Use Case:  Predicting Customer Churn Based on Feedback Sentiment

.

Objective:

To predict customer churn based on sentiment scores derived from customer feedback, identifying the most significant factors leading to customer attrition.

Methodology:

Approach: Logistic Regression (Binary Classification)

  • Dependent Variable:

    • Churn (1 = Churned, 0 = Retained)

  • Independent Variables:

    • Sentiment Score (from customer feedback, using NLP to classify sentiment as positive, neutral, or negative)

    • Frequency of Complaints (number of complaints reported by the customer)

    • Customer Support Interaction (number of interactions with customer support)

    • NPS Score (Net Promoter Score: score from 0–10)

Each independent variable is treated as a continuous or categorical input, and the model predicts the likelihood of a customer churning.

Key Findings:

Driver Odds Ratio P-value Interpretation
Sentiment Score (Negative) 3.20 0.001 Strong predictor — customers with negative sentiment are 3.2 times more likely to churn.
Frequency of Complaints 2.50 0.002 Significant — customers who frequently complain are 2.5 times more likely to churn.
Customer Support Interaction 1.80 0.045 Moderate impact — increased customer support interactions correlate with a higher likelihood of churn.
NPS Score (Detractors) 4.50 0.000 Very significant — detractors (score 0–6) are 4.5 times more likely to churn compared to promoters.

 

Insightful Takeaway:

Negative sentiment and high frequency of complaints are the strongest indicators of customer churn. Customers who express negative sentiment are significantly more likely to leave the company, as are those who have a high volume of complaints. Additionally, NPS score shows a strong correlation, with detractors being much more likely to churn. This analysis highlights the importance of monitoring sentiment and addressing customer complaints promptly to prevent churn.