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I'm looking at identifying trends within my data, particularly wondering if usage of my app on new sign ups is increasing over a week to week basis. As we are constantly improving our product, I'd love to be able to identify a correlation between app usage & new feature releases.

The data I have is split into weekly cohorts. Each week I have the number of new sign ups, and how many of those accounts are still active (e.g. users are logging into the system).

Let's say I have the following data

Week 1 - 10 new trials, 3 accounts active

Week 2 - 15 new trials, 5 accounts active

week 3 - 4 new trials, 3 accounts active

week 4 - 20 new trials, 12 accounts active

week 5 - 17 new trials, 9 accounts active

In my current approach of analysis, week 3 looks amazing because 75% of accounts are still active ... In reality though, the number of new trials is extremely small compared to other weeks. As a result, I don't feel like I'm accurately comparing apples to apples in a week to week comparison.

Is there a way I can normalize the weekly data so that I am performing accurate analysis - or is percentage based really the best way to look at this data?

I am pretty new to this, so any help is much appreciated.

Cheers

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  • $\begingroup$ Welcome to DataScience.SE! Consider modeling your activity with a Beta random variable. $\endgroup$ – Emre Jul 20 '16 at 18:18
  • $\begingroup$ @Emre Thanks - can you point to any tutorials on how to use beta random variables for a dummy. I'm struggling to understand how i'd apply this to my problem. $\endgroup$ – Gshock Jul 20 '16 at 20:23
  • $\begingroup$ I hope this helps. You haven't mentioned how you are modeling the features though; an binary variable to indicate whether a feature was introduced that week? $\endgroup$ – Emre Jul 21 '16 at 4:41
  • $\begingroup$ I literally have no idea what I am doing, hence the ask for tutorials. I have no idea where to start. $\endgroup$ – Gshock Jul 21 '16 at 12:26
  • $\begingroup$ Maybe you need to step back and get a book on statistics? Larry Wasserman's All of Statistics isn't bad. $\endgroup$ – Emre Jul 21 '16 at 15:37
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With this low number of sign ups I doubt you can profit from advanced statistics at this stage. Just chart your numbers in excel along the timeline and judge visually.

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  • $\begingroup$ What if signups were in the 100's? The above data was dummy data. $\endgroup$ – Gshock Jul 21 '16 at 14:34
  • $\begingroup$ If you can have the sign ups broken down per day you could try using CausalImpact from Google or Probablistic Programming github.com/CamDavidsonPilon/… to see what the effect is. $\endgroup$ – Diego Jul 21 '16 at 23:07
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Try Normalizing the trails and active accounts data with a mean or median. The resulting statistics will show week 3 as negative values which can be interpreted as a comparison to all the data. See the Excel graphs below.

mean normal mean median combo

As opposed to the data that has not been normalized. enter image description here

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