The major paradox in research in marketing: Can the researcher construct models that capture firm heterogeneities and achieve accurate prediction of outcomes for individual cases that also are generalizable across all the cases in the sample? This study presents a way forward for solving the major paradox. The study identifies research advances in theory and analytics that contribute successfully to the primary need to fill to achieve scientific legitimacy: Configurations that include accurate description, explanation, and prediction (i.e., predicting outcomes accurately of cases in samples separate from the samples of cases used to construct models having high fit validity.) The solution here includes philosophical, theoretical, and operational shifts away from variable-based modeling and null hypothesis statistical testing (NHST) to case-based modeling and somewhat precise outcome testing (SPOT). The study here provides examples of research contributing to knowledge and theory that advance prediction and control in business-to-business contexts. Shifting beyond linear model construction and symmetric tests (i.e., multiple regression analysis (MRA) and structural equation modeling (SEM)) and embracing complexity theory and asymmetric tests (i.e., constructing and testing algorithms by “computing with words,” Zadeh, (1996, 2010)) includes taking necessary steps away from examining “net effects” of variables to useful screening modeling of case configurations. Researchers embracing this shift in marketing benefit from recognizing that the current dominant logic of performing null hypothesis testing (NHST via MRA and SEM) is “corrupt research” (Hubbard, 2015) and from recognizing that predicting by algorithms via somewhat precise outcome testing (SPOT) advances business-to-business research toward achieving scientific legitimacy.