Building e-warranty business unit
Using AI-driven cost prediction and lead-scoring to develop a comprehensive e-warranty business strategy for a manufacturing company
Client
Leading Manufacturing Company
Challenge
A market-leading manufacturing company was looking to develop a new e-warranty business unit to extend customer value and generate additional revenue streams. They needed to understand the financial viability and customer acquisition potential of this new venture.
- Lack of visibility into warranty cost structures and prediction models
- No systematic approach to identify and prioritize high-value warranty prospects
- Risk of entering a new market without proper financial modeling and forecasting
Our Approach
We developed a comprehensive AI-driven approach combining cost prediction models with lead-scoring algorithms to evaluate the business potential of an e-warranty service offering.
- Developed an AI-driven analytics tool that integrated multiple data sources including demographic information, consumer behavior patterns, competitor locations, and historical sales data
- Created a sophisticated ranking algorithm that evaluated locations based on projected sales potential
- Built a predictive model to forecast sales volumes for specific product lines at each potential location
- Built a predictive model to forecast ROI of device installation
- Designed an intuitive interface for Head of distribution and sales teams allowing business users to visualize location data and run scenarios
Results
The comprehensive analysis provided the client with actionable intelligence and a clear roadmap for their e-warranty business launch, enabling them to make informed strategic decisions.
- Adapted by the client for providing actionable intelligence for the client's retail expansion strategy
- Created a prioritized ranking of existing store locations for specific product focus
- Identified several high-potential locations that had been overlooked by conventional analysis
- Generated precise sales volume predictions that exceeded traditional forecasting accuracy
- Tool continued to improve over time through machine learning from actual performance data
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