Predicting Customer Needs via Location-Based Cost Analysis
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Solution Overview
Problem
Current systems fail to effectively recommend onsite shopping locations to customers based on their real-time location and needs, and do not facilitate merchants in reaching potential customers with targeted advertisements efficiently.
Innovation Solution
A computing system that analyzes transaction data and location information to predict customer needs, recommending nearby locations and offers, while also aggregating data to suggest price points and promotions to merchants based on demographic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If customers manually research deals online and via phone calls to find the best deals nearby, then they can find some savings, but this consumes significant time and effort
Solution Approach 1:
The system automatically collects and analyzes transaction data, location data, and offer data without requiring customer initiation. The computing system proactively identifies recurring purchases, determines customer needs, calculates travel costs, and generates personalized recommendations, making the information service self-activating rather than requiring manual customer research
Solution Approach 2:
The system uses transaction data feedback to identify recurring purchases and infer customer needs. By continuously monitoring spending patterns and comparing them with available offers and travel costs, the system dynamically generates personalized deal recommendations that adapt to changing customer behavior and preferences
2Reliability
If merchants use targeted advertising technologies to place advertisements based on customer traits, then they can reach relevant customers, but the effectiveness is limited without real-time location and need context
Solution Approach 1:
The system pre-calculates travel costs between customer locations and various merchants before making recommendations. By determining geographic relationships and associated travel expenses in advance, the system can proactively identify and present deals that are both relevant to customer needs and logistically feasible, enhancing advertising effectiveness before the customer makes a decision
Solution Approach 2:
The system transforms static demographic targeting into dynamic, multi-parameter recommendations by incorporating real-time location data, transaction history, recurring purchase patterns, offer details, and calculated travel costs. This creates a comprehensive customer context profile that enables highly targeted and effective advertising
3Ease of operation
If the system calculates travel costs and recommends locations based on both product cost and travel cost, then customers can find truly cost-effective options, but the system complexity increases
Solution Approach 1:
The system merges multiple data sources and calculation methods into a unified recommendation engine. By combining transaction data analysis, location data processing, offer data integration, and automated travel cost calculation into a single cohesive system, it provides comprehensive cost-effective recommendations without requiring separate manual processes for each factor
Solution Approach 2:
The computing system acts as an intermediary that automatically performs the complex task of calculating travel costs between customer locations and merchants, then integrating this information with product costs and customer preferences. This intermediary function handles the computational complexity internally while presenting simplified, actionable recommendations to the customer
Data Source
AI summary
A computing system predicts a need of a customer based at least in part on a location of the customer, recommend a physical location to the customer based on the predicted need using location-based costs of living, and recommending offers to merchants based on historical activities of a plurality of customers.