Multimodal Product Image Similarity Analysis

This skill guides you on how to analyze and group products by the visual similarity of their main images. It helps Amazon sellers identify same-style products, detect competitor lookalikes, and organize product lists into visually coherent clusters.

Core Concepts

Product Image Similarity Analysis uses multimodal AI to compare the main images of products and automatically group them based on visual features such as appearance, color, composition, and material. It is a post-processing tool -- it operates on product data that has already been retrieved by a preceding step (e.g., product search, product recommendations).

Similarity threshold: The similarityThreshold parameter controls how visually close two products must be to land in the same group. It is an integer from 0 to 100 representing a percentage. A higher value means stricter matching (only near-identical images group together); a lower value means more lenient matching (broader visual clusters). The default is 60.

Single-brand group filtering: The includeSingleBrandGroups flag (default true) controls whether groups containing products from only one brand are included in the results. Setting it to false filters out single-brand groups, which is useful when the user wants to focus on cross-brand visual overlaps (e.g., competitor lookalike analysis).

Input Data Requirement

This tool requires a products list from a preceding step. It cannot fetch product data on its own. The typical workflow is:

1. Run a product search or recommendation tool to obtain a product list.
2. Pass that product list into this tool via refResultData for visual similarity grouping.