CPG | Customer Analytics
Customer Lifetime Value Analysis
Objective
CPG companies aim to accurately predict and optimize customer value across their lifecycle. They need a solution that can forecast future customer behavior, identify high-value segments, and develop targeted strategies for value maximization while considering both monetary and non-monetary factors in the CPG ecosystem.
Challenges
- Complex purchase patterns and seasonality.
- Multi-channel customer interactions.
- Data quality and integration issues.
- Dynamic market conditions.
- Varying customer lifecycle stages.
- Attribution modeling complexity.
- Limited customer identification.
- Long-term prediction accuracy.
Solution Proposed
- The solution implements Survival Analysis, Random Forest, XGBoost, and Recurrent Neural Networks for value prediction and pattern recognition.
- The computation steps include a predictive churn modeling system, customer segmentation module, value optimization recommender, multi-channel attribution model, and real-time scoring framework.
- Create a dynamic scoring system that adapts to changing customer behaviors and market conditions while incorporating both direct and indirect sales channels.
Outcome
- 40% improvement in CLV prediction accuracy.
- Increase in customer retention rates.
- Growth in the high-value customer segment.
- Reduction in customer churn.
- Better allocation of marketing resources.
- Increase in average customer spend.
Design & Thinking Wins
- Dynamic CLV calculation engine.
- Automated reporting system.
- Interactive visualization dashboard.
- What-if scenario analyzer.
- Customer journey tracker.
- Risk assessment module.
- Cross-sell opportunity identifier.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
