Attribute like color as an example can not be described in text. The traditional clustering techniques using text can hence recommend a poor performing ‘red’ to a store. With visual dimensions in fashion being many, there is a need for a new way to localize assortment.
# New Way to Localize Assortment
The new way must be able to
a) Understand the taste of a store like a human eye dynamically
b) Study what is on offer and come out with affinity for styles available
c) Prioritise the allocation
This is possible by using ensemble machine learning models that can study the product images, attributes and store performance data.
This needs a lot more data discipline including product images and functional attributes in the form of text.
We are calling this concept of Localization as moving from a ‘STANDARDISED” process to a “LIVE STORE”. In this method, the store is alive and providing signals in various forms for the allocation system to dynamically adjust the supply on the moving taste of the store and availability of styles.
Stylumia has a proven track record of using a proprietary methodology in predicting the demand for unseen products pre-season and demonstrated forecast accuracy improvements in the order of 10-40%. Being driven by a unique vision + text AI model, this is a higher-order performance than existing forecasting systems. In case you are an omnichannel retailer, the model can take into account both the store transaction details and also geo-specific details for the relevant stores from the online channel.
You can augment the product localization with experience at the point of sale. Product and experience curation are key levers of offline survival.
If you would like to move from Standardised to LIVE STORE movement and maximize the potential for your brand, please reach out to us.
Blog by Ganesh Subramanian