Consumer Spending Tracking via Centralized SKU Data Aggregation
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Solution Overview
Problem
Existing methods struggle to effectively track and collect information on consumer spending habits, especially in offline retail settings, due to difficulties in identifying repeat customers and sharing information between merchants, and challenges in monitoring cash payments and multiple payment card usage.
Innovation Solution
A communication device with a software application that receives and processes information about purchases made at retail locations, using SKU codes or other product identifiers, and uploads this data to a remote server for analysis, enabling the collation of consumer spending patterns and providing personalized offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If merchants collect detailed consumer information to improve marketing targeting, then marketing effectiveness is improved, but information sharing between merchants becomes difficult due to competitive concerns
Solution Approach 1:
A centralized server acts as an intermediary that receives consumer spending information from multiple merchants and payment networks. This mediator enables information aggregation and analysis without requiring direct sharing between competing merchants, thus resolving the contradiction between information collection and competitive privacy concerns
Solution Approach 2:
The system creates a universal platform that serves multiple merchants and payment networks simultaneously. The server processes information from various sources (online merchants, offline retailers, different payment networks) and provides consolidated consumer spending analytics to all participants, enabling broad information utility without direct competition
2Measurement precision
If payment networks track transactions to monitor consumer spending, then spending tracking accuracy is improved, but tracking becomes difficult when consumers use multiple cards from different networks
Solution Approach 1:
The system merges data from multiple payment networks and card types into a unified consumer spending profile. By consolidating transaction information from different sources at the server level, the system achieves comprehensive tracking accuracy without requiring individual networks to track multiple cards separately
Solution Approach 2:
The system segments consumer spending information by merchant type (online vs. offline), product category, and time period, allowing precise tracking and analysis of spending patterns across different dimensions while simplifying the overall data structure
3Loss of information
If consumers manually enter purchase information to enable tracking, then data collection completeness is improved, but consumer convenience deteriorates due to additional manual effort
Solution Approach 1:
The system enables automatic information collection where merchants and payment networks self-submit consumer spending data to the centralized server. This eliminates the need for consumers to manually enter purchase information, as the system automatically aggregates data from transaction sources
Solution Approach 2:
The system performs preliminary data collection and organization by merchants and payment networks before consolidation. Information is prepared and transmitted to the server in advance, reducing any potential consumer effort to minimal or zero input requirements
Data Source
AI summary
When a consumer makes a purchase at a retail location, a communication device associated with the consumer receives information specifying the type of product purchased. This may be done by the consumer entering the information manually, by the communication device receiving a data file over an interface, or by a camera of the communication device capturing a picture of a receipt. The information may be, or may be converted into, stock-keeping unit (SKU) data. The data can be analyzed to derive consumer spending patterns and to suggest product offers to the consumer.


