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We present a new affective-behavioral-cognitive (ABC) framework to measure the usual cognitive self-report information and behavioral information, together with affective information while a customer makes repeated selections in a random-outcome two-option decision task to obtain their preferred product. The affective information consists of human-labeled facial expression valence taken from two contexts: one where the facial valence is associated with affective wanting, and the other with affective liking. The new “affective wanting” measure is made by setting up a condition where the person shows desire to receive one of two products, and we measure if the face looks satisfied or disappointed when each of the products arrives. The “affective liking” measure captures facial expressions after sampling a product. The ABC framework is tested in a real-world beverage taste experiment, comparing two similar products that actually went to market, where we know the market outcomes. We find that the affective measure provides significant improvement over the cognitive measure, increasing the discriminability between the two similar products, making it easier to tell which is most preferred using a small number of people. We also find that the new facial valence “affective wanting” measure provides a significant boost in discrimination and accuracy.