Company Name: LoyaltyPro
Project Objective:
LoyaltyPro is struggling to retain its customer base due to limited insights into churn patterns and ineffective retention strategies. The inability to identify at-risk customers early has led to a decline in customer loyalty and increased acquisition costs. The objective is to predict customer churn using advanced data models and propose actionable retention strategies, such as personalised discounts and rewards, supported by visual dashboards.
Requirement:
- Develop a model to identify at-risk customers using transaction and behaviour data.
- Propose retention strategies like discounts, rewards, and follow-ups.
- Create dashboards to display churn risk and retention effectiveness.
Detailed Features:
- App Features:
- Churn prediction model using customer data.
- Retention strategy module for recommendations.
- Dashboards to visualise churn and retention metrics.
- Deliverables:
- Phase 1: Churn prediction framework and dashboard wireframes.
- Phase 2: Prototype for prediction and retention analysis.
Technology Suggestion: Excel for modelling, Power BI for dashboards, Python for Churn modelling using libraries such as scikit.
Expected Output:
- Phase 1: Churn prediction workflows and mockups.
- Phase 2: Retention analysis prototype.