WHITEPAPER: CDT 2.0: Understanding Consumer Decisions through Behavior Analytics
August 16, 2017
While many organizations use variations on the terminology and approach of a Consumer Decision Tree (CDT), the basic idea is the same: CDTs break down a single category in a logical, linear manner to inform a broad variety of marketing and merchandising choices.
A key application for CDTs is the concept of substitutability, or understanding which products shoppers will substitute when their preferred SKU is not available. Knowing key attributes of each SKU (such as price, brand, pack size and perceived quality) and understanding how shoppers substitute them enables intelligent assortment changes that truly address the needs of shoppers and retailers.
While CDTs are valuable, traditional approaches are insufficient at modeling the real behavior of real shoppers. CDTs are often fraught with inferences and assumptions based on sales data, online surveys, exit interviews and even some advanced techniques such as virtual reality. These techniques don’t directly measure the real behavior of shoppers in real stores.
Real shoppers often shop irrationally and do not follow a linear decision-making path. Shoppers are clearly influenced by in-store shopper marketing and can be swayed into making emotional decisions. Simulating a shopping environment or asking shoppers to recall their experience is not adequate to fully measure what affected their decision at the shelf. Traditional Consumer Decision Trees simply do not map to the full breadth of shopper needs and preferences.