Consumer Segmentation via Value-Based Vector Clustering
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Solution Overview
Problem
Conventional methods of consumer segmentation based on demographic or behavioral data are inefficient and ineffective as they fail to account for individual differences in motivations and values, leading to overly burdensome and costly personalized marketing efforts that may not resonate with all consumers.
Innovation Solution
A system and method that filters a population to identify groups based on value preferences and value gaps through surveys with paired comparisons, generating vectors and clustering members into segments, and creating predictive models to estimate segment membership, allowing for tailored promotions to be delivered to client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized promotional content is created for each individual consumer, then marketing effectiveness and revenue increase, but time consumption, complexity, and cost increase significantly
Solution Approach 1:
The patent segments the consumer population into distinct groups based on shared motivations and values rather than treating each consumer individually. This segmentation allows marketers to create targeted promotions for groups while avoiding the burden of fully personalizing content for every single consumer, thus resolving the contradiction between marketing effectiveness and time consumption.
Solution Approach 2:
The patent changes the segmentation parameters from conventional demographics to motivation-based and value-based characteristics. By using psychographic parameters (motivations and values) instead of demographic parameters, the system achieves better marketing effectiveness while reducing the complexity of personalization, as consumers with similar motivations respond similarly to promotional content.
2Productivity
If segmentation is based on conventional demographic or behavioral categories, then marketing efficiency improves, but effectiveness decreases because consumers with same demographics may have different motivations and values
Solution Approach 1:
The patent fundamentally changes the segmentation parameters from demographic/behavioral categories to motivation-based and value-based psychographic parameters. This allows the system to maintain efficiency through grouping while improving effectiveness by capturing the underlying motivations that drive consumer decisions, such as achievement, affiliation, and power motivations.
Solution Approach 2:
The patent adds a new dimension to consumer segmentation by introducing psychographic measurements (motivations and values) alongside or instead of traditional demographic dimensions. This dimensional shift allows consumers to be grouped by their internal drives and values, creating more homogeneous and responsive segments that improve marketing effectiveness without sacrificing efficiency.
3Loss of information
If detailed individualized marketing is implemented, then promotional relevance increases, but complexity and cost of the marketing system increase
Solution Approach 1:
The patent uses segmentation based on motivation and value profiles to group consumers with similar responses to promotional content. This approach maintains high promotional relevance by matching content to motivational drivers while reducing system complexity compared to fully individualized marketing, as the segmentation model requires fewer data points and computational resources than full personalization.
Data Source
AI summary
A system including a processor may filter a population to identify a group having members, and deliver, over an electronic network, a survey to members of the group to determine value preferences and value gaps. The survey may be made according to a set of paired comparisons or other related techniques. The system may generate vectors according to the determined value preferences and value gaps for members of the group, which may have lengths equal to numbers of determined values for the members. The system may cluster members of the group into segments by calculating patterns of differences between generated vectors. Results of this process enable the creation of a predictive model to estimate segment membership, which can be used in the generation of a promotion for display corresponding to a clustered segment, and deliver the promotion to a client device associated for display.