Analyzing Customer Churn in Telecommunications: Insights from Data Patterns and Trends

Customer churn has become a major issue for the telecom industry as the cost of getting new customers is much higher than that of keeping the existing ones. The patterns of churn are not straightforward and are affected by demographic, behavioral, and regional factors, which in turn interact in subtle ways. A thorough and descriptive analysis is carried out in this research using a real-world telecom dataset to identify the impact that a person’s age, gender, state or region, tenure, calls made, SMS usage and data consumption have on a person's decision to churn. The dataset goes through cleaning, processing and enrichment with engineered features in the form of tenure and age groups which allows for a deeper analytical insight. This study that focuses on three core research questions uncovers the following very clear churn patterns: young and mid-age groups have a higher propensity to churn, low-usage customers quit faster, and some states have consistently high churn rates due to regional competition. The results emphasize the significance of having the basic descriptive analytics before moving on to predictive modeling and also point out the demographic, behavioral, geographic dimensions which are vital for telecom retention strategies.

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