CHURN ANALYTICS
Predict and prevent churn before it happens!
Key Figures from our use case deployment
Key results from our use case deployment. These results were measured up until 3 months of the model implementation. If progress is tracked for longer period, these %ages would increase
90%
CUSTOMER SCORING ACCURACY
20%
REDUCTION IN CUSTOMER CHURN
15%
INCREASE IN CUSTOMER LIFECYCLE VALUE
10%
INCREASE IN CUSTOMER SATISFACTION RATE
Business Challenge
Research suggests new customer acquisition cost can be 25X more expensive than retention, leading to indirect costs for companies. U.S. businesses lose an estimated $136 billion annually due to customer churn. Customer churn is a critical issue for retailers, with the industry facing an average churn rate of around 20-25%.
This has resulted in increased revenue losses and and spiked marketing expenses for retailers. Identifying and addressing the factors contributing to customer churn is essential for retailers to maintain profitability and customer loyalty.
Our Use Cases for AI enabled Churn Analytics
- Customer segmentation
- Customer sentiment analysis
- Churn root cause analysis
- Predictive churn modelling
- Customer lifecycle value prediction
- Personalized marketing and communication
- Customer support optimization
- A/B testing and optimization
Business Outcome of our Churn Analytics Model
Based on top identified churn drivers and root causes of customer churn, our model recommended mitigation measures to reduce churn and increase marketing effectiveness.
- 25% increase in the effectiveness of targeted marketing campaigns to at-risk customers, leading to higher retention rates
- 30% improvement in the relevance of personalized offers and recommendations
- 20% reduction in the number of customers flagged as false positives, allowing retailers to focus their retention efforts on truly at-risk customers
- 15% increase in overall customer lifecycle value (CLV) due to improved retention strategies and tailored customer experiences
- 10% reduction in customer churn-related costs, including acquisition expenses, as a direct result of implementing AI-driven insights
Customer Churn Predictive Models Used
Finarb's Churn Analytics model collects customer data, preprocesses it, and extracts relevant features for predicting churn. Multiple machine learning algorithms, including GBMs, Neural Networks, and Random Forests, are trained and evaluated using metrics like AUC-ROC, precision, and recall.
Hyperparameter tuning and ensemble learning techniques are employed to improve predictive performance. Finally, SHAP and LIME provide insights into the underlying reasons for customer churn and the impact of individual features on model predictions.
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If you would like to know more or discuss our use cases in detail