
40+
Global brand resale sites powered by Marqo
+17.1%
Avg ATC rate uplift
How Archive Powers Smarter Search Across 40+ Brand Resale Sites with Marqo
Company Overview
Archive is the leading resale infrastructure platform for premium consumer brands, building and operating branded secondhand marketplaces for 40+ global brands including The North Face, lululemon, New Balance, and Oscar de la Renta. Archive gives brands full ownership of their resale channel. From product listing to shopper discovery, Archive handles all the underlying technology, operations, and intelligence. As resale becomes a core revenue channel for premium brands, Archive's platform scales across categories from apparel and footwear to accessories and lifestyle goods.
Challenges
Archive operates resale at a scale no single brand could achieve alone. With 40+ brand sites running simultaneously, each with its own catalog, shopper behavior, and discovery patterns, search quality becomes a platform problem, not a brand problem. A single weak link in search relevance affects every shopper across every site.
Before Marqo, Archive's brand sites relied on keyword-based search that struggled with the realities of resale commerce. Analysis of The North Face Renewed USA revealed that 95% of search traffic was lost before Add to Cart, with a 30% query abandonment rate. Shoppers searching for specific items received no results due to typo sensitivity and synonym blindness, forcing 11% of users to rephrase their queries entirely.
Resale adds layers of complexity that keyword search cannot handle: inventory that turns over constantly, one-of-a-kind items with no restock, and shoppers who browse very differently than they do on primary retail sites. Archive needed a search infrastructure that could learn each brand's unique shopper intent signals across dozens of sites simultaneously.
Solution
Archive partnered with Marqo to deploy AI-native search across its portfolio of brand resale sites. Marqo's pixel was installed across Archive properties, capturing click, add-to-cart, and purchase signals from day one. These signals fed into custom relevance models trained specifically for each brand's catalog, understanding the nuances of how a North Face resale shopper searches versus how a New Balance resale shopper discovers products.
Marqo was deployed for 100% of traffic on each site, with rigorous A/A testing used to measure impact precisely. As shopper data accumulated, the models improved continuously, delivering better ranking and relevance without manual merchandising effort from Archive's team.
Catalog-trained relevance per brand: Each Archive site received a relevance model trained on its specific catalog and shopper behavior, ensuring results reflect what that brand's resale customers actually want.
Typo tolerance and semantic understanding: Marqo eliminated query reword friction that caused shoppers to abandon search, understanding misspellings, synonyms, and natural language queries natively.
Resale-aware ranking: Marqo surfaced in-stock items intelligently while maintaining SEO coverage for out-of-stock listings, handling the unique inventory dynamics of resale.
Platform-scale deployment: Marqo's infrastructure deployed across Archive's full portfolio simultaneously, with Merchandising Studio and Analytics Center enabled across all sites.
Results
Marqo deployed across Archive's brand portfolio delivered consistent performance uplifts, validated through rigorous A/A testing on 100% of live traffic. Below are two brand-level deep dives.
What This Means
Archive's platform model means search quality has to work at scale, not just for one brand, but for dozens simultaneously. With Marqo, Archive now has a search infrastructure that learns the specific intent signals of each brand's resale shopper, delivering consistent uplifts across every site in the portfolio without a separate implementation per brand. As the model continues to learn from accumulated shopper data, results improve automatically, making Marqo a compounding asset for Archive's entire brand network.
One AI search infrastructure delivering consistent uplifts across 40+ brand sites with different catalogs and shoppers
Typo tolerance and semantic understanding eliminated the query abandonment that was losing 95% of search traffic before ATC
Continuous learning from shopper signals means relevance improves automatically as each brand site grows
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