Manthan Positioned as a Strong Performer by Independent Research Firm in Retail Planning Report

Manthan, a leading AI and advanced analytics solutions provider, announced that its Merchandise Analytics and Planning solution has been cited as a Strong Performer in The Forrester Wave™: Retail Planning, Q1 2020. According to the Forrester report, “This report shows how each provider measures up and helps CIO professionals select the right one for their needs. Architecture, assortment, and continuous planning are key differentiators.”

As per a separate Forrester report, “Retail moves from seasonal to continuous planning.” With this shift in the retail industry, winning retailers are no longer relying on the traditional season-based assortment planning. They are moving to a more dynamic, multi-dimensional continuous planning approach which is simplified though the use of AI and ML.

Manthan has helped several customers get granular predictions down to the attribute level for precise assortment planning; automatically identifying inventory exceptions and getting recommendations on the next best action to help them improve their full price sell-throughs.

“We believe our position as a strong performer in retail planning re-iterates the fact that we provide retailers with an edge with our customer centric, algorithmic and analytic driven retail planning solution. Our strong product vision and strategy is aligned to support top-line business outcomes for our customers,” said Seema Agarwal, Head- Merchandise Analytics at Manthan.

“We are constantly rethinking how our AI and ML driven solutions can cater to key omnichannel retail business usecases to help retailers deliver highly personalized assortment choices to customers. And our analytics first approach has helped us stay relevant to our customers,” said Sameer Narula, Chief Product Officer, Manthan.

Manthan Merchandise Analytics and Planning is designed to meet the specific assortment needs of fashion retailers. An algorithmic solution that helps retailers make better buying decisions.

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