Anonymous Transaction Data Profiling for Targeted Marketing
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Solution Overview
Problem
Retailers face challenges in understanding customer spending habits, as customers are typically secretive about their expenditures when asked by third parties, limiting the effectiveness of conventional market research in developing targeted marketing strategies.
Innovation Solution
A system and method that utilizes anonymous customer purchasing behavior to develop marketing strategies for known customers by sorting transactional data into profiles based on traits like age and gender, determining underlying purchasing behavior characteristics, and adjusting marketing strategies in real-time to influence known customers' purchasing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional market research asks customers about their spending habits, then retailers can gather customer insights, but customers become secretive and untruthful about their expenditures
Solution Approach 1:
The patent introduces anonymous aggregated spending data as an intermediary between the retailer and the customer. Instead of directly asking customers about their spending (which causes secrecy), the system collects anonymous data from multiple sources, aggregates it, and presents it back to customers as third-party information. This intermediary layer removes the direct confrontation that causes customers to become untruthful, while still providing the retailer with valuable spending insights.
Solution Approach 2:
The system creates copies of customer spending behavior data in anonymous form. Rather than relying on customers to report their own spending (which they may hide), the system collects actual spending data from various sources, anonymizes it, and uses these copies to inform marketing strategies. This allows the retailer to base decisions on real spending patterns rather than potentially false self-reports.
2Loss of information
If retailers collect detailed customer spending data, then they can develop targeted marketing strategies, but they face privacy law compliance challenges
Solution Approach 1:
The patent extracts the personally identifiable information (PII) from customer spending data, retaining only the anonymous behavioral patterns. By taking out the identifying elements while preserving the spending behavior information, the system enables targeted marketing based on purchasing patterns without storing or processing data that would violate privacy laws. This extraction allows the retailer to use spending data safely under privacy regulations.
Solution Approach 2:
The system changes the parameters of customer data from identifiable to anonymous by removing personal identifiers while preserving spending behavior characteristics. This parameter transformation allows the data to be used for marketing purposes without triggering privacy law violations, as the data no longer contains personally identifiable information while still maintaining the behavioral insights needed for targeted marketing.
3Reliability
If retailers use anonymous aggregated data to inform marketing, then they can avoid privacy issues, but they lose the ability to target specific known customers
Solution Approach 1:
The patent merges anonymous aggregated spending data with known customer information in a controlled manner. The system uses anonymous data to establish baseline spending patterns and categories, then selectively applies this information to known customers based on their purchase histories and profiles. This merging allows the retailer to maintain privacy compliance while still delivering customized marketing to specific customers by combining general anonymous insights with individual customer data.
Solution Approach 2:
The system performs preliminary analysis of anonymous spending data to establish spending categories, patterns, and benchmarks before applying this information to known customers. By doing the heavy lifting of pattern recognition and category development upfront using anonymous data, the system creates a framework that can then be efficiently applied to individual customers without requiring real-time processing of sensitive information, thus maintaining both privacy compliance and customer-specific targeting capability.
Data Source
AI summary
Methods and systems for enhancing customer purchasing behavior are disclosed. A transactional data evaluator receives a first set of aggregated customer transaction data, having no PII and a second set of aggregated customer transaction data, having PII. The transactional data evaluator uses the first set of aggregated customer transaction data to develop a plurality of customer profiles, compares the second set of aggregated customer transaction data with the plurality of customer profiles, and assigns each customer associated with the second set of aggregated customer transaction data to at least one of the plurality of customer profiles. A customer profile specific marketing strategy is then generated based on an evaluation of each customer associated with the second set of aggregated customer transaction data with respect to the assigned at least one of the plurality of customer profiles.