Mobile Device Movement Data for Store Product Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumer packaged goods (CPG) manufacturers face challenges in understanding geographic demand for their products, as existing systems lack the ability to efficiently track and recommend product distribution across disparate areas, leading to inefficient provisioning and marketing strategies.
Innovation Solution
An automated platform that generates product-based recommendations using mobile device movement data to analyze store visit patterns and predict consumer behavior at a granular level, such as Census Block Groups, allowing for targeted product development and distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CPG manufacturers use traditional geographic demand tracking methods, then they can understand demand at broad national or county levels, but they cannot efficiently track demand at discrete local levels such as specific retailer locations or neighborhoods
Solution Approach 1:
The patent segments geographic demand measurement into discrete retail trade areas and neighborhoods by analyzing mobile device movement data. Instead of measuring demand at broad national or county levels, the system divides the geographic space into smaller units (trade areas around specific retailers) and measures demand independently for each segment, enabling precise local demand tracking while maintaining manageable system complexity through modular data processing
Solution Approach 2:
The patent introduces mobile device movement data as an intermediary to bridge the gap between traditional broad geographic measurements and discrete local demand tracking. Mobile devices serve as mediators that naturally track consumer movement patterns, providing indirect but accurate information about which consumers visit which retail locations, thereby enabling precise geographic demand measurement without requiring complex direct tracking infrastructure at each store
2Loss of information
If CPG manufacturers implement comprehensive product distribution tracking across disparate geographic areas, then they can understand regional differences, but they cannot efficiently provision and recommend products across these areas
Solution Approach 1:
The patent implements feedback loops where mobile device movement data continuously informs product provisioning decisions. The system collects data on which consumers visit which retail locations, analyzes this feedback to identify unmet demand patterns, and uses these insights to recommend optimal product placements and distributions across different geographic areas, creating a continuous improvement cycle that enhances both information accuracy and provisioning efficiency
Solution Approach 2:
The patent changes the parameters of product distribution from broad regional allocations to location-specific recommendations based on actual consumer movement patterns. By analyzing mobile device data to determine which consumers visit which retail trade areas, the system dynamically adjusts product provisioning parameters (which products to stock, where to place them, and in what quantities) to match actual local demand, thereby reducing information loss while maintaining high productivity through automated data-driven decision-making
3Adaptability or versatility
If CPG manufacturers use broad geographic marketing strategies, then they can reach large audiences, but they cannot effectively target specific consumer regions with in-demand products
Solution Approach 1:
The patent applies preliminary action by using mobile device movement data to identify emerging demand patterns and consumer preferences before manufacturers commit to product development or marketing campaigns. The system analyzes movement data to predict which products will be in demand in specific geographic regions, allowing manufacturers to pre-plan and pre-position products and marketing efforts in advance, thereby enabling highly adaptable targeted marketing without losing time in the product development and distribution process
Data Source
AI summary
Product provisioning and recommender systems and methods are described for generating product-based recommendations for geographically distributed physical stores based on mobile device movement. In various embodiments, a server aggregates mobile device movement data originating from a plurality of mobile devices geographically distributed with respect to a physical store of a geographic trade area. A provisioning application (app) executing on the server and analyzing the mobile device movement data, assigns geographic region descriptors defining a geographical consumer region for each mobile device of the plurality of mobile devices. The provisioning app generates a network store graph model based on a set of regional probabilities and a set of trade area probabilities. The provisioning app implements the network store graph model to generate, and provide to physical store operator(s), product-based recommendations for a target geographic consumer region selected from the geographical consumer region(s) and in a proximity to the geographic trade area.