Product Desirability Models Using Implicit Consumer Interaction Data
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Solution Overview
Problem
Current product design methods rely heavily on explicit consumer feedback, which is prone to errors and can distract from the purchasing experience, and lack insights into consumer desires based on in-store touch and feel interactions, limiting the ability to inform design decisions effectively.
Innovation Solution
A computer-implemented method generates product desirability models using data from product designer studios, in-store interactions, and local business environments, including visual, touch, and kinetic data, to provide design element recommendations that reflect consumer preferences and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If explicit consumer feedback is used for product design, then design decisions can be informed, but errors increase and purchasing experience is distracted
Solution Approach 1:
The patent replaces explicit consumer feedback mechanisms (surveys, interviews) with implicit behavioral data collection through sensors and analytics systems that track actual consumer interactions with products in-store, eliminating the need for direct consumer input while capturing more accurate preference information
Solution Approach 2:
The system introduces an intermediary layer of analytics processing that captures consumer behavior data through sensors and translates it into actionable design insights, separating the consumer experience from the feedback collection process to avoid distraction while maintaining information accuracy
2Loss of information
If in-store touch and feel interaction data is collected, then consumer desires can be understood, but data collection complexity increases
Solution Approach 1:
The system uses multi-functional sensor arrays that simultaneously capture visual, tactile, and kinetic data during consumer-product interactions, allowing a single integrated system to gather multiple types of interaction information without proportionally increasing complexity
Solution Approach 2:
The analytics system automatically processes and interprets sensor data without requiring manual intervention, with algorithms that self-adjust to different product types and consumer behaviors, reducing operational complexity while maintaining comprehensive data collection
3Measurement precision
If multiple data sources are integrated for product desirability models, then design insights are improved, but processing complexity increases
Solution Approach 1:
The system divides the integrated data processing into separate analytical modules that handle different data sources (sales data, interaction data, demographic data) independently before combining results, allowing each module to be optimized separately while achieving comprehensive analysis
Solution Approach 2:
The analytics engine dynamically adjusts processing parameters and model complexity based on the specific product type and available data quality, simplifying processing when possible while maintaining high measurement precision when needed
Data Source
AI summary
Generating a product design is provided. A plurality of product desirability models corresponding to a product is generated based on analysis of data corresponding to the product and features of the product that affect physical interactions between potential consumers of the product and the product. A set of insights into design of the product is generated based on the plurality of product desirability models corresponding to the product. A set of design element recommendations for the product is generated based on the set of insights into the design of the product generated from the plurality of product desirability models.