Inconsistent Stickiness and Retention

Hey @stgo.larrain ,

Thank you for reading the articles and for the links! No permissions needed, thank you for the consideration! Let’s get to work so!

Stickiness: https://analytics.amplitude.com/vestua/chart/7aln69m

First, let us focus on your stickiness chart. Stickiness is for showing you how often users fire specific events over a given period of time. In your chart we are focusing on the cohort showing users who purchased 5 times in July, yes? I can’t see the cohort but I am guessing from the others. We want to see how often users are firing AddToWishList in August. The non-cumulative Stickiness chart (yours!) shows you the percentage of users who fired the event at least once on the exact number of days listed on the X-axis. So, targeting the cohort, there were 661 unique users who triggered this event. Out of that 661, 18.4% triggered AddToWishList once in August, on only one day. 2.12% triggered it 15 days of the month, meaning 2.12% of users came every second day to trigger it. So it is looking at how many times they are triggering this event. Please note, users can appear i more than one bucket. 

Retention Analysis: https://analytics.amplitude.com/vestua/chart/uofcbzu

The retention chart helps you drive product adoption by showing you how often users return to your product after taking a specific action (known as firing an event). We’re looking for users who did the event AddToWishList and came back and fired the return event of AddToWishList. Targeting the same cohort, same month and using N-day retention. N-day retention shows you what users came back to fire the event on a specific day. 24-hour windows mean we do not use calendar days to denote when one day ends but 24 hour rolling periods. Let’s say we have User A triggers Event X on 05:00 Tuesday. He then fires the return event on 00:01 wednesday, 19 hours later on the Wednesday. He would still be day 0 retained (same day) as 24 hours did not pass and we are going off that, not calendar. Here we are looking at how often does it take them to return, and not on how many days are they doing a certain event. Go to your breakdown chart and expand it like below ;

Let’s go to the bottom, Aug 1st with 198 Users. We can see 100% of these users triggered the event within 24 hours of triggering the first. 50%, 99, triggered it within 24-48 hours. 30 days from August 1st would be 31, which has passed, so let’s go to the end of this row. We see that 38.4% of 198 users from that cohort came back on day 30 and triggered that event. Not that they necessarily did this every day for 30 days. 

Let’s pop up to August the 15th. 149 users from the cohort. All of them triggered the event on Day 0, rolling hours. Well, day 30 from then would be the end of today, right? So we can see 12.4% have come back, 30 days from August 15th, and triggered the return event. However, note the asterix. This means data is not finished completing and some users may still have events to send (also it is not fully 30 days since August 15th yet!).

Let’s pop up to August 31st! Since August 31st, about 14 days have passed, so this is the maximum return time we can see. Our last day in the chart is August 31st. If we want to see which users are day 30 retained, we will have to wait until the end of September ! We can only see the users who triggered the event on August 31st and have come back each day and triggered it. For example, Day 2 after, 55.1% came back and triggered the event. So far, we know that users from that cohort who triggered the event on August 31st, 20.5%came back today, Day 15, and triggered the event!

I hope this explains the situation for you! Let me know if there is still any confusion!

Kind Regards,
Denis