Nextail Mentioned in Gartner Case Study on Guess’s Inventory Results driven by Machine Learning
Nextail, the intelligent in-season merchandising platform for fashion retail, was recently mentioned by Gartner in a rare case study titled Case Study: Improving Apparel Inventory Results Using Machine Learning, authored by Caleb Thomson and Mario Almanza Turrado. The study examines how global fashion brand GUESS implemented a machine learning–enabled approach to improve in-season inventory demand forecasting, allocation, and replenishment.
The case study highlights the challenges GUESS faced with legacy planning tools, including “inefficient and time-consuming manual processes” and “suboptimal forecasting and allocation practices [that] led to frequent distribution center (DC) stock-outs for top-selling products.” The initiative aimed to resolve these challenges by integrating a more data-driven approach to merchandise planning via Nextail.
GUESS implemented in-season solutions and a seasonal first-push DC-to-store allocation leveraging machine learning, allowing the retailer to optimize allocation quantities with greater accuracy. This shift enabled GUESS to “leverage granular SKU-store-day calculations” and treat “each store as a unique ‘cluster’ with its own behavioral pattern.”
As a result of the transformation, “GUESS realized a 7.5% reduction in store inventory coverage while also increasing in-season full-price sell-through to avoid margin impact from discounting and clearance.”
Additionally, the company achieved a 5% increase in full-price in-season sales, a 1% reduction in stock-outs in full-price stores, and a 7% increase in year-over-year sales in the EMEA region.
“It’s a proud moment for us having been mentioned alongside a forward-thinking global brand like GUESS who is leveraging machine learning for in-season inventory,” said Carlos Miragall, CEO and Co-Founder of Nextail. “We believe that it’s further validation of the role agile AI-driven merchandising solutions can play in helping fashion retailers address the realities of short life cycles and dynamic customer demand.”
Previously, Nextail has also been named a Representative Vendor in several consecutive Gartner Market Guides such as the 2022 and 2024 Gartner® Market Guide for Retail Assortment Management Applications: Short Life Cycle Products and the 2022 and 2024 Gartner® Market Guide for Retail Forecasting, Allocation and Replenishment Solutions. The full case study as well as these Market guides are available through Gartner’s research platform.
Download the Gartner Case Study: Case Study: Improving Apparel Inventory Results Using Machine Learning (Gartner subscription required).
Looking for broader market insights? Explore the Gartner® Market Guide for Retail Assortment Management Applications – Short Life Cycle Products on the Gartner Portal (Gartner subscription required).
Interested in learning more about the award-winning Nextail <> Guess partnership? Read our own version of the case study available on our web.
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