Fashion Industry

Accurately connect fashion shoppers with the relevant products they’re looking to buy.

Lily AI drives 8-9 figure revenue uplift for fashion retailers and brands by dramatically improving their on-site search conversion, relevant product recommendations and demand forecasting.

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Trusted by global retailers, brands and industry leaders

Women's arm with three beaded crystal bracelets on it.

From fast fashion to luxury, Lily AI is changing how retailers and brands use enriched product data to drive new revenues.

We work with Macy’s, The Gap, Bloomingdale’s, thredUP and many other fashion industry leaders to ensure that the language of their customers informs and enhances every point in their retail stack. We unlock new revenues and boost customer satisfaction by providing a depth and scale of product attribution that no other solution can match. 

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A lack of customer-centered product attribution data has compounding negative effects across the retail value chain.

That’s why Lily AI connects your customers with what they’re really looking for. Drive rapid product onboarding and eliminate days of manual work. Reduce cart abandonment to as low as 2% by matching shoppers to the exact products they’re searching for on your site. Then boost SEO and SEM with relevant product data that powers relevant search results.

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Fashion model holding a teal purse while posing in a maroon sweater.
Lily AI Fashion Chart Showing the Retail Chain with Proper Lily AI Attribution.
Male and female fashion models posing in fall attire complete with neutral coats and a floppy hat.

Predictable demand forecasting and merchandising, built to tame the unpredictability of fashion.

Now retailers and brands can increase their ability to sell customers what they’re looking for right now, and decrease the need to mark that inventory down later. Lily AI’s proxy products and robust computer vision can help accurately forecast demand for brand-new product lines, boost full-margin sales and keep warehouses operating “just in time”.

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“Lily AI’s platform solves for something extremely difficult: driving pinpoint results for the many long-tail queries that comprise more than 50% of our e-commerce searches. Lily's rich and detailed product attribution helps ensure that Bloomingdale’s customers find what they’re looking for, every time.”
Image of James Kim from Bloomingdale's.
James Kim
VP of E-commerce, bloomingdale's
“It all needs to start with the customer. The context of what they are trying to accomplish — their progress in refreshing their closet or finding a specific piece for an event — does not come from segmentation alone. Lily AI’s platform understands what our customer is trying to do and offers the right recommendations to them.”
Chris Homer
Co-Founder and COO, thredUP

Resources

Female fashion model posing in luxury fashion outfit with a round purse.

Major Global Luxury Retailer Increases Relevant Results for Descriptive Searches by up to 30x, Driving $20M in Incremental Revenue

A major global luxury retailer had been struggling with its conversion from online search, and turned to Lily AI and its rich product data enrichment for help.
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Woman in a tan geometric patterned sweater showing the connection between fashion merchandising and product attribution data.

The Connection Between Fashion Merchandising and Product Attribution Data

Fashion merchandising is continually evolving and there are clear ways to ensure the process is more seamless and effective – starting with better item set-up with the help of customer-centric AI.
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Woman in a black floral dress with attribute hotspots.

The Cascading Challenge of Legacy, Out-of-the-Box Product Attributes

Without a product taxonomy that describes products in the language that shoppers actually use, retailers start a cascading series of problems right at item set-up.
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Person's hands using a search bar to look for products while shopping.

How to Capitalize on Shoppers’ Search Diversity

Different shoppers search uniquely, making it essential for retail e-commerce brands to build the right product taxonomy to capture both common and long-tail searches.
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