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Effects of Customer Referral Programs on Mobile App Stickiness: Evidence from China Fresh E-Commerce

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Proceedings of the Thirteenth International Conference on Management Science and Engineering Management (ICMSEM 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1002))

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Abstract

Customer referral programs are an effective means of increasing customer acquisition and customer retention. Using a large-scale customer data set with 17, 116 observations from a Chinese fresh e-commerce app, we identify that participation in a referral program also increases customer stickiness. We apply propensity score matching to match each customer participating in the referral program with a similar customer (statistical twin) who did not participate. Then a regression model is constructed to compare their stickiness. Our empirical analysis shows that participation in a referral program increases stickiness of both the referrers and the referred customers by 54.13% and 10.88% respectively after matching. The results contribute to a growing literature on customer referral programs as a means for retaining and growing relationships with current customers. We also offer empirical evidence from China fresh E-commerce to contribute to the understanding of mobile app stickiness. The study is also important for marketers, who can differentiate customers with stickiness to realize more precise marketing.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant 71502019, 71472130), the Foundation for Humanities and Social Sciences of the Ministry of Education of China (Grant 17YJA630031), Sichuan University (Grant 20822041A4222).

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Correspondence to Yang Yang .

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Wang, Q., Jiang, Y., Liao, C., Yang, Y. (2020). Effects of Customer Referral Programs on Mobile App Stickiness: Evidence from China Fresh E-Commerce. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1002. Springer, Cham. https://doi.org/10.1007/978-3-030-21255-1_29

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