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In marketing we are often confronted with a continuous stream of responses tomarketing messages. Such streaming data provide invaluable informationregarding message effectiveness and segmentation. However, streaming data arehard to analyze using conventional methods: their high volume and the fact thatthey are continuously augmented means that it takes considerable time toanalyze them. We propose a method for estimating a finite mixture of logisticregression models which can be used to cluster customers based on a continuousstream of responses. This method, which we coin oFMLR, allows segments to beidentified in data streams or extremely large static datasets. Contrary toblack box algorithms, oFMLR provides model estimates that are directlyinterpretable. We first introduce oFMLR, explaining in passing general topicssuch as online estimation and the EM algorithm, making this paper a high leveloverview of possible methods of dealing with large data streams in marketingpractice. Next, we discuss model convergence, identifiability, and relations toalternative, Bayesian, methods; we also identify more general issues that arisefrom dealing with continuously augmented data sets. Finally, we introduce theoFMLR [R] package and evaluate the method by numerical simulation and byanalyzing a large customer clickstream dataset.

Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream