Deal Engine Predicting Conversion Rates for Merchant Offers
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Solution Overview
Problem
Merchants lack the ability to generate custom deals targeted to specific customer groups, limiting their effectiveness in marketing and sales strategies.
Innovation Solution
A deal engine that receives merchant information and historical data to predict conversion rates for candidate deals, allowing merchants to select and offer deals tailored to specific customer demographics, such as prospective, existing, and loyal customers, based on predicted conversion rates and revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If merchants offer generic deals to all customers, then implementation simplicity is maintained, but customer targeting effectiveness deteriorates
Solution Approach 1:
The patent segments customers into different types (prospective, existing, loyal) and generates customized deals for each segment. The deal engine divides the customer base into distinct groups and creates targeted deals for each segment based on their specific characteristics and behaviors, thereby improving adaptability without requiring complete system redesign.
Solution Approach 2:
The system changes parameters such as discount depth, deal structure, and promotion terms based on customer type. For example, prospective customers receive different deal parameters compared to loyal customers, allowing the system to adapt to different customer segments while using the same underlying deal generation platform.
2Productivity
If merchants use historical data and prediction algorithms to generate customized deals, then deal effectiveness and conversion rates improve, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing historical data, pre-segmenting customers, and pre-generating deal options before actual deal deployment. The deal engine prepares customized deals in advance based on historical performance data and customer segmentation, reducing computational complexity during real-time deal selection while maintaining high conversion rates.
3Measurement precision
If merchants analyze multiple candidate deals with historical information, then deal selection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by evaluating a limited set of pre-selected candidate deals rather than analyzing all possible deals. The deal engine identifies and evaluates only the most relevant candidate deals for each customer segment, achieving sufficient prediction accuracy without the need to exhaustively analyze every possible deal option, thus reducing processing time.
Data Source
AI summary
A server receives a request for a deal from a client device of a merchant, the request for a deal including a merchant identifier. The server then accesses merchant information based on the merchant identifier, the merchant information including a merchant category and a merchant location. Additionally, the server obtains a set of candidate deals based on the merchant category and merchant location. The server then obtains historical information for each candidate deal, the historical information corresponding to one or more conversion rates of the candidate deal when the candidate deal was previously offered by one or more merchants. Next, the server determines a predicted conversion rate for each candidate deal, based on the historical information for the respective candidate deal and selects a deal based on the predicted conversion rates for the set of candidate deals. Lastly, the server communicates data corresponding to the selected deal to the client device, the data including the predicted conversion rate for the selected deal.