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Archive Case Study
New Balance Reconsidered
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New Balance Reconsidered logo

+33.96%

Add-to-Cart Rate

+32.90%

Revenue per User

Industry:Footwear
Business Unit:Resale & Recommerce
Use Case:Product Discovery
Channel:Website
Key Objectives:Increase Conversion, Grow Revenue per User
Products:
SneakersApparelAccessories

How New Balance Reconsidered Lifted Add-to-Cart Rate by 34% with Marqo

Company Overview

New Balance Reconsidered is New Balance's branded resale platform, operated by Archive. It offers authenticated pre-owned New Balance footwear and apparel, serving fans looking for classic colorways, limited editions, and everyday styles at secondhand prices. The platform gives New Balance a direct channel to capture resale revenue and attract new customers into the brand ecosystem.

Challenges

New Balance Reconsidered launched with a clear question: would shoppers convert at the rates the brand needed? In the period before Marqo went live, the data showed strong top-of-funnel interest but significant drop-off before purchase. Shoppers were finding the site but not converting through search, pointing to relevance and discovery as the bottleneck, not demand.

With 6,356 searches in the control period, only 565 resulted in an Add to Cart and 140 in a purchase. The data pointed clearly to relevance and discovery as the bottleneck, not demand.

Session data reinforced this: more than 52% of sessions ended within 2 minutes before Marqo, indicating shoppers were leaving quickly when they couldn't find what they were looking for.

Solution

Marqo was deployed for 100% of New Balance Reconsidered traffic. The pixel immediately began capturing shopper signals including clicks, add-to-carts, and purchase events, training a relevance model specific to New Balance Reconsidered's catalog and the way footwear resale shoppers search.

The model learned how New Balance shoppers browse: by silhouette, colorway, model number, and lifestyle category. It handled the natural language of sneaker discovery and ranked results to reflect real demand signals, surfacing the products shoppers were most likely to buy based on what others like them had clicked and carted.

  • Footwear-specific relevance: Marqo trained on New Balance Reconsidered's catalog to understand sneaker discovery intent: model numbers, colorways, silhouettes, and lifestyle categories.

  • Conversion-optimized ranking: Results were ranked to reflect real purchase intent signals, not just keyword matching, surfacing what shoppers were most likely to buy.

  • Improved shopper engagement: With more relevant results, shoppers stayed longer and explored more, with queries per user rising from 5.39 to 5.72 and session duration increasing by 11%.

  • Continuous model improvement: As more shoppers interacted with results, Marqo's model compounded. ATCs per user rose 29.82% as the model learned New Balance Reconsidered's demand patterns.

Results

After implementing Marqo for 100% of site traffic, New Balance Reconsidered saw immediate and significant impact:

+6.72%

Click-Through Rate

+33.96%

Add-to-Cart Rate

+34.46%

Purchase Conversion Rate

+32.90%

Revenue per User

+19.48%

Avg ATC Value per User

+11%

Median Session Duration

What This Means

New Balance Reconsidered launched into a crowded resale market where shoppers have options. Marqo gave the platform a search experience that converts from day one and gets better over time. The 34% lift in ATC rate and 33% revenue per user increase are not just search metrics: they represent shoppers finding the right product faster and committing to purchase. As the model accumulates more New Balance Reconsidered data, those gains will compound.

  • More than 1 in 3 additional shoppers added to cart with Marqo, with ATC rate rising from 8.53% to 11.42%

  • Revenue per user grew 32.90% as shoppers found and committed to the right product faster

  • Session duration rose 11%, with shoppers staying longer and exploring more with relevant results

Results measured against a 10-day pre-launch control period on New Balance Reconsidered. Outcomes may vary depending on catalog size, traffic volume, and implementation configuration.

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