One of the most challenging parts of digital measurement is getting the data collected properly. You will want both accuracy and relevance in your page tagging in order to accomplish this.

No matter what vendor application you’re using to collect clickstream data, you will first need to define your reporting priorities (which means you’ll need to define what you want to know about your visitors’ behavior). For the sake of this post, let us assume you’ve gone through that exercise already and know what you want to report on.

Most web analytics applications today require page tagging to collect data. Tags are snippets of javascript that generally go in the header of your html and are comprised of a combination of a supplied formatting (specific to the tracking tool) plus a location in the script to place variables that are unique to your site and sometimes to the campaign, page or activity you are tracking. For some sites that need only basic, generic reporting, the placement of a single line of code with your domain as the variable will be sufficient–but most serious marketers need to go beyond this.

It’s key to success that a tagging expert create these tags, especially when you need to go beyond the generic tracking built into the vendor’s application.

Deployment of tags then moves to either a tag management tool or to your developers who will put the tags into the html in the appropriate locations. In order for this process to go smoothly, some tagging expertise is necessary, as developers typically do not concentrate on this as part of their skill-set. If you have a tag management solution, you’ll be going through a somewhat different process (we don’t have space to detail it here) but the tags still need to be carefully constructed by an expert.

Testing for data collection throughput and accuracy takes place after tag deployment and, usually in a test environment, some data is coming through (its not a good idea to launch before tagging QA). This is accomplished by checking each and every tag to make sure that when the relevant action takes place, it is picked up by the tag and delivered to the analysis engine. A good way to set up a QA report is to use a spreadsheet showing the tag name, its function, the expected result and the actual result. Unexpected or null results will then have to go back to the developers for adjustment–usually your tagging expert will know what was done incorrectly and can make sure it gets fixed.

Once a rigorous QA plan is completed and the tags are collecting data as they should be, it’s finally okay to launch and begin to collect actual user data. At this point you should expect to see live data flowing into your reports as expected.

Many marketers struggle with mapping Data to KPIs.

In other words, they want to review the reports that come in and see whether they achieved their goals. And in most cases, the data just sits there unyielding. It doesn’t tell the marketer what the marketer needs to know. It doesn’t say “here is your KPI and here is how well you did”.

Data doesn’t know what you want to know.

But that doesn’t mean you can’t get answers to your business questions.

You’ll need to put the data in context. Creating context is one of those cognitive abilities only humans have (so far). It’s a trait not unlike intuition, and relies on the combining of lots of experiential information, plus empirical data, plus what still passes for “gut” in parlance, even though we can be pretty sure even this murky sense of “what’s right” will one day be quantified by neuroscience.

“Key Performance Indicator” is a term that goes halfway towards a data definition of “why you have a web site”. Data itself has no idea, nor will it ever, why you built your site–no matter how large nor how small it is.

Think of KPI as metadata about your site. It needs to be layered on top of the reports.

Here is an example of how this mapping of KPI metadata might work. As you may begin to notice, the KPI for even a large, complex site may be rather simple, and that much of the content on the site is supportive in nature (in digital analytics, that would be called “engagement content” because it is supposed to help drive customers to perform the most important KPI, or the “conversion” event).

Let’s say your site wants to disseminate content about different topics your company has deemed important–or that your company wants to develop certain ideas based on some content popularity metrics.

Much of the work lies in reducing the ratio of signal to noise in your data. In other words, for this exercise, you will want to focus on certain reports while ignoring everything else (analytics tools are very good at giving you much more data than you need and burying what you want).

For the sake of this exercise, let’s say the concepts you are market-testing reside in a pdf or other downloadable format.

What you will want to look at is the following:

• reports that indicate which campaigns drove the most traffic to your “pdf section” (for instance)–possibly a landing page

• reports that indicate which campaigns drove traffic that moved beyond the landing page and got to the download page

• reports that tell you how many times a particular asset got downloaded, and which ones were the most popular

So out of thousands of possible reports, we have isolated just a few.

And they will not necessarily be sitting in a single dashboard. You may have to go find them.

Then, in order to describe them to others as KPIs, you’ll have to extract the data from them and create a story to tell.

That story (supported by charts or visuals) might read something like this:

• Our most popular pdf was “Comparing Icebergs to Big Data”.

• The campaign most successful at driving traffic to this pdf was called “Don’t Get Sunk in Oceans of Data”.

• We should devote more resources to our consulting offering about the dangers of mishandling Big Data.

• We should come up with another campaign that calls to mind disaster on the high seas.

Of course I have oversimplified. But decoupling what you want to know from actual reports; and finding reports that answer your business questions is an exercise that, at least until robots replace nearly all of us, can only be made by a human observer exercising some fairly straightforward powers of observation and deduction.

Join industry leaders @AndrewVEdwards and @RandSchulman when they discuss The Dawn of Convergence Analytics at SES NY on March 26, 2013.

The combination of “big data,” access to cloud computing, powerful algorithms, and unprecedented visualization capabilities has created an emerging new class of analytics tools for the marketer.

It’s being called “Convergence Analytics”. It’s the marketing equivalent of “one ring to rule them all.” Though still in its infancy as a discipline, there are many vendors in the market, and their goal is to pull together data sources from multiple touchpoints from the web and beyond. They’re also using advanced data gathering and data regularization strategies to create a correlative dashboard-like experience for the marketer.

Will all of digital analytics look like this in two years? Find out how fast the landscape is changing.  REGISTER

In Digital Marketing, two aspects of customer engagement seem to get the lion’s share of attention: Campaigns and Conversions. There’s always excitement about powering up a new way to reach customers and of course, when they convert, that rings the register and everyone is happy.

But the most costly errors and most critical customer losses occur during the engagement process. Engagement: that’s the rather unexciting middle part of the conversion funnel where users are right there on your site, looking at what you have to offer (whether its goods, services or content). And they either move on to conversion or they bolt. And you may see them again (there’s good data to suggest that some buyers need to visit at least twenty-five times before converting); but also, you may not. And you also may not know about the repeat visitors getting closer and closer to conversion as they continue to visit the site.

Does your funnel look like a distorted martini glass? Many enter but few get past the landing page? It means you’re losing your most valuable marketing asset (people who came to your site) almost immediately.

 Most of the time there are two reasons for this:

• your overall messaging is somehow driving the wrong traffic
• your content is devoid of interest to people who should be interested once they show up.

Both of them require some serious marketing work. You can test which of the above is your problem by looking at bounce-rates, though there are other ways as well. For instance, if you had an email open, but then a one page visit, it may be your messaging (or that your landing page is failing). However, if you know that users are going one or two pages into your site and then leaving, you may have a more general problem. You need to know where they go, and what their exit page is. If it is a page that was supposed to lead them towards conversion, that page needs attention. If it was a page leading not to conversion–then the bigger question is: why do you have that page?

Any of these measurements requires a careful implementation of analytics. Relying on campaign data and purchase data (or download data) alone gives only the barest indications of success, and is not really actionable.

In order to develop strength up the middle (as the baseball folks might say), you need to develop the middle of your site so it matches the quality of your reach and conversion capabilities. Whether you’re using Google or a paid application, this requires the careful attention it deserves.

By understanding the middle of your conversion funnel, you will probably get it to look less martini-glass and more like a coffee filter. That’s what a well implemented analytics effort is meant to do.