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The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.<\/jats:p>","DOI":"10.1007\/978-3-030-98581-3_16","type":"book-chapter","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T18:03:23Z","timestamp":1648058603000},"page":"211-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Online Prediction of\u00a0Aggregated Retailer Consumer Behaviour"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0908-9163","authenticated-orcid":false,"given":"Yorick","family":"Spenrath","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4027-4351","authenticated-orcid":false,"given":"Marwan","family":"Hassani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3978-6464","authenticated-orcid":false,"given":"Boudewijn F.","family":"van Dongen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining","author":"W Van der Aalst","year":"2016","unstructured":"Van der Aalst, W.: Process Mining, 2nd edn. 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