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 blog post we are going to bring the sexyback. I am going to attempt to significantly simply your life by recommending the critical few metrics you should use to analyze performance of your digital marketing campaigns and website. You'll be able to quickly go from "omg what can I do!" to "omg what am I going to do with all the money and fame I'm earning!"

The approach I'm going to use is to 1. Use my Acquisition, Behavior and Outcomes framework to ensure an end-to-end view of important activity and 2. Recommend metrics / KPIs you can use based on the size of your company.

Each recommendation comes with hints on what analysis to perform once you have the data, and what changes you could make to your campaigns, content and overall digital strategy. [A summary in pictorial format is at the end of this post.]

Excited? Let's do this!

Best Metrics / KPIs for Small Business Websites

Small business websites are a very fragile ecosystem. People working hard to do the best they can on the smallest possible budgets. But not to worry. They have to start with just four simple metrics to start rocking!

Acquisition:

Clicks? Visits? Backlinks? Impressions? No. We have something magnificent.

Cost Per Acquisition.

Obsess about this metric. You have very little money. You need to know, obsessively, what you get for it. This metric delivers that insight. Oh, and everything has a CPA (not just your paid search or display/banner ads). If you are doing SEO then you are likely paying for someone. That's the cost.

Kill things that don't have an optimum CPA. Invest more in ones that do. Simple enough, right?

Tip: Remember this is just cost, not profit. If your product costs you $15 to make then, in the above scenario, you are shipping a crisp $5 bill along with every Social Media order!

Where is it? Most likely in Excel. For Search it is in your Google Analytics or Omniture Site Catalyst reports. But for most other programs (Affiliate, Email, Social, Display) your Cost is likely sitting outside your web analytics tool. So extract the # of conversions, import into Excel, add a column for Cost, do the math, sing or weep (based on what the data says!:)).

If you are paying someone to do web analytics and this metric is not on top of the dashboard they've created for you, it might be time to say sayonara to them.

Behavior:

Page Views? Time on Site? No. You can do so much better!

Bounce Rate.

I continue to be a believer in trying to prompt love at first sight. Okay, okay, I'll settle for delivering relevance. :) Bounce Rate helps you identify campaigns where you might be targeting wrong people (who then come to your site and leave right away) or sending relevant traffic to irrelevant (and often flash-filled hideous) landing pages.

Bounce rate helps you find campaigns and landing pages that need to be killed / improved. Everyday.

Where is it? Standard metric in every web analytics tool worth anything. Look at your All Traffic Sources report and your Landing Pages report.

Checkout Abandonment Rate.

I find the fastest way to make money is to take it from the people who have already decided to give it to you. Obsess about checkout abandonment rate (the percentage of people who click Start Checkout to those who complete that process).

Focus on checkout steps with the highest abandonment. Tweak like crazy. A/B & Multivariate tests are a good option. But you are a small business… so just take away as many fields as you can, play with where to show shipping cost (I vote for way up front), reduce the number of checkout steps if you can, ask for account creation at the end of the process rather than at the start. Try, test, measure, be rich.

Where is it? In Excel. Or if you use Google Analytics: In Paditrack for free. (Google Analytics' native funnels are pretty sub optimal, ignore that entire feature.) For other tools: In KissMetrics. Create a funnel just for the checkout process (from clicking Start Checkout to Thanks for your Order) and both these tools will give you the metric automatically. They also allow you to segment the data! Make love to it.

[Bonus: What is abandonment rate?]

Outcomes:

My favorite Economic Value? No. As a small business I recommend…

Macro Conversion Rate.

You are a small business. Obsess about conversion rates, and everything connected to improving them. What products are people buying? Every single day (okay week) look at the All Traffic Sources report and seek out the Conversion Rate metric. Ruthlessly punish sources that are not working well and reward the pretty babies. Be they Earned, Owned and Paid media – oh and have a marketing strategy that has each of those elements or as a small business owner you are not going to win a lot.

I love creating an advanced segment with just the people who buy twice the average order size. I call them the Whales. Look at sources, locations, product bundles purchased, keywords and campaigns and all that to learn where/how you can find more Whales.

Where is it? Standard metric in all analytics tools. Remember to look at both the rate and the raw number of conversions for context. People make silly decisions when they don't do that.

That's it!

You are a small sized business and these four simple key performance indicators will literally rock your world as soon as you start measuring them. Cost Per Acquisition. Bounce Rate. Checkout Abandonment Rate. Macro Conversion Rate. Don't look at any other metric until you feel you've mastered them.

Tip: If you've hired the right analytics talent/consultant to help you, they'll be measuring these fabulous four.

Best Metrics / KPIs for Medium Sized Business Websites

What if you are a medium sized business? What key performance indicators are optimal for you?

First, you are going to measure the KPIs mentioned above. But because you are running a bigger and more complex business you'll also measure…

Acquisition:

CPA

+ Click-through Rate

While CPA is a macro metric about your campaigns' bottom-line performance, Click-thru Rate (CTR) is a deeper dive into analyzing the creativity and relevance of your affiliate deals / search listing / blinky banner ads.

In the context of Search (Paid or Organic), the text in your ads, the number at which your listing is ranked, the match between the user query and your ad's intent all help you receive a higher CTR. And if someone comes to your site (and does not bounce!) then you get an opportunity to convince them of your product or service's glory.

Small tweaks to the subject line of your email campaigns can have dramatic improvement in CTR. Recency and Frequency capping of your display remarketing campaigns can have a huge impact. Changing demographic targeting options in your Facebook ads can work wonders. Etc., etc., etc.

Put another way… CTR helps you understand if you showed up at the right place for your first date. Are you dressed okay. And if you are smiling the right smile. Helpful to know, right?

Where is it?

Everywhere. Start at a campaign level. Drill down to individual creatives. Kill badness. Promote goodness. Rinse. Repeat.

Behavior:

Bounce Rate

Checkout Abandonment Rate

+ Page Depth

A very tiny percentage of visitors to your site will see more than a couple pages. That's the internet for you. As you improve the user experience, information architecture and relevancy of content on your site, it is important to keep an eye not on the rather useless metric of Average Page Views per Visit or Average Time on Site but rather on the distribution of page depth. Here's how that picture might look like (from a post I wrote in July 2006!)…

From the deep detail reported by your web analytics tool you can choose to aggregate into buckets you most care about (like mine above). Categorizing the visits into Abandoners, Flirts, Browsers, One-off-Wonders, Loyalists will dramatically change your view of content consumption. Over time, as you move to deeper consumption, you'll see direct business rewards.

The above image emphasizes a sale/conversion at the end, but even if you are a content-only website improving Page depth helps you because more pages equal (at the very minimum) more ad impressions!

Where is it? The final table will be in Excel. If you use Google Analytics the data you need is here: Audience > Behavior > Engagement > Page Depth tab. If you use WebTrends, Yahoo! Analytics, Coremetrics please click around to find the data. They all have it.

+ Loyalty (Count of Visits)

If Page Depth helps you optimize for a single session experience, Loyalty helps you optimize pan session behavior. Put another way… how good are you at getting the same person to visit your website multiple times? For ecommerce or non-ecommerce websites, loyalty can mean the difference between life of survival and raking in profits like crazy.

First set a goal for the % of site Visits you would like for people who've visited more than x times. [Set a goal for x too. :)] For ecommerce websites use your Days to Conversion report (more on this metric below) to set your goal. For content sites perhaps mirror your content update schedule. If you are the New York Times and you update the website 24 times a day then should the average person be visiting the site at least 90 times per month?

Your BFF, as always, is analysis and not just reporting the metric. Create this simple segment in five seconds…

Apply to your keywords and campaigns and referring sources reports and identify which sources drive loyal traffic. Apply it to your content reports and figure out which content drives Loyalty (Sports? Op Ed? International? Cat Stories?).

Where is it? In every web analytics tool on the planet. If you use Google Analytics the data you need is here: Audience > Behavior > Frequency & Recency.

Outcomes:

Macro Conversion Rate.

+ Micro Conversion Rate

Pick your favorite benchmark and you'll notice that less than 2% of visitors convert. Focusing on just the Macro Conversion Rate means you don't care if you received any business value from the 98% that did not convert. I refuse to accept that uber-lameness.

Identify your Micro Conversions (/Goals) and obsess about the long and short term business value they deliver. You'll quickly realize the Economic Value they create for you is often far greater than the Revenue your Macro Conversion reports! And optimizing for that will ensure you win HUGE.

Where is it? In Google Analytics it is here: Conversions > Goals. Even if you are a content site the data is there. Details in the Goal URLs report. Setting up goals takes two minutes, setting goal values might take you a week (see measurement strategies here). If you use other tools, please check with your vendor.

+ Per Visit Goal Value

This Key Performance Indicator 1. helps you move beyond the obsession of focusing on the 2% (because it forces you to focus on Every Visit!) and 2. encourages you to create a business that uses the web to deliver multiple outcomes to your visitors.

Every visitor will not convert, but every visitor will, hopefully, deliver some Economic Value. Looking at this metric helps you identify Goals that contribute higher value, and and understanding of simple things like where you should focus on. If Twitter delivers 87 cents of Per Visit Goal Value and Google delivers 97 cents then perhaps I want to keep focusing on my SEO strategies rather than following the advice of the Social Media Guru who's just informed me Search is dead.

Where is it? In pretty much every single report in every single web analytics tool. Click on the Goals tab.

That's it!

For a medium sized business we ended up with nine metrics. Seems about right if you are making more than five million dollars of economic value. They key difference from websites that are in the small business category is that we are going to shoot for multiple conversions, deeper site engagement and better analysis of acquisition efficiency.

Time now to deal with the big boys and girls… large websites!

Best Metrics / KPIs for Large Sized Business Websites

Acquisition:

CPA

Click-through Rate

+ % New Visits

My choice of this metric perhaps betrays my refusal to rest on my laurels. There are clearly a finite number of people in the world relevant for any business. But staying hungry and staying foolish is a popular mantra for me. I use this metric to constantly calibrate my acquisition strategy to understand which inbound marketing efforts are bringing new "impression virgins" to the business.

If you look at your Earned, Owned and Paid media then this metric is especially important for your Paid media efforts. Except for your re-targeting / behavior targeting campaigns, you want your paid search, display, affiliate, and social efforts to bring new visitors to your franchise.

Where is it? It's like air, everywhere! Don't forget to segment for optimal analysis.

Behavior:

Bounce Rate

Checkout Abandonment Rate

Page Depth

Loyalty (Count of Visits)

+ Events / Visit

Every awesome large website delivers complex experiences (videos, demos, dynamic slideshows, configurators + + +) via sophisticated technologies (Flash, AJAX, Gadgets + + +). Almost all of the time we leave measuring their effectiveness on faith (or the HiPPO). I love event tracking because it helps us measure these often astonishingly, expensive initiatives.

Of 110,842 visits to the site, 9,054 interacted with your delightful experiences and each of those visits had 2.24 Events per Visit. Is that good? Bad? Could be better? Are these 2.24 interactions delivering higher economic value to your business?

In the above case the answer was a big NO. In your your case you'll decide based on your strategy and goals. At the end of the analysis you'll make significantly smarter decisions about your content (especially because the Analysis Ninja that you are, you'll triangulate performance of this metric with first, Page Depth and, second, Loyalty).

Where is it? Most web analytics tools do some type of event tracking. Please check with your vendor (it might not be called event tracking in their lingo, just describe my first paragraph above). In Google Analytics the data is here: Content > Events.

Outcomes:

Macro Conversion Rate.

Micro Conversion Rate

Per Visit Goal Value

+ Days to Conversion [or Time Lag for Content sites]

Another pan session metric I adore.

Life, no matter how hot you are, is not a series of one night stands. Yet because of how they analyze the data most companies end up optimizing their web marketing campaigns for one night stands. Come here and convert NOW! If yes: Oh, I love you. If no: Kill the campaign!

That approach is not just short-sighted; it is an insult to your visitors. Convert them at a pace they are most comfortable with. This metric helps you understand how quickly or slowly your visitors convert. You can, at the very minimum, change your campaign messaging and come hither calls to action and adjust your landing pages. If the Days to Conversion are much longer, then create a robust (slow dance) micro conversion strategy.

If you have a non-ecommerce website then there is something delightful for you in the Google Analytics Multi-Channel Funnel reports. Checkout the Time Lag report . It is showing you exactly the same data as the Days to Transaction for Ecommerce sites. The metric you see immediately above is called Conversions. It is essentially your Goals (/micro conversions).

Optimize your "hello, nice to meet you, what would you like, here is what I have to offer, why don't you check with your spouse, come back and check it out again, multiple times, I'm still here, you ready to convert / deliver economic value, here's how… " process.

Where is it? Days to Conversion is in the Ecommerce section of your web analytics reports. It is a standard report. (Don't forget to segment your sources. Deep insights await.) Time Lag may or may not be a standard report in your tool. Please check with your vendor. In Google Analytics it is a standard report here: Conversions > Multi-Channel Funnels > Time Lag.

+ % Assisted Conversions

This is the newest metric I've made standard for all my clients / partners / BFFs. And it is a sweetie.

Assisted Conversions builds on the above mental model. It takes a while for a majority of your visitors to convert (macro and micro conversions), so why does almost all of web analytics focus on single channel analysis and optimizing that single channel in a silo? Just because the Affiliate click was the last one before conversion should it be optimized for that conversion? Especially if the Visitor originally came via Facebook (or Google or whatever)?

How many of your conversions had more than one ad / media / marketing touch prior to converting? Really smart Analysts at really successful companies understand that…

…and then use that data to optimize the portfolio of channels rather than individual channels for the company.

Even if you don't do portfolio optimization (and desperately hope you do) you can easily see how the above data will cause you to execute a different marketing optimization and expectation strategy for Email (1.18 Assist / Last Interaction rate) vs. Organic Search (0.61).

I am being modest when I say that this metric and subsequent analysis will have a fantastic impact on your company.

Where is it? % Assist Conversions may or may not be in your web analytics tool. Please check with your vendor. In Google Analytics you'll find it here: Conversions > Multi-Channel Funnels > Assisted Conversions.

And we are done!

For large businesses we've identified 13 key metrics that would give a robust end-to-end view of business performance. The key difference vs. medium sized businesses is that we are really, really, really focused on pan-session (multiple visits) behavior. Put another way, we really care about people here and not just a single visit.

Here is a summary of the metrics I am recommending in this post…

I hope the picture above will quickly help diagnose where current gaps in your measurement strategy might be.

Additionally if you are a small business you'll know what else to measure when you start to become medium sized and if/when you cross that threshold you'll know the metrics that come with your large business status. :)

You'll notice that I'm not focusing on KPIs like AdSense Ads CTR or Page Load Time or Actions per Social Visit or Search Exits (I love this metric!) or Content Distribution vs. Content Consumption Rate or Conversation Rate (in case of a content site) etc. That's simply because these KPIs tend to be unique to the type of business you are running. My strategy above was to focus on just the KPIs that would be applicable across all types of businesses.

That brings me to a very important point.

While it is my hope that you'll find my recommendations above relevant and yummy… the most optimal way to identify that best key performance indicators for your company will come using the process and structure outlined in the Digital Marketing & Measurement Model.

I'll end with the thought I started this post with… we have more data than God wants anyone to have. But web analytics does not have to be scary or impenetrable. Use the roadmap above, focus on all three elements (acquisition, behavior, outcomes) and I promise you'll soon be on your way to being as happy as God wants everyone to be.

I wish you all the best!

Okay as always it's your turn now.

Does your business use the above recommended metrics / key performance indicators? Do you have an absolute favorite metric that's not mentioned above? Which metric above do you find most useful? Which one most useless? What is your strategy for identifying the most relevant metrics?

Please share your suggestions, critique, and helpful best practices via comments.

Thank you.

PS:
Couple other posts on metrics / KPIs you might find interesting:

• Web Metrics Demystified
• Your Web Metrics: Super Lame or Super Awesome?
• Kill Useless Web Metrics: Apply The "Three Layers Of So What" Test

Best Web Metrics / KPIs for a Small, Medium or Large Sized Business 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

I am not kidding. Direct traffic contains visitors that proactively seek you out, everyone else you have to "beg" to show up on your site!

Yet this question seems to bedevil a lot of people:

What the heck is Direct Traffic?

As if that was not sad enough, even people who do know what the definition of Direct traffic is rarely focus on it or work hard to tease out the opportunity that exists in Direct traffic.

I love analyzing Direct traffic because it contains a valuable set of visitors who deserve more love than we currently give them.

I want you to be just as excited.

So let's look at the definition, to make sure we understand, at least on paper, what this traffic is supposed to be. We'll also look at the challenges that exist in ensuring we are looking at the real unpolluted Direct traffic.

Definition.

Here is the simplest and cleanest definition:

Direct traffic contains all Visits to your website where in people arrived at your site directly (by typing the url) or via a bookmark.

Direct traffic is hence not traffic that results from people clicking on links on other sites to your site (that's referring urls traffic), it is not traffic that comes to your site by clicking on ads (that's Other in Google Analytics or Campaigns in other tools), it is not people who come from search engines (that is Search or Organic or PPC traffic).

The reason Direct traffic is a beloved of mine is that it represents (checkout the sweet contextual – red and green – numbers above):

1. People who are your existing customers / past purchasers, they'll type url and come to the site or via bookmarks.

2. People familiar with your brand. They need a solution and your name pops up into their head and they type.

3. People driven by word of mouth. Someone recommends your business / solution to someone else and boom they show up at the site. Uninvited, but we love them!

4. People driven by your offline campaigns. Saw an ad on TV, heard one on radio, saw a billboard and were motivated enough to typed the url and show up.

[If you were really smart you would use campaign tagged vanity url so you can segment them!]

5. [Remember the part below, but.] Free, non-campaign, traffic.

In a nutshell these are people show up without invitation (email, display, social campaigns) or they are people who already know you. There is an extra motivation connected to their visit which causes them to type your url of find the bookmark they made.

That little bit of extra intent, when compared to other visitor segments, is the reason that conversion numbers  (on ecommerce or non-ecommerce sites) for clean direct traffic usually look like these. . . .

The only goal that is red is supposed to be red (fewer registrations from people who already know you is not unusual right?).

Now you'll agree when I say your job is to be extra sweet to them?

Segment them in your data, the delightful numbers you see in your KPI's will show you why.

So if Direct traffic is so important and often the metrics show very positive results then why don't we all obsess about it a lot more?

The Problem: Unfortunately. . . You!

Most website tag and campaign tracking implementations are poor (to put it charitably). This is always disappointing but it is particularly harmful to Direct traffic.

You see if you don't implement your links properly the person shows up to your site without any tracking parameters and thus fail to help your web analytics tool to put that visitor in the right source bucket.

Typically Direct traffic also contains all the Visits that originated from improperly tagged campaigns, untagged campaigns and problems with your JavaScript tag. I am sitting in a puddle of tears as I write this, that is how often Direct traffic is polluted and that is how painful (and profoundly sad) this is.

Here is a simple example:

You are the Acquisition manager for a company called Omniture.

You have purchased banner ads in various Android applications using AdMob to target high value analytics decision makers. You goal is to get people to buy your Discover data warehouse product.

You are using Google Analytics to track all you display campaigns.

The proper way to link your banner to your Discover2 website is:

omniture.com/discover2awesoemness/?utm_source=nytimes_mobile_homepage&utm_medium=masthead_banner
&utm_content=188_92&utm_campaign=affluent_readers

You actually use this url:

omniture.com/discover2awesoemness/

Guess where this category will be categorized?

Direct.

:(

You see mobile applications don't send a referrer and it will look like all of a sudden you got very high converting Direct traffic.

With a simple stone you've killed two beautiful birds:

> The direct traffic is polluted and you'll never be able to focus on finding real insights for actual valuable lovely people who are seeking you out directly.

> Google Analytics will show that your mobile campaigns with AdMob stink. Of course that's not true, but you'll have no way of knowing that.

Not a great situation right?

Oh and what do you think is happening to the trackability of all your shortened urls in Social Media that you are not tagging with campaign parameters? 78% of people consume Facebook and Twitter content via applications and unless you use campaign parameters all that traffic is sitting in Direct. So sad.

Result?

Direct traffic is a fantastic segment to analyze because it contains desirable Visitors and yet because it is often polluted (due to our own inability to implement web analytics tools correctly).

Let's aim to fix this because it is too important not to.

. Why Does Direct Traffic Get Polluted / Mistakes You Should Avoid:

The good and the bad are all mixed in, and it is your job to ensure that that is not happening inside your web analytics data.

Here are the main reasons traffic that should not be Direct ends up there, try, please please pretty please, to ensure this is not happening to you:

1. Missing web analytics tag from landing pages

Perhaps the most common source of traffic being miscategorized.

Your urls are all tagged correctly with campaign parameters, or maybe people are just coming to from sites that link to you.

They land on a page that is missing the web analytics tag.

They click on a link on the landing page to go deeper into the site.

Guess what's the traffic source for this traffic?

Direct.

So sad.

You worked so hard to get that referring link / execute the campaign. Now not only do you not get rewarded for that work. you actually messed up your direct traffic.

Don't be that person.

Go purchase WASP from iPerceptions or an account with ObservePoint and address the cheapest problem to fix in Web Analytics. If you are a little bit tech savvy then go get REL Software's Web Link Validator, it's pretty good.

2. Untagged campaigns (search, email, display, social media etc)

This is perhaps the second biggest reason data in web analytics ends up in wrong places.

In case of untagged emails (to people who are using Outlook, Thunderbird etc) and mobile ads and mobile application links (think of all those Twitter / Facebook apps) and Adobe AIR applications (like my beloved NY Times Reader) and in rare cases where people are clicking on links in PDF documents etc, the data ends up in Direct (no referrer).

In case of untagged display campaigns usually there is a referrer so it will end up there rather than in Campaigns were you want it.

In case of untagged paid search campaigns it usually ends up in organic search data.

On behalf of your company you are spending precious budget on acquisition, not ensuring your campaigns are tagged properly is near criminal behavior. Don't be that person. Tag.

Oh one more thing.. if you are practicing bigamy and have two tools, say Google Analytics and Adobe's Site Catalyst you better remember to have campaign parameters for both GA and SC because they use different parameters for campaigns. Whichever one you forget to tag for will show your campaign traffic as Direct!

If you want to track the campaign in the first part of this post with both Google Analytics AND Omniture the url would look like this, as an example:

omniture.com/discover2awesoemness/?utm_source=nytimes_mobile_homepage&utm_medium=masthead_banner
&utm_content=188_92&utm_campaign=affluent_readers
&s_scid=TC-10013-3159426121-e-361634984

See both set's of campaign parameters? You don't do that one of them is wrong. Not so shiny to practice bigamy is it?

3. Improperly tagged campaign parameters / site tags

This one is probably not that hard to understand.

Instead of utm_source and s_scid you use utm-source or s-scid and you are. how to say this politely. screwed.

In both cases your two (or one) web analytics tool will most likely ignore the improper parameters and throw the traffic where it does not belong and mess up your ROI analysis.

Auditing your campaign tracking before they go live is a great idea. Do this at the very minimum for the 20% of the campaign that are responsible for 80% of your traffic / revenue.

If you use Google Analytics grab the Google Analytics Tracking Code Debugger. See this blog post for troubleshooting guide & detailed instructions: Debug Your Tracking Code.

Omniture, WebTrends, CoreMetrics, Unica all come with such debuggers. I can't link to them as location are not public (or you need to pay first!). Please reach out to your Account Managers to get access, just in case you don't already have them. Debug!

[Update:]

Ben Gaines from Adobe/Omniture was kind enough to share that a free debugger is available to Omniture clients. Log into the Knowledge Base and look for KB ID 534 and you are set! But here's something cooler. The debugger is actually a bookmarklet and here it is:

Omniture Analytics Tag Debugger

Create a bookmark in your browser. Copy the code in the above text file. Click edit on your bookmark. Paste the code where the Link is. Go to any page on your site with Site Catalyst. Click on the bookmarklet and bathe in bugs! :)

[/Update]

4. Improperly coded redirects / vanity urls etc

Another silly issue that causes problems with direct traffic.

When you get a email or a mobile campaign, and keep a close eye on the url window, you'll notice the click goes to your campaign solution provider and is then redirected to your site.

That's one example of a redirect. We use redirects / vanity urls in our multi-channel campaigns, in our display or search campaigns or even just for the heck of it.

That is not an issue.

Make sure they are permanent, 301, redirects. The delicious type of redirects that dutifully pass the referrer string to the landing page telling your web analytics provider where the person originally came from.

You use temporary, 302, redirects and the referrer never gets passed on. Depending on how the redirect server is configured either the click looks like it came from the redirect server or with a blank referrer (direct!).

5. Really heavy tag at the bottom of the page (switch to Async!)

A smaller problem for normal sites with just text and some images, but a huge problem for fat ugly flash heavy websites (especially the, still annoying, ones with flash intros).

It takes such a long time to load the flash file itself that person might have clicked skip intro or some other link on the page well before the fat flash file loads or before the web analytics JavaScript tag loads.

The data tracking behavior is exactly as if issue #1 above existed, no tracking code on the landing page.

I would recommend putting the tag in the header, except that is the selfish lover strategy and no one likes a selfish lover.

Make your pages as lean as you can, especially campaign landing pages. Keep the tag in the footer, you don't want the page to hang because of issues at your analytics provider.

If you use Google Analytics you are in a little bit of luck. Switch to the magical GA Async Code. It goes in the header, captures data without ever hampering your page loading and as if that were not enough is leaner and meaner.

One of these days all web analytics vendors will migrate to the Asynchronous making the Internet a faster place to live in.

6. Corner cases causing traffic to end up in Direct.

Here are some reasons that don't happen a lot but you should be aware of:

~ Links encoded in JavaScript clicked in some browsers will send a null referrer (i.e put traffic into Direct). Often times you can't help his because you don't have control over people linking to you can do whatever they want. But do check that your campaigns in Facebook or Yahoo or other places are not using this method.

~ [Update, via Alec Cochrane:] https to http and vice a versa also won't have referrers passed due to (good) security reasons. So if possible make sure you put campaign tracking codes in links from https pages to ensure those visits don't end up in direct. For this you would have to know this is happening and then be able to find the person who will oblige you by changing the link. Tough to do but when you can do it!

~ Some smart folks will make changes to their browser configurations that cause referrers not to be passed. Happens in a tiny minority of cases.

~ This might impact only some tools but check with your vendor how this scenario is credited. . .

First visit: From a campaign (search, referring url, social, display, whatever).

Second visit: Direct to the site.

If you are using Google Analytics then that second visit will still be "credited" to the campaign (non-direct) because the _utmz cookie will be present in the browser.

In your web analytics tool that might not be the cause. Please check with your vendor.

~ Multi-domain / sub-domain "unique" web analytics implementations across many websites. With any tool these are really hard to do right, and really easy to do wrong. If you have one of these polka dotted puppies then get your expensive Consultant to triple check the code and cookie customizations with a special eye on Direct traffic.

~ [Update, via Pritesh Patel:] You could also have polluted Direct traffic if your entire company (hopefully of a good size!) has their home pages in browsers set as your company's website. This will clearly skew your direct traffic (and your bounce rates, after all they don't actually care about your site :)). You can easily use your tools admin settings to filter out all your internal IP's which would solve this issue.

~ [Update, via Deric Loh:] #1. iFrame: Whenever someone links to your site via an iFrame it is possible for them to code it in such a way that it does not pass referral data and the visit will look like Direct. We can do much about this but in case there are sources where you can avoid this issue or get it done properly then it is worth the effort.

#2. Company Gateways: Some companies might have a security gateway which has been set up to strip the referrers from request calls. This of course is not great for your clean Direct traffic. It won't happen a lot of times and then limited to just one source. But it is something you certainly should be aware of as a cause.

That's it. Six simple problems for you to take care of. : )

All kidding aside know that you'll accomplish a major clean-up if you address the first three issues and then YMMV.

Also know that it is totally worth it to get this data clean, the orange line below is Direct traffic conversion rate and the blue is overall conversion rate. . . .



Not bad eh?

You want to know who these people are.

You want to know what you can learn from analyzing their geographic locations.

You want to know their Visitor Loyalty and Visitor Recency profiles.

You want to know what content they are consuming.

You want to know what products they are purchasing.

You want to know what the differences between their behavior on your site is from your other campaign traffic.

You want to know if any of the spikes are correlated to you offline campaigns or catalogs you have sent out (and then establish causality between offline campaign calls to action and behavior by these people).

You want to establish the value of these visitors and then pay special attention to them if they are of value to you.

For the New York Times website I'll always be Direct traffic. I use a bookmark, I go to the site at least once a day, I click on Ads (I have nytimes.com on my adblock white-list!), I subscribe to the Times Reader, I am a big evangelist of their brand.

But only if they care to ensure their Direct traffic is clean, and then analyze that traffic will they ever know that.

If they are like every other company that obsesses with PPC and Yahoo! Banners and Facebook Display ads and Email campaigns etc etc then they'll never know that some of their best customers they should make happy are right under their nose.

I know that the NY Times web analysis team is super sharp. Are you?

In the small chance that you were not before I hope I have convinced you to truly bring the "make love" type of passion to this valuable, and usually large, segment of traffic to your site.

Good luck!

UPDATE: A clarification specific to Google Analytics:

Every tool uses delightful sets of attribution rules when it comes to assigning visits or conversion to campaigns. To share with you how Google Analytics will attribute these things here are a couple of scenarios….

Scenario 1:

Visit 1: Came from SEO click on keyword "ASOS Fashion"
A few days later…
Visit 2: Came direct to the website

In Google Analytics you will see this in your reports:

Keyword "ASOS Fashion": Visits: 2
Direct: Visits: 0

In effect Google Analytics will "understate" direct visits. It is difficult to have a perfect scenario here, some people will vehemently make the case that GA is doing it right and that the Visit did come via the organic click first so second visit should be attributed to it. I am personally in the camp that that is sub-optimal and that because we can't read too much into anything (we just don't know what is influencing what) we should report keyword visits = 1 and direct visits =1. But at least you know what GA is reporting.

Scenario 2:

Visit 1: Direct to the site.
Visit 2: Came from Affiliate Campaign click.
Visit 3: Came direct to the site.

In GA it will show:

Direct: Visits: 1
Affiliate Campaign: Visits: 2

See how that works? Regardless of how you think it should be you now know how it is. : ) Make sure you keep this in mind as you analyze the GA reports.

[My heartfelt thanks to David Williams for his help with a test for above cases.]

This stuff is complicated right? Remember none of this takes anything away from the importance of direct traffic or how hard you have to work to make sure your reporting of it is clean (tips above) or that it is worth focusing on. Whatever tool you have, do all of the above!

Ok your turn now.

Do you obsess about Direct traffic just as much as I do? What insights have you found from you analysis? What methods have you deployed to ensure that your Direct traffic segment is as clean as possible? Do you also look at any "Direct" traffic to really long complicated url's on your site and instantly doubt that could be direct?

Please share your experience / feedback / tips / critique via comments.

Thanks.

PS:
Couple other related posts you might find interesting:

• Excellent Analytics Tip: Unsuspected Correlations Are Sweet!
• Excellent Analytics Tip: The Adorable Site Abandonment Rate Metric
• Excellent Analytics Tip: Measure the Real Conversion Rate & "Opportunity Pie"?
• Seven Steps to Creating a Data Driven Decision Making Culture
• Analyze This: Five Rules For Awesome Impromptu Web Analysis
• Analysis Ninjas: Leverage Custom Reports For Better Insights!

Excellent Analytics Tip #18: Make Love To Your Direct Traffic is a post from: Occam's Razor by Avinash Kaushik

]]> http://www.kaushik.net/avinash/excellent-web-analytics-tip-analyze-direct-traffic/feed/ 66 http://www.kaushik.net/avinash/actionable-tips-web-data-metrics-analysis/ http://www.kaushik.net/avinash/actionable-tips-web-data-metrics-analysis/#comments Mon, 26 Jul 2010 09:02:49 +0000 Avinash Kaushik http://www.kaushik.net/avinash/?p=2935 We lovingly craft reports every day. We try to make sense of what they are saying. When we hear nothing we try to bludgeon them, hoping for the best. My hope in this post is to share some simple tips with you that might make your reports and analysis speak to you a bit more. [...]

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

]]> We lovingly craft reports every day. We try to make sense of what they are saying. When we hear nothing we try to bludgeon them, hoping for the best.

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

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

Excited? Here we go. . .

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

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

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

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

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

Bottom-line: Quote, quotes, quotes.

And that is fine.

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

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

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

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



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

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



See how the ranking changed?

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

You betcha!

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

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

Go deep.

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

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

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

And so on and so forth.

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

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

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

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

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

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

So why not your metrics?

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



Ok great.

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

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

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

This is the problem with lonely metrics.

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

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



Much better right?

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

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

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

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

Kick it up a notch. Right?

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

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

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



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

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

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

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

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

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

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

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

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

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



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

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

For example. . . .

* SFAF helps prevention through information sharing and providing services. One key way of doing this is providing forms and information as downloads. Example see all the downloads on the Science & Public Policy page. Or the Bulletin of Experimental Treatment for AIDS.

I can track downloads easily (using event tracking or "fake" pageviews) and help quantify those micro conversions.

* There are a ton of micro conversions on the Advocacy Action Center page. Sign ups. Successful searches for elected officials. Tell-a-friend's.

* On the How You Can page, and other places on the site, there are links to other websites. Why not track these through outbound link tracking to see if we are sending people to the right place.

* Oh and of course the important micro conversion of signing up Volunteers!

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

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

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



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

Broaden your view!

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

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

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

Ninjas do that. You should too.

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

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

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



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

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

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

Two months do not a trend make. Important lesson.

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

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



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



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

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

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



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

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

So on and so forth.

Remember:

1. Don't look at just one month or just two consecutive months.

2. Understand your business and its cycles of up and down. Use that understanding to pick the right comparative time period / time horizon.

3. If you do present your data as a trend it does not hurt to include some "tribal knowledge" and throw in some annotations! Like this…



Sweet momma that is awesome!

Kick it up a notch, ok?

#5: Present data better, make insights obvious.

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

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

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

Three month trend for one very important business metric…



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

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

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

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

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

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

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

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

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

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

The presentation is ok, could be made more pretty.

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

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

Don't do that.

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

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

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

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

#6: Leverage segmentation, daily.

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

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



Learn how to:

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

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

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



Learn how to:

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

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

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

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

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


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

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



Learn how to:

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

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

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

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



Learn how to: Multichannel Analytics:

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

Feeling like an Analysis Ninja already?

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

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

Ok?

Good luck.

As usual it's your turn now.

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

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

Thanks.

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

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