It is hard.

This blog post has three ideas I've learned from other smart people, ideas that help surprise the "organization" with something non-normal and get it to take action. Each idea is wonderfully simple, and yet in its own sweet way makes us, Analysis Ninjas, think harder and deliver insights better.

Let's go.

Compute Actual Cost Per Acquisition Post-Facto Including Micro-Conversions.

I know that is confusing. Stay with me.

This idea, 100% of it, comes via my friend David Hughes. [He passed away recently and I miss his friendship, and our collaborations, tremendously.] From this post: Improve Search Marketing Conversion Rates through Email Registration. I'm going to redo the tables, just to make them fit the width of this blog.

David's idea is simple and genius.

Today when we measure our Cost Per Acquisition (CPA) for our campaigns (Search, Email, Affiliate, whatever), we just think of the macro-conversion and, perhaps worse, we think only of that session / visit.

Let's assume we are running www.macys.com and we got 1,000 Visitors to come to our site via a display advertising campaign. As dutiful Reporting Folks we will send this table out to reflect performance of that campaign.

$16.7 CPA might sound huge (or not depending on your margin), but on the surface it seems a lot. The flaw in this report of course is in assuming that all 1,000 visits were in play (wanted to convert / buy something). This is rarely the case.

I have repeatedly evangelized identifying all the jobs the site is trying to do (macro AND micro conversions) and then quantifying their economic value to the business. On the Macy's website of the 970 non-converting visitors, some might have signed up for a free account, some for email alerts or coupons, some opened a wedding registry etc.

If some of those 970 Visitors completed some micro-conversions, then shouldn't the CPA be on that basis rather than just the 30 orders above?

Simplifying the scenario a bit. if some of those 970 submitted an email address / signed up for price alerts and converted later then shouldn't the cost-per-acquisition include those future sales?

Say some of the Visitors did just that. What was the acquisition cost of each sign-up?

Nothing. Nice.

What will Macy's do next? Send the 100 folks the price alert they signed up for!

And what will come of it? Sales, of course.

This is a reasonable picture that will emerge.

So we got 30 orders from the original visits, and another 20 by re-targeting users via permission-based email.

What does the CPA of our original affiliate marketing campaign look like now?

A more respectable $10 compared to the original $16.7.

An immediate implication is that if at a CPA of $16.7 you were profitable, then you can communicate to your Senior Leaders that you were actually even more profitable since the final CPA is now only $10. And if you find yourself in a aggressive marketing siutation then you could even increase the bids on your display campaigns to get even more Visitors. Thanks to your clever micro-conversion and re-targeting strategy!

Lessons:

1. It is important to think in terms of micro-conversions, beyond your main objective. For the 98% of people who won't convert on your site, do you have a way of engaging them again in the future?

2. It is critical to have a robust re-targeting strategy (as in our case above). Hopefully it will be intelligent, relevant to the customers and non-torturous.

3. If you do #1 and #2 then be a dear and ensure you compute the "final CPA" of your original campaign (search or email or affiliate or social or whatever).

4. You can't do the above analysis inside Google Analytics (or even Site Catalyst or the base versions of WebTrends or CoreMetrics). You'll use Excel or a simple database (or possibly the data warehouse versions of Omniture, CoreMetrics, WebTrends).

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Some of you might be excitedly yelling "Attribution!" at the screen. For now, just immerse yourself in the simplicity of the analysis above. I won't cover attribution here but if you have Web Analytics 2.0 jump to page 358 for my thoughts. Also remember in this case at least it was deliberate re-targeting of the initial pool of people.
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Command Attention, Valuable Action, By Stating Raw Numbers.

This idea comes via Kaiser Fung, from this post: Further thoughts on the Facebook business model.

In a blog post with thoughts about a graph from WebTrends, that shows click-through rates (CTRs) and cost per clicks (CPCs) on Facebook. Kaiser made this simple insight:

"What does a 0.01% CTR mean? Yes, that's 100 clicks per 1 million ads shown to Facebook users."

Let me restate that astonishing number. If your ad shows 1,000,000 times, you get 100 clicks!

And of course that's clicks, not conversions.

It caused my eyes to open wide.

That is astonishingly low.

Somehow when someone tells you "Facebook's ads CTR is 0.01%" you don't quite get it. I mean, it does not feel pathetically minuscule, as it should.

I have championed the contextual use of raw numbers to deliver insights, especially when using Averages, Percentages and Ratios. [See: Actively Avoid Insights: 4 Useful KPI Measurement Techniques]

Yet the 0.01% number did not make the impact on me it should have. And that is exactly the problem when you present conversion rates (also pathetically low on every single website on the planet) and other such metrics.

So make sure you show raw numbers.

The first number might not get your management team to take any action; it just does not evoke any feeling.

The second set of numbers might get someone to scream: WTH!

They might ask:

1. Are we showing the wrong ads on Facebook?

2. Are we using any intelligent ad targeting strategy or just randomly showing ads?

3. If we double our budget to 2,000,000 impressions is there even relevant inventory (desired demographic / users) on Facebook for us?

4. Would it be worth it?

5. Why do we suck so much? Is it us? Is it Facebook?

All really great questions — ones that you have to find answers to as a Marketer and an Analysis Ninja. Answers that will help your company improve your Facebook advertising strategy, or quit.

Lessons:

1. Big numbers are not your enemy, especially if they lead to tiny numbers.

2. Resist the urge to turn everything into an average, percentage, ratio or a compound metric.

3. At least one person will tell you that 1,000,000 impressions is great branding. Tell them that branding is measurable and you can prove them right or wrong so you don't have to take their word for it. [See: Brand Measurement: Analytics & Metrics for Branding Campaigns]

Makes sense? If not please share your thoughts using the comment box below.

Either way, remember that your job is to divert people from becoming lulled into a false sense of everything's okay. Scare them into paying attention and asking you tough questions.

Face Reality By Computing "Convert-able Audience" & "Real Conversion Rates."

This idea comes via Thomas Baekdal, from this post: Converting Traffic to Subscribers.

In it, Thomas postulates that even if you have 1,000,000 Absolute Unique Visitors to the website, that does not mean that your possibly "convert-able" audience is a million.

Some people will visit once and never again. That was not an audience that would have converted, ever. For example, the link above is to Baekdal Plus. I pay $49 per year to access that premium content because it is so good. Many of you may not want to pay for content on the web. So for Thomas, not all the Visitors from the above link are actually in play for conversion. [Though I wish they were.]

So it is imprudent to count those folks; better to only count returning Visitors.

Then, some content attracts traffic, other content actually is "valuable and will convert people into subscribers." Thomas's guidance is to only count the latter in the in play for conversion bucket.

Now you can calculate the "convert-able" audience. In Thomas's example here's how his picture looks:

(1,000,000 less the 63% one-time Visitors) less the 20% valuable traffic = 74,000.

Possible convert-able audience = 74,000.

Real audience you even have a remote chance of converting: 74k.

So small, right? After starting with a million.

I rarely see Web Analysts doing this simple exercise and educating their Senior Leadership of this harsh truth. We assume every single person who will visit www.tesco.com is there to convert. Every single person who visits www.etsy.com is there to buy. Our conversion rate calculation, Orders/Visits (bad version) or Orders/Visitors (better version), reflects that, sub optimal, mental model.

We show our leaders that we suck more than we actually do by computing conversion on the basis of All Visits (bad version) or All Visitors (better version).

If Thomas has 3,700 conversions in a month, we would normally report that as 0.37% conversion rate. [(3700/1000000)*100]

Of course, the reality is that the conversion rate was 5%. [(3700/74000)*100]

Not that 5% is orgasmically higher. But it is more reflective of the truth than 0.37%.

You would take one set of actions with 5% and a completely different set with 0.37%.

Compute your "convert-able audience." Please.

Use whatever common-sense approaches you can find.

In a post written in Nov 2006, I presented a similar thought (though in a different context than Thomas). [See: Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"]

My graphics were a lot less sexy in comparison to Thomas's.

The idea was to get you to identify your "Real Conversion Rate", by identifying your "Opportunity Pie."

My recommendations were:

Throw out everyone who bounced, just for now, and also if you use log files (ohh those were the days!), then throw out "visits" by robots / junk. That gives you a rough idea of your "Opportunity Pie" (convert-able audience).

Or

If you have a qualitative survey deployed (with the three greatest survey questions ever), then throw out the percentage of Visitors who do not state their Primary Purpose as visiting your website to "buy" or "research products and services" (I generously assume we can convince the latter bucket to buy through amazing marketing on the site). So now you know just the people for sure in play and possibly in play.

This second path will also give you a great rough idea of your "Opportunity Pie" (connect-able audience).

My recommendations were different from the ones Thomas is using. But both reach for the same goal: To get you to understand that not every single Visitor will convert, and you should know, even roughly, how many are in play / convert-able.

Perhaps you'll come up with your own rules. You might throw out everyone who was there to check Order Status. Or those that logged into their account to update settings. Or those that only visited the /blog/ directory. Or the Social Media of course they will never every buy but eat our bandwidth daily digging diggers!

As long as they pass the common-sense filter, go for it. You'll be earning your Analysis Ninja chops, and delivering something extremely valuable to your management team (even if they perceive it to be a cold bucket of water on their faces, the first time).

Lessons:

1. Don't scam your Senior Management by lulling them into believing every Visitor is convert-able.

2. Ignore the standard Conversion Rate definition in Google Analytics, Omniture, WebTrends, CoreMetrics, whatever else you are using. Focus on People. (Unless your business model is that everyone must convert, and does convert, on every Visit.)

3. You might get resistance when you first present the "real conversion rate" or "convert-able audience" metrics. Worry not. Charge forward. Good will come.

After the initial shock, your Management team, if they are smart, which I am sure they are, will ask you this: "So what can we do with the majority of the traffic on our website that is not convert-able?"

Preen proud as a peacock; this is your moment of greatness. Tell them why having thoughtful micro-conversions is so important on the site. Tell them you are going to compute the micro-conversion rate for the non convert-able audience. Tell them that with some of the non convert-able audience you'll hence establish a longer term relationship: with some you'll just hope to create delight and make them your recommenders, and with others still you'll do re-targeting and use David's method (all the way up top of this post) to reduce cost-per-acquisition.

All really great business outcomes.

In a nutshell. the goal is not to abandon a majority of your traffic. The goal is not to just ignore all the bouncers (fix that, tips here: Six Tips For Improving High Bounce / Low Conversion Web Pages). The goal is not to be depressing. The goal is to face reality, give it a hug and then figure out how to kick things up several notches.

Are you Ninja enough to accept that challenge?

Of course you are. You read this blog! : )

Know that I'm rooting for you.

Okay, now it's your turn.

Does your company do re-targeting to captured email addresses? If not, why not? If yes, then do you compute real CPA? Have you computed your "convert-able audience?" Is it 100% of your website Visitors? When was the last time you used raw numbers to shove a dose of reality in front of your Senior Leaders? Are there other techniques you've used that worked?

Please share your Analysis Ninja tips with the rest of us Ninjas-in-training using the comment box below.

Thanks.

Three Amazing Web Data Analyses Techniques For Analysis Ninjas is a post from: Occam's Razor by Avinash Kaushik

Without a doubt the feature that I am most excited about is Advanced Segmentation. This has been a long time coming (can you sense my pushiness!), and in this post I wanted to share with you all how to use this awesome feature.

Along the way I'll share three different segments that you must have in your web analytics tool. Regardless of why your website exists or what tool you use, Google Analytics or an alternative. I'll close with a approach you can use to get answers to your ad-hoc questions / queries faster, in mere minutes rather than days.

But before we go on here are all the features released today:

AdSense and API are in Private Beta (access by invitation). Motion Charts, Custom Reports, Advanced Segmentation are all in Public Beta (being released starting today, gradually to everyone in the next few weeks).

[sidebar]

If you want to get really really good at web analytics segmentation I encourage you to read these two posts. . . .

~ Three Components of an Effective Segmentation Strategy

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

The above posts take a strategic view and get you to think optimally, regardless of the web analytics tool you are using.

[/sidebar]

Now to evolving from being Reporting Squirrels to being Analysis Ninjas!

Why Segmentation?

Analyzing data in aggregate is a crime.

Bold statement, but the reality is that a "monolith" does not come to your website. Your site does not exist for a singular reason either. The core drivers of traffic are magnificently different for each core group of visitors.

So your website's really a mix of Visitor Sources, Visitor Behavior and your Desired Outcomes.

When you look at all that in aggregate you get nothing. You think Average Time on Site means something. No! You think All Visits and Overall Conversion Rate gives you insights. Nyet! You think understanding Keywords without drilling down to each search engine will be awesome. Non!

If you want to find actionable insights you need to segment your web analytics data. You need to separate out the various Sources, Behavior and Outcomes.

Then you'll understand behavior of micro-segments of your website visitors, which in turn will lead you to actionable insights because you are not focusing on a "glob" rather you are focused on a "specific".

Need more convincing? Want a specific case study? See this post: Excellent Analytics Tip#2: Segment Absolutely Everything.

Segmentation in Google Analytics:

Google Analytics always had rich segmentation capabilities. The problem is that you needed to be Robbin Stief to use it. She is super bright (went to Harvard!). She is technically super competent (regex anyone?). She is extremely aggressive! (Of course I say that with love!!)

This release of Advanced Segmentation means that the simplest of folks (Me, hurray!) can now perform sophisticated analysis.

Even my seven year old Damini daughter created two segments in ten minutes. Its that simple!!

Click on the "Advanced Segmentation (Beta)" link and boom!

What you are seeing are a list of "default" segments. The GA team had a nice brainstorming session about what segments might apply to most people. The ones you see were a result of that, and are pre-created and available in every account.

You could just use the ones above that apply to you. You could also choose one of the above as a starting point, just click on Copy and then further customize them. For example I might click on New Visitors, Copy and then customize it to identify New Visitors from Iceland.

You could also simply click on the link that says "Create new custom segment", if you do you'll be on your way. . . .

What you are looking at is Step One in creating any segment. On you left are the Dimensions (customer & campaign attributes) and Metrics (numbers, key performance indicators) you can choose to create custom segments of your own.

Ok with that briefest of tutorials let's dive in and have some fun with this puppy.

[The first example is a lot more detailed, just to show you all the steps. After that we'll do the bare bones.]

Idea #1: Segment out your Brand Search Keywords.

Branding baby!

Most search portfolios (Search Engine Marketing or Search Engine Optimization) have a unhealthy obsessions with our brand terms. Yes it is important to care but for many it takes over all our life and thinking. That is not good.

So the first segment is to segment out your brand terms (this thus far was not that easy in Analytics). We want to get a reality check on how big that segment is, how important it is, and is it worthy of our obsession.

In the search box on top of Dimensions type in Key and Analytics will guess and find the relevant options for you. . . .

Pick up Keywords and drag it on to the the Dimension or Metric box. Take a breath. :)

To keep things simple I am going to use "avinash" to identify my brand terms. For you it could be "maxim" or "obama" or "quickbooks" or "sexyback44" or "dell" or. . . .

As I type in "av" in the Value box Google Analytics will do a real time look up and tell me my options to aid me in choosing the word I want. Pretty nice. My favorite "tealeaf avinash". :)

Click on "avinash" and I am done!

Of course I could also add another statement and include "avanish" or maybe even "occam's razor" (which I think of as my "brand" term). But for now I want to keep this simple.

One sweet feature that differentiates segment creation in Google Analytics from that of other vendors is the ability to do real time QA (quality control) and test the segment you have created.

In most web analytics tools (NedStat is a notable exception) you create the segment, process the data, take a nap, wake up and realize you made a boo boo. Then you repeat the process. With Analytics (and NedStat) you don't have to do that.

Its like making sure that you go out on a first date before you propose marriage. Always prudent.

When I click on Test Segment GA will run a life query for the time period I had chosen and bring me the actual data for my segment.

So of the 44,528 Visits in these weeks I only good 576 Visits from my brand terms?

Heart attack!

Denial!

Ego bruised!!

Breath. Breath. Look. Ahh… I made a mistake.

Turns out that in the Condition I choose "Matches Exactly" (look in the middle above).

So the segment I have created is how many Visits from people typing in the exact term "avinash", what I wanted was Visits generated by every variation of "avinash". avinash kaushik. avinash blog. analytics blog avinash. amazing avinash awesome author. Ok that last one's made up.

I'll make one small change to my segment:

I click on the Condition and change from Matches Exactly to Contains.

Notice above that without knowing too much about regex and what not you can easily choose a number of conditions you can apply to your segment.

At the bottom I type in Brand Keywords and click Create Segment (after testing it one time!) and I am done.

Next step, trying to be a Analysis Ninja.

You now have tha ability to apply this segment you have created on pretty much every single report that you have in Analytics.

For example you could go in and see what content (pages) do people who come on Brand Keywords consume. What products do they buy? If I were GM then which product brochures do the download? On a tech support site did they find the right tech support answer? For a social networking site, do people under brand terms show a higher Visitor Loyalty or lower? And on and on.

For me I wanted to start with understanding the size. Recall I have a fragile heart.

So I go into the Keywords report and look at the Visits graph. . . .

I have to admit it did make a cry just a bit.

That orange line is the number of Visits from my Brand Keywords and the blue line is All Visits. So every variation of my brand makes up such a pathetic number of my overall Visits.

Ok let's put the kidding aside. This proves a very important point I make in my presentations. Search has a very very long tail.

As is clear above I don't got a few visits from my brand terms, even though they dominate the top ten keywords report I look at every day in Google Analytics (or Omniture or WebTrends or CoreMetrics or IndexTools or ClickTracks or whatever else).

I have hundreds and hundreds of keywords that individually drive five or ten visits each.

If I were running a business here then I would use the above simple graph to get my Management team to start paying attention, and creating a solid strategy, to the long tail of search .

Remember your brand terms are important, these are people who know you. But it is equally important that you have a solid long tail strategy because that's where you'll find people new to your franchise.

Next I care about money. Is this segment of Visitors converting (meeting goal targets)?

Sweet.

While they form a much smaller % of my overall Visits, Visitors on my brand terms form a nice % of my overall conversions. 18% of the all important Goal 3 (which has a very high $$$ value!).

Looking at the percentage view confirms the obvious. . . .

Nice.

When was the last time that you understood this much about a valuable part of your traffic, all in less than 10 mins? This stuff is not that difficult, and now its even more straightforward.

Next I would look at landing pages for my brand terms and see if I could tighten them up.

I would certainly figure how to create a solid search long tail strategy.

And I'll do one more thing to help with that. Create a new custom segment for my non-branded keywords.

Here is one way of doing that. . . .

Notice all I did is:

~ Switch the Condition from "Contains" to "Does Not Contain".

~ Added Source Contains the search engines (Yahoo, Google, Live – those are 99% of my traffic).

I save it and I am done. 99% accurate but good enough. I can get the last 1% accuracy if I can afford to spend a couple hours. In my case it is simply not worth the investment of time. You'll make your own choice, just make sure you are balancing time invested with the reward you get.

I recommend looking at your key Visits, Traffic Sources, Content reports to identify how to start monetizing your long tail.

Trust me it is not that much work, and it is just so much sexy fun.

Idea #2: % Visits with a high "Degree of Engagement".

Yes I did use the word engagement! But atleast I am following the simplicity and guidance from Theo Papadakis.

From the dimension selector choose Page Depth. . . .

What I am looking for is the number of visits where more than a certain number of pages were seen. To convince you of my greatness you have to read atleast three posts I have written. Yes it is not instant impression! :)

In your case it could be that you have a shopping site and the cart, checkout it 4 pages, then to get to the cart it takes another three pages of product pages and cute babies on pages. How many people on your site are going through the torture?

Or how many people are you impressing enough to stick?

Pick Page Depth. Then pick Greater Than. Then type in the number (be honest). I choose three. Here are my results (remember to click on the super sweet Test button). . . .

2,916 visits out of 44,519!

Honestly that surprised me. This is after all a blog and most people come to read the latest post and nothing else (which is fine). But almost three thousand read three pages. Yea!

What's your website's number?

The actions I would take would be to understand what websites / affiliates / keywords / campaigns are sending me Visits that have a positive "degree of engagement".

I would also investigate what pages these people land at, what content they consume (compared to my other segment, the terrible people who bounce! just kidding!).

Learn from these Visitors and then apply them to other Visitors / Pages / Campaigns that have a poor "degree of engagement".

Excited? See I am.

Idea #3: Outcomes! Segment Precious Customers.

So you sell on your site. Good for everyone!

One good idea for you to maximize revenue (and admit it everyone's scaring you about how bad the economy is and what not). So your HiPPO's are pressuring you to make more money.

What you want to do is identify Valuable Customers (after they buy from you they move from Visitors to Customers, yea!), and learn something from them.

One easy way to do that is to find the "whales". :)

I am going to use the number of items that Customers purchased on my website as a proxy for "whales". . . .

I want everyone who bought more than three things.

The actions I could take it to again try to understand a lot more about the Visitor persona that ends up converting to a "whale" customer.

Quantity is just one of the ecommerce segmentation possibilities in Analytics. There are, if I am not mistaken, another 15 to 20 attributes about your ecommerce experience that you can track, and hence segment by.

It has never been easier to understand what makes people purchase, what items go together, what calls to action / content works etc etc.

Idea #4: A Surprise: Use Segmentation to do Ad-Hoc Analysis.

I have a lot of ad-hoc questions I want answered. I can do this using reports in the web analytics tools. But it takes time. And usually is a pain.

The addition of the Test feature in Google Analytics Advanced Segmentation allows me to answer those questions much much more easily.

I'll give you a example.

I was doing the page depth segmentation (Item 2 above). I got distracted and noticed that another post from my blog got "stumbled upon". This always warms my heart because it brings lots of people to the blog.

But the question I had was: Do these people from Stumble Upon have a high "degree of engagement"?

So start with page depth. . . .

and then add my source of traffic, Stumble Upon. . . .

and bada bing bada boom (!). . . .

653 people came from Stumble Upon in these couple weeks. Not bad at all.

But sadly only 17 of those met the "degree of engagement" test. There were a bunch more if I reduced degree to 2. But still not as many.

It reiterated to me that it is nice to get stumbled upon, but you build audience one person at a time.

In 15 seconds I answered a question. Prior to now it would have been really hard to do it that fast.

Now I can dump this and move on to the next thing my mind flies to.

Soapbox: :)

In Web Analytics there is too much report creation and too much trying to do things permanently and forever. Yet most interesting questions come ad-hoc and answers that require you to submit a ticket or justify the creation of a new report are answers that come late and are useless.

Of you use Google Analytics use the Test feature to get your ad-hoc questions answered in seconds, not days. If you are using Omniture or WebTrends or other web analytics tool, then use this feature in those tools.

Don't settle. Demand answers fast.

Summary:

Regardless of the web analytics tool you use it is extremely important to use the power of segmentation to understand Visitor Sources, Visitor Behavior and Outcomes.

There is no other alternative to actionability.

One last, perhaps even more exciting, thought. If you combine the power of new GA Custom Reports feature with Customer Segmentation then you'll never have to look at a standard generic data again. You can get precisely the data you want for your business. Check it out.

Want access now?

If you want access to the public beta features (they are slowly being rolled out as I write this) then you can send me a email with two items. 1) Your Google Analytics account id (UA-something) and 2) Your email address that has access to that account.

[Update : Several hundred (!!) of you wrote in for early access. Thanks to the team at Google, by end of day today, 23rd, you will all have access to the new public features. Now off to reply to all your emails individually!]

Ok your turn.

Have you tried segmentation in Google Analytics? Yes it is in beta and there is scope for improvement but what do you think of it? Have your absolute favorite segment you use all the time?

Your ideas, suggestions, critique are welcome.

Thanks.

Google Analytics Releases Advanced Segmentation: Now Be A Ninja! is a post from: Occam's Razor by Avinash Kaushik

There is nothing particularly wrong with the standard 19,000 reports in your web analytics tool. But they do represent the Vendor's best guess about what you should look at. Sometimes they even get it right.

Most of the time though your business is absolutely unique (even as it exists amongst hundreds of competitors) and it is absolutely important that you take your web analytics tool and mold it around you. The power that is given to you even in free tools like Yahoo! Web Analytics and Google Analytics can create a view of data that will help you find faster insights.

This post is inspired by a suggestion from Horia Neagu in reply to my tweet asking for blog post ideas. My thanks to Horia.

Horia's question was: How about a post entitled "10 Google Analytics Custom the Reports You Absolutely Must Set Up"?

I am not going to write about that, simply because the very idea that a report is custom means that there are probably no "ten standard custom reports" to set up.

I am going to share one recommendation and two ideas for making your own custom reports better.

This is a "teach a person to fish" type post. Sorry. :)

[UPDATE: If you want to download three advanced custom reports to do Page Efficiency Analysis, Visitor Acquisition Efficiency Analysis & Paid Search Performance Analysis please check out: 3 Awesome, Downloadable, Custom Web Analytics Reports]

No Goals, No Glory.

Here's a cliché: If you don't know where you are going, any road will take you there.

Nowhere is this more applicable than when it comes to trying to find insights from your data you can action.

You report your poor heart away, no one seems to be able to take anything you give and take action.

Often it is the case that you and I have not bothered to sit down with he HiPPO / the boss's boss and tried to understand what in the name of all that is holy and pure is our website trying to do!

What are the goals?

No custom report (or advanced segment, the life giving oxygen) was ever created without an answer to that question.

So ask that question. Get an answer before you go about your customization ways.

If your leaders / clients truly want wisdom from you they will answer the question. But it does happen sometimes that begging or throwing yourself at her/him does not elicit anything of value.

In those rarest of rare cases (after you have already submitted your resume to other companies that will cherish you for the golden child you are) try to figure these one or more of these three things out:

1. Where it the company currently spending money? Email marketing? Affiliate? Paid Search? Online PR?

And what's the biggest bucket?

Now go create your custom reports because if you can help the HiPPO's figure out how to reduce cost of acquisition they will love you more than you can imagine.

2. If possible, without violating HR policies, figure out what your boss's salary bonus is tied to.

Start doing analysis that will help your boss get a raise. A great goal to have, love and promotions likely.

3. Go visit your website, yes yes the one you have not used for a while. : ) Find out the single worst thing about it (should take you less than half hour of clicking around).

Now go look for data that will help you prove that the worst thing is the worst thing. Not a bad goal to have to fix what's completely broken, and people will listen.

Three good proxies if you have no goals to start with. Ideally you'll know what your Macro Conversion is so you'll start your analysis with a bang. Super ideal would be that you know both your macro and micro conversions!

Remember: No goals, no glory. Not for you. Not for your boss. Not for your company.

Custom Reporting Tip #1: Always, Always, Always Focus On The End To End.

One problem with standard web analytics reports is that the data you need is scattered all over the place, making it harder for you to find insights.

For example I am trying to figure out which pages stink and need fixing. In Yahoo! Web Analytics the standard report only shows Page Views and Average Time on Page. How much good will that do?

Or I want to figure out which sources of traffic I should make love to or divorce? The standard Google Analytics report spreads the data I need over four tabs.

Custom reports are good at solving this problem. Drag the dimension you need (traffic sources, landing pages etc) and analyze the data by choosing the metrics that tell the end to end story.

End to end has three pieces: Input. Onsite Activity. Outcome.

Here is my favorite, custom, traffic sources report:

By inputs I mean metrics that help you understand (in line with your goals) how well the "top of the funnel" (usually acquisition) is working.

In my case that is measuring Visits (to know who is sending how much) and % of New Visits (to know who is sending how much that is of value to me – new visitors are very valuable in this case).

At a glance I have the information to start making some preliminary superficial judgments about performance.

By onsite activity I mean choosing metrics that help you understand the behavior of your visitors on your website (thus absolving your Acquisition team of any blame, perhaps!).

In my case that is measuring Bounce Rate (not so fast Acquisition team, don't get me bad traffic! :)) and Average Time on Site (as a proxy of measuring if the landing pages are engaging visitors and as a proxy of how much each traffic bucket engages with the site).

Depending on my goals I would choose different onsite activity metrics for my custom report.

By outcomes I mean, well you don't need to know do you? You read this blog! I am all about outcomes, every day!!

In my case the outcome metrics are Goal 1 (my macro conversion) and Average Value created for my website.

I could also have used $ Index or Per Visit Goal Value metrics if I were analyzing a non-ecommerce / content only website.

Remember Without a crisp articulation of outcomes every battle you fight will be lost, every day and you will live a very very unhappy life.

With these end to end metrics my custom report tells me stories that would otherwise take too long to piece together (or stories I might have missed completely).

One of the stunning realizations was just valuing Twitter traffic for example. (Click on the above report for a higher resolution report).

My twitter (social media) campaigns were doing exceptionally well. Lots of traffic (#3) overall, the second highest conversion rate (0.78%) and a Average Value that was not the best but rather sweet ($136 – which looks ever better when you compare the cost which is negligible).

Yet non focused traffic from twitter is not doing that well. 0.33% conversion and $39 average value. Pathetic.

I can now jump, like a na'vi, from row to row understand performance quickly and efficiently.

The power for a custom report that shows the end to end story.

It is so easy too.

For example here is the exact same custom report created in Yahoo! Web Analytics, just 30 seconds of drag and drop:

[Click on the image for a higher resolution version.]

Always go e2e. If you don't, you better have a good excuse!

Custom Reporting Tip #2: Create "Micro Eco-Systems".

I think I can honestly say that I have get to meet a single decision maker or a department or a company that has yet to tell me: "You know what the problem is Avinash? I don't get enough reports."

:)

We love spewing out data and pretty soon your company has 200 reports and I'll bet you $50 that not a single decision is actually based on data.

So fix it.

Create micro eco-systems.

What I mean are custom reports that do three things:

Let's understand how to do this by looking at a real life example.

My goal is to create a "search ecosystem" report that collects different important pieces of data, for three different stakeholders, all into one place.

I do that by first understanding who all the stake holders are who'll need to use the data (let's hope!) and doing a simple stake holder interview to understand what their business goals are.

Now rather than spamming everyone with a report (that no one will find, I'll have a hard time version controlling, and other such pain), I'll just put it all together in one place (at least in Google Analytics due to a simple yet exceptional feature – tabs!!).

Here is a pictorial view of the process that I'll go through:

[Click on the image for a higher resolution version.]

My first "client" is the Acquisition team, they are responsible for spending the company's money wisely. They are measured on bringing new Visitors (potential customers) to the site.

I create a tab for them that shows Visits, New Visits, Bounce Rate and Average Time on Page (not site). I add the latter two because I want them to see their end to end view and I want them to realize they hold some level of responsibility for people not just coming, but also staying.

We have just one report, each day (God willing) they'll log in and see their own personalized sweet view of the data:

[Click on the image for a higher resolution version.]

But I am not done yet.

Next up is my HiPPO. Let's call him Paul.

Paul only cares about Revenue and all things connected to revenue. He does not care about any other metric. Nothing wrong with that.

Rather than creating another report for Paul I click on *Add Tab* simply do this:

Create a person view for Paul. I throw in Visits (I have to give him some context and some Input metric) and Goal Conversion Rate (so he knows efficiency), Goal Value, Revenue, Shipping (because Paul is having us charge lots for shipping because he thinks of it as a profit center (!!), not great but remember I am personalizing).

Here's the resulting output:

[Click on the image for a higher resolution version.]

You know what the result is?

Paul actually looks at the data every other day (and a bit deeper each Friday) because it does not contain crap. It contains just what he needs to do his job (find people to reward and find people to fire).

That is what you are going for. Taking people from data apathy to data usage.

[Oh yes, yes, I noticed Revenue and Shipping are zero in the above screenshot. I wish I could show you someone's real data! Not today. But you get what the report is trying to do.]

Finally there's Amy. Another key stake holder, but a tougher nut to crack. You see her bonus is tied only to Visits, a low bar if there ever was one.

So what do you do?

You can't over smart Amy, she is too smart for you (and probably a level or two higher).

You are going to lose her if you give her too much data.

You need to entice her to start using data, and restrain your smarts – you know you want to create a impressive 8 column report!

In this case I simply add a tab. It says Amy Chang (so she knows it is her very own personal report). It has Visits and Average Time on Site. I added Time on Site as a Outcome metric, just to keep up with my outcomes obsession.

[Click on the image for a higher resolution version.]

It is simple. It is effective.

It will get her to see just the data she wants (plus one more thing :)).

And here's the sweet part…. since this his an eco-system report perhaps she (and Paul as well) might see other pieces of data in other tabs and might be intrigued enough to ask you to add more metrics.

Then and only then and only at that time and only when you are asked (am I repeating myself?) add those metrics. It is vastly more likely that she (and Paul) will use the data.

There you go… one report that has all the data each stake holder needs personalized and customized.

No one is going to come to you and say: "hey want folder is my search report" or "I don't understand what all this data is saying" – it is personalized. And when you have to make changes, it is all in one place.

Well…

Such simple little things: tabs (or cup cakes :)).

Makes it so much easier for you to create a data democracy. And a bit sad that you can't do this with most paid web analytics tools today – yes you can create a custom report but the above report would be a one huge 13 column report that:

I hope all paid web analytics vendors will incorporate this feature, for the sake for our data democracy!

That's the story. Goals. End to End. Micro self contained eco-systems.

I was hoping to teach you how to fish, rather than just tell you which 10 reports to create. Regardless of the specifics of the reports and metrics above I hope you have learned a bit more as to how to think about approaching the issue and the important things to focus on.

Custom reports are a powerful way to take what looks overwhelming in web analtyics – REPORTS and DATA – and make it didapper. It is also a wonderful way to start the journey of your company, big or small, to start using data.

Ok now your turn.

Do you have custom reporting tips to share with us? What small or big thing you have done that really really worked for you? Have you tried end to end reports? How about micro eco-systems? What strategy completely failed? Got a custom report you think everyone in the world should be using?

Please share your stories / tips / bruises / successes.

I'll send the best one a copy of my new book Web Analytics 2.0.

UPDATE: It was hard to pick just one winner so a copy of the book goes out to SteveK (for advocating common sense!) and to Ali Shah (for emphasizing sharing of context). A bonus prize also goes to MGSeeley (for bringing a smile with his adorable analytics haiku!).

Analysis Ninjas: Leverage Custom Reports For Better Insights! is a post from: Occam's Razor by Avinash Kaushik

Web analytics gems lie deep in the data and we spend our lives looking at the top ten rows of data.

It does not matter which report you look at. Affiliates. Products sold. Referring URL's. Pages viewed. Search keywords. Promotions. Geographies. Really pick any report with any dimension you want to look at, we spend our time (and valuable space on our dashboards) looking at the top ten.

We look at the top ten rows of data because:

1. Too much data from our web analytics tools.

2. Lack of clarity from our business leaders about what the site is solving for.

3. Not enough hours in the day to overcome challenge #1 and #2.

But if you just look at the top ten rows of anything here are the two corrosive problems:

1. The top ten of anything rarely changes (with the exception of hourly changing content – news – sites).

2. The top ten only focuses on the head, while the magic is in the long tail of anything. Magic related to finding challenges in your business. Magic related to finding opportunities. Magic that will help identify things you can actually action.

Allow me to make the case for you to look beyond the top ten rows in your reports by sharing three short stories. In each case I request you to look beyond the specific request and tool, rather focus on the analysis and how you could possibly apply it. I hope is to inspire, not to prescribe.

For example take a look at this report. . . .

I am sure when you look at it now it appears all mysterious, full of potential. You can't wait to take it out for a first date and then another and by the third date if you do the same old thing it gets boring. You are done looking at the bounce rates and time on site and conversions of these keywords. In the best case scenario you have even optimized landing pages. Good.

The first week's over, now want? Why keep reporting the top ten keywords on you Executive Management Global KPI Dashboard?

Look at the top of that table. For this website 86,837 visits came from 8,939 keywords!

What's going on with the other 8,929 keywords?

We never bother with them both because it is really hard to look at more than 10 rows of data. Harder still to look at 30 or 50 or 70 rows of data. Not only do we have a hard time interpreting insights from lots of data, we can't actually physically look at that much data and find insights.

Last month this blog received 40,662 Visits from 26,137 key words. The top 12 keywords accounted for 5k visits. The other 26,125 keywords accounted for 35k visits! By analyzing just the top ten see how many visitors I would be ignoring?

Here are three techniques I use to overcome the trap of the top ten rows. . . .

1. Advanced Table Filtering.

In the past we all used the standard reports that our web analytics tools churned out.

I don't do that any more. If you show me a report and it is not a custom report that you have created to better pull relevant kpi's into one place then please know that I will think less of you.

A sign of non-laziness is that you bother to atleast create custom reports. A best practice is to pull atleast some input metrics (Visits) with some attribute metrics (% New Visits), have something that denotes customer behavior (bounce rate) and it is criminal not to have atleast a couple outcome metrics (goal conversion rate, per visit goal value).

That best practice gives me this report for my search keywords. . . .

[Click on the image for a higher resolution version.]

It is a useful report that helps me understand performance much better than the standard reports from Omniture, Google Analytics, WebTrends etc etc.

But remember I have twenty six thousand keywords referring traffic to this blog.

I want to very efficiently look through them to find something useful.

I want to locate my most important brand terms that perform magnificently for me. To accomplish that I click the link called Advanced Filter under that table and do this. . . .

I move beyond the limitation of the top ten rows by creating a simple inline filter.

Find keywords:

containing "avinash"
where the "per visit goal value" is greater than $1.7 (a higher bar since the average is 1.3) and
the Goal Conversion Rate is greater than 10% (again another high bar compared to the site average)

Notice that I can only run a smarter query because I had created a custom report, without it I would have to use the "lame" metrics that I might get in the standard report (or immediately proceed to extracting all the data, all metrics and spending next four hours in excel doing what I can in two seconds in Analytics).

In a second the table transforms into. . . .

[Click on the image for a higher resolution version.]

You know what's in the table?

Keywords we should make love to because regardless of if they bring a lot of traffic or little, they hugely deliver to the bottom line. Making love might be too small a emotion when you realize compared to a site average of $1.3 the per visit goal value is $5.5. And look at that conversion rate!

Also notice up top. . . I quickly went from tens of thousands of keywords to just 193 I should focus on. I need to analyze these keywords, search engines, landing pages, products sold, leads received, and so much more to figure out:

1. what is going on here that is so right that it works like magic and
2. how much can I replicate the lessons learned

Far too often we focus on our losers. Here I start by focusing on winners and see if I can do more of what I know already works.

I could just as well have mined my data to look for

people on non-branded terms
people who come on every variation of the names of my two books
people who come from China
people who use a cluster of terms I consider most competitive or. . .

The limit to the data I can mine (and I use that word loosely here) is the imagination I have (or better put: the intelligence I possess about important business questions).

So do this. Use inline advanced table filters to go from tens of thousands of rows to just a few that you need to focus on.

Yes you can absolutely do this in Excel. But it will take you five times the amount of time and effort required because. . . please pay attention. . . in this case you are querying the entire dataset of your website and in Excel you'll keep going back and forth to get more data or dump it or write more complicated queried or. . . you catch my drift.

Advanced Table Filters can be used in any report in Google Analytics (Yahoo! Web Analytics also has a very similar feature, actually YWA had it before GA! :)).

The one limitation of the approach is that you'll more optimally analyze your known knowns. You'll even get to understanding your known unknowns. But not your unknown unknowns.

2. Use Tag Clouds.

For pulling lots of data from lots of rows all together into one place I do so love tag clouds.

Download all your keywords into a text file (twenty six thousand or so in my case) and upload them into Wordle and bam!

[Click on the image for a higher resolution version.]

O M G!

Did you think that by doing something so simple you could get such a quickly glance-able view of so much data?

I love search keyword tag clouds because they can tell you about the health of the company when it comes to search.

In my case, above, for example I LOVE the fact that above and beyond everything the word Analytics dominates it all, and what makes me happier is that the word Survey is so prominent (you all know I love qualitative data, now here is proof that my blog attracts so much of that traffic).

I worry a smidgen that people will think this blog is about Google Analytics, it is not, so I am happy that the word Google appears atleast as much as the word Web (and variations like website and online etc). I can't drill down in wordle so easily, but I use technique #1 (above) and it turns out 30% of the time the word Google appears in search queries in the spirit of "working for google", and another 30% for queries like "google insights for search", "google ad planner" etc (tools I have blog posts on). My mind is relieved.

See how I can understand about a strategic concern, at least a bit, using a technique as simple as a tag cloud?

Another thing I am rather ecstatic about is the sheer diversity of the keywords in search queries. It is not my brand that dominates (boo hoo! cry cry!) but rather "category" terms (which bring the "impression virgins"). Metrics and Conversion and Data and Questions (look at that!) and Analysis and Customer and Intelligence and Bounce and Best. . . .

That sweet spread validates some my Search Engine Optimization strategy (something I spend a lot of time on) – go for diversity and attract new people to my "franchisee".

Or in your case it may not. It may tell you different things. The main point is it would be hard to understand some of these macro factors in your data by looking just at a table in your web metrics tool of the top ten keywords!

Here is a tag cloud for a small company you might have heard of, Gatorade. The data does not come from them (obviously), it is from Compete. . . .

[Click on the image for a higher resolution version.]

I am not a SEO expert, can't underscore that enough, but everything that could possibly be sub optimal about seo/ppc is wrong with Gatorade.

The one brand term dominates their referring search keywords. This in of itself is not a bad thing, Gatorade is a huge brand. But what it indicates clearly that their site attracts people who already know Gatorade and are "pre converted", when perhaps a greater use of the website would be to attract the impression virgins and blow them away with the greatness of Gatorade so they'll never consider Powerade or any other brand.

Look at the diversity of keywords.

Find any?

People use tens and thousands of different ways to find even a web analytics blog. Look at how much different types of content there is on Gatorade.com and MissionG.com and Gssiweb.com and it is quite clear that Gatorade's tag cloud is telling a sad story.

Finally for all the money that Gatorade is handing out to premier current athletes (and the really expensive content Gatorade has on its website related to those top athletes) only two show up in the tag cloud. One that used to be important (though he is still a big brand) and the other that sadly ran over a fire hydrant a couple weeks back. That shows how exposed the Gatorade brand (atleast online) is should something unfavorable happen to these two guys.

I would humbly dramatically change Gatorade's SEO and PPC strategies tomorrow morning.

It is amazing how when you have so much data in one place, using such a simple technique, that you can find some very intriguing patterns in your data, stories that might validate what you are doing right or expose everything that is wrong with your digital strategy.

Simple but effective. Try it for your site. What do you find?

3. Use Keyword Trees.

This is my latest love. I mean it.

I was so happy when I first saw it because of this constant quest I am on to take lots of data and show it on a page.

Zach and the team at Juice Analytics have created two powerful visualizations: Referrer Flow and Keyword Tree.

I adore that last one.

You simply go to http://analyticsvisualizations.appspot.com click on Keyword Tree and you are on your way!

The visualization uses the free, open and multifaceted Google Analytics API. In a few seconds you'll get something pretty and intelligent (how often have you seen those two together :)). . . .

[Click on the image for a higher resolution version.]

It's a tree. With branches. :)

While in the case of tag clouds it is difficult to understand the relationships between different words that exist in your search queries, that is not the case with keyword trees.

I was looking up the relationships for the word "avinash", image above, click for a higher resolution version. I am looking at hundreds upon hundreds of rows of data visualized all in one page.

I can easily see long tail queries like "top 10 key metrics web analytics avinash" or head ones like "kaushik blog" or even "kaushik web analytics 2.0 pdf" (my book is not available in pdf form but now I know lots of people are looking for it and so maybe we should get it out fast!).

I can simply walk through the various branches of the tree and it helps me understand in a very powerful way the relationships that exist in my data. It always throws up surprises (partly because of my top ten rows table driven existence I have never actually looked at so much data in such a easy to understand way).

The fun though does not stop here. I can actually look at keyword trees using different metrics.

In this view I am looking at the data for the keyword "tracking", the colors shown highlight the bounce rate metric for each relationship. . . .

[Click on the image for a higher resolution version to be truly impressed!]

Now I know the queries that stink like a skunk, the deep reds, and find some sweet ones, "event tracking" is one such word (lots of visits with very little bounce).

But I can switch and say. . . . well our bounce rates stink so we'll not use that as a success metric :), let's use the % of New Visitors as a success metric. Ok no problem, press the button on the control panel on the right and. . . .

[Click on the image for a higher resolution version to be truly impressed!]

Notice the relationships change, the queries you would have paid attention to will change, what you will action will change. Just with the press of a button.

You can also flip the size of the words. I am using Visits in both cases above, but you can just as easily go for quality (in this case) and use New Visits. I would love to see some kind of Outcome metric there, given my passionate and sustained obsession with measuring end success.

You can do lots of true analysis, for free, with your data and get the kind of insights tables from Google Analytics and Yahoo! Web Analytics and WebTrends and CoreMetrics simply can't provide.

Let me share two snapshots to make that point.

I was genuinely shocked at the complexity of the tree and branches associated with the word "google". . . .

[Click on the image for a higher resolution version to absorb the whole thing!]

The darn thing did not even fit my laptop monitor (1440×900), and there was so much going on that it took me a while to absorb all the lessons.

Meanwhile for the "analytics" branch I can see, at a glance, the 70 or so queries that cause the "main flare" and it gives me a peek into the the head of my visitors unlike anything else. Talk about collecting VOC (and actually understanding it!!).

On the other hand I was quite saddened to see the report for the word "metrics". . . .

[Click on the image for a higher resolution version.]

Remember this blog is all about data and metrics. Yet the tree is so "shallow". Or better put it is less a tree and more a bush. Or maybe just a shrub.

For all of the reasons I was less than thrilled with the gatorade data in #2, I am less than thrilled here. From competitive intelligence analysis I know that there is a ton of volume on Google for queries related to "web metrics", and variations, yet I have not done a good job of attracting that traffic.

The above picture does not simply tell me that I need to do a better job of doing SEO for "web metrics", the real lesson is that I need to put in a ton of effort to attract the long tail for "web metrics" because that is where most of the volume is.

You will probably find other lessons from this exercise on your data. Hopefully there is no doubt by now that valuable lessons do await you if you put in the effort to start switching from using tables and excel and shift to using other data analysis / visualization techniques.

Each effort above uses something very simple and while none of them are a panacea, your understanding of web metrics data will not be the same boring self.

Have fun.

[Bonus Item: #4: One strategy to escape the top x rows is listed in the second half of this post, jump to just after the picture of Tiger Woods (!!): Focus On “What’s Changed”.]

Ok… your turn now.

What do you think of these strategies? Have you used them before? Worked for you? Have you used other data visualization techniques that liberate you from the trap of top ten rows? What tools do you use? Got models / approaches / strategies you want to share?

We would all love to learn form you. Please share. Thank you.

PS:
Couple other related posts you might find interesting:

• “Dear Avinash”: Be Awesome At Comparing KPI Trends Over Time
• Consultants, Analysts: Present Impactful Analysis, Insightful Reports
• How Thick is Your Head and How Long is Your Tail?
• Make Web Analytics Actionable: Focus On “What’s Changed”
• Web Data Quality: A 6 Step Process To Evolve Your Mental Model

Analysis Ninjas: Move Beyond The Top Ten. Find Love (/Insights). is a post from: Occam's Razor by Avinash Kaushik

One is in the business of providing data.

One is in the business of providing, to use a old fashioned word, information.

This one of the core reasons why most dashboards are "crappy", i.e. they are data pukes that provide little in terms of context and even less in terms of actionable value.

Here are some examples of sub optimal dashboards, sub optimal in my mind from a actionable perspective. . . .

Perhaps the most common type is above. Lots of data, even drill downs included, but you can't look at it and go: "Wow we need to do . . . ". No sirrie bob you can't.

I wanted to point the above out purely because of a common feature of 80% of Web Analytics Dashboards, in excel with a billion tabs to look through. This is not a dashboard, it is the result of a massive sum of money paid to a Consultant who is trying to impress you with his / her excel skills – without actually telling you anything.

You are walking down the street. You look at someone from behind and you think "hmmm she's / he's pretty". So you speed up and overtake them and in the process you sneak a glance at them (yes you are married but looking is still ok :), and you are hugely disappointed. Not pretty. That's the dashboard above. Very sexy and Web 2.0'fied and a ton of data there, but a lot less actionable than you might have hoped.

Why is this so? All the above efforts are well intentioned, took lots of honest work and probably took months to put together. So why?

Here are some hidden (corrosive) reasons why most dashboards tend to stink when it comes to helping the Executive make any decisions:

1. They leave the interpretation to the Executive (/ customer / requestor / other Squirrels). This is a fatal flaw because most dashboards are highly aggregated views of any KPI and are missing all the nuance and analysis (that only you as Ms. Ninja have, and you don't go with dashboard).
2. Most Executives actually want insights / action recommendations but they don't trust the Squirrels / Ninjas / VP's / Data Providers. So they ask for numbers. We dutifully cram as many of them on to a A4 size paper in 3 size font and send it along with a magnifying glass.
3. Most Squirrels / Ninjas live in a silo. Going out to collect enough tribal knowledge to actually know what is going on to then make recommendations from the data is not something that we do, nor are we encouraged by our Executives or our organization structures. This incentivizes data pukeing.
4. Often dashboard creators tend to be "outsiders" (Consultants, Experts etc) and they often don't have deep practitioner experience that would allow them to understand the human / "below the surface" issues like the above three. That leads non-Practitioners to make the common mistakes like creating the above three dashboards.

If you want your Executives / Customers to take action, you have to give them information and not data. It takes effort to get there, it will take all your charms (though no violation of any HR intimacy policies), and it will take some time.

Step one as always is to become aware of the above three problems.

Step two is to get a possible solution from the Occam's Razor blog. :)

My attempt at solving this problem was to try and attack it from a human psychology perspective: How can I create a "dashboard" that will incent the right behavior from the Squirrels / Ninjas while giving Executives the information they need to make decisions (rather than engaging in a bitchfest which is the typical outcome).

Recommendation #1 was to move to a Critical Few philosophy for Executive reporting: Only report the three or five (at most!) metrics that define success for the whole business. Kill all the ancillary metrics that were nice to know (and my kill I mean let lower levels worry about it).

Recommendation #2 was my humble, admittedly ugly, attempt at a "Action Dashboard":

4Q. (Sorry Jonathan! :)

Each quadrant representing a solution to a human problem that lead to crappy dashboards.
(Apologies for having to redact some of the data above, to protect the innocent.)

Let me walk you through it.

First very up top a clear identification of what the Critical Few metric was, who was responsible for that metric from a business perspective (translate into "head on the line") and who was responsible for the analysis.

Also note the little red dot. That here indicated trouble. It can have two other colors, yellow for don't fire anyone yet but get ready and green for send someone a big hug and a box of chocolates. Next. . . .

The first quadrant (the graphic) shows the trend for the metric. Ideally segmented (as is the case here, cart abandonment is illustrated for four key customer segments).

This quadrant is to satiate Executive curiosity that you know what you are doing, it will be glossed over (and that's good!).

The second quadrant (Key Trends & Insights) is to add value by interpreting the trends and adding context. It says there that some things are up or down (in english :), and it also warns which data might be bad etc. You are starting to do your job here.

This quadrant is the one that Executives will read a lot initially, over time they will gain confidence in you, they will love that you share context (hello Ninja!), over time they will gloss over it (a good thing).

The third quadrant, clockwise, (Actions / Steps To Take) is force the shy Web Analyst to get out and talk to Marketers, Website Owners, VP's, Whomever it takes to get all the tribal knowledge, identify root cause for the trends in the metric and recommend solid action to take. The Analyst will rarely be able to do this by themselves. It will require human contact with others, it will require conversations, it will mean identifying solutions collaboratively. It is a fantastic opportunity to become smart about the business.

This quadrant is key to driving action. No longer do you leave things to interpretation or let's blame people etc. You are recommending what actually needs to get done. Your Executives will kiss you and over time this is the only quadrant they'll read. It will also mean that monthly meetings will move from bitch fests to deciding who does what. Amen!

The fourth quadrant, (Impact on Company/Customer) exists in case it is not clear to the Executives why they need to take action (listen to poor old you the lowly Analyst). I feel it is the key thing missing from any dashboard, they are normally missing the kick in the rear end and this quadrant delivers it. It is the answer to this question: "As a result of this trend (up or down) what was the impact on the company and its customers". It also forces you, Marketer / Analyst, to do hard work to estimate the impact and put it on paper.

This is the killer quadrant, if nothing else drives action this will, knowing exactly how much money was lost, how many customers were pissed, how much opportunity was wasted. Now when they ignore you they do that at their own peril and with their butt on the line. Trust me action you recommend will be taken.

See how simple it is?

You fix the human problems, you address the flaws in the system today and you actually become much smarter about the whole business (thanks to q3 and q4).

Win – win – win.

Over time you'll gain a lot more trust from your Executives and all the crappy dashboards can die and be replaced with one that looks like this one. . . .

Now you are asking your Executives to simply layer their own judgment on the recommendations and help the company take action. Who needs to see the numbers? They pay you and I to deliver actionable insights.

I stress that it won't happen overnight, but shoot for that nirvana state.

May the force be with you.

Ok now your turn. Care to share your own learnings and battle scars? Your success stories? Perhaps critique my "Action Dashboard" (sorry could not think of a better name, do you have suggestions?). Your perspectives are most welcome and would be greatly appreciated. Thank you.

PS:
Couple other related posts you might find interesting:

• Five Rules for High Impact Web Analytics Dashboards
• Web Analysis: In-house or Out-sourced or Something Else?
• How Should Web Analysts Spend Their Day?
• Seven Steps to Creating a Data Driven Decision Making Culture.
• Six Data Visualizations That Rock!

The "Action Dashboard" (An Alternative To Crappy Dashboards) is a post from: Occam's Razor by Avinash Kaushik

It is possible to create truly data driven organizations , but for my "Guru" talk at eMetrics summit in Washington DC tried to tackle a much more solvable problem: Creating a data driven boss (or the boss's boss or the boss's boss's boss's boss or….).

My hypothesis is that not only is this a solvable problem, but that it is also a way for you to be in an environment where you can be challenged while adding value to your big / small organization.

Before we dive in I must say that it is my assumption that you actually want to do this and that you have the passion to fight the good fight. Glory and higher salary will be the obvious end rewards, but by themselves they are not motivation enough.

You have to have the passion to want to roll the big boulder uphill, here are your weapons………

Six Steps to Creating A Data Driven Boss:

# 1: Get Over Yourself

# 2: Embrace Incompleteness

Many of us come to the world of Web Analysis with experiences in traditional analytics where things can be counted to the last drop. On top of that we are classically trained to not to take risk and to only make decisions based on data we can swear on to be "accurate".

The problem is that we live in the most perfect imperfect medium in the world: the Web.

For now it is impossible to collect data perfectly. It is ugly, it is dirty, it is incomplete and no matter how hard you try it is not going to get perfect.

Yet we can't resist.

We obsess about silly things like cookie deletion rates. We go down rat holes of chasing down the last 5% of the deltas.

Not only is that effort not worth it, it is futile. Simple reason: You can't ever know what the total number of Page Views were on your site, much less every thing else.

And that's ok.

Now here is the silver lining in this dark sky. We live in the most data rich environments were we can find a ton of actionable insights.

Think about it. Taking our ads in Fortune Magazine is a completely faith based initiative. Based on the number of subscribers the magazine has you assume there will be an outcome. Or maybe we do some primary market research.

You can do much better than that on the web.

You can see exactly how many people got the "magazine", you can see how many of them read each "story", you can see the ads they were "exposed to", you can….. and that's just the basics. You can see were each person came from, what drove them to have a interaction with you, what the outcome was, did it result in a positive brand impression and so on and so forth.

Yet we obsess about the last 2% perfection, and we waste the opportunity to use 80% great stuff we have.

So make sure you are on first party cookies, tag as many campaigns as you can in a clean way, collect data and make decisions. Resist the temptation to be perfect, it is the enemy of good enough.

Embrace incompleteness and it will set you free. Both you and your boss.

# 3: Give 10% Extra

Organizations run on reports and so does your boss. They ask questions and you give spreadsheets. Then they ask for more and you automate the production of spreadsheet.

The other day someone was recommending that the only tool you should use for web analytics is excel! Then they proceeded to share best practices on how to write complex macros and reference cells and reduce the pain of creating spreadsheets.

Here is the problem with that: You were initially providing data, and now you are providing data without even having to look at it!

How is anyone going to find actionable insights?

Your boss is fifteen steps removed from data, you are closest to it. Yet now you have become a reporting squirrel (I was going to say monkey but that sounds rude).

As the person closest to the data is it not your job to Look at it? Look and understand and make recommend?

Make a conscious choice what job you want to be in: Reporting Squirrel or Analysis Ninja ?

[Bosses: When you pay people / consultants to do "data work" also make this conscious choice - are you paying the consultant to be a RS or a AN? Don't hire a Squirrel expect them to do a Ninja's job.]

Next time Your boss asks for a report, give them 10% extra.

You do that by actually looking at the data. Stare at the table. Go visit the website, click around, experience it, then go back to the data, connect the dots that only you can because you are the smartest person in the room.

Now give your boss 10% extra: Your insights that your boss did not ask for.

Make a recommendation. Tell them what is working. Tell them what is broken. Tell them that xxx or yyy is a better metric to answer the question.

You create a data driven boss by giving them something they can drive from data. Not by giving them spreadsheets or reports or only what they want.

At the end of each week ask yourself, did you give your required 10%? 10% extra is all it takes.

Got Ninja?

# 4: Become A Marketer

I find that great Analysts are not simply "data people". They are "customer people".

Yes they have all the qualities that we have talked about before (critical thinkers , curious, common sense, etc). But a delightful quality I have found is that are Marketers.

The reason is that different parts of the organizations care about different things but Marketing cares about the business with a very unique perspective.

If you want to change your boss and your company then you'll have to become a Marketer, someone with an understanding marketing principles, someone who can be a customer advocate / champion, someone who can evangelize the purpose of data in creating customer centric decisions.

Sales cares for selling, IT cares about keeping servers and sites up, Engineering cares about building things that can hopefully be monetized, Marketing cares about customers and, most of the time, they care about a longer term success and not simply meeting this month's quota.

No matter what organization you are a part of, you have to become a Marketer. Think like a Marketer and execute with that mindset.

Your job is to "market" your data in unique and innovative ways that solve for the customer. Get an understanding of marketing.

Take a course in marketing at the local university, read up on it (I subscribe to Seth's RSS feed), partner with the Marketers in your company and absorb.

Your boss will love you. Your career will soar.

# 5: Business In The Service Of Data. Not.

Lots of companies are data rich and tools "richer". In fact in many of them extensive data efforts to mine the logs and extract and xml and data warehouse it and mix it and merge it and clean it and build for scale and BI it and……..

Nothing.

No insights come out.

In the obsession about capturing, processing, storing, moving, shaking, baking data the core reason for doing all that is forgotten.

When the question is asked, rarely : What has all this complexity delivered for the company? The answer usually is: We have lots of reports and know up to the moment exactly what is going on via our blackberrys.

Classic sign of a ecosystem were the business exists to produce data to employ people to do all of the above.

The business does not exist to produce data. Doh!

Data should exist to serve the needs to the business and provide insights that can be actioned. Get that mindset if you want to change your management's mindset about how decisions should be made.

Do an inventory, ask around, how many decisions have been made based on data that can be traced directly to have added value to the bottom line revenue numbers? [Bosses: Great filter to apply for Consultants you hire as well, ask them that last question.]

It is important, nay critical, to constantly check yourself and ask if the business is really serving the needs of data or vice a versa.

So what does this mean?

When you undertake data projects apply this advice: Do small, deliver in a month, measure if it had an impact on the bottomline (even if small). If yes continue to invest more. If not dump it, time to do something new.

Traditional IT projects tend to be long multi year undertakings that used to deliver in the traditional worlds. That does not work on the web.

On the web things happen too fast, they get complex too fast, and every data project you undertake starts to decay almost immediately. Embrace speed and flexibility and 80% good enough. Implement, measure value, if yes move forward, if not kill.



We don't kill enough, we plan too far, we "implement" for too long, we don't think smart and move fast . Change.

Data driven decisions are not made when you spend 95% of your time in "getting" data rather than analyzing what little (or a lot) you have. You want a data driven boss? Spend 80% of your time analyzing data and producing insights.

# 6: Adopt A New Mindset, Expand Horizons: Web Analytics 2.0.

One final bonus tip. Expand the data you use to make decisions, move away from clickstream clickstream clickstream all the time.

ClickStream data is good at the What. We have tortured it to find insights. We have done the best we can with just knowing the clicks. It has worked ok, but it has not done spectacularly. Hence we have tried to take it to the next level by adding a bunch more clicks together and making it more complex. That is not stuck at all.

The other problem with a clickstream only strategy is that your bosses don't get it. There is lots of confusion, still, about Visits and Visitors and Unique Visitors and Sessions and…. So when you and I add / divide / subtract / multiply our clicks and page views and present analysis it does not have quite the impact we want, because at some level our bosses still don't get it.

That's quite ok.

Web Analytics 2.0 is your friend.

There is one important reason for that: with Web Analytics 2.0 you are talking your boss's language, that of Customer Voices and Competitors and, get it (!), Money!

Most web analysts focus on analysis with Omniture and WebTrends and Visual Sciences and HBX and Google Analytics and Coremetrics etc.

If you are one of those consider expanding your skills and experience to understanding and executing Surveys and Remote Usability Testing and A/B – Multivariate and Testing and competitive intelligence and so on.

Doing that will mean that you can represent the customer voice back to your bosses with qualitative data. It will mean that you can fight the HiPPO driven opinions with data beyond clicks. It will mean that you can kindle a small fighting fire in your boss by showing how your competitors are doing.

You boss might not care about clicks, but you bet your bottom she/he cares about customers and competitors.

As you create your own execution strategies do a Web Analytics 2.0 checklist. How many cylinders (above) are you firing on?

Can you believe that I did the above presentation in 12 minutes with eight slides? :) [If you were there share your feedback! Looooong twelve mins?]

Creating a data driven boss is not difficult. All it takes is some or most of the above six things. I wish you all the very best, may your days be brighter and your bosses more data driven!

Ok now it's your turn.

Please share your perspectives, critique, additions, subtractions, bouquets and brickbats via comments. Thank you.

[Like this post? For more posts like this please click here, if it might be of interest please check out my book: Web Analytics: An Hour A Day.]

Six Rules For Creating A Data Driven Boss! is a post from: Occam's Razor by Avinash Kaushik

There are seven courses in all: SEO, PPC, Social Media, Web Analytics, Conversion Optimization, Marketing Fundamentals and Online PR. Each course is taught by a world class expert who passionately loves teaching. It really is a fun group.

At the start of each quarter I send a letter to the students enrolled in the web analytics certification course.  My hope with the letter is to set clear expectations about the course and how to maximize their chance of success in it. My hope is to inspire them to put in the immense effort required (John and I are tough graders). My hope is to give them the guide rails they need. My hope is to share my belief that it is not the certificate they get in the end that matters, it is what they do to get the certificate that matters. Small, but substantive, difference.

I love writing the letter, and in this post I thought I would share the one I wrote the last quarter with you.

Regardless of the method of ongoing education you have chosen, I hope that you'll find the guidance contained in it to be of value. You'll most definitely see my unique brand of web analytics woven through the manifesto.

Here we go. . . .

- – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - –

Hi,

I am thrilled that you have chosen to take the Web Analytics Master Certification course. My thanks to those of you who dialed into our first weekly call today (I cannot stress how important this is. You are in a very high expectation, high input course).

From your questions today it is clear that you are learning and already frustrated (a fabulous thing, by the way).

There are many ways to succeed and do the best you can with this course. There are an equal number of ways in which you won't.

To help you, I have written a little manifesto. It contains the recipes of what I think it takes to win. The secrets, if you will.

Print it, keep it handy, refer to it all the time.

We are supremely excited that you are here, and we can't wait to make it all the way through to a successful conclusion. If you have questions, bring them up in the weekly calls, or better still, don't wait and post them in the forum (and include screenshots and as much detail as you want!).

And here it is…. the manifesto…. good luck….

-Avinash.

What it takes to do well in the Market Motive Master Certification course.

#1: Focus on the business.

This course is about data and analytics, but it is also a lot about the business of online business and the business of online data.

Focus on learning the frameworks (there are so many of these throughout the course: the So What test, the PALM rule, the Web Analytics Measurement Model, the KPI life-cycle, the 10 principles of amazing business analysis, etc., etc., etc.). The thing that will accelerate your career is knowing the frameworks, developing a structured thinking capability, because they scale and can be applied in many different scenarios. Not your ability to pull a metric or a report out of your bff tool.

Visit the websites you are analyzing. Go through the whole process. See, actually see, the pages. Find the pain, and awesomeness. Talk to people who are around you, people you are helping. Understand the business.

Most people fail here.

#2: Pick two great websites to study.

Don't short circuit this part.

Like in any good course, you'll learn a lot of theory, yet greater than 50% of the value will come from doing the work. If you don't pick good sites to work with, you are simply not going to learn as much. That is the reason 100% of the weekly homework, and the super critical final dissertations, will be for real websites.

What's a good site?

+ You have access to *all* of the data (or at least enough source and behavior data and at least one solid outcome data).

+ You have access to the person who owns it so in the rare case when you need to you can ask  that person questions.

+ The site's using a good tool that allows analysis (you don't need to use Google Analytics or Omniture, but if you only have access to Statcounter data then choose a different website).

Choose one website that is non-ecommerce and one site that is ecommerce based. I want you to exit this course as a well-rounded Analysis Ninja. We are particularly biased here against one trick ponies. : )

If you don't have one of each you can use Occam's Razor and Brainwaves Toys.

If in doubt, choose Occam's Razor & Brainwave Toys.

If you can't analyze something solid, you are not going to win (in life, in this course).

#3: Be creative.

There is nothing that will keep your career down more than copy pasting what you have read in books or blogs or from so called gurus. Or just doing what you've always done.

Think broader, think of things / ideas / elements invisible to others, think of new ideas, think of two things you would do differently in your current actual job. Every single video in the course is built to expose you to new ideas. Use them!

Any one can pick new vs. returning visitors. Anyone can point to a standard report. Anyone can screenshot the standard Site Catalyst dashboard. Anyone can use the standard segments in Google Analytics (how lame!).

Life is better if you use the word custom in front of everything you do. Custom segmentation. Custom report. Custom dashboards. Custom frameworks.

All of that takes creativity.

My personal way of being creative is to fail a lot and figure out everything that's wrong, so I just happen to bump into what's right. Try that.

#4: Start now.

This is not a web analytics course, this is a web analytics 2.0 course.

If you need to use a different kind of tool on your site, implement it today.

If you need to use a competitive intelligence tool, find it and start poking around now.

If you need to have more data try and get it now.

If you need to….. start now.

This course is deliberately built to "snowball."

Everything you learn this week (and learned three weeks ago) will be required in the following weeks. Every single subsequent submission you make will have to be ever more complex and better than the one before (and include all the knowledge from the course up to that point).

So start now, retain as much as you can and keep getting better every week.

If you are not doing well let's *together* figure it out fast, and now, rather than at the very end.

#5: Make time.

You can't sail through this course. Sorry, that's not right… you won't sail through this course. It is a lot of work. I know that sounds tough.

You have made an enormous financial commitment to do this, and I am sure an enormous personal commitment. So make time.

Make time to review the material carefully and take notes.

Make time to come to *every single* office call.

Make time to experiment and play with the tools (a lot!). We have so many at your disposal.

Make time to submit your essays and quizzes and submissions on time and make sure they are complete.

Make time to review the feedback.

We are here to make you a hero. It won't happen without your help.

I am personally in awe of your commitment to do a full time job, have a family and still invest in your education through such an intensive course. I also feel a burden of responsibility, and am personally committed to your success.

In closing, I assure you that if you follow this manifesto you will do well and the fact that you'll earn a certification, (earn is the key word there,) won't be a surprise (to you or us). Anything less than what is recommend above will cause you to fall short (and now that won't be a surprise to you either!).

I wish you all the very best.

Carpe Diem!

- – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - – - –

What do you think? Would it inspire you to give it all you've got?

In some sense, there is nothing about the items outlined above is unique to Market Motive. I think these guiding principles should prove to be relevant regardless of if you are enrolled at Market Motive or the analytics courses at the University of British Columbia or the University of California at Irvine or Universite Laval. Or for that matter preparing to take the Google Analytics IQ certification exam.

Whatever your path, I hope you invest in becoming a true Analysis Ninja.

Okay it's your turn now. . .

What is the best advice you ever got when it came to education? What principle / mantra / magic trick has worked the best for you? How have you maximized the ROI of your education? What are you doing today to ensure your knowledge stays current tomorrow?

Please share your tips / feedback / best practices / secrets via comments.

Thanks.

The Market Motive Master Certification Manifesto: Web Analytics is a post from: Occam's Razor by Avinash Kaushik

My hope in this post is to share some simple tips with you that might make your reports and analysis speak to you a bit more. Suggestions that might increase the probability that you'll bump into things that might be insightful, and communicate data more effectively.

None of them are very hard to do, but I think they make a world of difference.

Excited? Here we go. . .

#1: Go as deep as you can. Then, a little bit more.

Far too often in our daily lives we let our job titles limit how deep we go in our analysis.

For example let's say I work at a delightful car / health / spaceship insurance company. Naturally all of my analysis is focused on the efficiency of the website in moving the Visitors quickly from the landing page to click on that delightful Submit Quote button.

I am focused on what the site does because that is what my job title says: Web Analyst

I am analyzing campaigns (which ones convert better and which worse), I am looking a little bit at the bounce rates, and of course I am totally obsessing about my seven step quote submission funnel (and how to reduce abandonment).

Bottom-line: Quote, quotes, quotes.

And that is fine.

The data is easily available in the web analytics tool so why not.

Here's my advice: You should kick things up a notch. Don't focus just on the quote (the part the site does), include the final conversion to a paying customer (even if that data is offline).

The picture you get from stopping at Quotes might be very different from stopping at Policies Purchased.

Here's what you are focusing on (and it is good):

All my experience in these things suggests that it is dangerous to think that the Conversions column is representative of the final outcome.

Here is what it probably looks like (and this is going from good to great):

See how the ranking changed?

You would make different recommendations right? Would it save your company money? Would it make you refocus your efforts on where improvements are needed?

You betcha!

For straight ecommerce websites the first picture is what you use every day. But for most other types of businesses the final success does not exist in web analytics tool. So what? Get the data out of the crm / erp / "backend" system. . . dump it into excel. . . write a simple formula!

Usually you don't need a complicated multi year data warehousing effort with expensive business intelligence tools to buy. At least for this scenario you just need a column and a short movie data with your online IT person and a longish coffee break with your "backend" IT person to get the right primary keys set up. Then you can bring your sexy back!

Go deep.

You are paid to find real bottom-line impacting insights (remember line of sight to net income?). Do that.

If you are a purely ecommerce business then you can go a bit deeper too. Consider doing quarterly analysis that focuses on calculating customer lifetime value. Up a notch.

If today you are a content site that is only focused on measuring content consumed try to go deeper to understanding CPA of the ads or Visitor Loyalty. Once again going one step deeper, up a notch.

And so on and so forth.

Make it a point to pause every Friday at 0900 hrs. Look at your most important work / report / dashboard. Then ask yourself this: "How can I take my view of the data one step deeper?"

Now figure out how to do that. That'll impress me, your boss and your mom.

#2: Join the PALM club. [PALM: People Against Lonely Metrics]

This rule actually comes from my second book, Web Analytics 2.0. [Page 318, Principles for Becoming an Analysis Ninja, if you have the book already.]

The rationale for this rule, joining the PALM club, is quite simple.

You need a someone in your life. I need someone. Everyone needs someone else. A boy friend. A girl friend. A cat. A "you complete me" person.

So why not your metrics?

We do reports / dashboards like this one all the time:

Ok great.

I know the top referrers sending traffic to my site in a month. Maybe I can appreciate more the power of Twitter or google.co.in or whatever.

You might even impress me next month with a updated version of this where some of these might have shifted a bit up or a bit down.

I might not do anything with the data… but you surely hypnotized me for a few seconds.

This is the problem with lonely metrics.

They don't have any context. They fail to communicate if 841 visits from Twitter were any good. In fact is any of the above good or bad? How do you know?

Why not find a BFF for your lonely metric and present something like this. . . .

Much better right?

I found a "you complete me" for my Visits metric, Bounce Rate.

Now in an instant I can not only see which referrers are big or small, I can see which ones are "good" or "bad".

I could have picked conversion rate as the bff. I could have picked % new visits. I could have picked connection speed or mobile platform or underwear size.

Whatever makes most sense for my business. But putting two minutes of thought into my metric would help make my report a little bit more useful.

Kick it up a notch. Right?

Never ever never never never ever present any metric all by itself.

If you want a cop out then at least trend it over time. If you actually want love then join PALM and don't let your metric be lonely.

Let me close with one of my favorite examples of this rule, this one's to inspire you if you have a pure content (non-ecommerce) website. . . .

Good to know what content's being consumed. Column: Pageviews.

Much much much better to know what the $ index value is for each.

See that crazy blue line that's literally off the chart? You would want to know that about the 1,414 pageviews right?

Now go find your dashboards, your reports, your data pukes (sorry!) and make sure that for every dimension you are not reporting success or failure using just one metric. Join PALM!

[Tip: Not that you are trying to but if you want to impress me but if you are then make sure the second metric you pick is as close to an outcome metric as possible. Or an actual outcome metric. I. Love. Outcomes.]

#3: Measure complete site success. Measure everyone's success.

One of my greatest passions when doing analysis is to look at the complete view of things. Rather than just the obvious.

An application of that passion is to look at all the jobs the website is doing, representing all the work that is being done by people in your company who touch the site.

Ecommerce is too easy an example of this so let me use a non profit example.

San Francisco Aids Foundation is a charity I support. It does incredible work to prevent new HIV infections.

The only way SFAF stays in business is if you and I make donations. As an Analyst I would focus all my energies on trying to figure out how many donations we are getting and where those people come from and what they are doing on the site etc.

But donations is just one measure of success ("macro conversion"). There are other jobs that the site is trying to do, and people who work on those jobs. So why not measure those?

For example. . . .

Measure the above four micro conversions, in addition to the macro conversion of donation, helps give a complete view of success. And what to do better.

Maybe Google is really good at Volunteers and not optimal for attracting people who donate. If you focus only on donations you'll devalue Google. Or maybe facebook is the best source for sharing information (downloads). And more such things.

Not only are you measuring all that matters. . . . you are validating the jobs of people who put together all that content.

Most of the time we don't do this. We, web analysts, just focus on one thing and then we wonder why we don't have the impact we want to, or why everyone does not pay attention to us.

Broaden your view!

If I were analyzing Amazon I would measure sales AND affiliate signups, signups for amazon prime, credit cards, wish lists, "like's" on reviews, self publish inquiries, free downloads….

If I were analyzing L'Oreal Paris it would be sales AND reviews, coupons downloaded, successful completion of "Profile My Skin", videos watched, sign ups for mobile alerts….

In both cases a complete view of the website and success of every person who works on the site.

Ninjas do that. You should too.

[UPDATE: A key next step, post micro conversions identification, is to identify the Economic Value. See this post for specific ideas about how to do that: Excellent Analytics Tips #19: Identify Website Goal Values & Win!]

#4: Be smart about using time. Move beyond MoM.

This is one of the most common view of data presented in web analysis…

The picture illustrates the performance of a metric over two consecutive months.

This is of course better than just showing data for June.

The problem occurs when you proceed to look at six such graphs on your dashboard and then proceed to use the trends to deliver insights. You are reading too much into the ups and downs, you are inferring things that might not even exist.

Two months do not a trend make. Important lesson.

Not even for the world's most flat line no seasonality business.

So here is a best practice. . . . at least add three months. . . . if the data looks like below you'll think one thing (and every different from above pic)…

But if the data looks like the image below. . . . you'll think something else. . . .

Worry in one case. Jubilation for the temporary awesomeness for May in the other.

The more time you put into this graph (and if you have as much space as above you can easily add at least six months and it will still look pretty) the better.

But if I can only have three I love using current, prior, same month last year.

Better context right? Will take you off on a completely different line of inquiry, all from adding June 2009 to look at June 2010.

If June is the last month of your quarter and you have a cyclical business then maybe you want to compare Apr, May, June 2010 and have the first column be March 2010 because you want to see how the last month of this quarter did vs last month of the last quarter (because Apr and May don't really show if the trend ended as high or low as it should have ended).

So on and so forth.

Remember:

Sweet momma that is awesome!

Kick it up a notch, ok?

#5: Present data better, make insights obvious.

There are so many ways to present data that a small section of a blog post is insufficient. And of course there are so many people who are better at this than I am.

Let me just say that the way you present data matters, a lot. I'm not saying you should make it pretty. I could not care less if it is pretty or not. I'm saying present it in a way that the insights you think exist in the data become more obvious.

Here is a "data element", from an actual dashboard, that I really like. It might not be sexy but it is extremely functional and it is super awesome at communicating the smarts of the Analyst.

Three month trend for one very important business metric…

First note that rather than just showing one column for the performance of this metric it shows four. One for each key segment of the customer that the company has.

This would require you to know the business (good thing), know its customers (great thing) and track the segmented data. IE have your act together.

Second note that the data is for three months. You could show more but in this case you don't want to overwhelm the Executive. If you go more months, shrink the segments.

Third, really important, note that the overall goal is clearly indicated in the picture. 80. And to get that number you would have to talk to Finance and Marketing and HiPPO's and get an agreement up front. This is absolutely magnificent, key to you building relationships and finding insights.

The nice thing about our picture above is that the overall metric would get averaged out and show a trend similar those we showed in tip #4 above.

But would it be insightful enough? A single metric trend would hide insights.

In this case it is pretty clear that Blue, Red, Green segments are doing fine. In fact the one that is absolutely most important, Green, we are doing ok.

The stink bomb in the pile is Purple. It has been dragging the overall metric down (and you know that even if the overall metric is not even shown!).

And you know how much gap you need to overcome for each segment, and were to prioritize your work (PURPLE!!).

This is just one tiny, I call it "functional", way of presenting data.

The presentation is ok, could be made more pretty.

What's precious is the process that went into creating the element – talking to leaders, meeting with Finance and Marketing, identifying the key metrics, finalizing customer segments, and establishing goals.

We often don't do all the above work (the things that are mandatory for data driven organizations) and even if we do it we don't show it because we show lame single line graphs.

Don't do that.

Kick it up a notch. You are working very hard at your job, make sure your work shows up and helps identify better insights.

Those were the five simple things you can do every day to make the most of your daily data analysis. They are not very hard to do, and they'll pay outsized dividends.

I am not someone who leaves the good enough alone. No sirree bob!

With love and affection here are 4 more bonus tips on improving your analysis and truly kicking things up a few notches:

#6: Leverage segmentation, daily.

All said and done the number one way to move from being a Reporting Squirrel to an Analysis Ninja is to leverage segmentation. Every tool has on the fly current and historical segmentation feature set. Use it.

I'll honestly not respect anyone is not applying at least some primitive segmentation to their data.

Learn how to:

~ Web Analytics Segmentation: Do Or Die, There Is No Try!
~ Google Analytics Releases Advanced Segmentation: Now Be A Ninja!

#7: Move beyond the top ten rows of data, seriously.

The "head" of your data will sustain finding insights for a month or two. You might even action something. The real gold lies in your ability to analyze tens of thousands of rows of data at one time. It is harder to do, and hence the rewards are bigger and your competitors will eat your dust more.

Learn how to:

~ Analysis Ninjas: Move Beyond The Top Ten. Find Love (/Insights)
~ Make Web Analytics Actionable: Focus On "What's Changed"

#8: Perform "pan-session" analysis, and win big.

One of the absolute criminal behaviors in web analytics (and indeed online marketing) is that we are so obsessed about Visits, and visits based analysis.

Few people sleep with you on the first date. So why is that your mental model?

Every true Analysis Ninja focuses on measuring customer behavior of one person (or in our case Unique Visitor) over the entire span of that person's interaction one our website.

Hence my devotion to measuring Days and Visits to Purchase. Truly analyzing how people buy. Or analyzing Visitor Recency and Visitor Loyalty to understand now just the first Visit (and conversion) but rather subsequent Visits (and conversions).

I tell you this is honestly kicking your web analysis up five notches, not just one.

Learn how to:

~ Measure Latent Conversions & Visitor Behavior
~ Measure Days & Visits to Purchase

#9: Evolve to multichannel analytics, achieve analytics nirvana.

There is perhaps nothing harder and nothing more impactful than getting good at multi-channel analytics.

Measuring the offline impact of your online activities gives your business a view of success that is truly orgasmic. If you get good at measuring the impact on your website of your offline activities (television, catalogs, billboards etc) then you have truly accomplished the rarest of the rate.

Learn how to: Multichannel Analytics:

~ Tracking Online Impact Of Offline Campaigns
~ Tracking Offline Conversions. 7 Best Practices.

Feeling like an Analysis Ninja already?

Of course not, you have to go do all these things! :)

Remember that tips 1 through 5 you should be able to do quite easily, just need to remember them and remember to use them. Tips 6 through 9 take time, they take a lifetime. Remember them, practice when you have time and slowly evolve over time.

Ok?

Good luck.

As usual it's your turn now.

What are your favorite tips for data analysis? When you present data what is the "trick" that you use most often to be awesome? Have you used any of the tips above? Got any favorites? What do you think it takes to morph from a Reporting Squirrel into an Analysis Ninja?

Please share your feedback / critique / tips and all via comments.

Thanks.

5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two is a post from: Occam's Razor by Avinash Kaushik

Even with a broken right hand I can't help but write this post!

The waterfall of positive feeling stems from the fact that this book was very hard to write.

I only had one job, at Intuit, when I wrote my first web analytics book. I now have several full time jobs, plus this blog, plus speaking around the world, plus a family, plus… so much more.

It took weekends of writing and nights of editing and days of research combined with practicing the preaching by doing oodles of analysis and, more importantly, the support of the most understanding wife in the world.

At the end of it all it is rather gratifying to see one's book at a bookstore, helps grasp the magnitude of the process. And there's absolutely nothing quite like hearing your five year old yell in a busy Borders bookstore: "I FOUND DADDY'S BOOK!"

This blog post is in three parts: The pitch. Request for help. A lovely contest [Contest closed now, thanks for the entries!].

You don't have to read the whole thing & skip ahead, but that would hurt my feelings. :)

Here we go. . .

The Pitch:

I invite you to consider buying my second web analytics book. It is not only the most current book on everything important and bleeding edge in Web Analytics, it is a labor of love that will help you transform your personal thinking and assist in revolutionizing your organization (big or small).

It is not a technical book, though it will make you technically dangerous. It is not just a business book, though every dna strand in this book is more about online marketing than online analytics. It is not a hard book to read, though it is brain food.

Here's why I think you'll love it:

Chapter 1 The Bold New World of Web Analytics 2.0

No dragging of the feet, the book starts with a bang by laying out the framework that will be the center of every company that will leverage data (qualitative, quantitative, competitive) on the web. It ends with a challenge to embrace Multiplicity – without this it's goodbye greatness.

Chapter 2 The Optimal Strategy for Choosing Your Web Analytics Soul Mate

It will be hard for you to find a more compelling four step process to choose the right web analytics tool for your company. Soul searching, questions to torture vendors with, comparing vendors, running a pilot and negotiating a contract, it's all in there. You be off to the races right.

Chapter 3 The Awesome World of Clickstream Analysis: Metrics

The thing I enjoyed about this chapter (I know I wrote it, but still. . .) was that the first half works really hard to evolve your critical thinking skills. I love that because we take too much for granted, now you'll be skeptical. A good thing. The second half shows exactly how to pick the best metrics for your org and, my absolute favorite (Page 64), how to diagnose the root cause of a metrics performance.

Chapter 4 The Awesome World of Clickstream Analysis: Practical Solutions

When people think of web analytics everything they think about is chapter 4, and yet you'll find so many yummy treats here. The best WA report, segmentation, site search, SEO & PPC analysis, email, rich media, cookies, data sampling. . . . I am out of breath!

Chapter 5 The Key to Glory: Measuring Success

If I have one jihad it is to massively convert every person who touches the web to focus on measuring Outcomes! It is the one reason we can't achieve the greatness we so richly deserve. No more! Glory will be yours!! B2B. B2C. Small Biz. Large Biz. Non-Ecommerce. We make love to 'em all! One thing you'll read here that you'll read no where else? Computing Economic Value, a concept that will liberate you.

Chapter 6 Solving the “Why” Puzzle: Leveraging Qualitative Data

Oh, oh, oh qualitative analysis!! I am a Mechanical Engineer with a MBA, a late covert to the power of understanding the super sexy "why" by leveraging lab usability studies, surveys, card sorts, online remote testing and more. You get a jump start. The thing you'll adore: Pages 190 – 192.

Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation

Sure you've heard of A/B and multivariate testing. But do you know how to truly win the game? There is no technical mumbo-jumbo here, just the real deal and how to get testing right. The thing you might not know / realize the power of: Controlled Experiments. I am convinced this is God's gift to online humanity, you'll agree with me by the time you reach Page 208.

Chapter 8 Competitive Intelligence Analysis

The most magnificent advantage the web possesses: everyone's data is available for everyone else to use. If Hilton Hotels has the data for Choice Hotels why not use it to "crush" them (sorry Sarah!). This chapter shows you how. I think the thing you'll be surprised by is at the start of the chapter (Data Sources, Types and Secrets).

Chapter 9 Emerging Analytics: Social, Mobile, and Video

The chapter I had the second most fun writing. Mobile, twitter, blogs, videos etc are just so darned hard to measure and so much changes every few hours that I had to really really work hard to find the essence of each and then make specific practical measurement recommendations that will stand the test of time. It was hard.

Chapter 10 Optimal Solutions for Hidden Web Analytics Traps

This is a collection of major reasons I think people fail at web analytics, and of course I boldly try to share how to avoid that fate. Behavior targeting, dashboards, accuracy, data mining, predictive analytics, and, the thing you'll appreciate the most IMHO, five steps for intelligent analytics evolution!

Chapter 11 Guiding Principles for Becoming an Analysis Ninja

All my life learnings laid bare. . . this is where you, yes you, start to evolve from a Reporting Squirrel to an Analysis Ninja! No metrics, data pukes, guidance on creating every more reports. No, none of that. Rather… analytical techniques, tips and tricks to apply to your job, how to evolve your thinking to a higher level.

Chapter 12 Advanced Principles for Becoming an Analysis Ninja

The chapter I had most fun writing (and rewrote the most number of times). It deals with two of the hardest practical challenges we face in the field of measurement: multi-touch campaign attribution analysis and multi channel analytics. Both are very hard to get right, both have a ton of fud out there, it was fun to share my recommendations.

Chapter 13 The Web Analytics Career

The chapter I should have had in the first book. How to plan a career in web analytics (paths, salary, longevity), and how to then cultivate the right set of skills. If you are a leader then how to spot great talent, how to interview them and make the right choice.

Chapter 14 HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture

Some might argue, rightly so, that the most elusive thing to accomplish is to truly bring data democracy to your organization. This chapter bravely hopes to help you do exactly that: excite people about data, remove organizational barriers, use data to change behavior, dealing with data quality, and creating data driven HiPPO's.

Convinced?

Nothing, absolutely nothing, in life is easy. But if you have the will and access to knowledge then that just might help you choose an optimal path, a path where your hard work will yield above normal results. That's my hope, and promise, with Web Analytics 2.0.

Jennie and I have decided to donate 100% of our proceeds from this book, just like for the first one, to two charities. This book benefits The Smile Train and Ekal Vidyalaya. We are very excited about that.

Request For Help:

As you all know my philosophy for this blog is eat like a bird, poop like an elephant. But if you are up for it I would love to ask you for a bit of help.

Recommend the book.
If you know someone who needs to turbocharge their online existence, please recommend Web Analytics 2.0 to them. Even in our hyper connected world, nothing works like a personal recommendation.

If you use a link please consider using: http://bit.ly/akwa20 That link has an affiliate code, all proceeds of which go to the above mentioned charities.

Review the book.
If you have a blog, website, twitter account, any kind of platform, it would be great if you could write a review of the book and help spread the word.

If you purchased the book online then please, pretty please, review the book on the store's website. Amazon. Borders. Target. Powells. Whatever you used.

Connect me.
I am very very bad at pimping. So if you know someone who is someone (or knows someone who knows someone) then please consider connecting us. Especially people outside our analytics / search circle. Authors. CEO's. Journalists. Influencers. TV anchors (or weather man/woman). Oprah (I can dream, can't I?).

Our world is separated by six degrees of separation, I am sure you know someone who just might consider helping me with my cause.

Share a picture.
I love getting to know my audience, and while your emails and tweets are pretty fun there is nothing like a picture.

I had a "Web Analytics: An Hour A Day Fan Mail" flickr group that has some incredible pictures from around the world, bringing my audience closer to me.

I would love to do the same again for my "Web Analytics 2.0: Fan Mail". Be as creative as you want to be. Babies. Cats. Posters. Cars. Places. Or the best, you. All would be welcome.

I will only post the pictures with your permission. Please send them to blog at kaushik dot net. Thanks!

A Lovely Contest:

[The contest is closed now. Winning entry details.]

Steve Cunningham invited me to be a part of a little "contest" he is running. The prize is a delight, you get to win a pack of seven books on online marketing & social media: Six Pixels of Separation, The New Community Rules, The Whuffie Factor, Trust Agents, Crush It!, Duct Tape Marketing, and Web Analytics 2.0.

How to win you ask? Two ways.

1. Answer this question in comments below: If you were to measure the success of a company's social media efforts how would you do it?

Pick any social media channel, or all. Only a short answer is required. The most innovative / interesting answer wins. No answer is too small or too simple.

[If you have my book already then my answers in the book to this question will win you major brownie points, but perhaps not the contest! :)]

2. You can get four more chances to win, if you want. Simply visit these blogs and answer a different question on each: Steve Cunningham, Beth Kanter, Tara Hunt, and John Jantsch.

Good luck!

A Word of Thanks:

This is from my book's acknowledgment page…

I would like to express my deep appreciation to the readers of my blog, Occam’s Razor. In approximately three and a half years I have written 411,725 words in my 204 blog posts, and the readers of my blog have written 615,192 words in comments! Their engagement means the world to me and motivates me to make each blog post better than the last. It is impossible to thank each person, so on their behalf let me thank three: Ned Kumar, Rick Curtis, and Joe Teixeira.

A very solid case can be made for the fact that neither one of my books would exist without you and your engagement and encouragement.

Gracias. Arigato. Ngiyabonga. Xie xie. Obrigado. Shukriya.

Web Analytics 2.0 Book: In Stores Now!! is a post from: Occam's Razor by Avinash Kaushik

An Analysis Ninja, let's call him Philip Walford, asked a delightful question. Philip wanted to know if the impact of a faith based initiative in his company, product demo videos, could actually be measured using data.

Hurray!

Faith is good. Data is better. : )

[And before you flame me: know that I love my religion more than you love yours. Wait. That did not come out right. Let me rephrase that.]

In this thanksgiving week 2008 post I'll share Philip's question about how to identify value of video product demos on an ecommerce site, and my answer about involving customers.

Here's Philip. . . .

We are a large retailer with a lot of product on our site. In the past we have invested lots of dollars and time producing things like demo videos for our products, or adding other features and tools to our website to provide more information about a product. Our goal is to inspire customer confidence in their purchase (by giving them as much information is possible).

The question is, what are the KPIs of things like a demo video.

My recommendation was to measure conversion rate for the segment that views the video. If conversion is higher then the videos are bringing value. Others in my company have presented the hypothesis only customers that are a lot more invested in buying the product are likely to click on the video link and hence "pre qualified", hence that segment would have had a higher conversion rate regardless.

I understand their perspective but I feel they are reading too much into the situation but I don't know how to argue this point. There are several directions we could go with this but I wanted to see if you could share some guidance on this issue.

My answer to Philip. . . .

This is a complex problem, more than might be apparent on the surface.

It is also an example where it can be easy to jump into bed with your web analytics tool to get satisfaction but you wake up in the morning feeling. . . . well. . . . less than satisfied.

But before we go there I have to give a ton of credit to Philip and his crew for being skeptical of reading too much into their own opinions or biases.

I firmly believe that people who work for a company rarely (never!) represent customers. They are too close to the company and too different.

Just because I work for Microsoft and use a Zune (yes I do!) does not mean I can be a effective customer representative of Microsoft Zune customers. Company employee opinions rarely reflect those of customers. Do please be aware of that.

So when looking to make decisions, look for data (quant or qual).

I'll present Philip with three solutions / options as he battles the challenge of figuring out if the investment of muchos dineros in creating product videos is worth it (besides the fact that these videos ooze sexiness!).

1) Use ClickTracks (Compute Contextual Influence)

There are two challenges with using clickstream data and the "typical" measure of conversion rate to determine success.

A] You might be looking at a "biased" segment (as challengers to Philip's recommendation mentioned). I.E. Only the highly motivated people.

B] By comparing all people who converted and viewed the video with those that converted and did not see the video you are not comparing fair segments. You are also lumping all other "convince our visitors to buy" tools into one large bucket. Tools like Comparison Charts and Product Screenshots and Product information and Customer Reviews and more.

It is quite possible that those other tools might be getting people to convert at a much higher rate and by dumping them all together you are not being fair.

And of course you'll get a wrong read on conversion impact of the videos.

So even if you use your web analytics tools (your Google Analytics or Omniture or WebTrends or CoreMetrics or whatever) try to compute "contextual influence" (value of each feature in context of the others).

It is actually very hard (damn near impossible) to do this in all those tools (even for the Paid solutions, even after you plunk down half a million dollars for the mandatory Data Warehouse "add on").

ClickTracks is the only tool I know of that can do this out of the box, using its terribly named "funnel report". No data warehouse. No extra tags or variables or sprops or wt_&*#$. In fact not even much IT, I just need admin access to my tool (not site, web analytics tool).

Its easy to use. Create a hierarchy of your website. Add individual or groups of pages into each stage (notice I did not say step because you can jump steps here). Add an outcome (in my case say "Thanks for placing your order" page). Click Calculate.

Boom!

[You are not supposed to be able to read the analysis, sorry, privacy dictates that.]

What I want you to note is two things.

This is a site where each stage means a view of the site (and like a traditional funnel how many people get in, get out, move on etc).

Secondly note that each box (which represents a page/'s or a tool – videos, comparisons, reviews etc) has a different stage of blue.

What this lovely report does for you is compute "the influence"of each of those pages/tools in driving the ultimate outcome – purchase here. The darker the blue the more "influential" that piece of content. [Influence is defined by the existence of that piece of content in the visitor session, regardless of what path the visitor took, regardless of when the content was seen.]

Ain't that super sweet?

The analysis you see above is for a real ecommerce website. What it proved to us, delightfully, was that the product videos, we had created at a cost of over one hundred thousand dollars, yellow star above, was the least influential tool we had on our site.

The most influential, sexy pink star above, was a tool that had cost us $8 to produce – it was a page that compared different versions of the product (information that was handily available in the company).

We used actual customer behavior. We analyzed contextual segments. Ultimately it allowed us to put our precious few resources in the right area.

Of course it is quite likely that everyone who came to the site and did not buy (convert) might have loved the videos and rushed to stores to buy our products (one HiPPO actually said that!). There is no way to prove that using just the web analytics data.

What we did is proved impact on online buyers.

As to the HiPPO. . . . read on. . . .

2) Use Surveys (Actively Collect VOC)

When in doubt (or confronted by a HiPPO, remember don't run) what better way to go then gather some Voice of Customer. Dare I say the voice of god? :")

Two things I have tried (of many!) that work a lot of the times. Each covers one unique bucket of visitors to your website.

A] Consider sending a simple post purchase email survey to customers who have purchased on your site and ask them for the key influencers of their purchase.

You could share with them the various tools you have on your site (product information, comparison tools, images, videos, customer reviews etc etc) and simply ask them to rank order them in order of importance.

Don't ask them to tell you how much they like them, or choose ones they like, they tend to pick all. :) Just ask them to rank order. Or use a tactic similar to that.

This tells you want works for those who buy.

For the 98% that will never convert on your website. . . .

B] Consider a onsite survey like 4Q (though 4Q can only be customized so much so perhaps you want to use either your own or one of the big daddy paid survey tools).

This will go to a small random sample of people who are on your site (who may or may not buy). You'll ask them three or four questions about why they were there (primary purpose) and then what tools/features of your website they liked (rank ordered if at all your survey company can do that).

That will give you what you want.

Since this can also be thought of as a page level problem, you can also use something passive, a page level survey / poll, like Kampyle on your product pages and ask people to quickly rate the various features. There is a Site Content feedback topic in Kampyle which you can customize.

Now you have the most important piece of data you need, your customer's. Few website owners / marketers / hippo's can argue with this. Leverage this advantage.

Finally one last option for you. . . . hopefully one you'll use before you write a chq for a hundred grand to create your videos. . . .

3) Use… wait for it….. Testing! (Measure Actual Customer Behavior)

I am sure this does not surprise you. Run a A/B or Multivariate Test and let your customers help inform you of the value of these features.

For 30% or 40% or whatever %, don't show the product demo videos and for the rest show the product demo videos and see the impact on the data. Boom (!) you have your answer, without any biased opinions.

It is certainly going to take you a small amount of effort, get the Website Optimizer, talk to your IT folks, create version of the page with no product tour link etc.

But you are making a very expensive decision for your company are you not?

And here is the additional benefit of testing. You are free to use any kind of "conversion".

You can measure success as conversions (submit order).

You can measure success (of the test) as number of people abandoning from the product page.

You can measure success as the time people spend on the product page. [There is a very cool javascript code that does this with the Google Website Optimizer, it is especially helpful for rich media / flash sites. Without a doubt other vendors can do this as well, just ask.]

You can measure success through your survey tool if it is integrated (this is some extra work sadly, but for big bets I recommend it).

You can integrate your analytics tool with your testing tool (say Google Analytics with Website Optimizer) and use other metrics to measure success such as bounce rate or electric shocks etc :).

[For GA and GWO ROI has integration instructions .]

The bottomline is that you can define success and then let the customers tell you.

That's my answer to Philip.

Sounds exciting?

Am I the only one who thinks when you do this kind of analysis you are in a nearly orgasmic state?

Yes these methods are some small amount of work. But nothing in life worth having is easy. The tools might be free, but that does not eliminate your need to investing your time and effort! :)

And on the positive side with a recession looming people who involve customers in making decisions, rather than their opinions, will win big. The "guessers" will not win big. They might even win small. Or fail.

Plus if you do this you'll be a Analysis Ninja, not a Reporting Squirrel.

Ok now your turn.

Have you tried to analyze the features like Video Demo's on your website? Or perhaps other complex features you have launched? What works for you? What totally failed? In my recommendation to Philip, what did I overlook?

Please share your feedback, critique and hurray's.

Excellent Analytics Tip #14: Measuring Value of Ecommerce Sales Tools is a post from: Occam's Razor by Avinash Kaushik

Guess what?

It's true!

All web analytics tools have a smattering of metrics and key performance indicators that were created just because someone decided it would be cute to add / subtract / multiply / divide some numbers.

Many of these don't pass the first sniff test and when if they do you are still left wondering: "What in God's name and all that is holy in this world am I supposed to action based on this metric?"

The answer?

Nothing.

With that gloriously upbeat set up let me tell you what we are going to cover today: Three metrics that are available in pretty much all "adult" web analytics tools. Daily, Weekly, Monthly Unique Visitors.

They are so common yet most people don't understand them well enough and fewer still realize how harmful these can be to your health even in day to day use.

So in this post we try to understand the most basic of the web analtyics basics, the Unique Visitor computation.

What's a Unique Visitor?

It is simple really. . . .

Technical Definition: Count of all the Unique cookie_id’s during a given time period.

English Definition: The first time someone visits your site a first party persistent cookie is set in their browser. This cookie lasts any where from several months to several years. Each time that person visits your site that cookie identifies them as the same browser.

Notice I said browser, not person. It is likely, but not always true, that each a unique visitor is a unique person.

You can learn a lot more about Visits and Unique Visitors in this post: Standard Metrics Revisited: #1: Visitors.

Very predictably every 18 months or so the blogosphere goes wild with how accurate, or not, the Unique Visitor metric is. Much mud is thrown around. Indignations are foisted on the world. Name calling ensues.

Regardless of that Unique Visitors remains a valuable metric that used correctly, in place of Visits, measures success of your online marketing efforts.

Oh and your best weapon against ignorance? Education. See above post on Visitors. And this one: A Primer On Web Analytics Visitor Tracking Cookies. It covers cookies and deletion rates and other such yummy stuff. Read that and you have my word you'll be the smartest cookie in the room.

See what I did there? :)

Daily, Weekly, Monthly Unique Visitors:

In many web analytics tools (say Yahoo! Web Analytics, Omniture, WebTrends etc, but not in Google Analytics ) you'll also see Daily Unique Visitors, Weekly Unique Visitors, Monthly Unique Visitors and, sometimes, Absolute Unique Visitors.

Each is trying to tell you something about Unique Visitors, yet if you pause and think about it, I mean really pause and think about it, you'll realize two of these are really bad for your health, and the third should be used with caution.

The core reason is that what looks attractive initially becomes progressively worse as you extend the time period. The Daily metric, so to speak, does not even last in value beyond two days!

So let's spend a second understanding this slightly yucky phenomenon.

Here's the data, from omniture.com, where WebTrends is used for tracking Visitors. . .

Now let's go measure the complex set of metrics that'll stare at you, let's say when you crack open Omniture or WebTrends (or pretty much any other competitive web analtyics tool).

The Web Analytics Unique Visitors Story:

Before that realize that what you see will depend on the time period you are looking at. [Arrrh!]

And before I really really jump in… you'll see a metric called Absolute Unique Visitor. I am going to use that as a proxy for how unique visitors should be computed correctly, regardless of what time period you are computing it for. Keep an eye on that number.

Looking at Month 1 and Week 1 at the end of Day One:

If you ran your reports at the end of day one here is what your analytics tool will report to you, with some delight and joy I might add. . .

Makes sense right? Do a happy dance, high five someone next to you, heck give them a hug and a kiss.

Now let's make this more "complicated".

Looking at Month 1 and Week 1 at the end of Day Two:

If you ran your reports at the end of day two here is what you'll see. . .

Slow down the happy dance a bit.

Note the silly effect on Daily Unique Visitors, even though it was the exact same folks, Dennis and Matt, from the earlier day who visited on day two. They get counted twice.

Life lesson: Daily Unique Visitors is a useless number if you are looking at a time period of more than one day!

Let's keep going.

Looking at Month 1 at the end of Week One:

Crack open your analytics tool, it has been a long week, look at the metrics, here's what you'll see. . .

Note the continuing uselessness of the Daily Unique Visitor number (and even if you trend it over time, as in the blue graph above, analyze what it is actually showing you? what's the insight?).

In your Web Analytics Tool you might see a report that looks like this:

By know you know why there is a sad frowny face in that last Total row. Right?

Repeat: Life lesson: Daily Unique Visitors is a useless number if you are looking at a time period of more than one day!

Looking at Month 1 at the end of Week Two:

Gather everyone in your close proximity in the office, form a circle, hold hands, close your eyes, say a quite prayer, now open your analytics tool. . .

The Weekly number is wrong because it counts: Avinash, Dennis, Matt, Matt again, Ian and Jim. It counts Matt again because he visited during both weekly time periods.

Life lesson: Weekly Unique Visitors metric is useless if you are looking across multiple weeks. We've covered above why Daily Unique Visitors is, to put it mildly, sub optimal.

Ok only two more scenarios left, hang in there, it gets better.

Looking at the end of Month 1, for the whole month:

By now I am sure you are 100% up to speed on what you are going to see. . .

There is now triple or double counting happening in both the Daily Unique Visitors and Weekly Unique Visitors numbers.

Life lesson: Both Daily Unique Visitors and Weekly Unique Visitors numbers are useless when you look at a time period of a month.

One last scenario, not to make your brain hurt but rather to ensure you reach the state of maximum Analysis Ninja enlightenment!

Looking at the end of Month 2, for the two months:

Tingling with excitement. . . here's what you'll see. . .

There is now triple or double counting happening everywhere, the Daily Unique Visitors, Weekly Unique Visitors and Monthly Unique Visitors numbers.

The correct measure of unique is the Absolute Unique Visitors metric because it de-dupes the unique visitors across the entire time period you are reporting on.

Life lesson: Both Daily Unique Visitors and Weekly Unique Visitors numbers are totally really useless when you look across months. Use Monthly Unique Visitors with caution, knowing it is only de-duping for each month and then summing the number for each month.

If your tool provides Absolute Unique Visitors you are in luck because then you are getting true unique visitors across whatever arbitrary time period you choose.

Google Analytics provides you with the Absolute Unique Visitors metric.

[Update: In the new version of Google Analytics this metric is called Unique Visitors. Everything about it described is the same in terms of it providing a unique de-duped count, it's just called Unique Visitors. You'll find it in standard reports, and you can easily add it to any custom report. Try it!]

It will do that across set time periods, like the month of March (or any number of months). . .

or across arbitrary time periods, as Monday March 9th through Thursday March 19th. . .

It will dedupe the numbers when it reports to you, rather than adding the totals of each day, week or month.

Complex but bonus for Ninjas: Depending on which graph you look at, daily, weekly or monthly, it will intelligently compute the number for each time period and also show you the aggregate deduped number for that time period.

Fly in the otherwise rather healing ointment?

Google Analtyics does not compute Absolute Unique Visitors when you segment the data, when you use the Advanced Segmentation feature. Those of you who read the blog know my utter infatuation with segmentation, so you can easily understand how sad this makes me.

You can get Absolute Unique Visitors for segments by using the "create a filtered profile that just data for the segment" method and that works if you have forethought. But it is sub optimal, just like some "enterprise" web analytics vendors telling you that you can only segment if you tell them before the fact what you might want to segment later.

Why do Web Analytics Vendors torture you with Daily, Weekly, Monthly Unique Visitors?

I knew you were asking yourself this question!

Good on you Mate.

If these metrics are that sub optimal, why do web analytics vendors put us through this torture?

Simple: Compute power (translation: cost, for them).

It is very computationally intensive to calculate for you the true real (Absolute) Unique Visitor number across any arbitrary time period or across multiple weeks or months.

Increased computational intensity for the vendor means more processing time and higher costs.

So doing Daily, Weekly and Monthly counts (and then summing them up) is cheaper for them.

After the first vendor decided to do this, and there were no major outcry from Web Analytics Users (or even Ninjas!), others quickly followed.

For the more prevalent vendors in the space Google Analytics is one the rarest that provides the truly de-duped Absolute Unique Visitor metric (in aggregate, not segmented, boo!). Only time will tell when Google will buckle under the computation/cost weight and stop providing it true Absolute Unique Visitors.

[Update: Both NedStat and Xiti, two wonderful European companies do allow for computation of Absolute Unique Visitors out of their standard packages, no additional payment or gyrations required. Add Unica's NetInsight to that list as well! Hurray!!]

There are some vendors that will tell you that you can buy their more expensive data warehouse solutions (at an additional cost on top of what you pay today) and then compute Absolute Unique Visitors yourself. True. Ask for the cost. Ask if its really Absolute. If prudent, pay more. Regardless, be informed.

Long lesson.

But now you are truly at a Analysis Ninja black belt level of proficiency!

Now your turn.

Please share your comments / feedback / critique / hugs / non-hugs about this post. What does your tool do? How do you think we should improve things? What would you eliminate? What would you add? What did I miss?

PS:
Couple other related posts you might find interesting:

• Standard Metrics Revisited: #1: Visitors
• A Primer On Web Analytics Visitor Tracking Cookies
• Five Rules for High Impact Web Analytics Dashboards
• The Ultimate Web Analytics Data Reconciliation Checklist
• Excellent Analytics Tip#5: Conversion Rate Basics & Best Practices
• Excellent Analytics Tip#4: Make Your Analysis/Reports “Connectable”

Standard Metrics Revisited: #6: Daily, Weekly, Monthly Unique Visitors. is a post from: Occam's Razor by Avinash Kaushik

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

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

Ask a famous blogger, a published author, a random twitterer or your mom how to succeed in web analytics, or how not to be a Reporting Squirrel. The answer will invariably be:

Before you provide the data, ask the requestor what is the business question they are trying to answer. Then fulfill that need.

It is a good answer.

Most of the time they, Marketers /bosses /HiPPO's, ask and we puke data out.

The result is also almost always the same.

After a while of doing this you, poor Squirrel, want to jump off a building. As if that was not painful enough, during the course of your employment the company made no actual decisions based on web analytics data. Ouch.

I call it a lose-lose.

If the outcome is so obvious. . .  why do we still have this problem? Why is it that we don't ask for the magical thing you were told to ask since the day you were born into this world? Business questions.

Part of it is that we might not be in the position to ask for that question (I don't buy this 80% of the time, sorry).

Part of it is the case that we don't often understand the difference between a business question and a report request. Even for experienced Analysts / Consultants.

Let' us solve this problem today.

I really want you to become an amazing Analysis Ninja and help ensure that this issue never comes back to bite you in the butt.

Come with me.

Why Ask For Business Questions?

One word: Context.

I am sure this will not surprise you, but it turns out you are a very unique person. You are distinct from all your other siblings, even your twin.

And it turns out every business is unique, every website is unique. There is one and only one of its kind in the world.

Even if you completely copy and paste someone else's website and start executing, your business is probably unique in terms of the individuals you have collected around you and how they work together.

Or perhaps while you sell via the retail channel like everyone else, your strategy is different in its focus on driving purchases through the web, or maybe you are obsessed only about offline store sales. Or perhaps while you have 100% copy pasted GroupOn's business and are executing it in Russia. It is 100% likely that you are solving the problem completely differently than the US GroupOn site you are copying.

Or maybe you are HP and are obsessed and 100% focused on solving for Customer Satisfaction on your eCommerce site and your closest competitor Dell is obsessed 100% on getting more and more conversions.

This is a long preamble to tell you that the strategy we normally execute in measurement is wrong. We read books / blogs on Metrics & KPIs and we think we know what to do for an ecommerce site or a blog or support site etc and we start reporting what the blog / book recommended.

Then we are shocked no one cares one bit about the data.

If you are unique, why should you crack open a standard analytics tool with its standard reports and metrics and get going?

Or why simply respond to a "report request" and start data puking? The person at the other end is probably uninformed about Analytics and Segmentation and what is possible (even as they are supremely qualified to do their job in Marketing / Sales / HR).

You need business questions because

1. Rather than being told what metrics or dimensions to deliver you want business context: What's driving the request for that data? What is the answer the requestor looking for? Then you apply smarts because you have context.

2. Best practices are highly overrated. If this is your first day on the job, sure go ahead and puke out what "industry experts" recommend. But know that it won't impress anyone because you don't actually know what the business is doing / cares about / is prioritizing.

Convinced you need to only accept business questions?

I am glad.

Three Attributes of Business Questions.

We have done a pinky swear that you are going to start your daily web analytics journey by asking the business what questions they want answered.

I have to warn my budding Analysis Ninja that what you will typically hear is:

+ I want to know how much traffic is coming to our website

+ I want a conversion rate

+ I want a path analysis for our visitors (oy vey!)

+ I want to the list of top exit pages on our website

+ I want to know how many leads we got on website this month

+ Or give me a report that shows click-through rates of our home page promotions

All of these are requests I am imploring you to rebel against. They are not business questions.

Take a deep breath. Simple. With your eyes, not your mouth, say: "I really want to help you."

At this point what we really want to do is refocus the discussion and increase the likelihood that you can be something more than a reporting squirrel. With a twinkle in your eyes politely say:

We are executing a true Web Analytics 2.0 and Multiplicity in our company. In as much as we have access to many sources of data that can help answer What, Why and What Else questions quickly and efficiently.

Based on my expertise I can help you pick the right tool and metric if you could share the question you are trying to answer. What problem are you are trying to solve?

That will probably take all of 20 seconds to say (don't forget to twinkle your eyes).

They'll be struck by your sincerity, and shocked that you want to help this much.

The trick now is to make sure that you are able to recognize if what comes out of their mouth (or in words over email) is actually a business question and not a rephrased report request.

Business questions have these three simple characteristics:

#1. They are usually open-ended and at a much higher level, leaving you room to think and add value.

#2. They likely require you to go outside your current systems and sources to look for data and guidance in order to measure success.

#3. They rarely include columns and rows into which you can plunk data you already have.

If what you hear fails any of the above tests then you have to go back and try again.

Try not to be impatient or show off how smart you are or pick a fight. Keep the twinkle in your eyes, highlight what was different about what they said compared to the first time around, and then gently ask them a specific follow up question.

If you read the three characteristics carefully you'll notice that they are encouraging the best from your requestor (context, business priorities, problem framing), while at the same time encouraging the best from you (your knowledge of data, systems, analytical strategies).

That is the basis for the magic that converts you and me from data puking Reporting Squirrels to Analysis Ninjas who leverage custom reporting, advanced segmentation, statistics, surveys, competitive intelligence tools and to deliver reports with specific insights.

Guess what's the result?

People who just wanted data are now running around taking action based on your insights. Why? Because you did not provide data. You answered questions that were important to the person, tied to business priorities.

In rare cases your requestors might not even know how to reply when you ask them for business questions. Let me send you off with a little gift for the times when that will happen. . . .

Sample Business Questions for Ninjas & HiPPO's.

Look the person in the eye, still with the twinkle, don't forget the twinkle, and say:

My dear friend Avinash Kaushik asked me to share these sample questions with you. He said it would help us identify what's most important for our business.

If you hear the questions below you've hit the jackpot, because these are questions, have the three characteristics outlined above. . . .

• How can I improve revenue by 15 percent in the next three months from our website?

• What are the most productive inbound traffic streams and which sources are we missing?

• Have we become better at allowing our customers to solve their problems via self- help on the website rather than our customers feeling like they have to call us?

• What is the impact of our website on our phone channel?

• How can I increase the number of customer evangelists by leveraging our website?

• What are the most influential buckets of content on our website?

• If we could only do one thing to increase revenue on our website what would it be?

• What is the incremental impact of our display ad campaigns?

• Are we building brand value via activity on our website?

• Do fully featured trials or Flash demos work better on the website?

• What are the top five problems our customers face on our website?

• What is the cost for us to earn $1.00 on our website?

• What is the effect of our website on our offline sales?

Open ended. Force you to go to new sources. Don't contain columns and rows.

That's when you know you are on the right track.

It is quite likely that you will run into other business questions that might be more pertinent in your case.

But the theme that you are looking for is tough, highest-level business problems that you can help solve by analyzing the data you have (or data you don't have but will figure out how to get).

These are the questions that give you goose bumps. These are questions that give you a drugless high. These are questions that validate your decision to come into this field. These are questions that get you out of bed in the morning and feel excited to be alive. (Ok so maybe that is just me. But you'll see what I mean!)

Questions that rock your world, and coincidentally make for a truly data-driven org.

Parting Words of Wisdom from a Practitioner.

For the longest time in the web analytics world we have been content to do one of two things:

1. Provide the data we have in our applications in the hope that in the deluge of visitors, page views, referring URLs, time on site, and exit pages, there is something that marketers and business stakeholders will find of interest and take action on.

2. (Eager to please.) Take requests for reports, create them, and figure out how to email them or publish them on the intranet.

Reality is rather messier as a result of this.

Business leaders feels frustrated that they are not getting insights that they can act on. On the other hand this can't be fun for you. It can't be easy for you to hold the title of Senior Web Analyst and you are reduced to running reports.

Hence the most important foundational element of any effective web analytics program is to ask real business questions, understand those business questions, and have the freedom to do what it takes to find answers to those questions by using Web Analytics 2.0 strategies.

So. . . .

If you are the Business Honcho, bare your soul and share the questions (samples above) that keep you up at night or the priorities that you think are required to go out and win against your competitors (again these are not reports you want).

If you are the underling, seek to get a peek into the said soul and understand the strategic questions that the business wants answered. When you learn what the questions are, go get answers, one at a time.  You will now be on your way to truly adding value to your company.

If you are a powerless underling, provide the reports, puke the data being asked, play the useless non value added game that you are being asked to play. But all the while know that real glory lies some place else. Be on a question quest at every given opportunity, with a twinkle in your eye.

Identifying business questions is a journey.

As you solve one set, the next will come up. Or you may be in the middle of solving one set, and suddenly that set will become irrelevant and there will be a new set.

This evolution and change is a sign that you are actually answering business questions and not just doing reporting, because business is always evolving and changing and you have to simply learn to change with it.

From the bottom of my heart I wish you all the very best.

Carpe diem!

Ok now its your turn.

How do you spend most of your time at your job or with your client? Providing data rich columns and rows and reports with pretty font, or answering business questions? If you have tried this strategy already what are the biggest barriers to you being asked questions rather than data? If a strategy has worked very well for you in this context what is it? Twinkle in your eyes? [I knew it! :)]

Please share your experience / feedback / pain / joy.

Thanks.

Rebel! Refuse Report Requests. Only Answer Business Questions, FTW. is a post from: Occam's Razor by Avinash Kaushik

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:

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