Search results

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Many companies focus considerable resources on managing and enhancing positive word of mouth (WOM). WOM management, however, has become increasingly complex given the rise of online channels and the corresponding increasing breadth of connections giving and receiving WOM. Given the generally believed importance of WOM to business outcomes, managers seek to leverage key drivers that they believe will enhance positive and minimize negative WOM.

Implicit in these actions is the belief that leveraging key drivers to enhance positive (or minimize negative) WOM results in generally positive outcomes across channels and connections. This research investigates whether this belief is correct. We examined WOM behaviors from over 15,000 consumers from 10 different countries in eight industry categories, as well as consumer attitudes toward the various brands investigated. Our findings indicate that efforts to enhance positive WOM typically have mixed effects – enhancing positive WOM in some channels while decreasing it (or even enhancing negative WOM) in other channels. Therefore, managers need to have a greater understanding of the complexity of leveraging attitudinal key drivers when seeking to enhance WOM to minimize potential negative outcomes.

Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

Database marketers often select households for individual marketing contacts using information on past purchase behavior. One of the most common methods, known as RFM variables approach, ranks households according to three criteria: the recency of the latest purchase event, the long-run frequency of purchases, and the cumulative dollar expenditure. We argue that RFM variables approach is an indirect measure of the latent purchase propensity of the customer. In addition, the use of RFM information in targeting households creates major statistical problems (selection bias and RFM endogeneity) that complicate the calibration of forecasting models. Using a latent trait approach to capture a household's propensity to purchase a product, we construct a methodology that not only measures directly the latent propensity value of the customer, but also avoids the statistical limitations of the RFM variables approach. The result is a general household response forecasting and scoring approach that can be used on any database of customer transactions. We apply our methodology to a database from a charitable organization and show that the forecasting accuracy of the new methodology improves upon the traditional RFM variables approach.