Real-Time Offer System Using Geolocation and Merchant Category Analysis
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Solution Overview
Problem
Merchants struggle to determine real-time market share and competitor activity, leading to inefficient use of offers that can either attract or fail to attract customers, as they lack data on competitors' transactions and market dynamics.
Innovation Solution
A computer-implemented method and system that analyzes transaction data from multiple merchants within a specific category and geographic area to determine real-time market activity, comparing data to initiate targeted offers based on market share thresholds, using geohash algorithms and machine learning to optimize offer timing and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If merchants use offers to attract customers, then customer acquisition may improve, but offer efficiency decreases when market conditions are not properly assessed
Solution Approach 1:
The system implements feedback by continuously monitoring real-time transaction data from multiple merchants and using this information to dynamically adjust offer strategies. The feedback loop collects market share data, competitor activity, and transaction trends to inform offer timing and targeting, ensuring offers are deployed when most effective rather than using static or guesswork-based approaches.
Solution Approach 2:
The system performs preliminary action by pre-calculating market share thresholds and competitor benchmarks before deploying offers. It proactively monitors market conditions and prepares offer strategies in advance based on predicted market scenarios, allowing merchants to act quickly when optimal conditions arise without delay for data analysis.
2Measurement precision
If merchants monitor competitor transactions in real-time, then market share accuracy improves, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by implementing a centralized processing layer that aggregates transaction data from multiple merchants through standardized interfaces. This intermediary layer simplifies the complexity by providing a unified data collection mechanism that translates diverse merchant data sources into standardized market share metrics, reducing the burden on individual merchants while maintaining high measurement precision.
3Productivity
If offers are deployed frequently to capture market share, then revenue opportunity increases, but offer effectiveness decreases due to poor timing
Solution Approach 1:
The system applies dynamics by making offer deployment strategies adaptive rather than static. It continuously adjusts offer timing, targeting, and parameters based on real-time market conditions, competitor actions, and transaction patterns. This dynamic approach allows the system to capitalize on emerging opportunities as they arise while avoiding offer deployment during unfavorable market conditions, thereby optimizing both revenue potential and offer effectiveness.
Data Source
AI summary
Provided is a computer-implemented method, system, and product for providing real-time offers based on geolocation and merchant category. The method includes receiving data associated with a plurality of transactions conducted by a plurality of merchants, the plurality of merchants including a first merchant associated with at least one first merchant category, determining an area of interest based on an estimated travel time to the first merchant from the plurality of merchants, determining a subset of merchants of the plurality of merchants within the area of interest associated with the at least one first merchant category, determining real-time market activity data associated with the first merchant and each merchant of the subset of merchants, and identifying a trend of increasing and/or decreasing transactions for the first merchant and each merchant of the subset of merchants.