Enhancing churn forecasting with sentiment analysis of steam reviews

Customer churn prediction is crucial for businesses seeking to retain their customer base. In this study, we present an enhanced approach for churn forecasting by integrating sentiment analysis of Steam reviews into the churn forecasting model, leveraging the vast Steam database. Steam, a prominent digital distribution platform for video games, boasts a large user dataset. Our dataset comprises 12,000 user reviews across four game types. Our approach involves extracting sentiment polarity to generate a sentiment score, which is then embedded in the time series data used for churn modelling. We developed a churn forecasting (CF) model using Vector Autoregression, incorporating the sentiment analysis (SA) predictive model with a Support Vector Machine using grid search and hyperopt, achieving an accuracy of 89%, precision of 84%, recall of 85%, and an F1 score of 84%. Experimental results demonstrate that our approach outperforms traditional churn prediction models, significantly enhancing churn forecasting accuracy. The CF model with SA yielded the lowest Mean Absolute Percentage Error (MAPE) of 10.37% among the four models developed for each game type, indicating its efficacy. These findings underscore the value of integrating sentiment analysis of user reviews to gain valuable insights for businesses aiming to reduce churn and enhance customer satisfaction. In information systems, this study emphasizes understanding game industry stakeholders’ demands, including shareholders and customers through machine learning development research. The game industry reacts proactively to stakeholder complaints by creating an inclusive and responsive organizational culture using sentiment analysis. Ultimately, the data analytics methodology gives insights into advanced consumer behaviour and information systems supporting long-term success and producing strategic business outcomes.