Predicting online-purchasing behaviour |
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| Institution: | 1. Department of Agricultural and Environmental Sciences, University of Bari “Aldo Moro”, via Giovanni Amendola, 165/a, 70126 Bari, Italy;2. School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, UK;3. Department of Agricultural, Food and Environmental Sciences, University of Foggia, Via Napoli, 25, 71100 Foggia, Italy;4. Business Economics Group, Department of Social Sciences, Wageningen University, Hollandseweg, 1, 6706 KN Wageningen, The Netherlands;1. Data Science Group, Internet Business Development Division, Recruit Lifestyle Co., Ltd.\nGranTokyo SOUTHTOWER, 1-9-2 Marunouchi, Chiyoda-ku, Tokyo 100-6640, Japan;2. Department of Information and System Engineering, Faculty of Science and Engineering, Chuo University 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan;3. School of Network and Information, Senshu University 2-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa 214-8580, Japan;4. Retty, Inc., Sumitomo Fudosan Azabu Juban Building, 1-4-1 Mita, Minato-ku, Tokyo 108-0073, Japan |
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| Abstract: | This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson's global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. Though our dataset is limited in size, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information. |
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