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Predicting player churn based on their historical gaming behaviour

Introduction

The gambling industry is a highly competitive sector and like in any other industry, the key to success is to not only acquire new customers but retain them. Player churn is one of the biggest problems for providers as it is the tendency of players to stop using a service. This is where the role of machine learning (ML) algorithms and artificial intelligence (AI) becomes crucial. ML AI looks at the historical gaming data of the player to prevent player churn by identifying patterns that may lead to player disengagement and thus help operators to take preventive measures to increase player retention.

Challenges

It is also important to note that the problem of player churn prediction is not without its challenges. The first challenge is the large volume of data that is produced in the gambling industry which makes it difficult to capture and understand. The second challenge is that developing accurate models is difficult due to the intricacies and variations of each player’s behavior. Some of the factors that may influence the models include game preferences, betting habits, win/loss record, and even the duration of play of each player. The level of customer engagement also expected from the companies in providing a personalized gaming experience also adds to the challenge of the task. Also, there are issues of regulations and data privacy as well.

AI Solutions

These challenges have been solved by AI and ML. Large data sets are not an issue for sophisticated ML algorithms that are capable of working through multiple layers of player data to search for trends that may indicate the player is likely to churn. For example, a change in the betting habits or a reduction in the number of bets placed may indicate that a player is likely to leave. Player behaviour can also be modelled with deep learning, which enables understanding of what might make a player churn. There are many AI-based predictive analytics platforms in use today including DataRobot and RapidMiner that are leveraged by companies such as BetBuddy and Playtech for customer churn prediction and prevention. In addition, there is a technique called reinforcement learning which is a type of machine learning that can be used to constantly improve the predictive models.

Benefits

There are a number of advantages that can be derived from the integration of ML AI in the process of estimating player churn. It reveals valuable information that can be applied to create powerful retention strategies. It enables the operators to provide targeted promotional offers and bonuses to the players according to their playing patterns thereby increasing the customer involvement. This also enables the operators to address problems that might make a player leave before he does so, which could prove costly in terms of revenue loss. Some of the other advantages include; better customer segmentation and targeting, increased player lifetime value and improved customer experience.

Return on Investment

The ROI of applying ML AI in predicting player churn can be very valuable. This is because the loss of customers can be very costly and the effects can be reduced through keeping customers. For instance, a decrease in the churn rate by 5% can boost the profits by 25% to 125%, as stated by Bain & Company. It also revealed that it is less expensive to keep the existing customers and this also adds to the overall ROI. Furthermore, the enhanced customer experience that comes with personalized interactions may also result to increased customer retention and spending hence increasing the ROI.

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