A shopping decision tree study helps you develop the most efficient and shopper-centric communications and strategies. With the gained insights and knowledge, you will be able to build a purchase decision tree and understand the ever-changing shopping decisions (such as planned or unplanned and in store or e-commerce purchases) for your products/services and for your competitors’.
By building a shopper decision tree and ranking features by attributes (such as importance, brand, size, price) you will gain a
strong understanding on:
The core part of the questionnaire consists of a set of product features/attributes from which respondents are asked to choose their most important/preferred and the least important/preferred options among a list of predefined attributes. Thus, consumers choose their most and least preferred/ important items among a subset of 4-5 items.
MaxDiff and Latent Class analyses are used to calculate and identify preference scores and patterns.
With the MaxDiff analysis we:
A latent class analysis can also be performed to identify and profile consumer segments with characteristic preference patterns.