Abstract
Customer journey optimization in email marketing is the art and science of finding the optimal time to deliver a sequence of marketing messages along the consumer’s decision journey. It aims to promote individual customer’s engagement and experience by optimizing the message delivery schedule over a time horizon, in order to maximize the customer’s opens, clicks, and avoid inducing fatigue (cancel subscription) to the customer. This paper proposed a solution by applying the genetic algorithm to optimize the customer journey with different objective functions for different business purposes. The simulation results show that the proposed method can effectively improve different business objectives.
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References
Ellis, C.F., Doherty, N.F.: Web advertising: the role of email marketing. J. Bus. Res. 65(6), 843–848 (2012)
Cho, C.H., Khang, H.K.: The state of internet-related research in communications, marketing, and advertising: 1994–2003. J. Advert. 35(3), 143–163 (2006)
Surveymonkey. https://www.surveymonkey.com/curiosity/map-customer-journey-keep-customers-happy/. Accessed 1 June 2019
New research. https://lab.getapp.com/new-research-getdata-why-do-people-unsubscribe-from-email-newsletters/. Accessed 1 June 2019
Oreski, S., Oreski, G.: Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert Syst. Appl. 41(4), 2052–2064 (2014)
Asadi, E., Silva, M.G., Antunes, C.H., Dias, L., Glicksman, L.: Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. Energy Build. 81, 444–456 (2014)
Soleimani, H., Kannan, G.: A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Model. 39(14), 3990–4012 (2015)
Deng, S., Yoshiyama, K., Mitsubuchi, T.: Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Comput. Econ. 45–49 (2015)
Hashem, S., Rafiee, Shahin.: Application of multi-objective genetic algorithms for optimization of energy, economics and environmental life cycle assessment in oilseed production. J. Clean. Prod. 140, 804–815 (2017)
Hao, J., Yu, Y., Law R., Fong, D.K.C.: A genetic algorithm-based learning approach to understand customer satisfaction with OTA websites. Tour. Manag. 48, 231–241 (2015)
Cui, G., Wong, M.L., Wan, X.: Targeting high value customers while under resource constraint: partial order constrained optimization with genetic algorithm. J. Interact. Mark. 29, 27–37 (2015)
Mahdavi, L., Movahednejad, M., Adbesh, F.: Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm. Expert Syst. with Appl. 38(1), 631–639 (2011)
Meena, P.L., Sarmah, S.P.: Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach. Transp. Res. Part E: Logist. Transp. Rev. 50, 84–97 (2013)
Fung, K.Y., Kwong, C.K., Siu, K.W.M., Yu, K.M.: A multi-objective genetic algorithm approach to rule mining for affective product design. Expert Syst. Appl. 39(8), 7411–7419 (2012)
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Zhang, L., He, J., Yan, Z., Dai, W., Pani, A. (2020). Application of Genetic Algorithm on Multi-objective Email Marketing Delivery Problem. In: Rocha, Á., Reis, J., Peter, M., Bogdanović, Z. (eds) Marketing and Smart Technologies. Smart Innovation, Systems and Technologies, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-1564-4_29
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DOI: https://doi.org/10.1007/978-981-15-1564-4_29
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