Making Decisions with Web Analytics Data: Low Hanging Fruit

My experience has been many groups have an aversion to enhancing their web analytics implementation. Everyone seems to agree that it is important, but their behavior suggests that by sticking with ‘slow and steady win the race’ will some how work out in our favor.

Perhaps it’s because there’s few pie fillings for your humble pie more bitter than empirical data  documenting your inefficiencies.  Like the dance wallflower or the artist who holds his art back for fear of rejection, too often, too many decisions are made by company philosophers who somehow divine customer intent through tradition. They fiddle on their roof without every really considering ‘the other hand.’

“This is how its done;”
“This has always worked for us in the past.”
“Tradition!”

So wonder of wonders, how can we help our stakeholder learn to stomach data? How do we help those that drive decisions to acquire the taste for meaningful data?

Consider these starter ideas:

Have some of your own? Be sure to include them in the comments.

 

What’s it worth to you?

Collect some data around some key indicators on your site. Maybe they are videos, social shares and other engagement indicators. Assign each interaction $1.00 value. Prepare a report along the lines of “If a social share is worth $1.00 to us, and it costed us ______ to make this, then our return on investment to date is ______.” Almost inevitably the response is, well that’s worth more than that to us. With a value assigned, the follow up question then is, “What can we do to maximize our return on investment?” The idea is to give your decision makers a monetary needle to move.

First Timers

Working for an organization like mine, it’s really easy for product managers to say, ‘well, it’s for everybody.’ While that might be true, when you try to look at the aggregate data for all visits, those same individuals find themselves wondering, “so, now what?” Since first impressions are so important, help stakeholders look at the first encounter. Collect some data around key indicators and try a phrase like. “Our site is perfectly designed to see 67 percent of first time visits abandon the site.” For a new site this can be a particularly valuable exercise.

Do you know what happens when you assume?

The Lean Start-Up really drives this point home: be aware of the assumptions you are making about your audience and find ways to validate or reject those assumptions as fast as possible. As you start or revisit a project, help stakeholders identify the assumptions they are making, and be prepared with ways on how data can prove or reject those hypotheses.