How SidelineSwap increased sales conversions by enriching product data
About
SidelineSwap is the leading marketplace for new and used sports equipment in the United States. At the beginning of 2024, we formed a partnership with a specific focus: enriching listings of used equipment with key data (model, year, size, and color...) to support customer decision-making.
The enrichment directly impacts sales conversion.
Problem
Low conversion due to lack of product data and difficulty in finding the desired product.
Koya's Solutions
How we use machine learning for enrichment:
01
Attribute extraction from the product title
We extract the required information, regardless of how it is written, and categorize it. In this example, we extracted the loft angle information from golf clubs.
02
Color Inference from Product Images
We extract the main colors of the equipment from the sellers' images. In the example below, we included blue and orange in the color attribute.
03
Attribute Extraction from Product Images
Through the sellers' images, we can determine whether the skis have foot bindings and include this information as a product attribute. We can also extract the curvature radius and categorize it within the available options.
04
Attribute Extraction from Public Data
By searching public data, we can complete information about attributes that are not included in the images and titles provided by the sellers.
Results
By enriching data, Koya improved search functionality, filters, and customer trust, reinforcing SidelineSwap's position as the leading marketplace for used sports equipment in the U.S.
48
.000
Enhancements Made
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