Dynamic Customer Behavior Data Processing for Personalized Marketing
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Solution Overview
Problem
Current solutions for generating customized marketing messages fail to utilize all available customer data elements, resulting in only 75% effective marketing, as they do not account for dynamic changes in customer behavior and lifestyle, leading to generic advertisements that do not cater specifically to individual customers.
Innovation Solution
A computer-implemented method and apparatus that processes customer behavior data, including metadata from various sources such as facial recognition, vehicle information, and shopping patterns, to generate highly personalized marketing messages dynamically, using a combination of internal and external data to create tailored marketing offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-generated advertisements are used to target large population segments, then advertising coverage is improved, but personalization to individual customers deteriorates
Solution Approach 1:
The patent segments customer data into multiple categories including demographic data, transaction data, profile data, and behavioral data. This segmentation allows the system to process and analyze different types of data separately before generating personalized advertisements, thereby achieving both broad coverage through aggregated data and precision through individual data analysis.
Solution Approach 2:
The patent implements dynamic advertisement generation where ads are created in real-time based on current customer behavior patterns, transaction history, and profile information. This dynamic approach replaces static pre-generated ads with living, evolving advertisements that adapt to individual customers' changing preferences and situations, achieving high personalization without sacrificing coverage.
2Quantity of substance
If customer profile data from questionnaires and surveys is used, then basic customer information is obtained, but dynamic behavior data and lifestyle changes are missed
Solution Approach 1:
The patent establishes continuous monitoring of customer behavior through multiple data sources including transaction records, profile updates, and behavioral patterns. This continuous data collection replaces one-time questionnaires, ensuring the system always has current information about customer preferences, purchases, and life changes, thereby preventing information loss while maintaining data completeness.
Solution Approach 2:
The patent implements feedback loops where customer behavior data is continuously analyzed and used to refine advertisement generation. The system learns from customer responses to advertisements and adjusts future ad generation based on this feedback, improving both data completeness and behavioral insight over time through iterative refinement.
3Device complexity
If limited data elements are used for marketing, then system complexity is reduced, but marketing effectiveness deteriorates
Solution Approach 1:
The patent segments data processing into distinct modules: demographic data processing, transaction data analysis, profile data management, and behavioral pattern recognition. This segmentation allows the system to handle complex data through organized, manageable processes, improving marketing effectiveness while keeping system complexity organized and controllable through modular architecture.
Solution Approach 2:
The patent introduces an intermediary data processing layer that transforms raw data from multiple sources into structured behavioral patterns and customer insights. This intermediary layer simplifies the complexity by abstracting the raw data processing details, allowing the marketing system to access processed insights without directly handling the complexity of multi-source data integration.
Data Source
AI summary
Customizing digital media marketing messages using customer behavior data is provided. In one embodiment, patterns of events in customer event data are identified to form customer behavior data. The customer event data comprises metadata describing a customer associated with a retail facility. The customer behavior data is processed to form dynamic data. A customized marketing message is generated for the customer using the dynamic data.