Measuring the Success of Retention Management Models Built on Churn Probability, Retention Probability, and Expected Yearly Revenues

In this paper, we claim that optimal retention management models should consider not only churn probability but also retention probability and expected revenues from target customers. To validate our claim, we develop and compare five retention management models based on churn probability, retention probability, expected revenues, and combination of these models along with different evaluation metrics. Our experimental results show that the retention management model with the highest accuracy in predicting possible churners is not necessarily optimal because it does not consider the probability of accepting retention promotions. In contrast, the retention management model based on both churn and retention probability is the best in terms of predicting customers who are most likely to positively respond to retention promotions. Ultimately, the model based on expected yearly revenue of customers accrues the highest revenues across most target points, making it the best model out of five retention management models

Customer Retention: Reducing Online Casino Player Churn Through the Application of Predictive Modeling

With the potential expansion of legalized online gaming in the United States as well as in the global market, customer retention is critical to the continued growth and success of an online casino. While customer churn prediction can be an essential part of customer retention efforts, it has received very little attention in the gaming literature. Using historical online gaming data, this study examines whether player churn (attrition) can be predicted through an application of a decision tree data mining algorithm called Exhaustive CHAID (E-CHAID). The results of this empirical study suggest that the predictive model based on the E-CHAID method can be a valuable tool for identifying potential churners and understanding their churn behavior. Additionally, this study shows how the classification rules and propensity scores extracted from a decision tree churn model can be used to identify players at risk of churn. The patron play and visitation parameters that are closely associated with churn are also discussed. This study contributes to the gaming literature by focusing on online players’ churn prediction through a data-driven approach. Finally, it discusses proactive approaches for churn prevention

Systematic Literature Review on Customer Switching Behaviour from Marketing and Data Science Perspectives

This paper systematically examines the literature review in the field of customer switching behavior. Based on the literature review, it can be concluded that customer switching behavior is a topic that has been widely researched, with a focus on various industries, particularly banking and telecommunications. Research trends in this area have shown a positive direction in recent years, and the amount of research being done in marketing and data science is relatively balanced. In marketing, correlational studies are predominant, with a focus on identifying relationships between customer satisfaction, price-related variables, attractiveness of alternatives, service failure, quality, and switching costs to switching behavior. The PPM model is also gaining popularity as an important development for switching behavior because it considers both push and pull factors. Data science research has shown promising results in predicting customer switching behavior, with each research paper achieving good predictive accuracy. However, research gaps spanning the fields of marketing and data science need to be addressed to provide a comprehensive understanding of the drivers of customer switching behavior. Overall, the literature review shows that customer switching behavior is an important concern for businesses, and further research in this area is essential to gain a better understanding of customer behavior and develop effective strategies to retain customers