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

One thing that I never want to be.

We all have a tendency to learn up to a point, we get comfortable and keep chugging along rarely investing in our ongoing education.

I call it the slow but sure path to irrelevancy.

Let me share my prescription for avoiding irrelevancy: Try new things.

Simple right?

At any given time I have six or seven interesting tools running on this website. That's not including others I actively seek out around the web. Most of them are not even related to my current job or problems I know of. And that's on purpose.

I want to constantly be in the know of new and more clever ways of working with data, tools that are often solutions to problems we don't know we have yet or tools that are sometimes seeking problems to solve!!

Irrelevancy is not fun. Stale people are not appealing (just like, as your mom taught you, a week old bread). If there is one thing you take away from it post I hope it is the importance in investing in yourself / your education / your ongoing awesomeness.

In this blog post I want to share four analytics tools that I have been playing with for a while… tools that solve an interesting problem… tools that point to what might be in terms of our near term analytical future… and in almost all cases they don't even know!

I love doing this, I hope you'll have as much fun as I do.

First Some Context.

Remember I am the creator of the 10/90 rule of investment in web analytics.

I had created the rule many years ago, early into my job at Intuit, and quite simply it states:

When I had created the rule Google Analytics did not even exist!

The rule was borne out from my own experience having inherited a world class tool we were paying $250k a year for and produced crap. Well not crap… lots of data that no one cared about or actioned. I threw out the world class tool, purchased ClickTracks for a fraction of the cost and put money into Analysts and boom!

Ok not boom overnight… but over the course of a few months the org started to be more data driven, because analysts we hired produced analysis. That fed a virtuous cycle. More analysts. More insights. More desire to be data driven.

So as you look at the tools below remember the 10/90 rule.

In the end it does not matter who has the coolest or the biggest tool. Or for that matter how many tools.

People matter.

You matter.

Remember that, at least for the rest of this post. Ok?

Let's go look at some tools…

Measuring "Invisible Virality": Tynt.

Tynt's promise is simple. Add a piece of javascript to your web page (do a View Source on this page to see it), and it will tell you how often your content is being copied.

Copied! Say it ain't so! :)

[Please click on the above image for a higher resolution version, including all the other metrics.]

In the last month data was copied off one of my posts 5,616 times, with most of it being content and some of it images.

But that's not all.

If you look at the higher resolution version (click above) you'll see it also reports other data like Visits Generated etc.

The way it works is that when someone copies a piece of content Tynt adds a little bit of additional text and a trackable code with a hash (#) at the end of the url from where content was copied.

Like so… the text that was copied from my blog is the first two lines… the Read More and link was added automatically by Tynt…

When people click on that link Tynt can report visits generated, page views, where the links were posted (in case there is a referrer) etc.

There is additional data like how many of your copies created links that were posted and then clicked on…

Gold are places were the copied text was pasted with the additional "Read more: http://…" text+link were also posted AND someone clicked on it.

You'll note that Tynt's selling point is connected to SEO. The idea that your copied text creates links back to you which in turn creates visits back to you, and per Tynt, better SEO goodness. You know links and page rank and what not!

I *personally* do not see much value in all that data. Two reasons:

So I am less enamored by Tynt data that focuses on all that.

I love the data you saw in the very first screenshot, and I absolutely love this…

[Please click on the above image for a higher resolution version, including all the other metrics.]

The first screenshot shows how often content is being copied and the above indicates the blog post / web page where the content is being copied from.

Why is this cool?

If you are a regular reader you'll notice that at the end of every blog post (before the start of the comments section) is a Topsy widget.

It measures how often a blog post is tweeted/retweeted. Goes viral. Higher the number the better, makes sense?

I also measure the # of Comments Per Post as a measure of how "engaging" / "valuable" people found the content to be. Looking at how often it was tweeted/retweeted is one more layer of information in understanding what subject / ideas in a post / things I write are well received by people and which are not.

But.

Both the above attempts measure two minorities.

The cool thing about Tynt is that it allows me to get some sense of "engagement" / "perceived value" / "Like" with the v a s t majority of people who will neither submit a comment nor write a tweet.

People who still use email. People who like something I wrote so much (or hate it so much) that they copy the text and paste it and forward it to others. Or copy the text and post it on their blogs (without attribution of course :)).

I like that a lot.

This entire interaction that was completely invisible to me is now a bit more visible. I can measure the "invisible virality" / "spread" by this big huge non-commenting, non-tweeting audience.

In the time period above I had written 4 posts (5,616 times copies). Check this out… It turns out the post with the fewest comments, just 25, and the fewest tweets, just 100…

…was copied an astonishing 506 times, when all other posts were copied in small double digits.

See what I mean… something I would have perhaps considered to be only a small success turns out was a huge hit with the blog's audience. I just would not have known that so far.

Here's another interesting application. . . Lots of people are measuring "influence" of a blogger (/ piece of content) using data from the "minority activity" (comments, retweets etc) and selling it as the complete truth. But how can you do that without some insight from the majority?

Tynt shares one very interesting piece to the puzzle that perhaps in the future fit some place where we can use it with all other context we have.

Invisible Virality. Cool right?

.

Applying Smarter Ideas to Measuring "Sentiment": Analyze Words.

Raise you hand if you are in the "If I am any more excited about doing sentiment analysis then I'll pee in my pants".

So many raised hands!

Here's the problem: Most solutions stink. Not just stink… dinosaur's breath after a meal stink.

We are algorithmically trying something that as yet does not lend itself to algorithmic measurement… "emotion". It is darn near impossible to cleanly buckets feelings and nuance into clean Positive, Negative, Neutral buckets.

We, computer programs, are simply not there yet. [Though I am absolutely confident that we will get there at some point.]

For now you are most likely wasting time (and money). Sorry.

Here's the other problem…

Even if it works… I don't think it works. [What!]

Let's say you have a 100% perfect human read and 100% human categorized analysis on hundreds of thousands of rows of text. Clean into the three desired categories. Like in the image above.

Now pause for a second and think… what could you do with this?

You have aggregated data into three pieces and we all know aggregated data stinks at delivering insights!

That does not mean wanting to identify insights from lots and lots of text is not prudent. It is.

I like a much more nuanced approach.

Analyze Words applies one such nuanced approach to text analysis.

It uses the well established and long use LIWC (Linguistic Inquiry and Word Count) methodology to categorize all your delightful text (in this case your tweets).

Why the LIWC? Here's the idea behind the LIWC:

Cool right?

You go to Analyze Words and you punch in your twitter id and bam (!) your "psychological" profile, or in this case mine…

Nice eh?

No simplified over promise under deliver aggregates!

The three categories and 11 sub categories provide much much much more nuanced understanding of what your text is saying, in this case for your twitter profile.

Why is this cool?

In this case measuring "Personable": Engaged in other people's well-being and at peace with expressing your own uncertainty about the world. High Scores in personable use positive emotion words, ask questions, express their own ambivalence and reference others frequently.

Better than positive, negative, neutral right?

Or "Analytic": "If law school exams were a persona, they would rank real high in this category. Ample large words and phrases that include complex thinking styles (e.g. "if – but not …")."

Love it!

Two magnificent things about this approach (remember it's not the tool, its what you do with it :))…

Of course you can do even more.

You know how you are doing… now compare it to your "competition" and find their strengths and weaknesses…

When you do competitive analysis, like above, find contrasts with your own profile, what your brand stands for in the world and their brand stands for.

Highlight differences where you brand strength is strong. Hopefully they'll discover where they stink and for the sake of humanity fix that.

Nice eh?

Analyze Words provides a glimpse of an approach that I hope others follow.

Rather than trying to find short cuts, where none exist, and provide aggregate data, where it just gets crapified, follow a well established methodology while leveraging segmentation and nuance.

We've applied it just for Twitter in the above case but you can easily see how you could apply it to call center data, tech support websites, forums, online survey open text voc answers and so much more.

Applying Simpler Ideas to Measuring "Sentiment": StatsIt.

StatsIt started off as a differentiated web analytics tool, but has morphed into a delightful social media monitoring tool.

[Update: Oct 18: StatsIt is evolving its solution. But in this section my hope is to focus less on the tool itself and more a type of analysis that we can use in our daily life.]

It's approach is to index blogs and tweets and delicious and twitter and youtube and on and on and analyze that data to find yummy actionable insights about your social media presence / activity.

Like all tools it gives you pretty charts…

Sweet, now you know how much "activity" is happening. Give it to your boss, she'll be impressed. You on the other hand realize "activity" rarely has insights.

I want to focus on just one part of StatsIt that I adore because of how simple it is in its brilliance when it comes to finding insights from lots of text.

StatsIt has indexed a ton of content from all the social web activity. When you tell it your brand terms (or just your brand name, in my case "avinash kaushik") and it churns through that social web data to provide you with something awesome…. a tag cloud!

[Click on the image for a higher resolution version, along with a peek at other metrics.]

Why is this cool?

Mikko and his team have taken 1,000 words from the English language that are connected to emotion. Good emotion, bad emotion, ugly emotion.

They look at their social web data and in that they look at the words around your brand mention and finally identify the emotional words people are using in context of… you!

The tag cloud above shows the emotional words use around mentions of me for a month's worth of time.

Without having to read all the text I can at a glance now get a really good understanding of the tone and texture of activity around my presence. More importantly it does not take all that long to figure out what emotions should be there but aren't.

A very simple, effective and elegant solution to a complicated problem.

Oh and guess that happens when you click on one of the words in the tag cloud?

You are right… it takes you directly to the text from all the data that StatsIt has collected!

By clicking on the words you are essentially segmenting your data and drilling down to the text (tweets, blog posts) where you can learn more about what the person was saying when they express, say, "great" as an emotion. :)

Effective "sentiment analysis" baby!

Why can't we be this simple in other places?

We are always seeking complexity. Here are two ideas that popped into my head from the StatsIt's approach that might apply in other places.

We collect lots of open text from our online surveys right?

Rather than finding the perfect answer to what's expressed in the text, and of course getting it wrong, why don't the vendors show us a emotional tag cloud?

Can there be a better / easier / faster way to allow us to make sense of all that text, leverage as a segmentation tool and find insights every day?

Vendors! Come on!!

Another idea.

Reviews are important. Most ecommerce sites have them.

But why is it that we only see "quantitative" analysis of the reviews? 5 stars. 3.2 moons. 61% rotten tomatoes. Etc etc.

The richness of the review is only partly in the quantitative analysis of the rating. The real sweet nectar is in the words people write in reviews.

I recently gave a talk at eBay. So let's use that as an example.

You get quick quant rating on eBay that you typically use. But perhaps the real gold is here….

This seller, me, is 100% positively rated.

Now let's say that you want to buy a Sony digital camera that is listed by both me and Emer. We both have 100% positive ratings for our 60 or so prior eBay auctions.

How can you best decide if you should buy from me or Emer? You can't possibly read 120 reviews, or even scan them quickly.

Now would your life be much much easier if eBay choose to provide an "emotional tag cloud" for both Emer and Avinash?

Very quickly you could see that while we both have same quant ratings it turns out that my emotional cloud shows a neutral to positive feelings expressed while Emer's is outrageously positive.

Now is it easier to decide who to buy from?

As our dear friend Sarah Palin would say: You betcha!

So why does eBay not provide this simple emotional tag cloud?

Or for that matter Trip Advisor or Amazon or any site that hosts reviews and ratings?

Simplicity rocks. Especially when it's actionable.

Quick, Efficient, Effective Mobile Analytics: Percent Mobile.

It is always a really good idea in web analytics to understand how data is captured (case in point the delightful blog post on Competitive Intelligence data capture).

No where is this more true than when it comes to mobile analytics.

There are many methods of collecting data depending on the platform you are on, and if Steve Jobs gets upset he can totally shut you down with a mere update of his TOS! :)

I am not going to cover all that here today. For those of you who already have my second book Web Analytics 2.0 please jump to Page 250 to learn all about data collection options, platform limitations, challenges with campaign analysis and finally reports and KPI's you should measure for mobile.

In this blog post I want to share a lightweight wonderful mobile analytics platform called Percent Mobile.

Now most web analytics tools, like Google Analytics and WebTrends and others, will capture and report data for javascript enabled smart phones (like the iPhone, Android and some Nokia phones). Honestly that is all the traffic that is of commercial value, so even if you miss the rest it is not the hugest of deals.

But all these "big boys" have simply "added on" mobile analytics to their tools. The result is that they suffer from both a lack of imagination and, this is important, truly great databases when it comes to devices and carriers and other unique mobile information.

Not Percent Mobile.

They have two incredible benefits:

Here is what the resulting data looks like…

[Please click on the above image for a higher resolution version.]

No hunting and pecking to find the data, like you would in Google Analytics or Site Catalyst or CoreMetrics. A quick at a glance view of how much traffic is mobile, key stats about the devices, the devices themselves (go iPad!!), vendors and operating systems.

If you compare this to your web analytics tool you'll notice almost immediately how much better this data is compared to what the "big boys" are reporting.

Click on the image above and you'll see a bit more clearly other really sweet metrics. % of mobile devices accessing your site via WiFi. Phones with touch screens and full keyboards etc.

[Can you imagine how cool it would be to segment your mobile traffic for full keyboard phone vs none and see which convert better. Or does access via WiFi mean more content consumption than via 3G? Etc. So cool.]

That is not all… if you scroll a bit more you can get a country map view, the networks used to access your site (AT&T still #1 for me!) and countries etc.

Of course it would be hard for me to like any tool that does not allow segmentation. :) You simply drag and drop on to the box on top..

And what would an analytics tool be without the normal search, referrer and all that data we have so come to love (and hate!).

I particularly like the "Activity Types" box at the bottom left, I don't know why web analytics tools don't categorize referrers by default.

I am also surprised at the long tail of referrers. Yes Google is big but there are 91 other referrers for this segment. More mobile SEO!

Why is this cool?

It might seem odd that I would like a tool that would give me similar data that I can get out of WebTrends or Omniture or Xiti or whatever.

The first reason is that, as mentioned above, the data is actually much better because of the databases that power Percent Mobile.

The other thing is that getting this data causes less pain than pulling my two front teeth.

Finally I so do like supporting pretty tools, especially if they have good data!

The one thing Percent Mobile lacks is some way of measuring any outcomes. I can certainly dig to my "conversion pages" but it would be great if they just let me just input them into the tool and then they could measure outcomes for me (even if it is like the Goals process in GA).

But if you want a light weight easy to use free mobile analytics tool just throw Percent Mobile on your site and have fun. Go to www.percentmobile.com , click Sign Up (top right) and use the Invitation Code "Avinash" (no quotes).

Mobile rocks no?

Summary Of Our Lovely "Let's Keep Learning" Cruise.

It is important to point out that I am not affiliated in any way with any of these tools / companies. I am also not recommending overtly or covertly that you buy / use them. That is totally your call.

Of course I would not personally use them or write about them if I did not thing they had value. :)

My sincere hope is that you'll internalize:

I hope you got that, more than names of interesting tools.

I cannot tell you how much fun it is to step outside the world of Omniture and Google Analytics and other traditional web analytics tools. It stretches your mind and sometimes you look at these new techniques and data and you notice you are smiling and feel so happy.

Try it, and have fun.

[In case you were curious at the moment I am playing with these incredibly cool tools: PostRank, Next Stage Sentiment Analysis, SEO Effect, and Colligent. Each in its own way does something magical and quite unlike anyone else.]

Ok your turn now.

What do you think of the work that Tynt, Analyze Words, StatsIt & Percent Mobile do? Have you tried any of 'em? What obvious flaws did I overlook? Are there other tools you are using in the Viral, Social, Sentiment, Mobile space that you really love? If so would you please post them in comments?

Please share your feedback / critique / ideas.

Thanks.

PS:
Couple other related posts you might find interesting:

• Multiplicity: Succeed Awesomely At Web Analytics 2.0!
• Actively Avoid Insights: 4 Useful KPI Measurement Techniques
• Web Analytics Tool Selection: Three Questions to ask Yourself
• Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test
• Web Analytics Career Advice: Play In The Real World!

Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions is a post from: Occam's Razor by Avinash Kaushik