Dynamic Retail Marketing System Using Sensor Data Segmentation
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Solution Overview
Problem
Current data processing systems for retail environments fail to utilize all available customer data elements to generate highly customized marketing messages, resulting in only 75% effective marketing efforts, as they do not account for dynamic customer changes such as unemployment, divorce, or recent lifestyle changes.
Innovation Solution
A computer-implemented method that processes external data from detectors like cameras, motion sensors, and pressure sensors to generate personalized marketing messages by combining this data with internal customer data using a data model, allowing for real-time analysis and delivery of customized messages on various display devices before or after the customer enters a retail facility.
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 (demographic data, transaction data, profile data, real-time data) and processes each segment through specialized modules. This allows the system to handle diverse customer types and situations individually while maintaining overall system efficiency, resolving the contradiction between broad coverage and individual personalization.
Solution Approach 2:
The patent implements a dynamic advertising generation system that continuously updates customer profiles with real-time data and adjusts advertisements accordingly. The system transitions from static pre-generated ads to dynamic, real-time personalized advertisements that adapt to changing customer situations, enabling both broad coverage and high personalization precision.
2Device complexity
If existing data elements are used for marketing, then data processing is simplified, but marketing effectiveness deteriorates to 75% of potential
Solution Approach 1:
The patent performs preliminary data collection and processing of multiple data types before generating advertisements. Customer profiles are continuously updated with real-time data in advance, so when marketing decisions need to be made, the system already has comprehensive, up-to-date information available, improving effectiveness without adding processing complexity at the decision point.
Solution Approach 2:
The patent introduces a centralized advertising generation system that acts as an intermediary between raw data from multiple sources and final marketing decisions. This intermediary layer processes, integrates, and contextualizes data from various sources, transforming complex multi-source data into actionable marketing insights and improving effectiveness while managing complexity.
3Manufacturing precision
If comprehensive customer data is collected and processed in real-time, then marketing personalization is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex data processing task into separate modular components: demographic data processing, transaction data processing, profile data processing, and real-time data processing. Each module handles specific data types with dedicated logic, making the overall complex system more manageable and maintainable while achieving comprehensive personalization.
Solution Approach 2:
The patent creates a universal advertising generation platform that can process multiple data types and serve diverse customer needs through a single integrated system. The core processing architecture is designed to handle various data formats and customer scenarios uniformly, reducing overall system complexity compared to separate specialized systems while maintaining high personalization capabilities.
Data Source
AI summary
A computer implemented method, apparatus, and computer usable program code for customizing digital media marketing messages. In one embodiment, external data is received from a set of detectors located externally to a retail facility to form external data. The external data is processed to form dynamic data. The set of dynamic data is analyzed using a data model to identify personalized marketing message criteria for the customer. A customized marketing message for the customer is generated using the personalized marketing message criteria. The customized marketing message is delivered to a display device associated with the customer for display.