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Network promoter score (NePS): An indicator of product sales in E-commerce retailing sector

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  • Supriyo Mandal

    (Indian Institute of Technology Patna)

  • Abyayananda Maiti

    (Indian Institute of Technology Patna)

Abstract

E-commerce companies want to predict their future product sales from the current customers’ feedback to frame a better business strategy. However, the conventional way of analyzing rating activities or quality and sentiment of reviews, volume of sales, or product prices is not enough for establishing a strong regression between these parameters and future product sales. Most of the existing works ignore the heterogeneous positional and influential effects of individual customer reviews and ratings. For the realization of these effects, we use review network i.e., a bipartite network between customers and products based on the customers’ review activities. In this paper, we present a concept named Network Promoter Score (NePS) based on the reliability, positional influence of each customer in the network. In-depth experiments on online review datasets show that NePS emerges as a strong indicator of product sales and can be remarkably futuristic compared to the existing parameters. Furthermore, we propose a predictive modeling technique to estimate the product sales of a company based on NePS.

Suggested Citation

  • Supriyo Mandal & Abyayananda Maiti, 2022. "Network promoter score (NePS): An indicator of product sales in E-commerce retailing sector," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1327-1349, September.
  • Handle: RePEc:spr:elmark:v:32:y:2022:i:3:d:10.1007_s12525-021-00503-1
    DOI: 10.1007/s12525-021-00503-1
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    1. Rossi P. E & Gilula Z. & Allenby G. M, 2001. "Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 20-31, March.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    4. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    5. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    6. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    7. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    8. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    9. Chung-Yi Lin & Shu-Yi Liaw & Chao-Chun Chen & Mao-Yuan Pai & Yuh-Min Chen, 2017. "A computer-based approach for analyzing consumer demands in electronic word-of-mouth," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 225-242, August.
    10. Eslami, Seyed Pouyan & Ghasemaghaei, Maryam, 2018. "Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 74-80.
    11. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    12. Dongpu Fu & Yili Hong & Kanliang Wang & Weiguo Fan, 2018. "Effects of membership tier on user content generation behaviors: evidence from online reviews," Electronic Commerce Research, Springer, vol. 18(3), pages 457-483, September.
    13. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    14. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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    Cited by:

    1. Rainer Alt, 2022. "Electronic Markets on platform culture," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1019-1031, September.

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