By Charlie Hammerslough
“Limited-Service Restaurants” (LSRs) is how the restaurant industry refers collectively to fast food and fast-casual dining establishments. Marketers who specialize in LSRs often employ marketing research to evaluate hypotheses about their brands or to detect segments within their markets. An important additional purpose of market research is to understand the total structure of a market, to find out what guests consider important about the LSR experience. Without understanding the way that LSR guests think, marketers fly blind about what innovations in menu or service will appeal to guests. Fundamental market research helps with brand positioning and allocating marketing resources (Marketing Mix Analysis), and also in generating unexpected directions for additional research.
Unsupervised Bayesian Belief Networks are a powerful innovation that allows the analyst to approach without preconceptions the underlying structure and dynamics of their marketing research data. The most important strategic questions that marketers ask and answer are nondeterministic. Such as, “What is the structure of our market?” or “What are the key facts to know about a dataset that characterize the underlying purchase process?”
Description and Exploration
Data analysis projects usually proceed in an orderly way through phases: Description, Exploration, Prediction, and Prescription. In practice, much of the glamour of data science is tied into Prediction (algorithm development such as Machine Learning) and deployment into Prescriptive systems (such as product recommenders or search engines). Such methods are fundamental to answering well-posed problems or evaluating specific hypotheses about how consumers behave.
Typical methods for Description and Exploration are univariate analysis of data variables, and an exhaustive search through all possible bivariate correlations. Analysts use multivariate methods such as cluster analysis or variable reduction such a principal components often to explore data.
Below we illustrate a newer tool for unstructured exploration, Bayesian Belief Networks (BBNs), which were developed in the 2000’s. BBNs use the paradigm of knowledge representation to simplify any data structure into its essential elements. This method can be applied to both unstructured and structured datasets, such as marketing surveys. BBNs generate an inference structure that represents each data variable a node in a network. Each node is probabilistically conditioned on the values of a “parent” node, and connected to the whole through arcs. In addition to the network structure discovered by the algorithm, we can examine the overall influence of each node, called node force, which is roughly, the overall strength of correlation of a variable with the rest of the network.
The data underlying the network below were a set of attitudinal questions asked of 2,294 guests who had visited Limited-Service Restaurants (LSRs) in the previous four weeks. The questions are grouped by domain areas, such as Service, Food, Convenience, Future, Company, and Brand. The question groups are in the minds of the researchers — the BBN algorithm has no prior knowledge of how the questions are grouped. All data are attitudinal questions of rated agreement or importance across a 5-point Likert scale.
Two analyses of the modeled network are relevant: node interconnection and node strength. The first is the analysis of the overall structure and interconnections of the Bayesian Belief Network. The nodes which are highly interconnected are often “gateways” to other domains of attitudes. In probabilistic terms, the value of many attitudes is conditioned on the value of key nodes.
A second analysis is of the overall strength of each node or variable (an individual attitude) on the network as a whole. The nodes which are highly influential show which factors affect how LSR guests evaluate the available restaurant options. The influence analysis supplements the interconnection analysis in giving an overall picture of the influence on the network.
Analysis of Structure