Automated Marketing System Using Consumer Data Segmentation
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Solution Overview
Problem
Retailers face challenges in quickly responding to changing marketing conditions, such as fluctuating consumer demand, due to limitations in adjusting inventory and pricing efficiently using existing electronic user interfaces.
Innovation Solution
A system and method that utilize consumer data from service information displays to segment data into groups for statistical analysis, compute projected marketing effectiveness, and apply rules to adjust marketing attributes like price or inventory levels based on pre-defined thresholds, using machine-learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If retailers use existing electronic user interfaces to display information to consumers, then information presentation capability is improved, but response speed to changing marketing conditions deteriorates
Solution Approach 1:
The system enables self-service by automatically analyzing consumer data, segmenting audiences, computing marketing effectiveness, and executing marketing actions without human intervention. The automated marketing system processes consumer inputs from information display devices, applies pre-defined rules and machine learning models, and adjusts marketing attributes autonomously, eliminating the need for manual analysis and decision-making while maintaining continuous operation.
Solution Approach 2:
The system implements feedback by continuously monitoring consumer data from information display devices, analyzing the effectiveness of marketing actions through computed metrics, and using this information to automatically adjust subsequent marketing strategies. The closed-loop system uses machine learning models that learn from historical and real-time data to optimize marketing attribute adjustments, creating a self-improving feedback mechanism that accelerates response to changing conditions.
2Adaptability or versatility
If retailers manually adjust inventory levels and pricing, then marketing attribute control is possible, but efficiency and speed of adjustment deteriorates
Solution Approach 1:
The system performs self-service by automatically adjusting inventory levels and pricing based on real-time consumer data analysis. The automated marketing system executes marketing actions defined in pre-defined rules without human intervention, continuously monitoring consumer behavior and autonomously modifying marketing attributes to maintain optimal performance while maximizing productivity.
Solution Approach 2:
The system applies dynamics by making marketing attributes adjustable and responsive to changing conditions. The automated system continuously modifies inventory levels, pricing, and other marketing attributes based on real-time data analysis and computed effectiveness metrics, enabling dynamic adaptation rather than static manual adjustments. This dynamic approach allows the system to respond flexibly to fluctuating consumer demand and market conditions.
3Measurement precision
If retailers use segmented data groups for marketing analysis, then marketing precision is improved, but data processing complexity increases
Solution Approach 1:
The system applies segmentation by dividing consumer data into distinct segmented data groups based on consumer characteristics, behavior patterns, and preferences. This segmentation enables precise targeting of marketing actions to specific consumer segments, improving marketing analysis precision. The system automatically creates and maintains these segments using pre-defined criteria and machine learning models, managing the complexity through automated processes rather than manual intervention.
Solution Approach 2:
The system handles data processing complexity through self-service automation. The automated marketing system performs data segmentation, analysis, and processing without human intervention, using pre-configured rules and machine learning algorithms to manage the computational complexity. This automation transforms complex data processing tasks into routine automated operations, maintaining precision while reducing the perceived complexity for users.
Data Source
AI summary
A method for automatically performing marketing actions. The method includes receiving consumer input relating to a product and/or service (“Product/Service”) from a service information display, receiving contextual input associated with the consumer input, placing the received consumer and contextual input into one or more segmented data groups, wherein each segmented group includes consumers data and associated contextual data having similar characteristics, and wherein each segmented group has sufficient consumers data and associated contextual data to enable statistical analysis. The method further includes computing for the Product/Service a projected marketing effectiveness corresponding to a change to one or more marketing attributes of the Product/Service, where the change to one or more marketing attributes defines a marketing action specified in a rule associated with one of the one or more segmented groups, and applying the rule in response to the projected effectiveness being equal or exceeding a corresponding pre-defined value.