Managing Retention Rate Benchmarks & Expectations

There are lots of factors to be aware of when growing an app, such as tool stack alignment, traffic quality, and product-market fit, as well as time constraints and implementation strategies.

To reach a point where all the teams and stakeholders on the agency and app publisher sides are truly satisfied with the results, it’s important to properly manage expectations and tasks throughout the collaboration. From a publisher’s perspective, it’s important to be aware of the industry benchmarks, be open to possible quick adjustments, and understand the capacity of various teams to collaborate with one another using clearly defined success metrics.

In this article, we’ll shed light on these topics and offer guidance on how to properly define and manage retention and CRM expectations.

Understanding retention rates

Growth teams always try to manage stakeholders’ expectations — but how realistic are they? The retention rate (RR in the mobile industry) is the percentage of people who continue to use an app over a given period of time (week, month, or quarter). In other words, it’s the number of users who still use an app a certain number of days after downloading. The more people retained, the better indication of an app’s long-term health. However, it’s sometimes the case that the publisher’s CRM expectations don’t correlate with industry benchmarks. For instance, if the industry benchmark shows that user retention barely reaches 5% by day 30, it’s optimistic to expect a 25% RR on Day 30.

In the Phiture CRM/Retention Team, one of our main focuses is to increase retention and engagement rates for our publishers. Usually, the RR is calculated on day 1, day 7, and day 30. These specific days reflect different characteristics of retention.

Day 1 is a good indicator that people aren’t deleting the app right after downloading.

Day 7 means they’ve been retained for a week, indicating that the activation strategy is good or the onboarding flow is working.

And day 30 suggests that a habit has been strongly formed around the product and the user might, depending on other factors, be considered “engaged”.

Managing challenges, expectations & results

There are plenty of factors to consider that might affect retention uplift and our ability to set realistic expectations with our publishers. For instance, it’s especially important to note that not all app categories can boast the same degree of user retention on day 30. News apps, banking, and sports apps often lead, with an RR average of 10-12%. Sometimes, what is considered as “great retention” for one industry, is average or even below average rate for another one (for example, for social consumer 25% is considered as good, while for enterprise is no less than 70%).

Educational, gaming, and social apps have the lowest index of around 2-3%. In this case, for each type of business model, there will be different characteristics of successful growth in user retention or engagement.

There are several challenges related to the app’s product, which can affect the publisher’s ability to improve retention. For example, if we’re comparing social apps with utility apps, apps with any social aspect like a feed or messaging features have a higher likelihood of regular interaction. They keep users engaged for longer periods due to the ever-changing and regularly updated content, whereas utility apps tend to be more static. Therefore, it might just be easier for social apps to pull their users back as it requires less cognitive load.

Another consideration is product-market fit. Some of the main indicators of product-market fit are, for example, a flattening retention curve, positive qualitative data, or decent sales. Simply put, if the app doesn’t have product-market fit, all the usual tricks to increase the retention rate won’t bring substantial results.

Traffic quality is another factor. If user traffic in the app is poor in terms of conversion to paying members, it will be almost impossible to impact trial starts and membership conversion rates.

Using a product analytics tool like Amplitude will help to analyze the performance of traffic coming from different sources and understand how the acquisition strategy should be changed — and if there’s a need to redistribute the paid acquisition channels. It’s nonetheless important to have a clear view of the volume of monthly/yearly users to understand how many users you can test and, therefore, how long it takes to reach a statistically significant result.

When it comes to analyzing the retention results, it’s crucial that stakeholders can quickly react to changes the growth strategy may require to reach the desired outcome. For example, A/B testing the onboarding screens or moving the APN prompt further/closer in the onboarding journey may significantly affect the user’s experience. This is something the publisher always has to keep an eye on and be open to testing different options.

There may also be situations where publishers can’t move quickly and amend their product/UX/CRM actions accordingly, which can affect processes and the time it takes to reach the desired impact.

We also encourage publishers to hone their focus on 1-2 initiatives at a time rather than trying to tackle all areas of the lifecycle at once. For example, onboarding and activation could be a good place to start as the results would have a knock-on effect on longer-term retention and engagement rates.

In general, driving retention impact is a more long-term strategy than other marketing initiatives. Sometimes, even if you have signs of good uplift in all of your retention campaigns, it takes time to see the real and proven aggregate impact of all lifecycle campaigns.

Let’s say, for example, we want to know the number of users retained on D30 after implementing a new onboarding flow. In this case, we have to wait until the moment existing users finish the old onboarding and other users start the new one and complete it.

Only after we get the results can we compare, and this step alone might take 1-2 months depending on the length of the onboarding and the sample size required to reach statistical significance. In terms of technical considerations, a properly aligned tool stack, clear taxonomy, and reliable data will be a win-win when it comes to fast retention growth.

The wrap-up

To summarize, there are a lot of factors that might impact CRM expectations and results. Among these factors are tool stack alignment, absence of product-market fit, app traffic, and internal restrictions. Setting realistic goals around CRM programs and choosing the proper metrics of success, as well as balancing expectations and actions, are the key to effective collaboration between the growth teams and the publisher. The more frank, the better.

Here at Phiture, we’re always happy to receive comments and feedback from our readers. If you have any follow-up questions or would perhaps like to recommend some additional reading or frameworks, don’t hesitate to get in touch.