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Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market. (English) Zbl 1325.90021

Summary: Online customer segmentation is a significant research topic of customer relationship management. Previous literatures mainly studied the differences between non-purchasers and purchasers, lacking further segmentation of online purchasers. There is still existing significant heterogeneity within purchaser-groups. This paper focuses on Chinese online purchaser segmentation based on large volume of real transaction data on Taobao.com, we firstly extracted and investigated Chinese online purchaser behavior indicators and classified them into six types by cluster analysis, these six categories are: economical purchasers, active-star purchasers, direct purchasers, high-loyalty purchasers, risk-averse purchasers and credibility-first purchasers; then we built an empirical model to estimate the sensitivity of each type of online purchasers to three mainstream promotion strategies (discount, advertising and word-of-mouth), and found that economical purchasers are the most sensitive to discount promotion; direct purchasers are the most sensitive to advertising promotion; active-star purchasers are the most sensitive to word-of-mouth promotion; finally, the implications of online purchaser classification for marketing strategies were discussed.

MSC:

90B18 Communication networks in operations research
Full Text: DOI

References:

[1] Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., & Wood, S. (1997). Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. The Journal of Marketing, 61, 38-53. · doi:10.2307/1251788
[2] Anderson, E., & Weitz, B. (1989). Determinants of continuity in conventional industrial channel dyads. Marketing Science, 8(4), 310-323. · doi:10.1287/mksc.8.4.310
[3] Barnes, S. J., Bauer, H. H., Neumann, M. M., & Huber, F. (2007). Segmenting cyberspace: a customer typology for the Internet. European Journal of Marketing, 41(1/2), 71-93. · doi:10.1108/03090560710718120
[4] Bart, Y., Shankar, V., Sultan, F., & Urban, G. L. (2005). Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study. Journal of Marketing, 69, 133-152. · doi:10.1509/jmkg.2005.69.4.133
[5] Bhatnagar, A., & Ghose, S. (2004). A latent class segmentation analysis of e-shoppers. Journal of Business Research, 57(7), 758-767. · doi:10.1016/S0148-2963(02)00357-0
[6] Chen, Y. F., & Wang, Y. J. (2010). Effect of herd cues and product involvement on bidder online choices. Cyberpsychology, Behavior, and Social Networking, 13(4), 423-428. · doi:10.1089/cyber.2009.0304
[7] Chen, Y., & Xie, J. (2008). Online consumer review: word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477-491. · doi:10.1287/mnsc.1070.0810
[8] Chiu, C. M., Wang, E. T., Fang, Y. H., & Huang, H. Y. (2012). Understanding customers’ repeat purchase intentions in B2C e-commerce: the roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal doi:10.1111/j.1365-2575.2012.00407.x. · doi:10.1111/j.1365-2575.2012.00407.x
[9] Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer-seller relationships. Journal of Marketing, 61, 35-51. · doi:10.2307/1251829
[10] Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics, 21, 768-769.
[11] Ganesh, J., Reynolds, K. E., Luckett, M., & Pomirleanu, N. (2010). Online shopper motivations, and e-store attributes: an examination of online patronage behavior and shopper typologies. Journal of Retailing, 86(1), 106-115. · doi:10.1016/j.jretai.2010.01.003
[12] Grewal, D., Iyer, G. R., Krishnan, R., & Sharma, A. (2003). The Internet and the price-value-loyalty chain. Journal of Business Research, 56(5), 391-398. · doi:10.1016/S0148-2963(01)00227-2
[13] Kau, A. K., Tang, Y. E., & Ghose, S. (2003). Typology of online shoppers. Journal of Consumer Marketing, 20(2), 139-156. · doi:10.1108/07363760310464604
[14] Kim, H. W., & Gupta, S. (2009). A comparison of purchase decision calculus between potential and repeat customers of an online store. Decision Support Systems, 47(4), 477-487. · doi:10.1016/j.dss.2009.04.014
[15] Kukar-Kinney, M., Ridgway, N. M., & Monroe, K. B. (2012). The role of price in the behavior and purchase decisions of compulsive buyers. Journal of Retailing, 88(1), 63-71. · doi:10.1016/j.jretai.2011.02.004
[16] Li, J., Wang, K., & Xu, L. (2009). Chameleon based on clustering feature tree and its application in customer segmentation. Annals of Operations Research, 168(1), 225-245. · Zbl 1179.68122 · doi:10.1007/s10479-008-0368-4
[17] Lin, Z., Li, D., Janamanchi, B., & Huang, W. (2006). Reputation distribution and consumer-to-consumer online auction market structure: an exploratory study. Decision Support Systems, 41(2), 435-448. · doi:10.1016/j.dss.2004.07.006
[18] Lohse, G., Bellman, S., & Johnson, E. (2000). Consumer buying behavior on the Internet: findings from panel data. Journal of Interactive Marketing, 14(1), 15-29. · doi:10.1002/(SICI)1520-6653(200024)14:1<15::AID-DIR2>3.0.CO;2-C
[19] Moe, W. W. (2003). Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1), 29-39. · doi:10.1207/153276603768344762
[20] Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on amazon.com. Mis Quarterly, 34(1), 185-200.
[21] Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000). Measuring the customer experience in online environments: a structural modeling approach. Marketing Science, 19(1), 22-42. · doi:10.1287/mksc.19.1.22.15184
[22] Peng, G.; Chen, J.; Liu, Y.; Lv, B.; Long, H., An empirical research on the determinants of sales and conversion rate of online auction, 1-4 (2008), New York
[23] Raju, C. V. L., Narahari, Y., & Ravikumar, K. (2006). Learning dynamic prices in electronic retail markets with customer segmentation. Annals of Operations Research, 143(1), 59-75. · Zbl 1122.90309 · doi:10.1007/s10479-006-7372-3
[24] Reynolds, K. E., & Beatty, S. E. (2000). A relationship customer typology. Journal of Retailing, 75(4), 509-523. · doi:10.1016/S0022-4359(99)00016-0
[25] Rho, J. J., Moon, B. J., Kim, Y. J., & Yang, D. H. (2004). Internet customer segmentation using web log data. Journal of Business & Economics Research, 2(11), 59-74.
[26] Rohm, A. J., & Swaminathan, V. (2004). A typology of online shoppers based on shopping motivations. Journal of Business Research, 57(7), 748-757. · doi:10.1016/S0148-2963(02)00351-X
[27] Shankar, V., Rangaswamy, A., & Pusateri, M. (1999). The online medium and customer price sensitivity. eBusiness, Research Center, University Park.
[28] Song, Q., & Shepperd, M. (2006). Mining web browsing patterns for e-commerce. Computers in Industry, 57(7), 622-630. · doi:10.1016/j.compind.2005.11.006
[29] Soopramanien, D. G., & Robertson, A. (2007). Adoption and usage of online shopping: an empirical analysis of the characteristics of “buyers”browsers“ and “non-Internet shoppers”. Journal of Retailing and Consumer Services, 14(1), 73-82. · doi:10.1016/j.jretconser.2006.04.002
[30] Tauber, E. M. (1972). Why do people shop? The Journal of Marketing, 36, 46-49. · doi:10.2307/1250426
[31] Wu, R. S., & Chou, P. H. (2011). Customer segmentation of multiple category data in e-commerce using a soft-clustering approach. Electronic Commerce Research and Applications, 10(3), 331-341. · doi:10.1016/j.elerap.2010.11.002
[32] Xia, L., & Monroe, K. B. (2004). Price partitioning on the Internet. Journal of Interactive Marketing, 18(4), 63-73. · doi:10.1002/dir.20017
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