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

This post is all about that.

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

Not today.

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

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

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

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

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

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

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

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

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

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

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

Ready?

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

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

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

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

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

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

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

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

Boom!

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

Charming!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Pretty cool. But that is not all.

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

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

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

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

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

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

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

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

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

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

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

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

1. Saw more than 3 pages during their visit AND

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

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

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

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

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

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

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

There are two ways to get to this report.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4. Bam! Bam! Bam!

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

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

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

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

But there is one fly in the ointment.

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

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

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

So, how do you make this report work?

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

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

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

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

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

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

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

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

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

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

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

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

For this business the results are fantastic:

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

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

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

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

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

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

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

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

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

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

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

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

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

My first example is a good representation of that.

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

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

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

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

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

Fun right?

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

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

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

Here it is…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Second tip on custom reports: Create micro-ecosystems.

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

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

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

Same outcome: Faster movement from data to insights.

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

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

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

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

Sometimes all the reports and features are simply not enough.

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

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

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

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

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

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

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

Happy?

Now here's the caveat.

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

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

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

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

Now allow your inner geek to rejoice!

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

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

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

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

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

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

So that's it.

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

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

Good luck!

As always, it's your turn now.

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

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

Thank you.

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

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

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

Awesome right?

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

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

The Fundamental Web Analytics Problem Is Not Data!

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

One of my biggest learnings?

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

Why?

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

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

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

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

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

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

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

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

And no one deserves that.

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

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

Why doesn't everyone do that already?

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

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

7 Rules for Digital Marketing Revolutionaries!

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

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

We expect more.

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

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

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

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

This is something we are most unprepared for.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

#4 Attention is the most precious commodity.

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

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

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

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

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

Get it? Attention. Via incredible daily utility.

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

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

#5 Brand destruction is insanely efficient now. Beware!

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

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

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

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

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

Imagine a better future for your company.

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

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

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

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

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

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

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

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

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

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

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

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

Bonus: #8 Data is your friend.

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

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

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

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

Data + You = BFF = Business & Personal Success.

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

I wish you all the very best.

It's your turn now.

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

Please share your feedback, ideas and awesomeness via comments.

Thank you.

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

In this post about an important Google change, I want you to focus less on the data and focus more on the methodology. And – so important – I want you to help me with your ideas of how we can do this impossible analysis better, in the complete absence of data :). So please share your ideas via comments and let's together make a smarter ecosystem.

On board? Let's go….

In an effort to make search more secure, on Oct. 18th Google announced that users logged into their Google accounts using www.google.com would be redirected to https://www.google.com. The search queries by these users would hence be encrypted and not available to website owners via web analytics tools such as Omniture, WebTrends, Open Stats, Google Analytics etc.

Switching from have all the search queries in the keywords reports was our normal state, not having them feels different. As the change ramped up and more user queries came to be represented, in at least Google Analytics, under the moniker "(not provided)" we all got worried. From our perspective it would be immensely preferable to be able to analyze all the keywords individually. Sadly we don't have that now.

The wonderful thing is that in addition to passionate commentary on Twittersphere / industry blogs / gurus, we also have access to data for our own websites. We can, and should, look beyond simplistic "it is this high or that low" to see if we can understand something (anything!) deeper.

Most analytics vendors, including Google Analytics, reacted immediately to the change in order help us quantify the impact of this change in multiple ways. As you can imagine my reaction was to unleash a flurry of custom reports and apply smart advanced segments and compare data pre and post change and go down a bunch of holes.

From that experience here are five steps I recommend you follow to gain a smarter understanding of this change…

1. Establish macro context.

On Oct 20th on my Google+ page I'd shared a custom report for Google Analytics that makes it extremely simple for you to look at this data. Visits, Unique Visitors, Bounce Rates, Goal Completions for (not provided).

You can download that report into your GA account by clicking on this link after you are logged into GA: Google httpS Change Impact.

Here's what the data for this blog looks like for one month:

Like me first you should compute the high level impact of the change. From Oct. 31 (when the trend started to spike and subsequently stabilized) to Nov 15…

Please note that this number will vary dramatically depending on the type of website you have, audience attributes, geographic location and a number of other factors.

Now you know what the number is for your site, and you can keep the custom report handy to continue to watch what happens over time. Remember to divide the number by total Google traffic. I see people using total search traffic or total site traffic or… other imprecise metrics.

All numbers in aggregate are at best marginally useful, and that rule applies to this one too.

We want to know more. Who are these people? Are they people I should care about? Not care about? And what kind of search queries are these? Brand? Non-brand? What else?

Sadly we can't answer all of those questions, but we can make a small clump of informed judgments based on data we do have. It just needs a pinch of passion, some smarts and a lot of effort.

Let's deep drive into some very cold and choppy waters…

#2: Understand the performance profile of the (not provided) traffic.

One of the things I hate about standard reports in all web analytics tools is that they scatter necessary data across tabs, multiple reports, or outright hide it. #aaarrrrrh

So I always use custom reports . In most web analytics tools it takes as little as 20 seconds to create one. I did one for this particular purpose. It provides me the end-to-end view of search keyword performance in one place.

Here is what it looks like:

You can download it into your Google Analytics account by clicking here: Keyword Performance Analysis Report

Two quick things to note.

So how does (not provided) look? Here's my end to end view:

The numbers in red were added to the report by me. I wanted to know what percentage of the total Visits and Goal Completions (not provided) was. [On that last point, if you have an ecommerce website you can use Orders or an appropriate proxy instead of Goal Completions.]

Bottom-line: 18% of the Visits and 22% of the Conversions.

Big numbers! But with a quick scan of the report, I think I already see that there is something delightful going on here. Stick with me. I think we have a surprise coming.

The custom report has eight metrics (two more than I normally use) simply to try to tease out some nuance of the performance as we look across keywords.

One hypothesis I had was that (not provided) might be mostly returning visitors. The overall search avg % New Visits is 67.96%, for (not provided) it is 65.06%. Very similar to the "average site visitor." But notice that all Brand Terms above (avinash, kaushik, occam's razor) have very low % New Visits. So it is possible that (not provided), contrary to my hypothesis, are mostly new people.

Overall bounce rate is 70.2% (not unusual for a blog/pure content site), and (not provided) is 66%. Again, scanning across the top ten terms you can see higher rates for non-brand searchers (people looking for specific, perhaps quick, answers) when compared to brand terms.

Content consumption, Pages/Visit, seems to be a bit on the higher side compared to the average (1.76). But like the other metrics above, there is a pattern between brand and non-brand (with brand higher on this metric).

I really, really care about Goal 2, hence that conversion rate is in the report. The average is 2.21%, (not provided) is around 2.37%. There's not much conversion going on with the broad non-brand terms (you can't get lower than 0% :).

Goal Completions is very interesting. (not provided) is a huge bucket of goal completions (and it is easy to understand why so many SEOs and Marketers and Lovers are in a tizzy!). The thing to note here are the numbers in red (% of each bucket compared to total Goal Completions, 4,816). See how quickly thing fall off the cliff. Note the difference between brand and non-brand.

Finally, my absolute favorite: Per Visit Goal Value. There is no obvious monetization on this blog, but I have 8 distinct goals and I have goal values assigned to each for the long term impact each adds. (How's that for focusing on customer lifetime value? :)). $1.27 for (not provided), compared to overall of $1.01, and the number does not come close to the other brand terms.

We still don't know what keywords are contained in the (not provided) bucket.

But what we do know is that for this site (not provided) visitors fits this bill: They seem to be new people with behavior that is quite distinct from the "head" brand terms and closer to the non-brand terms.

In the past I've lovingly termed non-brand long tail visitors as "impression virgins." The hint at the end of this step is that I've got myself a lot of impression virgins in (not provided)!

Let's go and see if we can validate that theory.

#3: Deep dive: Match up performance profile to Brand & Non-brand visits.

Based on the clues above, I'm going to try to understand whether the performance profile for (not provided) is indeed closer to brand searchers.

I create this simple segment in GA… should take you five seconds to do it for your own business…

Apply it to my custom report and boom!

[sidebar] A quick thing to note is the ratio of Unique Visitors to Visits. In context of % New Visits that makes sense. But just make a note of it. [/sidebar]

How does this compare, purely from a performance of the key performance indicators perspective, with (not provided) for the same period?

Quite a stark difference as you look across metrics like % New Visits, Bounce Rate, Pages/Visit, Conversion Rate and Per Visit Goal Value.

So how does the performance of (not provided) compare to that of non-branded keywords? Not a difficult question to answer.

Back into GA to create a segment like the one above, expect change "Include" to "Exclude" and I have my non-branded traffic segment.

Here's how those numbers look like in the aforementioned custom report:

When you do this with your data you'll have a similar image and you'll compare it to your (not provided) segment performance, and your brand segment perfromance. In the comparison above it is clear that these three buckets are distinct, but that the performance of (not provided) is not as close to brand as it is to non-brand. Even though the (not provided) segment is small (4.6k) compared to non-brand (21.9k) – thinking about impact on averaging these metrics.

There are two likely scenarios in terms of what you'll find…

In your case (not provided) segment might match overall Google traffic or one of the above segments. In which case you continue business as usual with the assumption of an even distribution.

It is possible that (not provided) segment does not match overall Google traffic, or one of the above segments, in your case. In this chase you understand a bit better how to treat it in your thinking (more keywords connected to your brand or non-brand segments). At the moment you can't take action based on this information (how to you react to visitors whose keyword you don't know at all). But when presenting to your senior executives you can give them a bit more context.

It does not eliminate all the questions, but it does help me go from "I have no idea who all these people/keywords are" to "Okay looks like it might be my non-brand possibly long tail traffic."

Something of value, right?

All of the above is still kind of at an aggregate level. But we all have a lot of keyword level historical data. At some point we should have enough post change data that we can throw it all into a delightful regression model to fine tune our understanding at a keyword level.

At the moment we just know a little bit more than "here's my total (not provided)."

#4: Tentative conclusions. Why this seems so scary, but might not be (at least for now).

Most, but not all, of my branded traffic is my "head" traffic, i.e. traffic that results from a few keywords used by lots of visitors. After all your brand is unique to you and, for any type of website, drives loads of search traffic to you because you rank high in SERPs for those brand queries.

Most of my non-brand traffic is my "tail" traffic, i.e. traffic that results from a lot of keywords used by a few people each. For example you'll notice at the very start of this post that during this time period I had 27k visits. Of this my "tail" traffic comprised of 21,921 visits. These delightful folks used 10,498 distinct non-branded key phrases to find my website.

10,498 distinct search queries drove 21,921 visits!

Remember the two scenarios I'd mentioned above? Let's look at one of them (performance closer to non-brand traffic) and understand what is happening a little more visually. What is happening when (not provided) shows up as your #1 metric in your search keyword reports?

In my case above, closer to scenario #2 for me, the performance of (not provided) as shown by the metrics above looks more like that of the visitors who came via those 10,498 non-branded search key phrases.

Here's what's happening when (not provided) shows up #1 for me (clear in the screen shot in part #2 above), as explained by my head – tail illustration :

Prior to this change by Google, the gray slivers above represent traffic that became (not provided) after the change.

In the past only a small part, if any, of this traffic, for me, would ever show up in the top ten or twenty keywords in the report (head traffic). Because much of it was in the long tail I never noticed it (it is hard to look at all 10,498 key words individually! :).

But after the change by Google, these tiny, in the past invisible, slivers combined look like one scary beast. I've painfully combined every pixel of gray sliver above:

OMG! I've lost a huge chunk of something that was a very important part of my traffic!!

Not really. It just looks scarier than it really is because tiny shavings of your other keywords (now used by logged in users who are opted into https sessions on google.com) appear in one big piece. Individual cells don't look that scary. But combined they look like Darth Vader himself. :)

Let me hasten to add that this does not mean that these "slivers" from user search queries are not important. Or that just because they are mostly non-branded traffic we should ignore them (I argue 100% contrary to that here: Monetize The Long Tail of Search ). Or that you should not worry and that the sun is shining, there is no US debt problem, we have universal health care and Ashton and Demi are still together.

No. Not at all.

But the sky is not falling either.

We can use the actual data we have to keep a very close eye on this traffic and its performance. We can use advanced segmentation and custom reports to understand where this big scary block of traffic used to be. Is it (to repeat the scenarios we outlined at the end of part 3 above) closer to the average performance and hence possibly evenly distributed or closer to non-brand and less evenly distributed.

We sadly still won't know what actual long tail or non-brand keywords or overall keywords they represent or how much of a particular keyword/phrase they used to be. But my POV is that we'll be in a better place.

You can be, if the data in your case justifies this, just a little less worried.

#5: Additional awesomeness: Landing page keyword referral analysis.

One final idea I had was to wonder if the (not provided) traffic enters the website at a disproportionate rate on some landing pages when compared to all other traffic from Google. If that is the case we could do pre post analysis on referring keywords to those landing pages and get additional clues.

It is not very hard to go checkout that theory.

First, create an advanced segment for the (not provided) traffic:

Then go and apply it to your standard Landing Pages report in Google Analytics (or SiteCatalyst or WebTrends or Yahoo! Web Analytics):

The analysis from here on is not very difficult (though in the new version of GA it is harder as the UI designers got rid of the % delta for comparative segments – what a shame). Just use our bff MS Excel.

For example 14% of the (not provided) traffic enters on the home page.

I was able to find a small clump of pages where the (not provided) traffic, at least currently, entered the site at a higher rate than overall Google traffic. I can see the referring keywords to those pages prior to the change and after the https change and attempt to identify which keywords might be contributing traffic to (not provided).

For me this analysis provided a better idea about some long tail non-brand keywords. But it was not as much as I would have liked to learn. Partly that is a function of the fact that those keywords are used by a handful of people and, this makes it worse, they are quite transient – they are not used too many times again.

But since everyone's site and visitor behavior would be different I did want to share this idea with you. It is not a hard bit of analysis to do, and you can let the data tell you something (or not).

That's it.

A simple five step process to go from reacting based on an aggregate number in your keyword reports to a much more nuanced (if imperfect) understanding based on your own data.

Caveats:

Before we go, a few important reminders that are spread throughout the post above but bear repeating….

Anything else I should have here that I've forgotten?

I would love to know how you would go about doing this impossible analysis? What other path would you take in your web analytics tool? What segment, report, metric, walk on water effort would you undertake? Regarding my five step effort above… what flawed assumptions am I making? What would you change in terms of the approach/conclusions in any of the steps?

Was this nuanced understanding of what might be happening better than where you started?

Please share your alternative ideas (please!), critique of the above analysis, ideas for world peace via comments.

Thank you.

P.S: A request. This blog focuses on digital marketing and web analytics, it is not a policy blog. If you are up for it I would love for your comments to focus on the former and not the latter. If for no other reason than that my skills don't extend to the policy part and I would not be able to share anything of value with you.

I appreciate your consideration.

Smarter Data Analysis of Google's https (not provided) change: 5 Steps is a post from: Occam's Razor by Avinash Kaushik

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

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

Ready for a can of concentrated compressed energy?

Let's do this.

1. Never Compare Apples to Watermelons.

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

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

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

Found the problem?

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

On the surface this is hard to detect.

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

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

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

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

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

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

Nice clean segments that provide you nice clean answers.

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

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

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

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

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

Look at it carefully? Found it?

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

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

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

Another important thing.

Label your x axis. Please.

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

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

3. Calibrate Your Time Series Optimally.

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

Don't scroll down yet.

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

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

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

It is impossible to find anything of value above.

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

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

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

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

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

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

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

Sweet, right?

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

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

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

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

Simple best practice. Use it.

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

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

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

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

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

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

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

Wait. That did not make sense.

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

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

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

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

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

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

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

Sometimes you just want to skip the graph.

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

Why do the graph at all?

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

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

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

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

Scroll back up.

Look at the graph.

Now look at the table above.

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

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

5. Statistical Significance is Your BFF.

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

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

What's the problem with that?

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

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

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

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

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

Remember ecstasy awaits!

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

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

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

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

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

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

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

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

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

Why yes of course we can!

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

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

7. Celebrate your new found awesomeness!

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

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

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

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

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

A variation on the above "simple" mistake.

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

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

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

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

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

Pretty close.

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

Do other useful things.

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

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

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

7. Pie Charts Are Evil.

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

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

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

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

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

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

Repeat five more times.

Remember to remember only the interesting bits.

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

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

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

Much easier, right?

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

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

Just go with this…

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

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

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

So do that.

Okay it is your turn now.

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

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

Thanks!

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

Actually I get that question almost every single day. :)

The interest is not surprising. There is a ton of excitement about web analytics. Companies are starting to think innovatively about the web (no more unintelligent banner ads or digital "crimes against humanity"), and they are starting to understand the power of data to delight customers and drive accountability.

I've said repeatedly that if I look into the next xx number of years Analyst is essentially a recession-proof job. In our field specifically, good Web Analysts will continue to be in high demand, for any conceivable time period. [Therein you see one way to "break into" analytics, or anything really, you have to be good!]

If you want to "break in," or if you've already "broken in" (welcome!) but desire greater awesomeness, here are some tips on taking a step back, thinking things through, and being strategic about your approach. . . .

#1: Figure out the optimal career path for you.

This might seem odd. Not asking you to jump into JavaScript classes or make love to Yahoo! Analytics or start pimping your resume left and right.

Why before what. Always.

It only takes two minutes of talking to most current "Web Analysts" in the world to realize that they actually are:

A. Web Analytics Implementers (they obsess about the latest tweak to the Omniture JavaScript code to eek out one more little bit of lemon juice)

B. Web Analytics Data Reporters (99% of their effort is taking in requests and working with above folks and simply regurgitating data out)

Before I go on I must stress that both are much required roles, without A you've got nothing, and without B most corporations would not function (as they believe sending data out is all it takes to be successful).

But neither role is that of a Web Analyst.

Having been around the block several times in several roles on all sides (practitioner, consultant, team leader, vendor, advisor) I've distilled the web analytics field into four distinct roles. Each requires very different sets of skills, delivers very different career paths, and, of course, leads to different salaries.

So first really, really, really understand what your actual skills are, and then, second, identify which of of the four career paths (at least initially) is the right fit. If you do, there will be happiness (forget money, at least initially, and focus on happiness). If you do, there will be progress in your career as you start (and you can always evolve).

How?

Read this post and the step by step process: Analytics Career Advice: Job Titles, Salaries, Technical & Business Roles. For each path it outlines Career Prospects, $$$ (Salary) Prospects, Long Term Job Title Growth.

On page 392 of Web Analytics 2.0 you'll also find this summary of the four paths, in a simple 2×2 matrix:

The numbers are there to give you a point in time perspective. They are there mostly to give you contextual guidance (between paths).

Remember:

1. The best path for you is the one you have the aptitude for.

2. Think of the four paths not as one point in the matrix, but rather as a dominant role with some (small) shades of the other.

3. If you don't figure out what your best (dominant) path is, you'll be very miserable.

4. Only one of the four dominant paths is that of a Web Analyst. The other roles are important in the world of web analytics, but they are not Web Analyst roles.

5. You can evolve over time. Your choice is for the time horizon just in front of you – the next couple years.

So what is your dominant quest? Individual contributor? Team lead? And which facet are you going to focus on? Business? Technical?

Read the analytics career paths post (or a much updated version on Page 386 of Web Analytics 2.0) and figure it out, before you do everything below. And trust me when I say this. the process is not easy. Especially for people who won't be honest with themselves. But of course that is not you!

[Update: This blog post is overwhelmingly for those who want to become Analysts ("Business" in the matrix above). Hence I am a little biased in emphasizing analytical skills development and the acquiring of business problem solving skills from practical work. Technical skills are important, perhaps I am under-emphasizing them here. Please see Alex's dissenting view (to mine), it is important and please take it into consideration in your evolution.]

#2: Pick Your First Two Web Analytics Tools.

It might seem silly to close in this early on a tool, after all you barely figured out what your skills are. Sadly tools are so dominating in our world as the determining factor for so many things that it is wise to make this choice up front (for the first x amount of time).

For example, if you choose Omniture or WebTrends or ICoreNica as your tools. then your choice to get smart about them and smart about your career path will lead through training and certification via those companies. You can't have free versions or free education or books on the subject matter.  Let me hasten to add that these are wonderful tools; they are used by some of the biggest companies in the world who hire tons of people. So it is not features etc., it is how you are going to get your first job.

If you choose Yahoo! Analytics or Google Analytics or Piwik then you are a little better off in terms of your starting path. The tools are free. Anyone can download and implement them anywhere. There are books galore. There are very cheap trainings (YWA and GA) all the time if you desire. There are free sources like Conversion University (video, audio, more). So you can start on your own, tomorrow morning, get very good at the tool if you want to. DIY.

Important: Neither one of these paths is ultimately better or worse. Neither one of these sets of tools is superior or inferior, no matter what some silly vendors and some sillier consultants will tell you. Neither one will mean you will ultimately win or lose (see rest of this post). They are just different.

When you are starting out just make an explicit choice, simply because it will dictate your immediate next steps and, I cannot stress this enough, f o c u s!

I said two tools. Did you notice that?

Every Analysis Ninja I know (not implementers, not data providers, not excel cross data store integrators – all good jobs) is very, very good at Multiplicity (answering a complex set of digital business questions using the best – often non-clickstream source).

So in addition to becoming good at Omniture, Google Analytics, Baidu Analytics, pick one other tool from the Experimentation, Voice of Customer, Competitive Intelligence buckets of Web Analytics 2.0.

This will ensure two things:

1. It will be a very, very strong signal to your future employers that you are not one of the numerous one-trick-clickstream ponies out there. You get the world we live in, you understand sophistication.

2. You will start learning all of the awesome things I said above your skills / experience should signal!

If you can't make up your mind here are the simplest tools to pick up, primarily because they are easy to get into (and provide a lifetime of sophistication development). Surveys: KissInsights or 4Q Survey. Competitive Intelligence: Compete, Google Insights for Search, DoubleClick Ad Planner, Trends for Websites.

If you can't make up your mind do KissInsights and Insights for Search. It will teach you how complicated, hard, beautiful, datagasmic the world we live in is.

Pick two starting points. Start on day one knowing you are going to be a Ninja.

#3: Get Educated.

Initial career path choice? Check. Initial tools focus? Check. Getting smart about it? Let's go!

Books:

It might be old fashioned but I like starting with a book. Notice we are on #3 and I have not yet asked you to install anything! :)

Buy two books.

Get an overall web analytics strategy book, one that covers the ecosystem, the mental models to apply, key analytics techniques. essentially a "how to think" book. I would recommend my book Web Analytics 2.0 (in 5 languages!) or Steve's Cult of Analytics or Gemma's and Tristan's El Arte de Medir or Juan's Meta Analytics. [Important: If you have other suggestions that are current, please add them in comments. Thanks.]

Get a really good tool book. For Yahoo! Analytics please get Dennis's book. For Google Analytics get Brian's book or Justin's book or Timo's book (in German). While some things (like UI) about tools change over time, these books are your best, structured bet at learning about the tool and the power at your disposal.

Blogs:

There is no doubt that the most current knowledge exists in blogs (yes, yes I know that the fad of the month is Twitter & Facebook, for someone starting to build a career the most current distractions exist there :)).

Pick two blogs.

Pick one practitioner blog, someone who can teach you about web analytics (thinking, approaches, pure practitioner education, complete lack of generic stuff). There is a very long list in the blogroll on the right navigation of this page (and every blog post on this blog).

Pick the blog of the vendor you've chosen previously. Every decent vendor in the world has an active blog teaching their practitioners how to use their tool. My favorite is the Omniture blog. It is the prefect balance between pimping (a little bit) and teaching (a lot).

A list of my personal favorite top ten blogs are also in the right nav of this page (in the blogroll section). You'll notice they are a mix of Marketing, Design, Analytics, Critical Thinking blogs. A clue as to what I personally think it takes to be successful.

Two blogs are not overwhelming. Really read both that you pick. Stay hungry, stay foolish.

Certification Courses:

If you want to jumpstart your education (and this is in addition to the above, not a replacement) consider taking a course with Market Motive (Disclosure: I'm the co-founder), University of British Columbia or the University of California at Irvine or Universite Laval. If you chose GA earlier, consider taking the Google Analytics IQ certification exam (the educational materials are free, the exam is $50).

My experience is that structured courses teach you how to think. None of them (okay except GA IQ) teach you how to use the tool. When you are starting out that is so important. Once you get sucked into s.vars and e.props and events and all that crap it is very hard to get your head to rise to a strategic thinking level, business analysis level, the things that really matter level. So if you can afford it, take a certification course.

You don't want to spend two years on this (look for fresh content); find the fastest three or six month jump start (because you'll still have to do the above and below).

University / College Level Courses:

For a career in Web Analytics you don't need a special degree (at least not yet). I've hired people with no college degrees, forest rangers, financial analysts, database programmers (as long as they have an analytical bent of mind). But if you don't have any exposure to Statistics then I strongly encourage taking an evening / part time course in Statistics 101. If an institution near you provides a course in quantitative or qualitative analytics (even traditional analytics such as direct marketing or market research) then that is also well worth the investment. It will absolutely jump start your career.

The trick will be how to make that massive investment as you'll have to read your two books (one time at least!) and two blogs (continuously) and take one course.  So here's my recommendation: Read the web analytics strategic book while taking the three/six month course. Start reading the tool book when you start using the tool. Start reading the blogs when you get to the below.

#4: Play In The Real World.

This is where everyone messes up. People show up at interviews having just used Omniture with no experience of any other Web Analytics 2.0 tools. They show up with limited theoretical knowledge or just the UBC degree.

Ain't gonna happen. The job. Won't happen.

You can learn everything there is to learn about fishing in a book, or at a University. You won't actually get any good unless you grab that pole and sit for hours on end on the water.

A free very good tool is available for every element of Web Analytics 2.0. Go get a site. Your mom's. Favorite charity's. Your friend's business. Your spouse's sibling on whom you have a crush. Or. . . start your own!

The specific details on how to really, really practice (without permission from your boss or your company or Guru) are outlined here: Web Analytics Career Advice: Play In The Real World!

Do it.

It turns out the greatest thing you can pour into becoming awesome is your sweat.

And don't stop at website data analysis. Remember you want to be a Ninja because they earn more money, get any job they want and are 900% happier than the average human.

I love Romy Misra's approach. She is taking publicly available data, (bravely) publicly doing analysis using tools like Many Eyes and publishing her results on her blog! Here are two examples:

~ Data Deconstructed -US Foreign Assistance

~ Data Deconstructed -Earthquake data

Impressive is it not? It takes effort. It takes love. It takes a deep desire to get good. And yes you'll get something wrong (Romy's example), but can you think of a better way to learn?

And Romy, and everyone similarly brave, is not just writing a blog. . . she is creating the greatest resume a person can create. Think about that.

Don't wait for your boss to allocate budget. Don't wait for permission from your mom. Don't wait to sign up for a project. Don't wait to be picked. Don't wait. Go and play in the real world. Now. And if you want to stay good, do so constantly.

#5: Find Your First Web Analytics Job.

By now you've probably spent three to six months investing in yourself. In the latter part of that journey you've likely practiced using the Romy method or the Avinash method or the iamawesomesoicreatedmyown method. You have a publicly available portfolio of your work, no matter how small or basic.

Time to find a job.

Look in the obvious places. Dice (315 jobs today), Indeed (809 jobs today with salaries estimated over $100k!), Craigslist (82 in SF Bay Area) et al.

But don't top there, try less obvious options. . .

Look in local locations. Washington Post Jobs in DC (11 jobs). You have a newspaper in town right?

Look at associations outside your core area. For example SEMPO's jobs site (31 jobs). Or the DMA career center (240 jobs with minimum salary of $100k!).

Look at non-profit entities (who have paid jobs). For example my favorite idealist.org (272 jobs – some tangential. 252 North America, 11 Europe, 6 Asia, 1 Africa, 2 LatAM).

With non-profits you also have an option of just writing to them and offering to implement Yahoo! or Google Analytics and helping them with insights. If you are interested post / read NTEN Affinity Groups.

And don't forget the obvious hidden places. . .

Look at web analytics consulting companies. There are so many of them, literally exploding with growth, and looking for even junior people (especially those with a "public resume") willing to work hard. Reach out to them.

Look at the Google Analytics, Yahoo! Analytics authorized consultants. Choose your local geographic location in the drop-down, visit their site, bada bing bada boom!

Look at web analytics vendors. The industry is stuffed with people who started as junior / senior consultants with analytics vendors. Omniture, WebTrends, IBM, Google, and everyone else out there.

I am not going to mention friends and family as your best network to get jobs. You know that already.

I am not even going to mention company websites. You know where to check them. With companies the challenge is that they often ask for pink elephants, but don't let that deter you. Ensure that on top of the resume you send them is a link to your above mentioned "public resume." It is a great way to show your actual hard work and give the company some room to shift from looking for pink elephants to an enterprising person with a visible track record of hard work and learning investment. If you don't have a "public resume" then you might be in less luck than optimal.

Lots of places, lots of jobs. But. . .

Be open to contractor to permanent positions. Just don't pick a slimy placement companies whose core earnings come from having you stay a temp. I have done contractor to permanent (both in taking a job myself and hiring Analysts) and it has worked out well.

Be open to starting below where you were before (if you are switching careers). My first web analytics job came with a downgrade in job title (ouch!) and $14,000 less in annual earnings (ouch! ouch!). I was confident it was the right move. Joined. Worked hard. Both things got fixed astonishingly quickly.

Be open to the type of company you'll work for. For profit, non-profit, universities, Fortune 10,000, Forbes 10, B2B, B2C, A2Z etc., etc.

Bottom-line: Look everywhere, but mostly focus on the non-obvious, and even for someone starting out there is a job waiting in Web Analytics.

That is it. Five simple steps to "breaking into" web analytics.

The hardest are developing a true Web Analytics 2.0 skill-set / mental model, and creating your "public resume" (body of work).

You'll be busy with that for most of your first xx months. So before I close this post, here is a bonus item. . .

Bonus: #6: Avoid Massively Over-Rated Activities.

Your time is precious. I feel obligated to spill two "secrets" that you might benefit from as you plan your career. Here are two over-rated investments:

1. Spending four hours a day tweeting and getting into loads of "conversations."

Prioritize your time. And remember, I am going to Bing you before I meet you for the interview. It will find your Twitter feed. I will expect massively more from you if you are invited (and if your Twitter / Facebook feed was sub-optimal you are not getting the call – benefits of a social world!).

If you have time, at least early in your career, invest in yourself education and evolution (see steps above).

If you have four hours a day to tweet, take three and a half hours and invest in your long term personal and professional goals.

2. Attending five analytics conferences a year.

Maybe one or two, regardless of who is paying (you or employer). And if you've attend a conference this year, don't go to the same one two years in a row. The content simply does not change enough.

Also, think Web Analytics 2.0, think breadth. Try other conferences: Search, Affiliate, Direct Marketing, etc. Seek analytics sessions, broaden your mind rather than let it rot by staying in a silo.

I am sure there are others I'm forgetting. If history is any guide you are going to oblige me by sharing them via comments below!

I want to close by welcoming your interest in a career in Web Analytics. I am immensely excited at its potential to transform companies and lives. It is a rewarding career from any perspective: work, salary, satisfaction. It does take a special kind of person who is willing not just to do the work, but love the work and love the special joy that comes from a day's hard work. All you need is the will.

Ok it's your turn now.

If you are new to the field. . . did you find this five step guide to be helpful? Was there a step you were not aware of? Are you executing your entry plan as outlined in the above priority order? Are you trying something else? What did I miss?

If you are an old hand. . . what advice would you share with someone who is just starting out? Which step above do you think is most important? What surprised you about your own career? What did I miss above in my guide?

Please share via comments. Thanks.

Web Analytics Career Guide: From Zero To Hero In Five Steps! is a post from: Occam's Razor by Avinash Kaushik

I am so proud of having reached this incredible milestone, because when I started I was not sure I would make it to the first anniversary.

Let me tell you how utterly improbable it seemed.

My first blog post, on May 15th, 2006, was titled Traditional Web Analytics is Dead (let me emphasize the first word, traditional). Per my plan, I wrote two posts a week. At that time I had done just two public speaking engagements, and the love of my life had been to one of them, and she knew what I did and my body of work.

At the end of the first month, having read every one of my 8 blog posts (reason #2691 I love her so much!), she turned to me and said:

"You should slow down your posting schedule, you've already written almost everything you know."

And let me tell you she was not that far away from the truth! I did worry about that as I went into the second month.

But my persistence in learning new things every day by being an active practitioner never went way. My quest to share *everything* I know with you stayed, and here we are. . . it has already been five years!

The secret to the blog's longevity is simple: your engagement with it. . .

In five years I have written a total of 433,273 words in 245 blog posts, and, this is simply awe-inspiring, those posts have received 9,706 comments with a total of 884,699 words! (Around 10% of those words in comments are my replies to you.)

Together we've written 1.3 million words in 5 years! One point three million!!

Isn't that amazing?

From day one my plan was to make analytics accessible, practical, non-IT centric, and real. I wanted to relate to you, your problems and provide solutions and frameworks you could actually use. No theory, just the unvarnished useful truth. My hope was that would encourage conversation (and yes Loyalty and Recency!) and that Occam's Razor would not be like every other blog – a broadcast.

Yet I had never ever imagined that you would write 2x the amount I would! Never in a million years. Every post I have written has been a ton of work; I cannot tell you how powerful your engagement is in terms of motivating me. Thank you.

Other numbers have been equally impressive (to me). Here's a simple graph of the number of visits to the blog and the number of posts I've written, by month. . .

Clearly the less I write the more popular the blog becomes! :)

Over time my posts have become longer (and l o n g e r!), more strategic and expanded to cover a lot more ground than "web analytics." It has been fun.

Those of you familiar with my Blog Metrics post will know that in addition to Conversation Rate (first set of numbers above), I place tremendous importance on Loyal Audience Growth (the number of RSS Subscribers). Here's five years worth of Subscriber count data. . .

[Apologies for the ugly visual, Feedburner is an unfortunately neglected product. The dark steady line is Subscribers, the squiggly lines are Reach.]

When I started the blog, my "I'll die and go to heaven" RSS goal number was 1,000. I did not think I would get to it (there were already so many well established Analytics blogs and people). As you can see above, the number crossed 52,000 sometime last month.

I still don't believe it.

Numbers are great, but as I reflect on the last five years I am struck by the impact of the blog on my evolution. . . my life has changed dramatically in the last five years.

I was Sr. Manager of a small analytics team. They were helping me ignore what web analytics was at that time, and imagine a different future. I became a director, then a consultant for a flirtatious period of one year. Then an Analytics Evangelist, working in fundamental ways to transform some of the largest companies in the world. Today that's coupled with keynotes at conferences around the world, co-founding an online marketing and analytics certification company Market Motive Inc, and advisory roles thrown in for good measure. So different from where I started.

There were other bits of surprises. 3 Bs.

Books.

Six months into this blog Wiley expressed interest in making the blog into a book. That initiative became Web Analytics: An Hour A Day. My sweetheart and I decided to donate our $10,000 advance to charity (to convert what we did for love, this blog, into a small benefit for others – The Smile Train and Doctors Without Borders). WA Hour was translated into 7 languages (vastly surpassing my expectations). In the middle of the third year I started writing Web Analytics 2.0. We decided to donate 100% our proceeds from that book to charity as well (adding Ekal Vidiyalaya as the third charity).

In the three or so years that WA Hour as been out and a year that WA 2.0 has been out, our proceeds from the books sales are around US $170,000.

Though that is a small sum, there is no way that I could ever have managed to donate that much money to charity. I am so incredibly grateful to everyone who bought a copy of the book. The result is this unimaginable joy of writing, publishing, donating. What else can a person ask for? I feel incredibly blessed.

Bleeding Edge.

This is a deadly cocktail: Pressure to write every week. Self-imposed pressure to create very high quality content. Your deep engagement.

These three things mean that I have to constantly be curious, understand deeply the world we live in, and the challenges we face as practitioners. That is the only way I can stay current and create solutions that matter – to provide something incredible, relevant, of value.

I firmly believe that is the "secret" to a longer "shelf-life." No opportunity to become stale, siloed, out of touch, irrelevant. Thank you!

Brand.

This was less of a surprise. How much bigger and broader the impact was is a surprise.

I have an MBA in Finance and Marketing so it should not be surprising that I went into this knowing it would be a platform for "brand avinash kaushik." I had a rough idea about what I wanted the brand to stand for. Passion. Integrity. No BS. Real value. Exciting. It is what I am; it is what I wanted this blog to be.

I have tried very hard to live those brand attributes. In ways small and big. Here and in every channel I participate in (Twitter, Facebook, Flickr).

This week I had to give a keynote on "Managing Your Personal Brand." A brand is not what you want it to be, it is what others perceive it to be based on your actions. So I asked people on Twitter to tell me the three words that come to mind when they think of "brand avinash kaushik."

There were hundreds of responses (!). A simple way to visualize all that data, and identify trends, is to create a tag cloud. Here it is, for all the words,. . .

[For more detail checkout the higher resolution version.]

There is an amazing amount of insight in that simple tag cloud, and it is inspiring to see the things that brand now stands for. Far beyond what I had originally envisioned.

While I care a little bit about the professional side of things, everything I do is people centered and so for me there were three words that meant less. So I took those three words (Web, Analytics, Google) out of the responses and got to the data I am really, really solving for ("people feelings"). . .

[For more detail checkout the higher resolution version.]

Looking at the above tagcloud, in Tagxedo, was one of life's quiet moments of amazement.

There is nothing quite like seeing five years of walking the talk summarized like that.

The words there are so beyond my expectations, and often underserved (though I thank all of you who said "sexy"!).

And now there is even more motivation to work even harder for the next five years.

I am immensely grateful for your attention. I have never taken it for granted, and I hope to keep earning it on Occam's Razor. Thank you. Arigato. Tusen takk. Gracias.

As always, it is your turn now. Let's celebrate with some of your stories and perspectives in comments.

When did you start reading this blog? Do you have a favorite idea, ah-ha moment, blog post? Are there small ways in which it has had impact on your professional (or personal) life? What would you do to improve it?

Thank you again for everything, especially your attention and engagement.

An Incredible Analytics Experience: 5 Years of Occam’s Razor is a post from: Occam's Razor by Avinash Kaushik