Dynamic Retail Route Optimization for Product Exposure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing navigation systems in physical retail stores lack the ability to dynamically recommend routes that pass by products of interest to the user based on their purchase history and merchant-specific criteria, leading to suboptimal customer experiences.
Innovation Solution
A computer-implemented method that identifies a target product and recommends additional products by analyzing user profiles and merchant criteria, generating routes that guide users through the store while passing by recommended products, using cost values assigned to segments of the store layout to optimize the path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the system provides only the shortest route to the target product, then the navigation efficiency is improved, but the opportunity to expose users to recommended products is lost
Solution Approach 1:
The route recommendation system dynamically adjusts the path based on multiple factors including user preferences, purchase history, and merchant priorities. Instead of providing a static shortest route, the system generates dynamic routes that balance navigation efficiency with product exposure opportunities, allowing the route to adapt to different user profiles and merchant goals
Solution Approach 2:
The system changes the parameters of route selection by introducing cost values that incorporate both distance metrics and product exposure metrics. By modifying the cost function to include recommended product proximity, the system transforms the optimization problem from purely shortest-path to a multi-objective optimization that balances navigation efficiency with sales opportunity
2Adaptability or versatility
If the system customizes routes based on user profiles and purchase history, then the personalization is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user profile data, purchase history, and product recommendations before route calculation. User preferences and purchase patterns are analyzed in advance to create personalized product interest profiles, which are then used to quickly generate customized routes without requiring complex real-time computations
Solution Approach 2:
The system creates simplified representations or copies of user profiles and purchase histories that capture essential patterns without requiring full data processing. These condensed user models enable fast route customization by matching users with pre-computed recommended product sets, reducing the computational burden during actual navigation
3Adaptability or versatility
If the system incorporates merchant-specific criteria and dynamic product priorities, then the merchant control is improved, but the system complexity increases
Solution Approach 1:
The system implements a universal interface that allows merchants to set priorities for different product categories and individual products through a standardized configuration mechanism. This multi-functional approach enables merchants to control various aspects of product promotion (category-level and item-level priorities) through a single integrated system, avoiding the need for multiple separate control mechanisms
Data Source
AI summary
A merchant may operate a retail store that users are able to visit in person in order to view and purchase products. When a user visits the store, the user might not know where a desired product (“target product”) is located. Computer technology may help direct the user to the target product in real-time. In some embodiments, a model of passable areas and the location of products in the retail store may be determined, e.g. by a merchant device. In some embodiments, when the user visits the retail store, a computer generates a product recommendation, e.g. based on user-specific information, and a route in the retail store is determined for the user. In some embodiments, the route in the retail store may be determined using the model based on the target product, the user's location in the store, and one or more recommended products.