HIVERY's Demand Transfer Model Explained

Our model technology uses machine learning algorithms and store-level data analysis techniques to identify shoppers' behavior, score them, and predict demand transfer based on these scores. In our whitepaper, "Unlocking Sales Potential: A Guide to Understanding Demand Transfer," you'll learn how we:

1. Identify latent shopper segments and determine their purchase probability.
2. Analyze shopper behavior patterns for each store across the chain and determine possible store mixtures.
3. Predict demand transfer based on scores and consider the possibility of a no-purchase option or "walk-rate."

The key point of difference for the HIVERY model is that it is trained with store-level data, allowing for store-level data analysis and insights. This analysis is done for every SKU in every store, providing a comprehensive understanding of shopper behavior. Additionally, the model is dynamic and constantly learning as new data is used, generating new insights that can be applied to improve assortment strategy simulation.

The model is also interactive, enabling users to change the assortment and see how demand is impacted in real-time. This unique combination of store-level data analysis, dynamic learning, and interactivity makes HIVERY's Demand Transfer Model an essential tool for retailers looking to optimize their assortment strategy and stay ahead of the competition.

If you would like to read more on this topic, check out Maximizing Incrementality and Minimizing Cannibalization: How AI Builds Confidence and Trust in Category Strategies.