Stylumia Apollo
What is Stylumia Apollo?
Stylumia's Appolo is a solution used to analyze True Demand, to help brands increase the accuracy of key product decisions. Prevailing demand planning methods are either associative, conventional consumer research-based models or at best, analyses of textual attributes. Inspired by human visual perception, Apollo scans every pixel of images of products on the internet and analyses that along with text-based attributes through an ensemble machine learning model for informed demand planning.
Apollo helps map True Demand using omnichannel data combined with True Demand of consumers from C.IT at an internet-scale. This advanced predictive analytics helps assess relative demand potential at the prototype/design stage itself so that users can accurately choose relevant styles for future ranges. It creates contextually relevant predictive analytics for new products and helps develop a consumer-right assortment strategy for launch assortment.
Apollo also helps users to optimize open-to-buy (OTB), pre-season and in-season demand planning decisions, in-season overbuy and underbuy predictions to take prompt actions. It leverages enterprise data and market trends to maximize sales velocity, revenue, and profit. No style is a poor performer if bought in the right quantity at the right time. Apollo predicts absolute True Demand for products at various levels of distribution channels. It eliminates guesswork and helps users to decide how much to buy or recommend to stores and channels by analyzing True Demand. Users can minimize the common errors of overbuying and underbuying, leading to inventory leftover and lost sales opportunities, respectively, due to inaccurate demand planning.
Categories & Use Cases
Videos
Technical Details
| Mobile Application | No |
|---|
FAQs
What is Stylumia Apollo?
Stylumia's Appolo is a solution used to analyze True Demand, to help brands increase the accuracy of key product decisions. Apollo scans images of products on the internet and analyses them with text-based attributes through an ML model for informed demand planning.