Online gaming churn-prediction – 2024 challenges and new methodologies

Online gaming churn-prediction – 2024 challenges and new methodologies

We all know that in the online gaming sphere, where player loyalty is both a coveted currency and a constant challenge, being able to accurately predict churn is an important key.

Accurate churn prediction is essential to optimizing player retention, unlocking opportunities like precise cross-promotions, tailored re-engagement incentives, and ad-based monetization that does not interfere with experience or cannibalize retention.

In a nutshell, churn prediction analyzes player behavior patterns to identify those at risk of leaving, using advanced algorithms to trigger targeted interventions like re-engagement campaigns and game design optimization, ultimately boosting player retention and profitability


Online-gaming retention – Industry benchmarks

Recently, research by AppsFlyer found an average retention rate of less than 30% on day 1 for mobile games, dropping to less than 8% on Day 7, as you can see in the chart below (These numbers are also dropping year over year as you can see):


Another research by GameAnalytics reveals a retention rate of between 2% to 20% after 7 days:

Day 30 data shows, unsurprisingly, much lower retention rates:

Do you need extra proof – this table is based on Adjust research:


As it may look harsh in the eyes of outsiders, industry professionals are familiar with these trends and are hardly surprised. However, understanding these benchmarks is important when looking at the soaring UA costs.

Retention, hence churn, directly determines whether a user shows a positive or negative ROI, making accurate churn prediction crucial for extending engagement and maximizing lifetime value.

Moreover, retaining existing players is significantly cheaper than acquiring new ones. Researches show that acquiring new users can cost 6-7x more than retaining existing ones, and can increase revenues by over 25%!


The problem with today’s churn prediction strategies:

Traditional churn prediction methods, while providing a valuable starting point, struggle to keep pace with the dynamic reality of modern online gaming. Their reliance on historical data and static player profiles creates several key limitations that result in insufficient accuracy:

1 .Data acquisition lag, model stability issues, resource limitations, engineering bottlenecks, all these challenges result in models being trained on months-old data, therefore struggling to adapt to the ever-evolving landscape of games. Major releases, feature updates, and changing player behavior can render their predictions obsolete. Retraining on fresh data becomes a constant, resource-intensive effort that impacts scalability.

2 . Newbie Blind Spot: With a typical requirement of 6-12 months of player data, these models are blind to the nuances of new players. Early churn signals are missed, leaving valuable opportunities for engagement untapped.

3 .Frustration in the Shadows: Real-time behavioral patterns, crucial indicators of frustration and impatience, often go undetected by traditional methods. Subtle changes in gameplay, interaction with features, and communication patterns can be missed, leading to inaccurate churn predictions.

4 . The Blurry Lens of Granularity: Traditional models often paint players with a broad brush, failing to identify specific cohorts or sub-segments within the churn-prone user base. This lack of granularity damages targeted interventions and personalized re-engagement strategies.
As a consequence, game publishers, including tier-1 developers that are known to have enormous resources turned to technological innovation, are openly reporting their frustration driven by the relatively low accuracy of their churn prediction capabilities.


AI-based, sensor-based – Behavioral analytics for the rescue

As discussed, traditional churn prediction methods often grapple with static player profiles and data silos.

Enter behavioral analytics, a transformative approach that leverages the power of AI and mobile device sensor signals to paint a dynamic picture of player behavior right from the first session.

By analyzing touch gestures, screen tilt, and other subtle cues, behavioral analytics unlocks deep insights into player engagement, satisfaction, and even frustration with unmatched accuracy.

Unlike methods that rely on long-term historical data or must wait for specific events to trigger predictions, behavioral analytics operates in real-time and is based on immediate physical signals that aren’t predefined by the developers like logs.

Think of it as having a window into the player’s mind, offering an immediate understanding of their emotional state and potential churn risk. This allows for timely interventions, like personalized incentives or targeted re-engagement campaigns, before players reach the point of departure.

The accuracy of AI-powered behavioral analytics is surpassing industry standards by a significant margin due to richer data sets, real-time processing, and of course the advantages that come with AI technology.

Studies have shown models trained on mobile sensor data can predict churn with much higher accuracy compared to all other tools, even within the first 24 hours of gameplay.

This opens up a world of possibilities, especially when it’s about new players, where churn prediction is no longer a guessing game.

Also, by capturing the nuances of player behavior, behavioral analytics offers a significant advantage over traditional methods that are dependent on pre-defined events. It tracks subtle shifts in engagement, pinpointing potential churners before they become lost causes and right when frustration starts, for example.

In essence, behavioral analytics isn’t just about predicting churn, but about truly understanding players’ emotional state as reflected in their behavior.


Specific advantages:

Insights as early as 24 hours from the initial session: Game professionals are struggling to gain insights from new players. Waning playtime, hesitant gestures, and frustrated taps –all these are gold-worth indicators when discovered before they escalate into full-blown churn. Sensor-based early warning system allows developers to intervene before players reach the tipping point. It can therefore predict churn for fresh players, within the first 24 hours of their journey.

Churn risk segmentation – A laser-focused approach: Gone are the days of one-size-fits-all interventions. Behavioral analytics segments players based on their churn risk and playing behavior, enabling developers to craft targeted strategies. Players on the brink receive immediate attention, while others at moderate risk benefit from personalized nudges to keep them engaged.

Engagement Optimization: By analyzing player in-game behavior within specific game features, developers can pinpoint what sparks joy and what breeds frustration in means that are much more accurate compared to event-based logs. This data-driven insight allows product teams to optimize in-game elements, balancing challenge with reward, and ultimately creating experiences that keep players hooked.


The future of churn prediction: AI at the forefront

As the gaming landscape evolves, the ability to predict and prevent player churn becomes ever more critical.

The reliance on static data and limited ability to capture real-time emotions is no longer enough. As in many other industries, AI and machine learning step into the spotlight here as well. Continuous advancements in AI and machine learning algorithms are already paving the way for more accurate and nuanced churn prediction. By leveraging advanced neural networks and deep learning techniques, these models can analyze vast amounts of data, including mobile sensor signals, in-game behavior, and even player communication patterns. This allows them to identify subtle shifts in player engagement, frustration, and satisfaction, providing early warning signs of potential churn before it becomes irreversible.

Their ability to analyze ultra granular data signals at scale and in real-time allows them to accurately recognize patterns that otherwise would have stayed hidden, just because such behavioural patterns are not limited to the human mind.

Today, the standard in the industry is rapidly changing and churn models can predict a player’s risk of leaving within hours of their first login, supporting developers, product, and acquisition teams to exceed their KPIs.

This is about deeper connections with players and getting to know them better, just like humans do with each other, fostering a more engaging and satisfying gaming experience for all.

By understanding the player’s emotions, motivations, and frustrations, developers can create games that truly resonate and keep players coming back for more. In this way, AI becomes not a tool for manipulation, but a bridge to a healthier, more sustainable gaming ecosystem.
Why it makes so much sense? Think about human beings. We all tend to “learn” other people that we interact with. What’s their style? What makes them happy? Annoyed? Impatient? We are not simply leaning on explicit communication, but also on nuanced gestures. Eye movements, body position, hand movements, and more.

That is because we all know that body language tells a story that is worth a thousand words. Behavioural analytics is harnessing AI-based device sensor data in order to do the same when humans interact with software, just like in online gaming.

The future of churn prediction and retention optimization will be about crafting games that will “read’ the player and will instantly adapt to minor behavioral changes, as reflected in the device sensor signals.



In the dynamic world of online gaming, predicting and preventing player churn is paramount for sustained success. Current churn prediction methods face challenges such as data lag and a lack of granularity, making them inaccurate and therefore prompting frustration among industry leaders.

Enter behavioral analytics—a transformative, AI-powered approach that advances churn prediction capabilities. By analyzing mobile device sensor signals, this method provides real-time insights into player emotions and behaviors, predicting churn with much higher accuracy compared to all other methods, and as fast as within the first 24 hours of gameplay.

Its advantages include early insights, precise churn risk segmentation, and data-driven optimization of in-game features.

Looking ahead, AI and machine learning take the spotlight, using advanced neural networks to decode nuanced player emotions. This shift towards predicting churn risk within hours of a player’s first login heralds a transformative era, fostering deeper connections with players and creating a healthier gaming ecosystem.





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