Manthan Enables Algorithmic Merchandising with Automated Insights and Machine-Driven Business Recommendations
Manthan, a global leader in AI-powered Analytics announced the launch of its next generation Algorithmic Merchandising Solution to aid merchandisers drive the best outcomes by providing the ability to auto-generate algorithmic insights and receive machine-driven recommendations on actions to take at the right time across different stages of the business. For example –
“We believe the solution will enable a decisive shift in the way merchandisers operate with a bias for action and a focus on driving the best outcomes with an AI-augmented analytics approach”
- Sales Forecasting and Planning: Forecast demand for products at a granular attribute level like style, fabric, pattern, color, size, fit levels that ultimately influence shopper decisions. The solution automatically considers the right business context, picks the appropriate algorithm, factors in external variables such as events, weather, trend data, demographic data to predict demand for products and aid in sharper planning decisions around clustering, assortment, buying and allocation during the pre-season.
- Smart Inventory Management: It helps match inventory movement closely with the plan based on actual sales and pre-empt inventory exceptions early on during the season. It automatically provides merchandisers with appropriate recommendation such as inter-store transfer, price off or OTB along with the impact of the recommended actions, instantly.
Once users receive recommendations, they can choose to simulate other potential scenarios. For instance, if the machine recommends 15% price off on a specific product to achieve a 5% sales lift, the user can easily simulate the sales lift that can be achieved at other price off percentages – like 12% or 20%.
Users have full control to override recommendations or enable faster execution of decisions through approval workflows. Users can also seek answers to business questions through a conversational engine using simple voice or text commands.
Using the same technique described above, a wide range of retail use cases like market basket analysis, promotion effectiveness, store cluster recommendations, assortment optimization, on-shelf availability, auto-replenishment and price optimization can be addressed.
“We believe the solution will enable a decisive shift in the way merchandisers operate with a bias for action and a focus on driving the best outcomes with an AI-augmented analytics approach,” said Seema Agarwal, VP – Merchandise Analytics, Manthan.
The solution is designed to meet specific needs of seasonal, general merchandise, fast moving consumer retail businesses. It recognizes nuances at both strategic and daily decision-making levels. A single solution with strong data management capabilities, out-of-the-box algorithms and easy-to-use interfaces make it a compelling solution for digital-age retail.
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