One of the things I built that term was a tool to visualize user retention, implementing triangle heatmaps, which I believe were invented in-house by Danny Ferante.
The idea here is to exploit the very quick visual pattern matching we're able to do as humans, and turn that into actionable cohort analysis. From the screenshot in the article (http://i.imgur.com/qBbkZv8.png), I think we can agree that it would become unwieldy with a large number of datapoints.
Compare this to http://imgur.com/sOQ4vrm, a screenshot of the triangle heatmap generated for tcreech's Cover Photo Finder Facebook App. The x-axis represents the cohort (the set of users that installed the app on the same day) broken down by day instead of by week like the article (hence patterns are more granular). The y-axis represents number of days after installation. Each datapoint is then coloured to represent the percentage of users that return to the app on (installation date + number of days).
A number of patterns are captured quite easily:
- A vertical pattern is local to a specific cohort. A new promotion or redesigned sign up page often results in this.
- A horizontal pattern is local to a specific vintage. If your app has a trial period that expires after 7 days, then you'll see your retention plummet across all cohorts horizontally at y=7.
- A diagonal pattern is local to a specific date. If your app is down on January 2nd, then there will be a diagonal blue line (0%) across all cohorts.
I wrote up a work term report for the University of Waterloo detailing triangle heatmaps: http://zeroindexed.com/triangle.pdf
Video released by Facebook explaining triangle heatmaps: https://www.facebook.com/video/video.php?v=3707283286197
One thing that CohortMe is doing that keeps the thing from being unwieldy is that I only go back 12 periods. So only 12 weeks, or 12 days or 12 months.
Not a perfect solution, but it's version 0.0.1 :) I really only wanted to see 12 weeks right now anyways. Until I have some decent data going into the months.