Crossing incentive alignment and adaptive designs in choice-based conjoint: A fruitful endeavor

Verena Sablotny-Wackershauser, Marcel Lichters, Daniel Guhl, Paul Bengart, Bodo Vogt

Journal of the Academy of Marketing Science · 2024 · Volume 52 · Issue 3

Abstract

Choice-based conjoint (CBC) analysis features prominently in market research to predict consumer purchases. This study focuses on two principles that seek to enhance CBC: incentive alignment and adaptive choice-based conjoint (ACBC) analysis. While these principles have individually demonstrated their ability to improve the forecasting accuracy of CBC, no research has yet evaluated both simultaneously. The present study fills this gap by drawing on two lab and two online experiments. On the one hand, results reveal that incentive-aligned CBC and hypothetical ACBC predict comparatively well. On the other hand, ACBC offers a more efficient cost-per-information ratio in studies with a high sample size. Moreover, the newly introduced incentive-aligned ACBC achieves the best predictions but has the longest interview time. Based on our studies, we help market researchers decide whether to apply incentive alignment, ACBC, or both. Finally, we provide a tutorial to analyze ACBC datasets using open-source software (R/Stan).

Keywords

Adaptive choice-based conjoint (ACBC) analysis, Discrete choice experiments (DCE), Incentive alignment, Predictive validity, Purchase forecasting

Enable JavaScript for citation tools and additional paper interactions.