Online marketing research

IBM Journal of Research and Development, Sep-Nov 2004 by Agrawal, A, Basak, J, Jain, V, Kothari, R, Et al

Marketing decisions are typically made on the basis of research conducted using direct mailings, mall intercepts, telephone interviews, focused group discussion, and the like. These methods of marketing research can be time-consuming and expensive, and can require a large amount of effort to ensure accurate results. This paper presents a novel approach for conducting online marketing research based on several concepts such as active learning, matched control and experimental groups, and implicit and explicit experiments. These concepts, along with the opportunity provided by the increasing numbers of online shoppers, enable rapid, systematic, and cost-effective marketing research.

1. Introduction

Estimating the relationship between marketing and response variables is fundamental to marketing- and merchandizing-related business decisions. Consider a simple example in which a retailer must select the price at which to sell a certain item. A systematic decision requires the retailer to know the relationship between the price of the item (the marketing variable) and the demand for the item (the response variable) at the various price points.

As a (slightly) more complex example, consider a situation in which the retailer feels that running a promotion on an item will lead to increased overall revenue. The promotion may take the form of a temporary price reduction achieved through the use of a coupon. Setting the face value of the coupon determines the effective price at which the item is sold, and this can be determined only if the demand at various price points is known. However, the decision is more complex if one considers other effects. If the retailer sells multiple brands of the item, reducing the price of a particular brand may result in shifting the sales from a competing brand to the promoted brand, leading to flat overall revenue. Also, shoppers may stock up on the item during the promotion period, leading to reduced sales of the item following the promotion period and net flat revenues.

Though simple, these examples illustrate the complexity of marketing and merchandizing. One may pose the problem so as to be amenable to analytical techniques by saying that an informed marketing and merchandizing decision requires estimating the multivariate relationship between marketing and response variables. Put simply, it involves knowing how the response variable(s) will change when one or more marketing variables are changed.

Estimating the behavior of a response variable to a change in the marketing variable requires data. Typically, data is collected through marketing research conducted through direct mailings, mall intercepts, telephone interviews, focused group discussion, and the like. In the simple example considered above, through telephone interviews one may simply ask the consumers to indicate the likelihood of their buying the item at different price points and use the collected data to infer the relationship between the marketing variable of interest (price) and the response variable (demand). The one-on-one interaction required in some of these modalities of collecting data (for example, in telephone interviews) coupled with the large turnaround time (for example, due to the transit time of a direct mailing to and from the respondent) and the significant number of person-hours required renders this traditional form of marketing research expensive, slow, and susceptible to inaccuracies.

The rapid growth of the Internet creates an opportunity for conducting online marketing research (OMR). Indeed, by some estimates, about 60% of the population of the United States and the European Union has Internet access. Collectively, these regions also account for a substantial amount of the world purchasing power according to the British Market Research Association (BMRA) [1] and the World Association of Opinion and Market Research Professionals (ESOMAR) [2]. Separately, various regions in Asia are also showing signs of increased Internet access. This widespread adoption of the Internet makes a large cross section of the population accessible through the Internet and ensures that the needs and preferences of a substantial and representative population of the consumers can be obtained online.

This paper is motivated by the possibility of providing actionable business intelligence rapidly, systematically, and cost-effectively through OMR. Given the complexity of marketing and merchandizing decisions in modern businesses and the limitations of space that are necessarily enforced by the paper format, we have chosen to focus on some fundamental aspects of OMR. Specifically, we focus on those aspects that will benefit any serious attempt at realizing an online marketing research implementation.

We have organized the rest of the paper as follows. In Section 2, we provide a conceptual overview of a system and describe a basic setup that can be used to conduct OMR. Our focus is not on system-level internals, since these are dependent on the commercial server in which OMR is implemented. Rather, we seek to provide some idea of the chain of events that occur, the various control points for OMR, and the various innovations proposed in this paper. These innovations, we believe, are central to OMR, and we detail them in Section 3. In Section 4, we present an overview of some types of actionable business intelligence that can be obtained on the basis of the proposed system and the algorithms. We conclude in Section 5 with some discussion.