A New Era of Consumer Decision Trees

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.

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 CDTs simply do not map to the full breadth of shopper needs and preferences.

By incorporating shopper behavior, directly measured across multiple categories, VideoMining is able to produce a next gen CDT, which captures the true decision making process from a broad perspective. This new approach is to the most accurate method for mapping the shopper’s purchase decision and representing that process with an easy-to-use format for category planning.

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CDT 2.0: Understanding Consumer Decisions through Behavior Analytics

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