Operation essentials ▏How to effectively reduce user churn

How to reduce user churn, I believe this is an issue that each of our product operators is concerned about, and it can even be said that this is a very troublesome issue. There are many reasons for user churn: product content cannot impress users, information updates are slow... If we cannot solve the problem of user churn well, it will also be very detrimental to the stable growth of the product.

I will analyze how to effectively reduce user churn from both product and operational aspects.

In order to prevent product loss due to too large changes in product functions, what we need to do is to break down product updates and revisions, make small changes in each version, and gradually guide users to understand and use the product. It will not lead to the loss of users because of too big a revision.

At the same time, the company can also let angel users use the new version of the product on a small scale first, and then decide whether to promote it on a large scale after understanding their opinions.

In today’s fast-paced life, no user is willing to stay in a product with complicated operating procedures. Imagine this: a user opens your product and searches for a long time but cannot find the content they want. Let’s not talk about whether the user will be annoyed at this time. The minimum user experience is already 0 points, so streamlining the product operation process is also An important part of effectively reducing user churn.

The main work of operations before a product is launched is product warm-up and market exploration.

After the product is launched, the operational goals have changed: releasing products and related official documents, carrying out social marketing around the product, ASO optimization, user behavior incentives, launching special topics/activities to promote activity, etc. If the above two stages are done in place, explosive growth may not occur, but stable growth is definitely possible.

After the above two stages, product operations will enter a period of stable growth. At this stage, interaction with users is still the main focus. When it comes to interacting with users, there are currently two commonly used methods: one is group messaging and the other is push notifications.

Blueshift conducted a survey based on the efficiency comparison of these two methods.

Let’s take a look at a set of data first:

Obviously, message push is more effective than group messaging.

We can also see from the data that in the same month, the retention rate with push function is significantly higher than the retention rate without push function.

Speaking of message push, in fact it is not that simple. Writing push copy, choosing push time, and how to achieve accurate push are all a science.

Taking U-Push, Umeng’s message push service, as an example, I would like to introduce how to do it: push the right content to the right people.

When selecting users to receive push messages, you must filter the conditions. Only by limiting the conditions can you ensure that the appropriate content is pushed to the right people, so that irrelevant messages can be avoided. The information disturbs the user.

Once the conditions are filtered, the effect of sending push messages will naturally be high. The figure below shows the usage analysis of different dimensions for your reference.

Only messages that conform to users’ reading habits are valuable! We also have rules to follow in terms of time for message push:

Using the powerful LBS function, we can push messages in and out of the target range We can push targeted messages at a certain time, for example: there is a traffic jam on a certain road section. Based on the geographical location, we can push the traffic jam information to the user in real time to improve the user experience and enhance user stickiness.

In the growth process of a product, there must be successes and failures. When the number of users increases linearly, we need to analyze which point and function attracts users and respond in time. When the number of users gradually decreases, we need to use specific data analysis to find out the reasons for user loss and correct them. Finally, don’t forget to make friends with users.