3 (49 mins, 18 mb)

Web Strategy by Jeremiah : MicroMedia: Utterz : Nov 10, 2007     Interview with Avinash Kaushik

EightBlack Blog: Video: Nov 09, 2007     Avinash Kaushik talks with Simon Chen at Blogworld Expo

ClickZ – Mike Grehan : Podcast : Oct 22, 2007     Online Marketing Crimes and Misdemeanors
      Download Podcast : MP3 (46 mins, 18 mb)

Six Pixels Of Separation: Mitch Joel : Podcast : Sept 30, 2007     CarCast With Bryan Eisenberg And Avinash Kaushik
      Download Podcast : MP3 (37 mins, 11 mb)

The Globe And Mail : Sept 27, 2007     Data give miners the shaft

Google Inc.: Video: Web Analytics: An Hour a Day : Sept 05, 2007     Authors@Google Presents Avinash Kaushik

IT Management: Aug 29, 2007     Top 100 Tech Blogs: the Datamation List

Google Inc.: Video : Aug 01, 2007     Rules for Web Analytics Revolutionaries

Duct Tape Marketing : Podcast : June 29, 2007     Marketing podcast with Avinash Kaushik
      Download Podcast : MP3 (17 mins, 3 mb)

Future Now Post : Grokcast : June 18, 2007     Interview with Analytics Evangelist Avinash Kaushik
      Download Podcast : MP3 (16 mins, 4.4 mb)

Future Now Post : Grokcast : June 15, 2007     Avinash Kaushik on “Web Analytics An Hour A Day”
      Download Podcast : MP3 (18 mins, 4.9 mb)

Eight Black : Podcast : May 03, 2007     An Interview With Avinash Kaushik
      Download Podcast : MP3 (65 mins, 10.4 mb)

CRMguru : Feb 12, 2007     You Are Not the Target Audience

Stone Temple Consulting : Podcast : Feb 09, 2007     Competitive Intelligence Tools Interview with Avinash Kaushik
      Download Podcast : MP3 (41 mins, 4.8 mb)

Web Analytics Association : Podcast : Nov 14, 2006     Measuring RSS and Blogs: Interview with Avinash Kaushik
      Download Podcast : MP3 (34 mins, 3.9 mb)

Web Analytics Association : Podcast : Nov 14, 2006     Measuring Rich Internet Applications: Interview with Avinash Kaushik
      Download Podcast : MP3 (39 mins, 4.6 mb)

Chicago Tribune : Sept 27, 2006 [Northwestern University: Media Management Center, 27 Sept, Newsday, 27 Sept, Belleville News-Democrat, 04 Oct, The News Sentinel, 04 Oct, The Wichita Eagle, 07 Oct, Oman Tribune]
    They know their audience: Page views? Unique visitors? Hits? How the Internet methodically measures its users

Your SEO Plan : Aug 21, 2006     Interview with Avinash Kaushik

Google: TechTalk – Video : July 24, 2006     Customer Centric Web Decision Making (@ Google Video)

Ecommerce Guide : June 26, 2006     How to Use Web Analytics

Marketing Monger : Podcast : May 28, 2006     Interview with Avinash Kaushik of Intuit
      Download Podcast : MP3 (22 mins, 5.12 mb)

MindComet : Guide : March 13, 2006     Top Ten Things to Know About Web Analytics (PDF)

Internet Marketing Voodoo : Podcast : March 06, 2006     Traditional Web Analytics is Dead      Download Podcast : MP3 (21 mins, 4.9 mb)

MediaPost Search Insider: January 31, 2006     Laziness And Personalization In Phoenix [Cached Version]

Jim Stern Foviance Best Practices Newsletter: December 2005:     Your Website is Not a Website

ClickZ: November 11, 2005:     Is Online Marketing Growing Up?

ClickZ: April 18, 2005:     Questions for Analytics-Driven Marketers

Press, Videos, Podcasts is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/articles-multimedia/feed/ 0 http://www.kaushik.net/avinash/4182/ http://www.kaushik.net/avinash/4182/#comments Wed, 06 Jul 2011 21:15:13 +0000 joostdv http://www.kaushik.net/avinash/?p=4182

...  26, ’08 Advanced Web Analytics: Enlightenment through KPIs Online Marketing Bootcamp, Burlington: Jun 17, ’08 Multiplicity: ...

is a post from: Occam's Razor by Avinash Kaushik

]]> I have had the privilege of speaking at a number of wonderful industry conferences and Universities around the world. This page lists my current schedule of keynotes and speeches (and for the sake of posterity, the past engagements as well).

Upcoming Public Engagements:

Gulltaggen (Golden Tag) 2011, Oslo, Norway: Apr 13, ’11
Conference Keynote: Jumping To Your Global Digital Maxima With Web Analytics!

The Art of Marketing, Vancouver, Canada: Jun 09, ’11
Data Driven Leaps Of Faith: Re-imagining Digital Marketing

IAB Conecta 2011, Mexico City, Mexico: Jul 28, ’11
Conference Keynote: Web. Data. Revolution: Rethink Decision Making!

MozCon, Seattle: Jul 29, ’11
Conference Keynote: Advanced Inbound Marketing Analytics

MIMA Summit 2011, Minneapolis: Oct 12, ’11
Conference Keynote: The Digital Analytics Decade!

Prior Engagements:

Be-Wizard Web Marketing Event – 2011, San Marino, Italy: Mar 11, ’11
Conference Keynote: Winning Big With Web Analytics

The Art of Marketing, Toronto, Canada: Mar 07, ’11
Brand Measurement: Metrics & Analytics

eMarketing Conference – Fanshawe College, London, Canada: Mar 01, ’11
Conference Keynote: Delivering On The Promise of Online Marketing With Data.

HSMAI Digital Marketing Strategy Conference, New York: Feb 01, ’11
Conference Keynote: Rethinking Online Analytics: Customer Centric Data Driven Strategies.

CMO Executive Summit, Los Angeles: Jan 27, ’11
Goodbye Faith, Hello Data Driven Agile Marketing.

TWTRCON SF 2010 , San Francisco: Nov 18, ’10
Analytics Driven Social Media Awesomeness!

NTEN Technology Leadership Academy , Virtual World: Nov 05, ’10
Web Analytics Uncomplexified, In An Hour!

Stamats Integrated Marketing Technology Conference, Las Vegas: Oct 21, ’10
Analytics That Rock: Converting Data Into A Marketing Advantage

AMA Marketing Analytics Training Camp, Durham, NC: Oct 20, ’10
Conference Keynote: Web Analytics 2.0

Search Engine Strategies – 2010, Chicago: Oct 19, ’10
Conference Keynote: Rethinking Search Analytics.

Marketo 2010 User Summit, San Francisco: Oct 14, ’10
Keynote: Marketing Analytics

Young Presidents’ Organization Iowa Annual Summit, Iowa: Oct 07, ’10
Online, Offline, Nonline: Empowering Business Through Data!

Hartford Institute Annual Conference, Durham, NC: Oct 03, ’10
Incredible Analytics, For Incredible Non-Profits.

The Art of Marketing, Montreal, Canada: Sept 30, ’10
OMG Marketing Powered By Data!

WSI Global Convention, Miami: Sept 24, ’10
Keynote: Actionable Web Analytics: Unleash the Marketing Power of Web Data.

Search Engine Strategies – 2010, Hong Kong: Sept 13, ’10
Bringing Accountability To Search & Search Marketing.

Connected Marketing Week, San Francisco: Aug 20, ’10
Global Marketing in a Digital World

Marketing Profs, Online:May 03, ’10
Multi-Channel Analytics: Tackling the Toughest Measurement Challenge.

CMA 2010 National Convention, Toronto: May 26, ’10
Web Analytics 2.0: Achieving Minor Orgasms Online By Using Data.

eMetrics Summit, San Jose: May 04, ’10
Leveraging Bleeding Edge Analytics Goodies.

eMetrics Marketing Optimization Summit, Toronto: Apr 08, ’10
Social & Search: Rock Both Worlds With Data.

Search Engine Strategies – 2010, New York: Mar 24, ’10
Be Awesome: Ideas for Approaching Search Analytics Differently.

The Business Intelligence SIG , Palo Alto: Mar 16, ’10
Rethinking Decision Making in a “2.0″ World.

Measure Up 2010, Chicago: Mar 12, ’10
Empowering Sustainable Innovation: Ideas for Creating a Data-Driven Organization

Search Marketing Expo – SMX West, Santa Clara: Mar 03, ’10
The Search State Of The Union.

Search Engine Strategies – 2010, London: Feb 16, ’10
Be Awesome: Ideas for Approaching Search Analytics Differently.

Stanford University, Graduate School of Business, Palo Alto: Jan 15, ’10
The Power of Social Technology.

Search Insider Summit – 2009, Utah: Dec 03, ’09
Awesome & Actionable Paid Search Analytics.

Listrack Webinar Series, Online: Dec 01, ’09
Actionable Web Analytics: Five Tips For Insightful Analysis.

Rimini Web Marketing Event – 2009, Rimini, Italy: Nov 22, ’09
Conference Keynote: Web Analytics 2.0.
Advanced Search Analysis: Paid + Organic = Awesome.

Marketing Profs, Online: Nov 12, ’09
Monetizing the Long Tail of Search.

Internet Marketing Conference, Vancouver: Sep 16, ’09
Rethink Online Analytics: Accountability, Outcomes, Experimentation & Intelligence.

The EpikOne Summit, Burlington, VT: Aug 14, ’09
Multichannel Analytics: Challenges & Solutions.

Search Engine Strategies, San Jose: Aug 11, ’09
Making Your Web Data Work For You!

Marketing Profs, Virtual World: Jun 25, ’09
Cracking the Code: Web Analytics for Paid Search Advertising.

GSI Commerce Connect, Philadelphia : Jun 24, ’09
Beyond Faith Based Initiatives: Online, Analytics, Marketing.

Practitioner Web Analytics Lisboa 2009, Lisbon, Portugal: May 21, ’09
Data Driven Decision Making.

Practitioner Web Analytics 2009, Barcelona, Spain: May 19, ’09
The Current & Future Challenges for Web Analytics.

American Statistical Association, Chicago: May 15, ’09
Math Hearts Analytics.

eMetrics Summit, San Jose: May 06, ’09
Rules for Analytics Revolutionaries!

Web Strategy Summit, Calgary, Canada: May 04, ’09
Conference Keynote.

CRE8 Summit 2009, Orlando: Apr 15, ’09
The Sexiness of Data.

MPA – Magazines 24/7: Navigating the New Reality, New York: Mar 03, ’09
Rethinking Decision-Making in a 2.0 World.

Graduate School of Business, Stanford University, Palo Alto: Feb 26, ’09
Applied Marketing Analytics.

Shop.Org Strategy & Innovation Forum 2009, Orlando: Feb 02, ’09
Moving Beyond Faith Based Initiatives: Lower Risk, Innovate, Be Great

ANA Brand Building Workshop, New York: Dec 09, ’09
Accountability in Advertising.

Monaco Media Forum 2008, Monaco : Nov 12, ’08
Online Marketing Revisited: Monetizing the greatest channel on earth.
[Watch It Online Now]

INMA Conference 2008, Norway : Oct 29, ’08
Conference Keynote.

Conversational Marketing Summit, San Francisco : Oct 15, ’08
The Super Metrics: Data that Matters in Conversational Media.
[Watch It Online Now]

SMX East, New York : Oct 07, ’08
Paid Search Analytics: Increasing ROI, Optimizing Conversions.

Online Market World, San Francisco: Oct 01, ’08
Super Session: Analytics
Managing Online Marketing Disconnects.

JMP Innovators’ Summit, San Francisco: Sep 26, ’08
Extending the Reach of Analytic Visualization.

Web 2.0 Expo, New York: Sep 17, ’08
Rethinking Decision Making in a “2.0″ World.

Marketing Profs, Online! : Aug 28, ’08
Actionable Web Analytics: Unleash the Marketing Power of Your Web Data.

Search Engine Strategies (SES), San Jose : Aug 19, ’08
Measuring Success in a 2.0 World.

Virginia Commonwealth University, Brandcenter, Richmond: Aug 05, ’08
Measuring Success in a Digital World: Its Different (& More Wonderful!).

Stanford University – University of Berkeley New Media Workshop, Palo Alto: Aug 07, ’08
Social Media Measurement.

ElasticPath Ecommerce Events, Online!: Jul 17, ’08
3 Things to Die For: Web Analytics Unleashed
[Watch It Online Now]

Market Motive Online Workshops, Online!: Jun 26, ’08
Advanced Web Analytics: Enlightenment through KPIs

Online Marketing Bootcamp, Burlington: Jun 17, ’08
Multiplicity: Be Massively Successful at Web Analytics 2.0.

Practitioner Web Analytics 2008, Barcelona, Spain: Jun 05, ’08
Web Analytics 2.0
Actionable Web Analytics
A Deep Dive into Experimentation & Testing
Rules for Analytics Revolutionaries

Web Analytics Congres 2008, Amsterdam, Netherlands: May 28, ’08
Multiplicity: Be Massively Successful at Web Analytics 2.0.

iCitizen – The Open Imperative, Columbus: May 21, ’08
The ever-evolving adventure of metrics and analytics.
[Watch It Online Now]

CMA NEXT:2008 National Convention & Trade Show, Toronto: May 12, ’08
Web Analytics 2.0: Putting the Marketer Back Into Marketing. Finally!

Compete Client Forum, Cape Cod: May 14, ’08
Keynote: Post Click Marketing

eMetrics Marketing Optimization Summit, San Francisco: May 04, ’08
Hunting Actionable Insights: Tips & Techniques From A Practitioner
All Your Search Questions Answered
The eMetrics Ecosystem

Journée InfoPresse: Web Analytics Conference, Canada, Montréal: Apr 16, ’08
The Future of Web Analytics

Search Engine Strategies (SES), New York: Mar 17, ’08
Web Analytics: Measuring Success

Santa Clara University – Leavey School of Business, Santa Clara: Feb 29, ’08
Actionable Web Analytics

Association of Strategic Marketing : Mar 04, ’08
Website Metrics and ROI

Integrated Media Association’s Public Media Conference, Los Angeles: Feb 20, ’08
Your Best Friend Online: Your Website Data!Rich Media Analytics Done Right.

SIPA’s 24th Annual Mid-Year Conference, Miami Beach: Dec 13, ’07
Web Analytics: Less Boring & More Life-Changing Than You Think

NCDM 2007, Las Vegas: Dec 10, ’07
Using Web Analytics to Optimize the Customer Experience

BlogWorld Expo, Las Vegas: Nov 08, ’07
Just the Numbers: Understanding Blog Analytics

University of Virginia – Darden School of Business, Charlottesville: Oct 22, ’07
Rethink Web Analytics: It’s Different, it’s Actionable, it’s a Strategic Advantage

eMetrics Marketing Optimization Summit, Washington DC: Oct 15, ’07
Advanced SEM/PPC Analysis: Ego Bidding, Cannibalization, Long Tail Exploitation and More
Guru Talk: How To Create A Data Driven Boss?

Creating Engaging Facebook Apps, Stanford University, Palo Alto: Oct 04, ’07
Analyzing applications and sharing insights.

BayCHI (San Francisco Bay Area Chapter of ACM SIGCHI), Palo Alto: Sept 11, ’07
Marrying the Qualitative + Quantitative: A Match Made in Heaven for User Centered Design

Search Engine Strategies 2007, San Jose: Aug 23, ’07
Analyzing The Analytics Players
[Watch It Online Now]

Shop.org 2007, San Diego: July 19, ’07
Conference Keynote

Searchnomics 2007, Santa Clara: June 27, ’07
Advanced Web Analytics: Tips, Techniques and Best Practices

SF Bay ACM Data Mining SIG, Palo Alto: June 13, ’07
Cutting Edge Web Analytics: Beyond Clickstream and Towards True Customer Centricity

Annual Conference for Catalog and Multichannel Merchants (ACCM), Boston: May 21, ’07
Coming Full Circle: Improving the Customer Experience & Increasing Conversion

Emetrics Summit, San Francisco: May 07, ’07
Guru Session: Competitive Analysis: Why You Can’t Live Without It

Web 2.0 Expo 2007, San Francisco: Apr 15, ’07
“Click the Big Red Button : Tips & Techniques for Optimizing Conversion and A/B Testing.”

Emetrics Summit, London: Mar 29, ’07
Competitive Analysis: Why You Can’t Live Without It

ClickTracks Web Analytics Education Series, Online!: Feb 26 & 27, ’07
“Unleash the Power of Web Analytics.”
[Watch It Online Now]

E-consultancy’s Online Marketing Masterclasses 2006, London: Nov 22, ’06
“Conversion Rate Optimization: What, Why, How.”

Emetrics Summit: The Big Picture, Washington DC: Oct 16, ’06
“Creating a Data Driven Web Decision Making Culture.”

Emetrics Summit 2006, London: May 03, ’06
“Customer Centric Decision Making: Steps for Foundational Web Success”

ATG InsightLive 2006, Las Vegas: Apr 23, ’06
“From Web Analytics to Web Insights: How Intuit Leverages Web Behavior, Outcomes, and Customer Experience.”

Emetrics Summit 2006, Santa Barbara: Apr 18, ’06
“Customer Centric Decision Making: Steps for Foundational Web Success”

Ad-tech Impact, Seattle: Feb 28, ’06
“Campaign Optimization: Ad Formats, Creative and Program Measurement Options”

Frost & Sullivan Internet Marketing Strategies 2006, Phoenix: Jan 26, ’06
“Extracting Greater ROI for Online Efforts: Strategies & Lessons from Measuring End-to-End Customer Interactions”

Ad-Tech, New York: Nov 07, ’05
“Lowering Customer Acquisitions Costs – Multiple Efficiency Tactics”

University of Utah, David Eccles School of Business, Salt Lake City: Sep 26, ’05
“Business Intelligence and Decision Making on the Web”

Emetrics Summit 2005, Santa Barbara: Jun 01, ’05
“Attempting the perfect Trinity: Website Experience + Behaviour + Outcomes”

is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/4182/feed/ 0 http://www.kaushik.net/avinash/difference-web-reporting-web-analysis/ http://www.kaushik.net/avinash/difference-web-reporting-web-analysis/#comments Mon, 04 Apr 2011 09:30:23 +0000 Avinash Kaushik http://www.kaushik.net/avinash/?p=3568

Someone asked me this very simple question today. What's the difference between web reporting and web analysis? My instinct was to use the wry observation uttered by US Supreme Court Justice Potter Stewart in trying to define po rn: "I know it when I se ...

The Difference Between Web Reporting And Web Analysis is a post from: Occam's Razor by Avinash Kaushik

]]> Someone asked me this very simple question today. What's the difference between web reporting and web analysis?

My instinct was to use the wry observation uttered by US Supreme Court Justice Potter Stewart in trying to define
po rn: "I know it when I see it."

That applies to what is analysis. I know it when I see it. : )

That, of course, would have been an unhelpful answer.

So here I what I actually said:

If you see a data puke then you know you are looking at the result of web reporting, even if it is called a dashboard.

If you see words in English outlining actions that need to be taken, and below the fold you see relevant supporting data, then you are looking at the result of web data analysis.

Would you agree? Got an alternative, please submit via comments.

I always find pictures help me learn, so here are some helpful pictures for you. . .

This is web reporting:

And so is this, even if it looks cuter:



And while you might be tempted to believe that this is not web reporting, with all the data and the colors and even some segments, it is web reporting:



See the common themes in all the examples above?

The thankless job of web reporting, illustrated vividly above, is to punt the part of interpreting the data, understanding the context and identifying actions to the recipient of the data puke.

If that is your role, then the best you can do is make sure you have take the right screenshots out of Site Catalyst or Google Analytics, or charge an extra $15 an hour and dump the data into Excel and add a color to the table header.

So what about web analysis?

The job of web analysis mandates a good understanding of the business priorities, creation of the right custom reports, application of hyper-relevant advanced segments to that data and, finally and most importantly, presentation of your insights and recommended action using the locally spoken language.

See the difference? It's a different job, requires different work, and of course radically different skills.

Examples of web analysis? I thought you would never ask. . .

This is a good example of web analysis:



[And not only because it is my work! Learn more about it here: Action Dashboard.]

Notice the overwhelming existence of words. That's not always sufficient, but I humbly believe always necessary.

When you look to check if you are looking at analysis or reporting look for Insights, Actions, Impact on Company. All good signs of analysis.

Here's another example of really good web analysis:



[Click on the image above for a higher resolution version.]

Ignore how well or badly the business is doing. Focus on approach taken.

Here are some things that should jump out. . . . A deliberate focus on only the "movers and shakers" (not just the top ten!).  Short table: just the key data. Most of the page is taken up with words that give insights and specific actions to take.

Another example that I particularly like, both for the style of presentation and how rare it is in our world of web analytics. . .



[Click on the image above for a higher resolution version.]

No table, no rows, no pies. And yet data holds center stage with clearly highlighted actions.

Normally, we all do the column on the left (it might look different, but we have it). Unfortunately we don't appreciate is the power of the middle column ("segmentation reveled"). That is super important because it gives the recipients exposure to the hard work that you have done and in a very quiet ways increases their confidence in your work. Guess the outcome of that? They take the actions you are recommending!!

Analysts constantly complain that no one follows any of their data-based recommendations.  How do you expose your hard work? In a garish Las Vegas show girl fashion where all the "data plumes" are, unsexily in this case, hanging off the body? Or, in quite concise ways? Only one of those two work.

One more? Okay here you go. . .



[Click on the image above for a higher resolution version.]

Diana has loads of observations, supported by visuals (sometimes it really helps to show the search results or the emails or the Facebook ad) with highlights (actually lowlights) in red, and finally recommendations.

And note the tie to outcomes (another common theme in all examples above). In this case, the search improvements are tied to the increase in donations I can make because of sales of my book. 1.5 extra smiles per month! (All my proceeds from both my books go to charity.) A good way to get attention from the "executive" and get him or her to take action.

Do that. A lot. Be creative. Yes it is hard work. But then again glory is not cheap, is it?

Exceptions to the rule.

Not every output you get from your Analyst, or "Analyst" :), with loads of words on it, instead of numbers, will be analysis. Hence my assertion that "I know it when I see it." Words instead of data pukes is just a clue, read the words to discern if it actually is analysis or a repeation of what the table or graph already says!

In the same vein not every output that is chock full of numbers in five size font, with pies and tables stuffed in for good measure, is a representation of web reporting. It is hard to find the exceptions to this rule, but I have seen at least two in nine years.

Top 10 signs that you are looking at / doing web analysis.

Let's make sure this horse is really and truly dead by summarizing the lessons above and using a set of signs that might indicate that you are looking at web analysis. . .

#1. The thing that you see instantly is not data, but rather actions for the business to take.

#2. When I see Economic Value I feel a bit more confident that I am looking at the result of analysis. Primarily because it is so darn hard to do. You have to understand business goals / outcomes (so harrrrrd!) and then work with Finance to identify economic value, and then you have to configure it in the tool and then apply advanced segments, and then figure out how things are doing. That is love. I mean that is analysis! Or at least all the work that goes into being able to do effective analysis.

#3. In the same vein, if you see references to the Web Analytics Measurement Model (or better still, see it in its entirety on one slide up front), then you know that the Ninja did some analysis.

#4. Any application of algorithmic intelligence, weighted sort, expected range for metric values (control limits), or anything that even remotely smells of ever so slightly advanced statistics is a good sign. Unknown unknowns are what it's all about!

Also mere existence of statistics is not sufficient. All other rules above and below still apply. :)

#5. If you see a Target mentioned in the report / presentation, then the Analyst did some business analysis at least. See the top right of the picture immediately above.

#6. Loads and loads and loads of context! Context is queen! Enough said.

#7. I have never seen web analysis without effective data/user segmentation. I think this statement is in both my books. . .  "All data in aggregate is crap." Sorry.

#8. If there is even a hint of the impact of actions being recommended then I know that is analysis. It is hard to say: I am recommending that we shift this cluster of brand keywords to broad match. It is harder to say: I am recommending. . . and that should increase revenue by $180,000 and profit by $47,000. Look for that.

#9. If you see more than three metrics in a table you are presented with then you might not be looking at analysis.

#10. Multiplicity! If you see fabulous metrics like Share of Search (competitive intelligence) or Task Completion Rate (qualitative analysis) or Message Amplification (social media) then they are good signs that the Analyst is stepping outside Omniture / WebTrends. I would still recommend looking below the surface to ensure that they are not just data pukes, but the good thing is these are smarter metrics.

User Contributions:

#11. From Carson Smith: If someone looks at your analysis / report / presentation / dashboard and has to ask "and… as a result?", then it might be reporting. What happened should be obvious.

[I love applying the "Three Layers of the So What" test to any analysis I present or see. I ask "so what" three times. If at the end of it there is no clear action to be taken then I know it is just web reporting, not matter how great it looks or how much work went into it. Ask "as a result?" or "so what?" to your work!]

#12. From Chuck U: 1) If it can be automated, it's probably not analysis 2) If your data warehouse team says they can automate it for you, then it's definitely not analysis. [#awesome! -Avinash]

Can you think of other signs? Please share your suggestions via comments. I'll add the best ones to this list.

In the list above, and in the examples in this post, you see my clear, and perhaps egregious bias for business analysis and business outcomes and business actions and working with many parts of the business and business context. But I've always believed that if you and I can't have an impact then why are we doing what we do?

I hope you've had some fun learning how to distinguish between web reporting and web analysis. It is a fact of life that we need both. The bigger the company, the more they want data pukes, sorry, reporting.

But if you have "Analyst" in your job title then you perhaps now have a stronger idea of what is expected of you to earn that title. If you have hired a "web analysis consultant" and are paying them big Rupees then you know what to expect from them. Don't settle for data pukes, push them harder. Apply the rules above. Send their "analysis" back. Ask for more. Raise your expectations!!

I hope now "you'll know it when you see it," and have more datagasms!

Okay, it's your turn now.

How would you answer the question about the difference between web reporting and web analysis? What signs do you look for when evaluating the work of your Analyst or Consultants?

Please share your thoughts via comments below.

Thanks.

PS: In case you are curious here's the current official definition of po rn, as outlined in Miller v. California:

(a) whether the 'average person, applying contemporary community standards' would find that the work, taken as a whole, appeals to the prurient interest,

(b) whether the work depicts or describes, in a patently offensive way, sexual conduct specifically defined by the applicable state law, and

(c) whether the work, taken as a whole, lacks serious literary, artistic, political, or scientific value.

The Difference Between Web Reporting And Web Analysis is a post from: Occam's Razor by Avinash Kaushik

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