Our Solutions
Apollo
Predict The Unpredictable
No style is a bad style if made/bought in the right quantity.
Demand Forecasting of a new product in fashion is a non-trivial problem. Current methods are either associative, traditional consumer research-based or prediction models using attributes. Apollo is a customized prediction engine for you, which is inspired by human visual perception through an ensemble machine learning model combining images and textual attributes powered by global fashion intelligence and your transaction data.
Customized
Data Augmented
Proprietary Demand Sensing Algorithm
Benefits
Test new products at scale
You can do consumer research (what consumers do not what they say) using big data at internet scale combined with your private data to create a contextually relevant prediction for your new product. You will get to know trend score with respect to market and with respect to your consumer with a potential $ demand for the new product.
Buy just the right amount of inventory
No style is a bad style if you bought in the right quantity. You can predict absolute $ demand at various levels of distribution (country, region, city etc) to either decide how much to buy or recommend to your partners how much should they buy. By buying the right quantities, you optimize overbuy and underbuy impacting inventory and loss sales opportunities.
Grade relative potential of new ranges
You can predict relative demand potential at proto stages in your calendar and prioritise the effort to choose the relevant styles in the upcoming/ future range. You can also use this to recommend to your wholesalers (if you have this channel).
Relevant for: Buyers, Planners,
Category heads.
Want to know how?
Our Solutions
Apollo
Predict The Unpredictable
No style is a bad style if made/bought in the right quantity.
Demand Forecasting of a new product in fashion is a non-trivial problem. Current methods are either associative, traditional consumer research-based or prediction models using attributes. Apollo is a customized prediction engine for you, which is inspired by human visual perception through an ensemble machine learning model combining images and textual attributes powered by global fashion intelligence and your transaction data.
Customized
Data Augmented
Proprietary Demand Sensing Algorithm
Benefits
Test new products at scale
You can do consumer research (what consumers do not what they say) using big data at internet scale combined with your private data to create a contextually relevant prediction for your new product. You will get to know trend score with respect to market and with respect to your consumer with a potential $ demand for the new product.
Buy just the right amount of inventory
No style is a bad style if you bought in the right quantity. You can predict absolute $ demand at various levels of distribution (country, region, city etc) to either decide how much to buy or recommend to your partners how much should they buy. By buying the right quantities, you optimize overbuy and underbuy impacting inventory and loss sales opportunities.
Grade relative potential of new ranges
You can predict relative demand potential at proto stages in your calendar and prioritise the effort to choose the relevant styles in the upcoming/ future range. You can also use this to recommend to your wholesalers (if you have this channel).
Relevant for: Buyers, Planners,
Category heads.
Want to know how?
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