5  Conclusion

In this research, we have explored the factors influencing customer churn within a highly competitive telecommunications industry by analyzing a dataset containing demographic, service usage, and satisfaction information. The analysis primarily aimed to address key questions, such as how demographic and service-related factors contribute to churn, the influence of customer satisfaction on retention, and any geographical trends in churn behavior.

Our findings show that “Competitor” churn is the most significant reason for customers leaving, with several other reasons such as “Dissatisfaction,” “Attitude,” and “Price” also contributing. Specifically, we observed that customers who churn for “Competitor” reasons are generally characterized by higher data usage, higher monthly charges, and shorter tenures. These customers are more likely to switch to competitors offering better services or pricing. The “Dissatisfaction” and “Attitude” categories, however, are associated with lower data usage and charges, and appear to correlate with longer customer relationships, suggesting that negative experiences build over time.

Geographically, churn reasons show variation. In cities, “Competitor” churn remains dominant, but other reasons like “Attitude” and “Price” become more prominent in specific regions, such as “La Cruz” and “Hollis.” Conversely, the churn in California displays a more balanced distribution of churn reasons, with “Competitor” leading, but other factors like “Attitude” and “Dissatisfaction” also contributing substantially. This highlights the importance of regional strategies for customer retention.

Furthermore, the research revealed that factors like internet type, contract type, and satisfaction scores play key roles in churn behavior. Customers with no internet access are more likely to churn due to attitude or price issues, while those with internet access, particularly fiber optic, are more likely to churn due to competitor-related factors. This suggests that the type of internet service, as well as contract length, significantly influences churn patterns. Customers with short-term contracts tend to churn sooner, with “Competitor” being the main reason, while those in longer-term contracts show more varied churn behaviors, indicating that contract type should be considered when designing retention strategies.

Satisfaction also plays a crucial role in influencing churn. Across various internet types, customers with lower satisfaction scores are more likely to churn due to “Attitude” or “Dissatisfaction,” further underscoring the importance of service quality in retention efforts. Interestingly, satisfaction seems to have a less significant role for customers in certain service categories like DSL, where churn reasons are more varied and complex.

In conclusion, this analysis highlights that customer churn is influenced by a complex mix of factors, including service quality, pricing, customer satisfaction, and competition. By understanding these drivers, businesses can develop more targeted retention strategies tailored to specific customer segments based on usage patterns, satisfaction scores, and contract types. Additionally, geographic differences suggest that regional strategies could enhance retention efforts, making it essential for businesses to consider local factors when optimizing customer retention. This comprehensive understanding of churn behavior provides actionable insights for improving customer loyalty and ultimately enhancing business outcomes in the telecommunications industry.