Get granular and precise demand-driven recommendations on what to buy, how much to buy and in what ratio specifically for every store.
Merchandise Analytics offers forecasts, automated store clustering, assortment wedge and size-pack recommendations out-of-the-box so you can buy right for every season, easily.
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Get early signals on assortment performance based on actual sales, pre-empt exceptions and mobilize inventory with timely analytical recommendations.
Merchandise Analytics automatically surfaces anomalies and inventory exceptions early and provides recommendations on how to rectify them so you can maximize full price sell through.
Easily identify products to markdown, get optimal price recommendations for every SKU and accelerate execution of the price change.
Merchandise Analytics offers intelligent markdown recommendations by factoring in business constraints and instantly displays the impact of the price change so you can maximize margins even as you liquidate inventory.
Easy to use sophisticated analytics for business users across your enterprise
Ready to use solution with a retail data model. Insights engine, advanced algorithms and natural language interfaces
Full spectrum of analytics from descriptive to prescriptive
Enterprise class solution that can be implement in weeks
To learn how advanced analytics can help drive profitable merchandising decisions
Download DatasheetGartner Hype Cycle for Retail tech ’19 across Algorithmic Retailing, AI in Retail, Cognitive Expert Advisor and Algorithmic Merchandise Optimization
Frost & Sullivan – 2019 Retail Technology Innovation Leadership Award
Forrester Now Tech: Pricing and Promotion, Q3 2019
Markets and Markets - Visionary leader in retail global forecast 2017-2022
5 Reasons retailers using enterprise BI should migrate to modern analytics platform
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With Manthan we grew from $ 600M to $ 2Billion.
By making decisions faster and more precise, and our execution more efficient, we believe it has the potential to realize a 7-9% reduction in out of stocks, recover an average 5-7% in lost sales, and achieve 10-12% reduction in churn. All of this would not be possible without the ability to spot and act on analytics-driven opportunities.
