Regression Tree Product Feature Recommendation
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Solution Overview
Problem
Traditional industries face challenges in product planning due to costly and risky in-person polling methods and reliance on intuitive professionals, while emerging industries leverage data effectively to target consumer segments, necessitating improved techniques for combining stakeholder interests and consumer preferences to enhance product performance.
Innovation Solution
The use of regression trees to recommend compositions of product features for weighted heterogeneous consumer segments by analyzing historical data, prioritizing segments based on stakeholder investment, and dynamically selecting features that optimize performance while adhering to production constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If in-person polling services are used to gather consumer preference data, then consumer preferences can be obtained, but the cost increases and sampling reliability decreases
Solution Approach 1:
The patent uses historical consumer data from existing product items as a copy or proxy for current consumer preferences, eliminating the need for costly in-person polling. This historical data serves as a reliable alternative that captures consumer preferences without the drawbacks of traditional polling methods.
Solution Approach 2:
The patent introduces regression trees and machine learning models as intermediaries between historical data and product planning decisions. These computational tools process historical consumer data and translate it into actionable insights about consumer preferences for new product compositions.
2Reliability
If highly intuitive professionals are used for product planning, then expert judgment is obtained, but cost increases and decision reliability decreases
Solution Approach 1:
The patent replaces the mechanical system of human expert judgment with an automated machine learning system. Regression trees and computational algorithms process data and generate product recommendations, substituting human intuition with a systematic, data-driven approach that is both more reliable and cost-effective.
Solution Approach 2:
The system enables product planning to be self-service through automated data processing. The machine learning models independently analyze historical data, identify patterns in consumer preferences, and generate product composition recommendations without requiring expensive human expertise at each decision point.
3Adaptability or versatility
If traditional product planning methods are used, then product development can proceed, but adaptability to different consumer segments decreases
Solution Approach 1:
The patent segments the consumer base into heterogeneous groups and uses regression trees to analyze preferences for each segment separately. This allows the system to identify optimal product feature compositions tailored to specific consumer segments, significantly improving adaptability compared to traditional one-size-fits-all planning methods.
Solution Approach 2:
The patent changes the parameters of product composition by using machine learning to identify optimal combinations of product features. The system varies product parameters (features, attributes, characteristics) based on predicted consumer preferences, enabling flexible adaptation to different market segments and conditions.
Data Source
AI summary
Product planning techniques are provided that recommend compositions of product features for weighted heterogeneous consumer segments using regression trees. An exemplary method comprises obtaining historical consumer data comprising product preferences for existing product items for multiple consumer segments; obtaining product features indicating characteristics for each existing product item; prioritizing the consumer segments by obtaining a weight indicating an interest in each consumer segment; computing a total performance metric, for each product item, by calculating a dot product between the consumer segment weights and respective preferences of the consumer segments regarding a given product item; obtaining a regression tree from the existing product items to predict the total performance metric in terms of corresponding product features; and selecting a combination of the product features to be used in future product items based on identified paths in the regression tree.