+19.0%

Increase in Search Conversion rate

+17.0%

Increase in Search Revenue per user

Industry:

Fashion

Business Unit:

Ecommerce

Use Case:

Product Discovery & Conversion Optimization

Channel:

Website

Key Objectives:

Enhance Product Relevance, Streamline Shopper Experience

Products:

Apparel

Accessories

Footwear

How Fashion Nova Achieved a $130M Revenue Uplift with Marqo

Company Overview

Fashion Nova is one of the most recognizable fast fashion brands, known for trend forward drops and a high velocity catalog. Founded in 2006 and headquartered in Los Angeles, California, the brand operates primarily through a direct-to-consumer model. Running on Shopify as one of the platform’s largest customers, Fashion Nova relies on fast, accurate product discovery to support its high-volume e-commerce business.

Why Fashion Nova Evaluated Marqo vs. Algolia

As Fashion Nova’s catalog expanded and customer expectations increased, the team saw an opportunity to elevate its on site search and discovery experience. Shoppers were using more descriptive, nuanced language to find products, and Fashion Nova needed relevance that could interpret intent more accurately and evolve with an assortment that changes week to week.

The goal was to modernize product discovery in a way that could keep pace with new trends, new product language, and shifting shopper behavior, without relying on intensive manual tuning. This led Fashion Nova to evaluate next generation AI native search and discovery solutions. Marqo stood out for its ecommerce built foundation and its ability to deliver catalog trained relevance designed for fast moving retail environments.

Challenges

Fashion Nova’s scale and rapid product drops created an opportunity to evolve search and discovery into a fashion native experience. The team prioritized stronger performance across long tail queries, including highly descriptive, multi attribute, and misspelled searches, helping shoppers reach the right products faster with fewer re searches.

Fashion Nova also wanted discovery that performs consistently across the catalog even as product content and attributes vary by collection and launch timing. Beyond core search, Fashion Nova aimed to unlock richer discovery experiences powered by semantic and visual relevance and multilingual support. Bringing a more intuitive and natural discovery journey to Fashion Nova shoppers.

The team evaluated AI native solutions built to capture product, style, and shopper intent across fashion catalogs and support discovery at scale with less ongoing manual effort. In a controlled A/B test against their existing baseline, Marqo demonstrated the impact of catalog trained, intent aware relevance tailored to Fashion Nova’s products.

Solution

Marqo installed a pixel on Fashion Nova’s site to capture shopper interaction signals, including clicks, add to cart events, purchases, and browsing behavior. This interaction data allowed Marqo understand how Fashion Nova shoppers search for specific styles, fits, and occasions, and how they explore new drops across category and collection pages, ensuring relevance reflects real engagement patterns.

Marqo ingested these signals to train a custom LLM tailored to Fashion Nova’s catalog. As new styles launched each week and new shopper signals were collected, Marqo continuously updated relevance over time.

This approach enabled Marqo to deliver intent aware relevance across both search and browse that stays aligned with Fashion Nova’s rapid product drops, evolving trends, and the long tail of descriptive queries, without relying on generic models or heavy manual setup.
‍

Enhanced intent recognition: Powered by Marqo’s custom LLM training approach and vision capabilities, Fashion Nova reported improved interpretation of descriptive queries, automatic handling of misspellings, and support for multilingual searches without heavy manual configuration.
Visual search capabilities: Fashion Nova reported that visual signals from product imagery improved semantic relevance, enabling shoppers to find items by attributes such as color, style, and occasion even when queries were imprecise.
Merchandising agility: The team gained control over ranking through boosting, burying, and pinning rules that could be targeted by category or attribute, without requiring engineering intervention.
Learning from onsite signals: Fashion Nova reported ongoing ranking improvements over time by leveraging anonymized onsite behavioral and conversion signals.

Results

Fashion Nova tested Marqo against in a controlled, production A/B test over a two-month period, with 30% of traffic assigned to Marqo.
During the measured period, Fashion Nova reported the following outcomes:

Search performance

+19%

Increase in Search Conversion rate

+17%

Increase in Search Revenue per user

Overall business impact

~80%

Reported decrease in hours spent on manual merchandising

$130M

Additional Annual Revenue

Fashion Nova attributed these gains to improvements in discovery quality across the shopping experience, supported by semantic understanding, image based relevance, and behavioral signals that aligned results more closely with shopper intent.

Moving Forward

With Marqo powering search and collections, Fashion Nova is continuing to evolve product discovery across the shopping experience. As AI becomes embedded in how consumers find products and make purchasing decisions, expectations are shifting toward faster, more intuitive, and more personalized journeys. Fashion Nova is setting the pace for the next wave of commerce, exploring agentic discovery capabilities such as automated collection creation, conversational search experiences, more predictive personalization, and context aware refinements designed to reduce friction while browsing.
Fashion Nova described rapid experimentation and faster iteration cycles as a foundation for ongoing optimization, helping the team evolve discovery experiences quickly and stay ahead as AI reshapes online shopping.

Results reflect Fashion Nova’s experience during the measured period and may vary based on implementation, seasonality, traffic composition, and other business factors. Reported outcomes are Fashion Nova’s own measurements and do not constitute a guarantee of future performance. Metrics were collected using Fashion Nova’s onsite tracking pixel. Marqo processes only anonymized behavioral signals and does not collect personally identifiable information.Marqo™ is a trademark of Marqo AI Ltd. Fashion Nova® is a registered trademark of Fashion Nova, Inc. All other trademarks belong to their respective owners.

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