A study reveals how Amazon’s A9 search algorithm can be enhanced to better connect shoppers with relevant seasonal products. By optimizing listings for seasonal search patterns, sellers stand to significantly boost visibility and sales of seasonal goods on Amazon.
On Amazon, shoppers use the same search queries year-round, even though their intent may differ by season. For example, “toys” retrieves different relevant products in December vs July. These are known as seasonal queries, where the desired results vary over time.
Most seasonal queries are implicit – they don’t contain obvious seasonal keywords like “summer” or “winter”. Identifying and handling these implicit seasonal queries is challenging:
Prior research modeled seasonality via monthly sales distribution per product. However, this is a product-level signal that doesn’t fully capture time-dependent query semantics.
To address this, researchers developed an enhanced search algorithm incorporating both query text and the time of year. This Season-enhanced BERT (SE-BERT) model better matches shoppers with relevant seasonal products.
SE-BERT detects seasonal queries based on the variability of top purchased products across months, using Jaccard index to measure variability. Queries with high variability are labeled as seasonal.
Tested on search logs with 2800 seasonal queries and 120 products per query. SE-BERT achieved gains of 1-2% on relevance metrics vs. non-seasonal BERT baseline.
These search improvements could significantly increase visibility for sellers with seasonal inventory when shoppers are most likely to purchase:
To maximize sales, optimize listings to align with seasonal search patterns:
This study demonstrates both the opportunity and viability of seasonality modeling in ecommerce product search. While focused on Amazon, the core principles could also benefit other product search engines. With thoughtful adaptation, these innovations in time-aware, semantic, and contextual modeling can substantially improve product relevance.
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