When buyers search on eBay, the default order of results is called Best Match. It’s designed to show the most relevant listings and incorporates a variety of quality, demand and market-driven factors, such as item popularity, pricing, shipping, regions, seller details, etc.
eBay says it has significantly invested in efforts to enhance the machine learning algorithms to improve the quality of search results for buyers. The most recent update to Best Match personalizes the search results for each buyer.
Because eBay’s marketplace is extremely diverse, a single category can find inventory ranging from a few cents to hundreds or thousands of dollars. There are 174 million active buyers on eBay, and every individual has their criteria to define a perfect item and price range for the product they are shopping for.
Previously, when users searched for a product on eBay, the Best Match algorithm would show the same results, irrespective of the individual needs. eBay has now introduced the price propensity feature in the search ranking to customize the search results based on a user’s price preference.
Price propensity takes into consideration a user’s past purchases at eBay, as well as the inventory they were interested in. Based on this information, a machine-learned model tries to predict the user’s price propensity for future purchases and uses this to surface search results that are personalized to the user’s price preferences.
As an outcome, shoppers will see customized search results according to their preferences, which should lead to higher engagement with those results.
eBay Best Match Tweak Only First Step
The company didn’t provide further details on what other ideas it is exploring. But changes to the Best Match algorithm usually lead to significant changes in how seller listings surface and explain why some sellers will see drops in sales, while others will see sudden increases in sales.
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