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2-(3-Chloropropyl)-l-phenyl-2,3-butadien-l-ol (3k): yield 840 mg (93%) from Id (1.17 g, 6 mmol) and benzaldehyde (0.42 g, 4 mmol) using procedure B; purified by flash chromatography (hexane-EtOAc (98.2)); 3k:4k = 97.3; IR (neat) 3390 (br)

This research aims to understand and predict online customers’ store visit and purchase behaviors. To this end, we develop a model that accounts for different patterns of online store visits at the individual level. Given the latency of visit patterns, we employ a changepoint modeling framework and statistically infer them using a Bayesian approach. The inferences obtained are then used to examine the effects of visit patterns on purchase dynamics across store visits. Using Internet clickstream data at an online retailer, we find that online store visit patterns tend to be clustered with significant variation across customers in terms of the number and size of visit clusters as well as the visit frequencies, both within and between clusters. Furthermore, the conversion rates vary significantly, depending on store visit patterns, such that they tend to be higher at later visits within a visit cluster, compared with earlier visits. The proposed model thereby offers superior fit and predictive performance than benchmark models that ignore clustered visit patterns and their impact on purchase behavior. We demonstrate the model’s ability to better identify prospective customers by utilizing their visit patterns, which can assist marketers in scoring customers and making targeting decisions across individuals for marketing activity. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0990 .

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