Development of a hybrid model to plan segment based optimal promotion strategy

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dc.authoridGURAN, Aysun/0000-0001-7066-0635
dc.authorwosidGüran, Aysun/AEK-6151-2022
dc.contributor.authorEkinci, Yeliz
dc.contributor.authorGuran, Aysun
dc.date.accessioned2024-07-18T20:49:01Z
dc.date.available2024-07-18T20:49:01Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThe study addresses the long-term effects of promotions in terms of movement in a value-based segmentation (lead, iron, gold, platinum), instead of simply looking at response rates that occur shortly after the promotion. The study develops a framework for planning an optimal promotion strategy via Markov Decision Processes and Machine Learning methods for an online department store. In the first phase, the states are set as the customer profitability segments in order to conduct the MDPs. Then, MDP model is solved, and the optimal decision for each segment is determined. In the second phase, in order to aid the company for making their plans for the next year, the segment that the customer will belong to next year should be predicted. Prediction of the future customer profitability segment is performed by using several machine learning algorithms, and the best performing model is selected. Using this best performing model, the company can predict the future (potential) profitability segment of the customer and make plans which include the optimal promotions that will be directed to the customers depending on their segments (these optimal promotions are the outcomes of the first phase). The proposed framework can be applied by practitioners in e-commerce companies which keep customer data.en_US
dc.identifier.doi10.1177/14707853221139599
dc.identifier.endpage662en_US
dc.identifier.issn1470-7853
dc.identifier.issn2515-2173
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85167817278en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage642en_US
dc.identifier.urihttps://doi.org/10.1177/14707853221139599
dc.identifier.urihttps://hdl.handle.net/11411/8026
dc.identifier.volume65en_US
dc.identifier.wosWOS:000886516300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofInternational Journal of Market Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMarkov Decision Processesen_US
dc.subjectClassification Algorithmsen_US
dc.subjectEnsemble Learningen_US
dc.subjectSynthetic Minority Over-Sampling Techniqueen_US
dc.subjectOptimal Promotion Strategyen_US
dc.subjectCustomer Lifetime Valueen_US
dc.subjectBig Data Analyticsen_US
dc.subjectCluster-Analysisen_US
dc.subjectClassificationen_US
dc.subjectOptimizationen_US
dc.subjectBehavioren_US
dc.subjectReturnen_US
dc.subjectStoreen_US
dc.subjectCrmen_US
dc.subjectClven_US
dc.titleDevelopment of a hybrid model to plan segment based optimal promotion strategyen_US
dc.typeArticleen_US

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