Our TURF uses hierarchical Bayesian modelling to get insight into the preferences of each individual for each product/idea being tested. The model uses all the swipes in the Idea Screen exercise to derive the preferences, and the results let us simulate many scenarios, including purchase likelihood, or how likely an individual would buy a product if it was the only thing available on the shelf. If the calculated purchase likelihood surpasses a certain threshold, the individual is classified as being reached by that specific product or idea. This approach provides valuable insights into consumer behavior and helps in making informed decisions based on the simulated scenarios.
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