This paper attempts to find characteristics of product categories, brands, and ad copy that lead to either increased or decreased effectiveness of advertising spending on ad awareness, brand awareness, or purchase intentions, as measured through tracking data.
The authors describe a model of the effects of advertised and observed quality on consumer expectations about new product quality. They test the model using data from two computer-controlled shopping experiments. In both studies, quadratic and gamma specifications for the effect of advertising claim discrepancy on expectation change fit better than a linear model. Furthermore, the adaptive expectations framework describes the updating of consumer expectations when the consumer observes the quality of the new product.
We empirically explore the accuracy of the simple stationary peak hour approximation (SPHA) for estimating peak hour performance in multiserver queuing systems with exponential service times and periodic (sinusoidal) Poisson arrival processes. We show that the SPHA is very good for a range of parameter values corresponding to a reasonably broad spectrum of real systems. However, we do find and document that there are many situation in which this approximation will be very inaccurate.
Bundling of products is very prevalent in the marketplace. For example, travel packages include airfare, lodging, and a rental car. Considerable economic research has focused on the change in profits and consumer surplus that ensues if bundles are offered. There is relatively little research in marketing that deals with bundling, however. In this article we concentrate on some tactical issues of bundling, such as which types of products should be bundled, what price one can charge for the bundle, and how the price of the bundle should be presented to consumers to improve purchase intent.
In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e. information pertaining to a limited set of adjacent or nearby stages. This holds in particular for planning problems over time, where an optimal decision in a given period depends on information related to a limited future time horizon, a so-called forecast horizon, only. In this paper we identify a general class of dynamic programs in which an efficient forward algorithm can be designed to solve the problem and to identify minimal forecast horizons.
This paper seeks to establish a parametric linkage between fuzzy set theoretic techniques and commonly used preference formation rules in psychology and marketing. Such a linkage helps to benefit both fields. We accomplish this objective by using a linear model with interaction term which nests many common preference protocols; conjunction (fuzzy and), disjunction (fuzzy or), counterbalance (fuzzy xor) and linear compensatory.
We model the effects of variety-seeking and marketing-mix variables on consumers' purchases of coffee using a nested logit model. We premise that on any given purchase occasion, the utilities of brands other than the one purchased on the previous occasion may be correlated due to the consumer's tendency to seek variety or to avoid variety. This results in a two-level hierarchical model where choice on any purchase occasion is conditioned on the brand purchased on the immediately preceding occasion.
A unique dataset collected from the personnel records of a large company is used to study the relationship between on-the-job training and worker productivity. The analysis shows how information contained in a company database is useful for eliminating heterogeneity bias in the estimation of training's impact on wages and job performance.
A decade of work in marketing meta-analysis has produced empirical generalizations concerning parameters in models of advertising, price, diffusion, and consumer behavior. Results from these meta-analyses should replace the now discredited zero null hypotheses of such parameters in future work. Probably more important than nonzero "grand mean" average effects is an approach called Parametric Adjustability, which provides estimated parameter values for specific conditions reflecting markets and research technologies.