Users that didn't convert

A question frequently posed to our services teams is "I devote time and attention to learn all I can about users that convert, but what can I learn from users who do not convert?"

(Such questions regarding converting users tend to come up more among advertisers and agencies using DoubleClick for Advertisers, but even if you use Floodlight/Spotlight tags in DoubleClick for Publishers you've probably asked this same question.)

First, some ground rules about the term "non-converter".  We find that the term "non-converter" in practice refers to a number of different things; deciding which is most important to you is a good place to begin answering this question.  The following are groups that are commonly referred to as "non-converters":
  a) Users who saw your ads and visited your website, but did not convert.
  b) Users who saw your ads but did not visit your website (and thereby did not convert).
  c) Users who did not see your ads but did visit your website.

Data Transfer is especially good at helping you develop valuable insights about these groups.  Let's focus on the first two cases (however you can learn things about all three categories using DT).  How do you identify which exposure pathways or interaction patterns are truly valuable vs. those that are not?  How do you identify user interactions that tend to generate a large amount of conversions but also drive users away?  What patterns can be observed from those users who do not convert?  Your results may be quite surprising.  (Note, the following explanation requires that you receive and process all three Data Transfer types:  Impression, Click and Activity.)

Start by inventorying the user IDs from your Activity files over the exposure window of your preference, say 30 days.  Next, use these user IDs to compare against those found in your Impression and Click files over the same date range (you may choose to add one additional day to your Impression and Click files to account for visits that occur right after midnight).  At this point you're processing 60 days of Impression and Click files.  Now, identify those user IDs occurring in your Impression and Click files that either do not have an Event-ID value of '1' or '2' (the identifier signaling a click conversion or an impression conversion) -or - that do not appear in your Activity files at all.  Finally, sort the resulting Impression, Click and Activity records for each user ID in reverse chronological order by time.  The conclusion is a list of your users that were exposed to and may have interacted with your ads but, for whatever reason, did not convert.

It's at this point that you can take action such as using Remarketing, better content targeting, Creative Optimization to deliver a better message, or site optimization to improve user experience.  You may even opt to run a DFA Experiment which divides users into treatment and control segments for comparison of ad effectiveness.

--Matthew Trojanovich and Ryan DeVito, Data Transfer Team