Predictive Attitudinal and Message Responsiveness System
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Solution Overview
Problem
Current database marketing systems lack accuracy in predicting consumer attitudes, motivations, and behaviors, often relying on assumptions rather than empirical research, leading to uncertain and ineffective marketing strategies with low response rates.
Innovation Solution
A system and method for independently predicting attitudinal and message responsiveness by using empirical research from a population sample to classify individuals or households in a database, providing actionable insights into preferred message themes, communication channels, timing, frequency, and sequencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If consumer attitudes and preferred marketing message themes are assumed and assigned to a segment without independent empirical research, then the marketing process is simplified and can be implemented quickly, but the accuracy and reliability of the predicted attitudes and behaviors deteriorate
Solution Approach 1:
The system performs preliminary empirical research on a sample population before full implementation. Attitudinal and behavioral data are collected in advance through surveys and research, then used to create predictive models that can be applied to the entire target population, combining advance preparation with accurate predictions.
Solution Approach 2:
The patent replaces assumption-based manual segmentation with automated statistical modeling and predictive analytics. Computer-based algorithms analyze empirical data to generate predictions about consumer attitudes and behaviors, substituting mechanical data processing for human judgment and assumption-making.
2Adaptability or versatility
If all individuals in a database are segmented into groups sharing distinct demographic and lifestyle characteristics, then the marketing can be targeted to specific groups, but the response rates remain low at 1-2% for direct mail marketing
Solution Approach 1:
The system applies different predictive models and messaging strategies to different consumer segments based on their specific attitudinal and behavioral characteristics. Each segment receives tailored communications designed to match their unique preferences and motivations, improving relevance and response rates.
Solution Approach 2:
The system uses empirical research data and actual consumer responses to continuously refine and validate predictive models. By comparing predicted attitudes with actual consumer behavior and responses, the system learns and improves its accuracy over time, leading to better targeting and higher response rates.
3Quantity of substance
If the target audience is communicated with using excessive and ineffective communications, then the marketing coverage is increased, but the marketing returns are reduced due to saturation and audience fatigue
Solution Approach 1:
The system applies the principle of partial action by sending communications only to consumers who are predicted to respond positively, rather than to the entire target audience. This selective approach avoids saturation and waste while maintaining adequate coverage of responsive segments.
Solution Approach 2:
The system optimizes communication parameters such as frequency, timing, and channel selection based on predicted consumer preferences and behaviors. By adjusting these parameters according to segment-specific insights, the system maximizes impact while minimizing waste and audience fatigue.
Data Source
AI summary
The present invention provides a system, method, software and data structure for independently predicting attitudinal and message responsiveness, using a plurality of attitudinal or other identification classifications and a plurality of message content or version classifications, for a selected population of a plurality of entities, such as individuals or households, represented in a data repository. The plurality of predictive attitudinal (or identification) classifications and plurality of predictive message content (ore version) classifications have been determined using a plurality of predictive models developed from a sample population and applied to a reference population represented in the data repository, such as attitudinal, behavioral, or demographic models. For each predictive attitudinal (or identification) classification, at least one predominant predictive message content or version classification is independently determined. The exemplary embodiments also provide, for each predictive attitudinal classification, corresponding information concerning predominant communication media (or channel) types, predominant communication timing, predominant communication frequency, and predominant communication sequencing.