Marketing and Data Science: Together the Future is Ours

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data

An empirical analysis of shopping behavior across online and offline channels for grocery products: the moderating effects of household and product characteristics

We study the moderating effects of household (e.g., shopping frequency) and product (e.g., sensory nature) characteristics on household brand loyalty, size loyalty and price sensitivity across online and offline channels for grocery products. We analyze the shopping behavior of the same households that shop interchangeably in the online and offline stores of the same grocery chain in 93 categories of food, nonfood, sensory and nonsensory products. We find that households are more brand loyal, more size loyal but less price sensitive in the online channel than in the offline channel. Brand loyalty, size loyalty and price sensitivity are closely related to household and product characteristics. Light online shoppers exhibit the highest brand and size loyalties, but the lowest price sensitivity in the online channel. Heavy online shoppers display the lowest brand and size loyalties, but the highest price sensitivity in the online channel. Moderate online shoppers exhibit the highest price sensitivity in the offline channel. The online-offline differences in brand loyalty and price sensitivity are largest for light online shoppers and smallest for heavy online shoppers. The online-offline differences in brand loyalty, size loyalty and price sensitivity are larger for food products and for sensory products.This project is partially supported by the Singapore Ministry of Education Research Project R-317-000- 073-113 and the Government of Navarre and the Spanish Ministry of Science Research Project SEC2002- 04321-C02-02

Return on investment implications for pharmaceutical promotional expenditures: The role of marketing-mix interactions

The authors empirically explore the revenue impact of marketing-mix variables and their interactions. The findings include the following: pharmaceutical direct-to-consumer advertising and detailing (sales force) affect demand synergistically, detailing raises price elasticity, and detailing has a higher return on investment than does direct-to-consumer advertising. The authors also discuss other implications and provide future research directions

Wireless Carriers’ Exclusive Handset Arrangements: An Empirical Look at the iPhone

Since the Apple iPhone’s first launch in 2007 with an exclusive arrangement with AT&T, it has garnered overwhelmingly positive responses from consumers and from the media. With its success, exclusive contracts between handset makers and wireless carriers have come under increasing scrutiny by regulators and lawmakers. Such practices have been criticized by regulators, by the media, and by “locked-out” consumers, due to the fact that a consumer has to subscribe to a particular service provider if he or she strongly prefers one handset to others. In this paper, we empirically examine the impact of handset exclusivity arrangements on consumer welfare. First we study consumers’ purchase decisions in mobile services that include the choice of a handset and of a service provider. We do so by combining survey data on consumers’ purchase decisions with supplemented data on prices and features of common handsets. Next, assuming a Stackelberg leader-follower relationship between the handset manufacturers and the service providers, and using our demand estimates, we recover the marginal costs for the players in the market. We then simulate what would have happened in the counterfactual scenario when the iPhone is available from all carriers. Our results suggest that, if we take into account price adjustments from handset manufacturers and service providers in response to the change in market structure, consumer welfare will increase by $326 million without the exclusive arrangement. We view our analysis as a starting point to a more complete characterization of consumer behavior and the complex relationships among players in this industry

Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments

The multinomial probit model of brand choice is theoretically appealing for marketing applications as it is free from the “independence of irrelevant alternatives” property of the multinomial logit model. However, difficulties in estimation have restricted its widespread use in marketing. This paper presents an application of the method of simulated moments, a new methodology that enables easy estimation of probit models with a large number of alternatives in the choice set. We describe the theoretical development of the technique and using pseudo-simulated data, conduct numerical experiments to compare the method with existing techniques for estimating probit models. Using the scanner panel data on the purchases of catsup, we provide an empirical application of the method of simulated moments to the estimation of the parameters of a multinomial probit model. Estimating the covariance structure associated with the underlying latent variable probit model enables us to identify broad patterns of similarities across alternatives. It also enables us to derive a pairwise similarity matrix across choice alternatives which when input into a multi-dimensional scaling routine provides us with a graphical representation of competitive structure in the catsup market. For completeness, we compare the substantive implications for the effects of marketing variables obtained from the multinomial probit model with those obtained from models in the extant marketing literature.brand choice, econometric models, estimation