Companies have been creating consumer decision trees for many years. However, the traditionally used methods have some clear drawbacks. CDT’s based on household panel purchase data and market structure output are limited to the objective features of a product, providing a limited understanding of how the shopper thinks about a category. A qualitative approach like ethnography or shop-alongs capture category structure through observation of shopping and detailed questioning of consumers as they makes purchases. Shortcomings of this method include no quantitative assessment of resulting trees and a base of a small group of respondents.

Decision Insight’s approach to CDT’s allows an understanding of how purchases are influenced by subjective features of a category (convenience, taste, quality, value, etc.) as well as the objective characteristics differentiating products (form, type, flavor, etc.). Our approach combines a method based on virtual shopping with an opportunity for shoppers to sort and categorize the products in a category.

The result is a CDT that provides a deeper understanding of the category structure, including product distinction and substitutability. It also comments on what drives products from shelf to basket and provides thought leadership within a category. As a result, manufacturers can relate convincing stories to retailers, influencing the importance of brands and product sub-segments, shelving decisions , retailer hierarchies, and category databases. Clients have also used the learning internally to help with brand strategies, innovation strategies, and brand messaging.

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