While the smooth transition (ST) model has become popular in business and economics, the treatment of unobserved heterogeneity within these models has received limited attention. We propose a ST finite mixture (STFM) model which simultaneously estimates the presence of time-varying effects and unobserved heterogeneity in a panel data context. Our objective is to accurately recover the heterogeneous effects of our independent variables of interest while simultaneously allowing these effects to vary over time. Accomplishing this objective may provide valuable insights for managers and policy makers. The STFM model nests several well-known ST and threshold models. We develop the specification, estimation, and model selection criteria for the STFM model using Bayesian methods. We also provide a theoretical assessment of the flexibility of the STFM model when the number of regimes grows with the sample size. In an extensive simulation study, we show that ignoring unobserved heterogeneity can lead to distorted parameter estimates, and that the STFM model is fairly robust when underlying model assumptions are violated. Empirically, we estimate the effects of in-game promotions on game attendance in Major League Baseball. Empirical results show that the STFM model outperforms all its nested versions.for this article are available online.

The hidden Markov model (HMM) provides a framework to model the time-varying effects of marketing mix variables. When employed in a panel data context, it is important to properly account for unobserved heterogeneity across individuals. We propose a new random coefficients mixture HMM (RCMHMM) that allows for flexible patterns of unobserved heterogeneity in both the state-dependent and transition parameters. The RCMHMM nests all HMMs found in the marketing literature. Results of two simulation studies demonstrate that 1) averaging across a large number of different data generating processes, the RCMHMM outperforms all its nested versions using both in-sample and out-of-sample performance and 2) the RCMHMM is more robust than its nested versions when underlying model assumptions are violated. In addition, we apply the RCMHMM to an empirical application where we examine the effectiveness of in-game promotions in increasing the short-term demand for Major League Baseball (MLB) attendance. We find that the effectiveness of four promotional categories varies over the course of the season and across teams and that the RCMHMM performs best.

This article introduces a new data enrichment method that combines revealed data on consumer demand and competitive reactions with stated data on competitive reactions to yet-to-be-enacted, unprecedentedmarketing policy changes. The authors extend the data enrichment literature to include stated competitive reactions, collected from subject-matter experts through a conjoint experiment. The authors apply theirmethod to investigate hypothetical and unprecedented sales force policy changes of pharmaceutical companies. The results from the data enrichment method have high face validity and lead to various unique insights compared with using revealed data only. The authors find that only a very large sales force decrease initiated by the market leader triggers all competitors to decrease their sales force as well, leading to substantial profit increases for each firm. With respect to sales force allocation, when competitors decrease their sales force, they mainly decrease the reach of detailing across doctors, rather than decreasing the number of details to the most-visited doctors. The proposed data enrichment method provides managers with a powerful tool to, ex ante, predict the consequences of unprecedented marketing policy changes.

We study the effects of information content in 59,814 pharmaceutical sales calls on doctors’ prescription decisions for statins, in the face of entry of competing brands and generics, using a hierarchical Bayesian distributed lag model. We conclude that adding information content to the prescription response model improves the in- and out-of-sample performance of the model. In the first six months following generic entry, it is more effective for incumbent brands to detail on drug contraindications and indications, compared to other periods, to positively differentiate from generics. In the first six months following branded entry, it is less effective for incumbent brands to detail on drug indications and costs, given increased competitive clutter. We also document substantial heterogeneity among doctors in their response to information content. Our model is helpful for analysts to more accurately assess the effectiveness of detailing. Our empirical results are also informative for drug manufacturers as they set or change their messaging policies in response to entry and help firms to tailor their message content at the doctor level. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2015.0971 .

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).

Game attendance resulting from ticket sales is the single largest revenue stream for Major League Baseball (MLB) teams. We propose a general multiple distributed lag framework following the Koyck family of models for estimating MLB attendance drivers and focus specifically on the differential direct and carryover effects of in-game promotions. By setting various model constraints, the proposed framework incorporates different forms of serial correlation and promotion-specific dynamic effects. Using information model-selection heuristics, we select an optimal model of attendance drivers for the Pittsburgh Pirates' 2010–2012 MLB seasons. We demonstrate that our newly proposed model with an unrestricted serial correlation structure performs best. We find that although kids promotions have the highest direct effect on attendance, giveaway and entertainment promotions have substantial carryover effects and the largest total effects. We use our results to optimize the Pirates' promotional schedule and find that a reallocation of resources across promotional categories can increase profits between 39% and 88%. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1856 . This paper was accepted by Eric Bradlow, special issue on business analytics.

The combination of higher costs of drug development and an increasing share of generics has lead pharmaceutical firms to focus on alternative strategies to make profits. An important development in the pharmaceutical industry is the focus on strategies that increase the returns from an already approved drug. Firms have various possibilities to extend the lifecycle and profitability of a branded drug, before and after its patent has expired. These lifecycle extension strategies can be divided into marketing strategies (pricing, promotion, divestiture, differentiation, over-the-counter drugs, and branded generics), R&D strategies (new indications, reformulations, combination drugs, and next-generation drugs), and legal strategies (generic settlements and patenting). For example, when the patent of the blockbuster drug Prilosec was about to expire in 2001, its manufacturer was pursuing many different lifecycle extension strategies concurrently. Already 6 years before patent expiry its legal, marketing, and R&D experts had started with the development of over 50 different strategies to soften the impact of the patent expiry, such as a next-generation product, introducing branded generics, and improving the patent protection of the product. This chapter provides a comprehensive framework to classify the various lifecycle extension strategies, gives an in-depth overview of the research on the different strategies, and identifies gaps in our knowledge on these strategies to guide future research.

Pharmaceutical drugs are rigorously evaluated through clinical studies. The commercial consequences of such clinical studies, both to the promotion for and sales of drugs, are largely under-researched. The present study answers the following research questions: 1) How does the evolution of clinical study outcomes affect product sales? 2) How does the evolution of clinical study outcomes affect a firm's promotion expenditures to physicians and consumers? 3) Is the assessment of the responsiveness of sales to promotion expenditures biased when the analyst omits the role of clinical studies? We summarize a comprehensive body of clinical studies in three metrics: valence, dispersion, and volume. We extend the literature with the following findings. A higher valence and volume of clinical studies (i.e., more positive and larger number of studies) increase sales. A higher valence of clinical studies increases spending on both direct-to-consumer advertising and direct-to-physician promotion. A higher dispersion among clinical studies decreases spending on direct-to-consumer advertising. A higher volume of clinical studies has no effect on direct-to-physician promotion, but decreases direct-to-consumer advertising. Furthermore, the results show that omitting these metrics from a market response model leads to an overestimation of the responsiveness of sales to promotion expenditures.