3 Churn Prediction Strategies to Boost Player Retention

Player loyalty is everything in the gaming industry. Yet, it can be difficult to quantify. While some games have a natural endpoint where players will lose interest, others might enjoy a longer game lifecycle. You need a model you can follow to understand if your players are on their way out. That model? Player churn prediction.

What is Churn Prediction?

Player churn prediction is the ability to analyze player behavior and user engagement to forecast when a player is likely to leave your game within a certain timeframe.

In many industries, the head of customer success will develop a churn prediction model to aid the company’s player retention efforts. But in gaming, developers are integral to churn prediction’s success. Why? Because they contribute to both sides of the process—analytics and optimization.

Why is Churn Prediction Important for Game Developers?

It has never been more difficult to keep gamers engaged today. The following numbers perfectly demonstrate this:

• In the third quarter of 2023 alone, mobile games had an average retention rate on Day 1 of less than 30%.
• This average dropped to less than 8% on Day 7.
• By Day 30, the average was 3.81% on iOS and 1.70% on Android, respectively.
• In 2024, Android games had an average churn rate of 72.4% on day one, reaching 97.3% by day 30.
• iOS games had an average churn rate of 64.2% on day one and 94.6% by day 30.
• In terms of game genres, strategy games had the highest churn rate on day one (match games had the lowest).
• By day 30, hypercasual games had the highest churn rate (match games also had the lowest).

In short—game developers have plenty of work to do.

 

There are many benefits to creating an effective churn prediction model for your gaming company. For example:

• It’s bound to boost your player retention rate. 
• It can help your developers build preemptive measures to improve those numbers before they’re even generated. 
• Generally, it is much less expensive than letting churn happen and then reacting.

Like many other kinds of data analysis, predicting churn helps to:

• Create more enjoyable, successful games
• Improve developers’ decision-making
• Increase the company’s revenue.

Churn prediction data can help developers create well-organized UI optimizations and re-engagement campaigns. Ultimately, these projects can improve your company’s bottom line.

Now that you’re more familiar with the significance of churn prediction, it’s time to put it into motion.

3 Ways to Build a Churn Prediction Model

Implementing the three strategies below can help predict player churn rates to prevent them from happening in the future.

1. Pinpoint Early Signs of Player Churn

Players might be “checking out” at a certain point in the game for the following reasons:

• A game stage is too difficult
• A story beat negatively impacts the player’s experience
• A bug or glitch is damaging gameplay.

By highlighting these early indicators, you can edit and optimize any problematic game zones. 

2. Use Real-Time Data to Gain Valuable Churn Insights

Never underestimate the power of data. By analyzing classic player engagement metrics like stage clear rate, virtual currency acquisition, level-ups, and skill building, you can understand gameplay patterns better.

Analyze the churn rate at each stage of the game. This way, you can better understand which stages are working and which are not.

Prerequisites for Churn Prediction

The following metrics can help you predict churn accurately:

• Monthly churn rate
• Number of customers
• Number of new customers in the last month
• Monthly customer loss.

Instead of relying on spreadsheets to predict your churn rate manually, invest in game analytics solutions that can provide you with instant insights to inform future decisions. 

This kind of platform can generate churn prediction line graphs, showing you what your company’s churn rate will look like over the next 12 months. If the results aren’t looking promising, then maybe it’s time to take some preventative measures.

3. Leverage AI to Predict Player Churn

From fast changes in player behavior to outdated data, many developers can grow frustrated with inaccurate results driven by traditional churn prediction strategies.

According to this study, AI can accurately predict churn, depending on its sensitivity. Ideally, your chosen platform includes predictive analytics for gaming. 

How to Predict Churn with AI

To successfully predict churn with AI, feed the relevant data into your AI tool and supply clear, effective prompts. Such as:

• Setting the context: Clarify that it’s a gaming company, the kind of game you’re analyzing, and the purpose of predicting churn.
• Data background: Supply key data metrics and the early signs of churn.
• Prediction request: Instruct the AI to predict the churn rate.

Note: AI platforms are constantly evolving and may not be primarily designed for gaming analytics. Users must balance their AI-driven results alongside proper analytical tools.

The Bottom Line on Player Churn Prediction

Churn prediction models are pointless if you can’t act upon them and stop players from leaving. Implementing preventative measures is vital. By adopting the above strategies, you can be proactive and ensure that many more users continue to enjoy your game.