Dynamic Digital Shelves Personalizing Retail Displays
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Solution Overview
Problem
Current digital shelves in store environments are not personalized to individual customer interests and are static, failing to display products and services relevant to customers, unlike online commerce which leverages big data for personalization.
Innovation Solution
A dynamic digital shelf system utilizing big data to create a customer insights profile by combining internal and external customer data, product metadata, and store criteria to identify and display personalized products and services to customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital shelves display generic products available in the store, then all products can be shown to all customers, but the shelves cannot be personalized to individual customer interests
Solution Approach 1:
The system segments customers into different groups or individuals based on their profiles and interests, allowing personalized product displays for each customer rather than a generic display for all customers. This segmentation enables the digital shelf to adapt its content based on the specific customer viewing it.
Solution Approach 2:
The digital shelf transitions from a static, generic display to a dynamic, personalized display that changes based on customer identification and profile data. The system continuously adapts the product recommendations in real-time based on customer interests, making the display flexible and responsive to individual needs.
2Adaptability or versatility
If digital shelves display static product information, then the display system remains simple, but the shelves cannot adapt to changing customer interests or market conditions
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing customer profile data, interests, and purchase history before the customer actually visits the store. This advance preparation allows the digital shelf to quickly retrieve and display personalized products without requiring complex real-time analysis during the customer interaction.
Solution Approach 2:
The system uses feedback from customer purchase history, browsing behavior, and profile information to continuously refine and update product recommendations. The digital shelf learns from customer interactions and adjusts its displays based on this feedback, improving personalization over time while maintaining processing efficiency.
Data Source
AI summary
A dynamic digital shelf system includes an interface for identification of a customer: a digital shelf manager in communication with the interface to: retrieve a store's products and services available according to the store's inventory; retrieve an enriched customer profile of a customer's interests wherein the enriched customer profile is enhanced by big data; retrieve a product to product metadata map; develop a customer insights profile to weight the customer's interests with respect to the store's products and services according to the store's criteria in the product to product metadata map; identify the store's products and services matching the enriched customer profile for products and services using the customer insights profile; and output the identified store's products and services. Included is a display in communication with the digital shelf manager to display the identified products and services output from the digital shelf manager which are personalized to the customer.