In the world of online gaming, the competition is fierce and operators are constantly looking for ways to improve customer retention and reactivation. Building customer loyalty and keeping players engaged within a brand is crucial for success in this industry.
Operators often analyze player churn rates to determine how many players remain active after a certain period of time since their first deposit. This could be three, six, 12, or 18 months. By studying this data, operators can identify patterns such as 30% of players leaving within the first quarter, 20% within six months, and the remainder gradually dropping off until a stable point of loyal players is reached.
One common question that arises in this analysis is, “I may be getting 3,000 new players every month, but why do I only have 1,400 active players in total?” This discrepancy highlights the importance of understanding player behavior and implementing strategies to retain and engage them.
Machine learning (ML) plays a significant role in this process. ML is a subset of Data Science that uses data to make predictions based on a predictive model. Developing these models involves a lifecycle of eight crucial steps, including tasks like data exploration, model development, and validation.
While machine learning may seem like a mysterious black box, it requires a deep understanding of data and complex challenges to be tackled by data science teams. These challenges include tasks like task definition, data exploration, and model development, all of which are critical to the success of implementing ML solutions.
One of the key applications of machine learning in the gaming industry is predicting player churn. By developing a model that scores existing players based on their early activity, operators can identify which players are most likely to churn and target them with specific retention strategies. This approach has been shown to increase the lifetime value (LTV) of player cohorts by 20%.
The diagram above illustrates how this targeted approach outperforms a random distribution, with the top 20% of players identified by the model being 3.5 times more effective at retaining players. By leveraging machine learning and data analysis, operators can make more informed decisions and optimize their player retention strategies for long-term success.
