AI-Driven Lead Scoring, Win-Back Targeting, and Price Optimization for Scalable Growth

A B2C subscription-based service provider with a nationwide footprint, offering digital and physical products to customers across various channels. The company aimed to improve conversion rates, reduce churn, and optimize pricing strategies using data science.

The Need:
The client faced challenges in identifying high-potential leads, understanding customer drop-off patterns, and setting price points that aligned with customer sensitivity. Manual segmentation was time-consuming and subjective, leading to missed opportunities and inefficient marketing spend. They needed an AI system that could score leads, suggest retention campaigns, and recommend optimal pricing.

Our Solution:
Using AWS cloud services—including SageMaker, Lambda, S3, and QuickSight—we built a robust data pipeline and deployed ML models for three interconnected use cases:

1. Lead Scoring: Built predictive models using historical CRM and marketing data to classify leads by conversion potential.
2. Win-Back Targeting: Identified dormant or churned customers likely to re-engage based on behavior history and time decay models.
3. Price Optimization: Modeled price sensitivity and willingness-to-pay across customer segments using regression and uplift modeling techniques.

These models were integrated into the client’s CRM and marketing platforms to trigger automated campaigns and pricing adjustments in real-time.

Outcome Achieved:

• Delivered real-time lead scoring with over 80% accuracy for conversion prediction
• Increased marketing ROI through precise win-back campaigns on high reactivation potential customers
• Improved customer retention and revenue through personalized discounting strategies
• Enabled dynamic price testing and recommendations for various customer cohorts
• Reduced manual effort in segmentation and analysis by over 60%, enabling faster decision cycles