What is the retention of the onboarded accounts?

How do we define retention?

Retention = the percentage of the accounts who come back and perform any action in your product/app "N" days/weeks/months after they start using it.

In order to make retention’s analysis more relevant, we consider that it’s not ok to analyse the cohort of all the accounts who signed up in a certain period of time; instead we should filter only those who finished the onboarding. The accounts that don’t finish the onboarding have an onboarding problem, not a retention problem. You can identify and analyse them separately in a dedicated report entitled: How are users converting during the onboarding process? These are the accounts that didn’t understand or received the value of your product. 

This aspect is important because it helps you understand what optimization strategies you have to use according to the situation: 

  • optimizing onboarding for the accounts who don’t finish the onboarding process, 
  • optimizing retention for the accounts who finish the onboarding process (so they understand the value of your app) but, for other reasons, don’t come back. 

You can read more about this different approach in this blog post.

How to check the link between retention and onboarding?

A way to understand how important onboarding is for optimizing retention is to analyse the retention of the sign-ups that didn’t finish the onboarding process.

If your onboarding process is well-defined and explicit, and you deliver on the promise you make to the clients with respect to the product, you will notice that the number of users who come back to your product without finishing the onboarding is around zero in the first few weeks after creating an account.

On the contrary, if the onboarding process is cumbersome, many sign-ups will abandon the product way before finishing the onboarding and reaching the wow moment.

This shows the impact of onboarding on retention.

So the cohort to analyse in this report is represented by the users who created an account during the selected period and finished the onboarding process, regardless of when that happened (whether this happens during the selected period or afterwards). Because these users understand the value of your app (they are onboarded), they have a reason to come back to your product.

You can also filtre this cohort by applying the segments which are already defined.

Retention time ranges

The retention mode: daily, weekly or monthly can be selected from the calendar section. Which is the retention mode that is relevant to your app? The frequency of usage of your app (daily, weekly or monthly) is the one that will represent the relevant option on this analysis too.

We suggest you select an older period from the calendar to be able to analyse retention for a longer period of time.

What are the activities which are taken into account for retention?

We consider that an account comes back to your app when it performs any of the in-app actions that are tracked. You can also include any other action that takes place outside the app which you consider it reflects the account is active. It can happen that certain actions are performed by users outside your platform for various technical reasons. E.g.: actions happening in an iframe outside the app, etc.

The overall retention curve

When you look at the overall retention curve, it’s important to consider 2 aspects: 1st week retention (day or month, depending on the frequency of usage of your app), and where exactly the retention curve stabilizes

Your overall retention curve will look like one of the following graphs: 

1. A good, stable retention curve 

This is how the graph looks for apps where users love to come back (except Facebook. We’ll get to that graph in a moment.)

You’ll lose some in the first week, maybe they found a competitor’s product that they’ve decided to try. You’ll lose some more in the second and third weeks, as some people who didn’t really need your app drop off, but it stabilizes after that. 

Potential optimization: depending on your actual situation, you can increase the retention rate for the weeks that are stable, but don’t expect wonders in this direction. Still, there are SaaS businesses that have stable retention rates of around 70% - 80%; these are unicorn apps that we all aspire to. 

2. Cliff Falling Retention Curve

If you happen to come across this situation, you might notice that only a small amount of users are returning during the first week, but those that do will stick around for a long time.
A retention that stabilizes at over 40% - 50% (depending on the type of business) is considered a good retention rate. But if the retention stabilizes somewhere under 40% then it’s clear that there is a big optimization potential. 
Potential optimization: The first weeks (and especially the first one) in which retention drops a lot represents the period where you can implement optimization strategies. One situation that is common for this retention graph is when the targeting is incorrect. Those few users that stick with you on the long term are the ones that find your app useful and better than the competition’s. Try to make a profile of these users (through questionnaires, direct calls, and the analysis of the actions they perform and features they use compared to those who don’t stick around). Then see if their profile coincides with the ones of the targeted users. 
If you manage, for example, to increase the retention rate for the first week, you will notice an increase in the retention table, in the first column (week 1).

3. Highway to Zero Retention Curve

In this scenario, the retention rate is really high during the first couple of weeks. The sad part is that it’s slowly but surely going down. This situation shows the fact that your product is not useful for the long term. 

Potential optimization: The investigations and optimizations should go towards identifying what would make your users stick to your product for the long term. 

4. Drop retention curve

If your product’s retention resembles what you see in the report above, it might be that there’s something fishy going on within the product.

It looks like you are able to convince your users to finish the onboarding, but once they get to experience the product they leave, never to return again.

Make sure you break it down step by step and see exactly what makes your users act this way. I’m sorry to have to be the one to break it to you, but this is about as bad as it gets.

Neither of the following options are an easy fix since they require radical changes and iterations to your product:

  • The problem you’re already trying to fix isn’t an actual problem 
  • Your product fixes the problem, but not the way people expect it

5. No problem retention curve

If you happen to come across this scenario where your retention rate is constantly really high, it could be because of any one of three things:

  • Your tracking is bugged. (Take a deep breath, check your tracking code, make sure everything is reported properly.)
  • You work at Facebook. (Good job! Ask Mark for a raise.)
  • You own the next unicorn. (Congratulations. Profit!)

Who are the users that return?

You can see the exact list of accounts behind this number, just like in every other InnerTrends report.

In this report, in the table section you can drill-down and get the list of accounts that came back within that range, as well as the list of those who didn’t return. 

Optimising retention

The first report you can check out during your optimization process is the one in which we identify the difference in actions between onboarded users that return and those that churn. You can also check the usage of your app's features to identify which are the most and least used features. 

An interesting analysis you can do is to compare the accounts created during a certain period of time that returned during the following week / month vs. the accounts that were created during the same period and returned much later (6 weeks after the account was created for example). From this retention report you can create temporary segments with these 2 cohorts, which you can then apply in the feature usage report to see what exactly those accounts did in the week following the account creation vs. after 6 weeks. You can thus identify potential features that are used less often. 

In case you want to learn more about how to optimise your retention, check this guide we wrote for you on our blog.

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