Triangulation and best practices in web analytics

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I do an incredibly amount of work in analytics in order to increase conversion rates, and one of the things I do is help the tech-types create better reports. (Well, I help them because it helps me, but that’s teamwork, right?) So, for example, I helped one customer take his thousands of products and put them into about fifteen categories. That way, we can create a product report with the click of a mouse (and not get a list of meaningless part numbers.)

The report defaults to Product Category and Revenue. This week, one of the company’s employees created a third column, product page views. “Look,” he said in an email, “Product Category A gets hundreds of thousands of page views and very little revenue. Product Category B gets few page views and lots of revenue.”

In general, this is a great kind of analysis to do with one’s analytics — for each product, what is the purchase to view ratio? Often, we find that “hot” products do well only because they are seen often (e.g. they’re on the home page.) Notice that I wrote, “For each product.” However, the employee’s analysis was way off base. What he didn’t realize is that if a page includes 47 products, the number of category page views increases 47 times when a visitor looks at it once.

There is a more important point here, though. Whenever I have some big analytic pronouncement to make, I do two things. 1) I ask, does this make sense? In the case I described above, the numbers were way out of line. 2) I triangulate. If the data leads me to a strange or unexpected conclusion, can I find it in a different way? In this particular case, I looked at page views of individual products and saw the problem right away. But triangulation can take other forms. For example, maybe you do have a really awesome analytics package. Have you thought about using a little free one at the same time? That enables you to say, is this a problem with the package I am using, with how I am interpreting the report, or is this truly something that matters?

Recently, I did some keyword work for this same customer. They have one of those big functional analytics packages, SiteCatalyst by Omniture. But I still installed Google’s conversion tracking and waited a month (to collect data) before making any drastic decisions. The SiteCatalyst report gave me lots of data that I couldn’t get from the Google AdWords conversion tracking, such as conversion over multiple visits. Nonetheless, having the Google data made me feel really good about making an important AdWords decision, because I could tell from the Google data that the Omniture data was in the right ballpark.

But I guess that to truly triangulate, you need three sources of data….

Robbin
LunaMetrics

Our owner and CEO, Robbin Steif, started LunaMetrics twelve years ago. She is a graduate of Harvard College and the Harvard Business School, and has served on the Board of Directors for the Digital Analytics Association. Robbin is a winner of a BusinessWomen First award, as well as a recent Diamond Award for business leadership. You should read her letter before you decide to work with us.

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