Item Substitution Identification Using Need State Segmentation
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Solution Overview
Problem
Current methods in retail environments rely on broad product categorization based on branding, which does not fully capture consumer decision-making processes and need states, leading to inefficiencies in product presentation and customer targeting.
Innovation Solution
The system generates customized messaging using stock-keeping unit (SKU) information and customer data to create personalized messages by transforming received information into standardized attributes, defining message recipes, and using machine learning algorithms to rank and select products for dynamic message generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If broad product categorization based on branding is used, then product presentation is simplified, but consumer decision-making process is not fully captured
Solution Approach 1:
The patent segments products into need states rather than broad categories. Each need state represents a specific consumer requirement (e.g., 'quick meal', 'healthy snack') and groups items that fulfill that need, enabling detailed capture of decision-making processes while maintaining organized presentation.
Solution Approach 2:
The system applies local quality by creating granular need state categories that reflect specific consumer preferences and decision criteria. Instead of uniform broad categorization, each need state is tailored to capture particular consumer needs, allowing differentiated product presentation based on local consumer requirements.
2Device complexity
If broad product categorization is used, then device complexity is reduced, but customer targeting effectiveness decreases
Solution Approach 1:
The patent divides the product universe into discrete need states, each representing a specific consumer requirement. This segmentation enables precise customer targeting by matching products to specific need states rather than using broad categories, significantly improving targeting effectiveness while maintaining manageable system complexity through structured organization.
Solution Approach 2:
The system changes the classification parameter from brand-based or category-based to need-state-based organization. This parameter transformation enables direct alignment with consumer decision-making criteria, improving targeting effectiveness without requiring overly complex analysis mechanisms.
3Productivity
If need state-based categorization is implemented, then customer targeting is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-defining need states and their associated product criteria before actual customer analysis. This allows the system to efficiently match products to customer needs using pre-established frameworks rather than performing complex real-time analysis, improving targeting while managing processing complexity.
Solution Approach 2:
The need state acts as an intermediary between raw product data and customer requirements. This intermediate layer simplifies data processing by providing a structured translation framework that converts product attributes into need state matches, reducing the complexity of direct product-to-customer matching while maintaining precise targeting.
Data Source
AI summary
Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.