An application of model-based clustering in market segmentation  (Page 5/5)

Implications of mbc

Our application of MBC in market segmentation offer insights about consumers in two ways. First, the models that describe each cluster are easy to interpret since the parameters that make up the model represent directly the consumer's “sensitivities” to observable exogenous variables. There are three exogenous variables considered in our simple bivariate model fitted to the simulated data: product price, product display, and quantity purchased during previous time period. Several useful interpretations can be made from studying the parameters. For example, a negative β _b ¹ reveals that price affects bacon purchase negatively, but a positive β _b ² implies the advertisement of bacon promotes sale of bacon. We can also compare the magnitudes of β across consumer segments to detect how differently consumers react to price, display and past purchases.

Second, the bivariate nature of the model also contains information about the cross-product effects between products. This is useful if the two products are closely related, which is true in our case of bacon and eggs. For example, the examination of β _b ⁴ , which is the parameter for display of eggs, indicates how much egg advertisements influences the consumer's bacon purchase. We incorporate time series components into the model by included quantities of bacon and eggs purchased at the last time period. We might expect the products to have a negative autocorrelation but a positive cross correlation. For instance, if a customer purchased a large quantity of bacon last shopping trip, he might purchase less bacon but more eggs on the subsequent trip. In addition, the parameter for the correlation term acts an overall indicator of how bacon and egg purchases are related. This information is useful especially in deciding if a cross-product marketing strategy is appropriate in the first place.

Finally, the hierarchical bottom-up clustering tree allows the manager to group consumers in a way that makes most sense. It is easy to visualize the size and memberships of consumer groups with this clustering algorithm.

Directions for future research

To discover the true potential for MBC in marketing research settings, we still need to apply it to actual consumer data. If studies using simulated data are promising, we would like to collaborate with marketing researchers to implement MBC on actual store scanner data and other marketing TSC data.

One interesting type of TSC data is e-commerce data. As more business is conducted online, company must be adapt to respond optimally to consumer behavior on the web. Many market segmentation protocols are already in place for some companies. For example, Google displays “Sponsored links” or advertisements related to the user's search term. Large online stores such as Amazon.com, Buy.com and eBay.com all offer customized recommendations for users. MBC's ability to reveal consumer sensitivities can contribute to make online marketing more effective.

With new sets of data available for MBC research, we will likely encounter TSC data that contain many extra zeroes. Zero-inflation is a common feature in count data and can cause problems for simple models that do not treat the extra zeroes properly. In the development of univariate MBC in pollution study [link] , a technique called zero-inflated Poisson (ZIP) regression [link] was used to accurately capture the zeroes that were prevalent in the TSC. Some consumer TSC data that might have extra zeroes include durable goods such as appliances or cars. The application of ZIP regression model in these instances may broaden the scope of applications of MBC in marketing research.

From statistical modeling point of view, an immediate extension for the project is to generalize the model beyond the bivariate case. The model for multivariate Poisson regression becomes more complex as the dimension size increase because the number of correlation terms increases. The problem can be illustrated using the trivariate case. In the Data section, we described how the bivariate Poisson is constructed via the trivariate reduction. For the trivariate case the multivariate reduction becomes more complicated:

now with $U_{ab,t}$ , $U_{ac,t}$ , $U_{bc,t}$ ,and $U_{abc,t}$ as correlation terms. In general, for the N-variate case, the total number of correlation terms is ${\displaystyle \sum _{i=2}^N\left(\genfrac{}{}{0pt}{}{N}{i}\right)}$ .

Acknowledgements

The authors would like to thank Dr. Kathy Ensor, Department of Statistics, Rice University, and Dr. Bonnie Ray, IBM J. T. Watson Research Center, for their valuable insights and guidance.

This Connexions module describes work conducted as part of Rice University's VIGRE program, supported by National Science Foundation grant 0739420.