Digital Shelf Performance Scoring via Machine Learning
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Solution Overview
Problem
Merchants face challenges in effectively measuring and improving the visibility and performance of their products on digital shelves in ecommerce channels, lacking quantitative and qualitative analysis tools to optimize their online presence.
Innovation Solution
The development of methods and systems that utilize machine learning algorithms to calculate a score indicative of product performance on digital shelves, incorporating factors like shelf share, price, ratings, and packaging quality, and provide recommendations for enhancement, which can be dynamically generated and displayed on graphical user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If merchants increase product visibility on digital shelf through traditional marketing approaches, then product presence improves, but lack of quantitative measurement prevents effective optimization
Solution Approach 1:
The patent replaces traditional qualitative marketing assessment methods with a quantitative machine learning-based scoring system. The system automatically collects data from multiple sources (product listings, customer reviews, pricing data, competitor analysis) and processes it through trained ML models to generate objective performance scores, eliminating the need for manual market research and subjective evaluation.
Solution Approach 2:
The system creates a digital replica of the physical retail shelf environment by scraping and analyzing product data from ecommerce platforms. This digital twin allows merchants to measure and optimize their product performance in a virtual space before implementing changes in the actual marketplace, enabling risk-free experimentation and precise measurement without physical intervention.
2Measurement precision
If the system incorporates multiple factors (shelf share, price, ratings, packaging) to calculate performance scores, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the complex performance measurement task into distinct factor components: shelf share metrics, pricing analysis, rating evaluation, packaging quality assessment, and competitor comparison. Each factor is calculated independently using specialized algorithms, then aggregated into an overall performance score. This modular approach allows for precise measurement of each dimension while simplifying the overall system architecture and enabling targeted optimization of individual factors.
3Loss of information
If the system provides detailed recommendations for improvement, then actionable insights increase, but information processing requirements increase
Solution Approach 1:
The system implements a closed-loop feedback mechanism where performance scores and detailed analysis are continuously provided to merchants, who can then implement recommended changes. The system re-evaluates performance after changes are made, comparing new scores against previous baselines to measure improvement impact. This iterative feedback process ensures that actionable insights are not only generated but also validated, creating a continuous optimization cycle that maximizes the value of information provided while systematically managing data processing requirements.
Data Source
AI summary
The present disclosure provides methods and systems for quantifying item performance in a digital shelf. A method for quantifying item performance in a digital shelf may comprise: calculating a value associated with a shelf share of the given item; determining a set of factors for calculating a score indicative of the item performance on the digital shelf, wherein the set of factors includes the shelf share; generating, using a trained machine learning algorithm, the score based on the set of factors; and displaying the score within a graphical user interface (GUI) on an electronic device.