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Since standard methods based on purchase recency, frequency, and past profit and revenue statistics often have limited predictive power, advanced machine learning (ML) techniques were applied to this task in recent years. However, existing approaches are often not fully capable of modeling certain temporal patterns that can be commonly found in practice, such as periodic purchasing behavior of customers. To address these shortcomings, we propose a novel method for CLV prediction based on a combination of several ML techniques. At its core, our method consists of a tailored deep learning approach based on encoder\u2013decoder sequence-to-sequence recurrent neural networks with augmented temporal convolutions. This model is then combined with gradient boosting machines (GBMs) and a set of novel features in a hybrid framework. Empirical evaluations based on real-world data from a larger e-commerce company and a public dataset from the domain of online retail show that already the sequence-based model leads to competitive performance results. Stacking it with the GBM model is synergistic and further improves accuracy, indicating that the two models capture different patterns in the data.<\/jats:p>","DOI":"10.1145\/3441444","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T22:24:14Z","timestamp":1620685454000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Improved Customer Lifetime Value Prediction With Sequence-To-Sequence Learning and Feature-Based Models"],"prefix":"10.1145","volume":"15","author":[{"given":"Josef","family":"Bauer","sequence":"first","affiliation":[{"name":"University of Klagenfurt, Klagenfurt am W\u00f6rthersee, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dietmar","family":"Jannach","sequence":"additional","affiliation":[{"name":"University of Klagenfurt, Klagenfurt am W\u00f6rthersee, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,5,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECACE.2017.7912903"},{"key":"e_1_2_1_2_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 ( 2014 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)."},{"key":"e_1_2_1_3_1","volume-title":"Competing Risks And Multistate Models With R","author":"Beyersmann Jan","unstructured":"Jan Beyersmann , Arthur Allignol , and Martin Schumacher . 2011. Competing Risks And Multistate Models With R . Springer Science & Business Media . Jan Beyersmann, Arthur Allignol, and Martin Schumacher. 2011. Competing Risks And Multistate Models With R. Springer Science & Business Media."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Derya Birant. 2011. Data mining using RFM analysis. In Knowledge-Oriented Applications in Data Mining. InTech.  Derya Birant. 2011. Data mining using RFM analysis. In Knowledge-Oriented Applications in Data Mining. 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In Advances in Neural Information Processing Systems. 4765--4774."},{"key":"e_1_2_1_33_1","volume-title":"Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE 13, 3","author":"Makridakis Spyros","year":"2018","unstructured":"Spyros Makridakis , Evangelos Spiliotis , and Vassilios Assimakopoulos . 2018. Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE 13, 3 ( 2018 ). Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. 2018. Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE 13, 3 (2018)."},{"key":"e_1_2_1_34_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S. Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg S. Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1520-6653(200021)14:2<43::AID-DIR4>3.0.CO;2-H"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/2780513.2780514"},{"key":"e_1_2_1_37_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V . Le . 2014 . Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems . 3104--3112. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. 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Deep forest. arXiv:1702.08835 (2017)."},{"key":"e_1_2_1_41_1","volume-title":"Le","author":"Zoph Barret","year":"2016","unstructured":"Barret Zoph and Quoc V . Le . 2016 . Neural architecture search with reinforcement learning. arXiv:1611.01578 (2016). Barret Zoph and Quoc V. Le. 2016. 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