Transferable demand with unparalleled accuracy inspired by GANs
The transferable demand model within HIVERY Curate goes beyond traditional Consumer Decision Trees (CDT) methods by leveraging advanced Machine learning (ML) techniques to infer incrementality and demand transferability.
This model has been inspired by a specific area of ML and Deep Learning called Generative Adversarial Networks (GANs).
The model is able to determine both the transferability and incrementality impact of adding or removing SKU into a category at unparalleled accuracy.
It's the world’s first AI-driven cannibalization model using this approach, and it drives HIVERY Curate's hyper-local assortment optimization and recommendations.
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In this report you will learn:
- The key to HIVERY Curate's transferable demand model is deciphering the unique purchase behaviour of each store and the ‘latent’ shopper segments within these stores
- How it can identify ‘latent shopper segment' by observing sales data within the store as well as the universe of stores
- How does the model infer the impact of demand transference beyond CTD methods
Other resources you might find interesting...
If you get the fundamentals right, everything else often falls into place. And in retail analytics, there are a number of fundamentals to grasp.
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