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No Respect: Where’s the Love for Qualitative Data?

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In the Web Marketing universe, qualitative data is often seen as the poor cousin of quantitative data. Information that can’t be reduced to a spreadsheet (or better yet, a graph or chart) isn’t trusted. It’s treated with skepticism, even derision.

It’s easy to understand why. Many practitioners have technical-leaning backgrounds, where precision is king. And so, “Data-driven” marketing is code for analytics and testing-driven marketing. (Where “testing” is defined as A/B and multivariate testing, i.e. backed up with statistics. Traditional usability testing just isn’t part of the game plan.)

Well, I’m a fan of data-driven marketing too. But there’s more than one type of data. Can’t we make room for others?

The Case for Parallel Data Collection

I appreciate the value of quantitative data. But it can only take you so far. Though it’s great for identifying problems, when it comes to finding solutions to those problems, it often falls short.

Take a simple example: A review of site analytics reveals that far too many visitors are bouncing from an important page. That’s definitely good to know!

But why are they bouncing? And how do you fix it? Sorry, but your graphs and charts and spreadsheets may not tell you that.

“Run some conversion tests”, you say? Great idea. But how do you decide what changes to test?

To gain the insights needed for solutions, don’t be afraid to look to qualitative data. Things like:

  • Interviews with customers. (Yes, actually talk to them.)
  • Interviews with CSRs.
  • Surveys and other direct customer feedback.
  • Most of all, usability tests: Watch customers try to use your site, and see where it lets them down.

“Data-Driven” marketing doesn’t have to mean “spreadsheet-driven” marketing. There’s room for a healthy sprinkling of qualitative data, and (dare I say it?) even creative hunches.

  • Ryan Ekins

    And don’t forget CEM. Tealeaf can replay the users experience. You see what they saw and understand what their experience was on the site. It’s a great bl end of quantitative and qualitative.

  • Michael Straker

    Ryan,

    Good point. My list wasn’t intended to be exhaustive, but great tools like Tealeaf definitely deserve to be included. 

    Michael

  • http://twitter.com/timflint Tim Flint

    I love this. This is something that always bothers me. Often testing and making changes only with data will make you hit a wall. Unless you talk to the users and see what they do and understand their needs you will hit a ceiling as to what quantitative data can do for you.

  • http://twitter.com/timflint Tim Flint

    I love this. This is something that always bothers me. Often testing and making changes only with data will make you hit a wall. Unless you talk to the users and see what they do and understand their needs you will hit a ceiling as to what quantitative data can do for you.

  • http://twitter.com/timflint Tim Flint

    I love this. This is something that always bothers me. Often testing and making changes only with data will make you hit a wall. Unless you talk to the users and see what they do and understand their needs you will hit a ceiling as to what quantitative data can do for you.

  • Derffred

    Rodney Dangerfield is awesomeness!!


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