Ekinci, YelizGuran, Aysun2024-07-182024-07-1820231470-78532515-2173https://doi.org/10.1177/14707853221139599https://hdl.handle.net/11411/8026The 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.eninfo:eu-repo/semantics/closedAccessMarkov Decision ProcessesClassification AlgorithmsEnsemble LearningSynthetic Minority Over-Sampling TechniqueOptimal Promotion StrategyCustomer Lifetime ValueBig Data AnalyticsCluster-AnalysisClassificationOptimizationBehaviorReturnStoreCrmClvArticle2-s2.0-8516781727810.1177/147078532211395996625Q264265Q3WOS:000886516300001