In-store product promotion system using shopper image prediction
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Solution Overview
Problem
Shoppers face frustration in locating specific products in large retail environments due to overwhelming signage and promotions, which can distract from finding desired items, and existing technologies fail to effectively direct consumers to lesser-known but highly desired products.
Innovation Solution
A computerized product promotion system that uses cameras to capture images of shoppers and processes them to identify selected products, employing a product prediction model to estimate likely purchases and display targeted messages on electronic devices near the shopper, allowing for personalized promotions without overwhelming the shopping experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stores promote popular products through signage, then sales of popular products increase, but less popular products go unnoticed and popular products may distract shoppers searching for other items
Solution Approach 1:
The system segments promotions by individual shopper needs rather than displaying uniform promotions for all customers. Each shopper receives personalized promotion recommendations based on their shopping cart contents, dividing the promotion strategy into individualized segments that avoid overwhelming any single shopper while increasing relevance.
Solution Approach 2:
The system applies local quality by providing different promotion information to different shoppers based on their specific shopping context. Rather than uniform signage, each shopper sees promotions tailored to their current shopping session, with promotions displayed locally on their mobile devices rather than through general store signage.
2Adaptability or versatility
If stores display multiple promotions to serve diverse consumer needs, then product visibility increases, but consumers become overwhelmed and experience frustration
Solution Approach 1:
The system applies partial action by selecting only the most relevant promotions for each shopper rather than displaying all available promotions. The promotion recommendation engine filters and prioritizes promotions based on shopping cart analysis, presenting a limited set of highly relevant options rather than overwhelming shoppers with excessive promotion information.
3Adaptability or versatility
If stores stock many products to satisfy diverse consumer wants, then consumer choice increases, but individual consumers experience frustration locating specific products
Solution Approach 1:
The system uses feedback from shopping cart analysis to dynamically adjust promotion recommendations. By continuously monitoring what products are already in the shopper's cart, the system provides feedback-driven recommendations for complementary products, enabling shoppers to efficiently discover relevant products without manually searching through the entire inventory.
Data Source
AI summary
A computerized product promotion system for use in a store is provided. The system comprises one or more processors configured to receive a plurality of captured images of a shopper in a current shopping session in the store and process the plurality of captured images to determine one or more products selected by the shopper during the current shopping session. The one or more processors are further configured to determine an identity of at least one target product, selected by a product prediction model, that is estimated to have a threshold minimum likelihood of being purchased by the shopper during a remainder of the current shopping session. The product prediction model receives as input the one or more products selected by the shopper during the current shopping session, and outputs the identity of the at least one target product.