The main objective of this paper is to provide a decision-support system of micro-level customized promotions, primarily for use in online stores. Our proposed approach utilizes the one-on-one and ...
This paper examines a market where the provision of information service is costly, but information service has the characteristics of a public good. Consumers, on the other hand, can use the inform...
The paper Manufacturer's Returns Policies and Retail Competition by Padmanabhan and Png 1997 argues that returns policies intensify retail competition and therefore raise the manufacturer's profi...
Store brand entry has become a key issue in marketing as it may structurally change the performance of and the interactions among all market players. Based on their multivariate time-series analysi...
One of the main problems associated with early-period assessment of new product success is the lack of sufficient sales data to enable reliable predictions. We show that managers can use spatial di...
Managers are very interested in word-of-mouth communication because they believe that a product's success is related to the word of mouth that it generates. However, there are at least three signif...
We use the results of three large-scale field experiments to investigate how the depth of a current price promotion affects future purchasing of first-time and established customers. While most pre...
In 2003 the INFORMS Society for Marketing Science (ISMS) introduce and conducted its inaugural Practice Prize Competition. The reports and papers that follow are the finalists from that competition, representing the best examples of rigor plus relevance that our profession produces.
This paper proposes the Additive Risk Model (ARM), first used by Aalen (1980), to explain households' interpurchase times. Unlike the Proportional Hazard Model (PHM), first proposed by Cox (1972), the ARM incorporates the effects of covariates on the individual hazard function in an additive (as opposed to multiplicative) manner. While a large number of previous studies on interpurchase timing have dealt with the question of correctly specifying the parametric distribution for interpurchase times, no study has explicitly investigated the question of correctly specifying the effects of covariates in the model. This study looks at this issue. We propose an ARM that is suitable for purchase-timing data, and compare its empirical performance to that of the PHM and the Accelerated Failure Time Model (AFTM) using scanner panel data on laundry detergents, paper towels, and toilet tissue. We find that the ARM not only estimates and validates the observed interpurchase times better than existing models, but also recovers a time-varying price elasticity and shows a high degree of robustness in the estimated covariate effects to alternative parametric specifications of the baseline hazard. The estimates of covariate parameters under the PHM, on the other hand, are highly sensitive to alternative parametric specifications of the baseline hazard.