Merchandising Analytics|Retail Merchandising Analytics|Predictive Analytics Merchandising|Manthan | Manthan

Fast and Accurate
Merchandising Decisions for
Time-Sensitive
Fashion Retailing

Fast
Merchandising
Decisions for the
Time-Sensitive
Fashion Retailing

[Enhance the way you work with automated insights and prescriptions] AI

Eliminate human bias in key
merchandising decisions
with machine driven recommendations

Automatically predict anomalies and receive
automated prescriptions to maximize
full price sell through and avoid inventory exceptions

Capitalize on limited time window to drive
profitability with automatic replenishment, high
performance promotions and algorithmic markdowns

PRE-SEASON

Aid Algorithmic Planning

  • Automated Store Clustering:
  • Create store clusters based on performance and demography to implement localized assortments, inventory allocation policies, pricing and promotion strategies.
  • Accurate Forecasting:
  • Project demand for new products; forecast at the attribute level, factor in seasonality for greater precision
  • Improve demand accuracy by including causal variables such as trends, weather, events and promotions
  • Algorithmic Buying:
  • Provide analytical inputs to buying teams for effective buying and presentation of merchandise.
  • Get recommended styles, size profiles and quantities to purchase based on business goals.
  • Customer-Centric Assortments:
  • Tailor localized assortments by overlaying enterprise data, customer buying patterns with key external data such as market trends, demographic, geographic and social data.
  • Smart Allocation:
  • Place right products at the stores in right quantities, with math driven allocation.
  • Define initial inventory to be distributed based on product and store ratings.
  • Maximize full price sales and sell through.
  • Automated Store Clustering:
  • Create store clusters based on performance and demography to implement localized assortments, inventory allocation policies, pricing and promotion strategies.
  • Accurate Forecasting:
  • Project demand for new products; forecast at the attribute level, factor in seasonality for greater precision
  • Improve demand accuracy by including causal variables such as trends, weather, events and promotions
  • Algorithmic Buying:
  • Provide analytical inputs to buying teams for effective buying and presentation of merchandise.
  • Get recommended styles, size profiles and quantities to purchase based on business goals.
  • Customer-Centric Assortments:
  • Tailor localized assortments by overlaying enterprise data, customer buying patterns with key external data such as market trends, demographic, geographic and social data.
  • Smart Allocation:
  • Place right products at the stores in right quantities, with math driven allocation.
  • Define initial inventory to be distributed based on product and store ratings.
  • Maximize full price sales and sell through.
MID-SEASON

Maximize full price sales and
sell through

  • Predict Anomalies, Trigger Alerts:
  • Predict anomalies with inventory and in–season, by comparing initial sales performance projections with actual inventory to highlight exceptions.
  • Get proactive notifications of anomalies in sales plan deviations, inventory exceptions and assortment performance for immediate action.
  • Automated Prescriptions:
  • Address anomaly incidents and maximise full price sales, sell through and margins with automated prescriptions, mid-season.
  • Get recommendations on mobilising excess inventory or making inventory available through a combination of return to vendor, inter store transfers, liquidation, cancellation of P.O.s or acceleration of purchase orders and re-ordering respectively.
  • Automatic Replenishment:
  • Study early demand signals for styles, brands and attributes to finalise inventory replenishment to the most appropriate stores, based on potential sales and minimum-maximum inventory.
  • Auto replenish items at every location to maximize the return on inventory and prevent out of stocks and understocking.
  • High Performance Promotions:
  • Identify items to promote at optimal price points to achieve targeted sales uplift and sell through.
  • Maximise promotional sales by ensuring items on promotion and associated high affinity products are in stock, using a combination of association mining and demand forecasting.
  • Predict Anomalies, Trigger Alerts:
  • Predict anomalies with inventory and in–season, by comparing initial sales performance projections with actual inventory to highlight exceptions.
  • Get proactive notifications of anomalies in sales plan deviations, inventory exceptions and assortment performance for immediate action.
  • Automated Prescriptions:
  • Address anomaly incidents and maximise full price sales, sell through and margins with automated prescriptions, mid-season.
  • Get recommendations on mobilising excess inventory or making inventory available through a combination of return to vendor, inter store transfers, liquidation, cancellation of P.O.s or acceleration of purchase orders and re-ordering respectively.
  • Automatic Replenishment:
  • Study early demand signals for styles, brands and attributes to finalise inventory replenishment to the most appropriate stores, based on potential sales and minimum-maximum inventory.
  • Auto replenish items at every location to maximize the return on inventory and prevent out of stocks and understocking.
  • High Performance Promotions:
  • Identify items to promote at optimal price points to achieve targeted sales uplift and sell through.
  • Maximise promotional sales by ensuring items on promotion and associated high affinity products are in stock, using a combination of association mining and demand forecasting.
END OF SEASON

Reduce risk of margin erosion
and locked-up capital

  • Markdown Optimization:
  • Algorithmically recommend the right products for markdown at the optimal price point by factoring in business constraints and markdown objectives.
  • Instantly view the impact of price change on key business metrics like sales uplift, margin and sell through.
  • Close the loop by simulating multiple price-offs, comparing business outcomes and accelerate execution with approval workflows.
  • Markdown Optimization:
  • Algorithmically recommend the right products for markdown at the optimal price point by factoring in business constraints and markdown objectives.
  • Instantly view the impact of price change on key business metrics like sales uplift, margin and sell through.
  • Close the loop by simulating multiple price-offs, comparing business outcomes and accelerate execution with approval workflows.

Gartner Hype Cycle for Retail Technologies 2018 features Manthan for Algorithmic Retailing, AI in Retail, and Cognitive Expert Advisors. Read more

Manthan named visionary

in the Global Retail Analytics Forecast to 2022. Read more

Manthan listed in the Gartner Hype Cycle for Retail Technologies 2018 for Algorithmic Merchandising Optimization and Customer-Centric Merchandising and Marketing. Read more

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Explore Product in Detail

Retail Analytics

  • Use advanced analytics for profitable merchandising decisions
  • Drive the best outcomes with machine driven prescriptions
  • Improve accuracy of decisions with AI-driven insights from new, additional data sources

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