Looking at the user life cycle from the data: Will lost users come back?

Looking at the user life cycle from data

One of the ultimate goals as an app marketer is to acquire new users and retain them. We all know that user engagement is a useful benchmark metric: not only can it be used to quantify the success of an app, but maintaining high user engagement can also lead to better revenue returns.

However, user churn is inevitable. In fact, we estimate that around 80% of users stop using an app one day after installing it , and by day seven, only 12% are still using it . Does this result mean that lost users will leave forever and never open your app again? According to our data, the truth may be more optimistic than you think. Next, let’s take a closer look at the distribution of returning users in different industries.

Do users return after a long period of inactivity?

To answer this question, we analyzed eight industries over more than one year of data to show you the average return rate of churned users: users who had no sessions return for more than two months.

The results of the analysis are encouraging: the number of users returning after two months is equivalent to 17% of all new installations during that period . After three months, an estimated 11% of churned users return , and in the sixth month, the return rate is still 4%.

Industry Comparison

Different industries have different churn rates , and their respective return rates are naturally quite different. We use the median value of the regression rate as the analysis target and compare the differences among the eight industries from the two charts below.

The return rate is particularly high for e-commerce . The regression rate three months following the last session was as high as 18% .

This result is not surprising. E-commerce apps are different from other apps for browsing or interaction. Users usually only open the app for specific needs (such as searching or ordering products). Unless you have a particularly large purchase volume, the frequency of opening the app is generally every few months rather than every few weeks. On the other hand, many consumers browse the Internet every day, which is why e-commerce applications have a higher retention rate from the beginning.

A similar situation exists for tool applications. Typically, these apps have a single purpose, such as acting as a scanning app, and are only opened for a specific need. This type of usage demand does not occur every week or even every month, but users will keep it on their phones for next use based on practicality.

Game apps also have a large number of returning users. The return rates for other industries are more or less the same as the average, but in the gaming category the return rate for churned users is almost always twice the average.

Gaming apps have a special appeal: after six months, about 8% of lapsed users return, and even after a year, an average of 2.3% of users return. This number may seem small at first glance, but if it is accumulated over a short period of time, it will still have a big impact on your retention data analysis .

Now, let's look at the following chart. Although the return rates for these industries are lower than average, they are still helpful in helping you gain a comprehensive understanding of the user life cycle and conduct effective remarketing .

Travel apps show a relatively high return rate, with about 8% of users returning after three months. This result is not surprising when you consider that online travel agencies, airlines, and hotels are the most popular travel apps.

Most of these apps are purely functional and are usually used for a specific purpose on a recurring basis (e.g., booking flights and hotel rooms).

However, for remarketing, it’s important to know when users start a new session or reinstall your app: Generally, we recommend launching customized campaigns or offers to bring users back after three months if they churn.

On the other hand, the user return rate of social applications is very low. Even after two months, only 1.3% of churned users are likely to return. For social networking applications, if users do not participate in them from the beginning, their interest will decrease rapidly.

Why use an infinite lookback window?

For the reasons stated above, we quickly realized the importance of adopting an infinite lookback window. First, if the sessions are initiated by users who have already downloaded the app and then returned after a period of time (say, three months), these sessions are likely to be counted as new installs.

Additionally, these sessions may be attributed to paid marketing campaigns , increasing advertiser costs and scrambling the data.

Looking at the averages, assuming your user records don’t go back far enough, your install data will be off by about 11%, and you may be paying ad networks for 11% of your existing users who appear to be new users.

What is the cost of this misattribution?

If user data is only kept for three months, how much money will you waste on these data differences? Taking the gaming industry as an example, suppose your app acquires 2,000 new users per day, and 26% of them come from paid installations.

Four months later, about 60 of the churned users from paid channels will open a session again.

The problem is, these users are not counted as returning users, but as new installs and are credited to the promotion . The cost of this misattribution is the addition of unnecessary costs.

In addition to unnecessary costs, misattribution can negatively impact the accuracy of your data and subsequent reattribution campaigns.

With unlimited lookback windows and uninstall and reinstall tracking , you can get a more complete overview of the user lifecycle, which helps you fully understand the user lifecycle and conduct effective remarketing.

Source: Adjust GmbH