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dc.contributor.advisorThompson, Richard H.
dc.contributor.authorPapetti, Ryan Henry
dc.creatorPapetti, Ryan Henry
dc.date.accessioned2020-02-13T02:59:58Z
dc.date.available2020-02-13T02:59:58Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10150/637024
dc.description.abstractAs the legal cannabis industry emerges from its nascent stages, there is increasing motivation for retailers to look for data or strategies that can help them segment or describe their customers in a succinct, but informative manner. While many cannabis operators view the state-mandated traceability as a necessary burden, it provides a goldmine for internal customer analysis. Traditionally, segmentation analysis focuses on demographic or RFM (recency-frequency-monetary) segmentation. Yet, neither of these methods has the capacity to provide insight into a customer’s purchasing behavior. With the help of 4Front Ventures, a battle-tested multinational cannabis operator, this report focuses on segmenting customers using cannabis-specific data (such as flower and concentrate consumption) and machine learning methods (K-Means and Agglomerative Hierarchical Clustering) to generate newfound ways to explore a dispensary’s consumer base. The findings are that there are roughly five or six clusters of customers with each cluster having unique purchasing traits that define them. Although the results are meaningful, this report could benefit with exploring more clustering algorithms, comparing results across dispensaries within the same state, or investigating segmentations in other state markets.en_US
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.titleCUSTOMER SEGMENTATION ANALYSIS OF CANNABIS RETAIL DATA: A MACHINE LEARNING APPROACHen_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelbachelorsen_US
thesis.degree.disciplineHonors Collegeen_US
thesis.degree.disciplineInformation Science and Technology
thesis.degree.nameB.S.en_US
refterms.dateFOA2020-02-13T02:59:58Z


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