How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation
<p dir="ltr">What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in custome...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513507645456384 |
|---|---|
| author | Joni Salminen (7434770) |
| author2 | Mekhail Mustak (19450519) Muhammad Sufyan (6335450) Bernard J. Jansen (7434779) |
| author2_role | author author author |
| author_facet | Joni Salminen (7434770) Mekhail Mustak (19450519) Muhammad Sufyan (6335450) Bernard J. Jansen (7434779) |
| author_role | author |
| dc.creator.none.fl_str_mv | Joni Salminen (7434770) Mekhail Mustak (19450519) Muhammad Sufyan (6335450) Bernard J. Jansen (7434779) |
| dc.date.none.fl_str_mv | 2023-07-06T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1057/s41270-023-00235-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/How_can_algorithms_help_in_segmenting_users_and_customers_A_systematic_review_and_research_agenda_for_algorithmic_customer_segmentation/26788423 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Business systems in context Information and computing sciences Artificial intelligence Data management and data science Customer segmentation Machine learning AI Algorithms |
| dc.title.none.fl_str_mv | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Marketing Analytics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1057/s41270-023-00235-5" target="_blank">https://dx.doi.org/10.1057/s41270-023-00235-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_090bbfb1a923529b7230217e0e16598c |
| identifier_str_mv | 10.1057/s41270-023-00235-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26788423 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentationJoni Salminen (7434770)Mekhail Mustak (19450519)Muhammad Sufyan (6335450)Bernard J. Jansen (7434779)Commerce, management, tourism and servicesBusiness systems in contextInformation and computing sciencesArtificial intelligenceData management and data scienceCustomer segmentationMachine learningAIAlgorithms<p dir="ltr">What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Marketing Analytics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1057/s41270-023-00235-5" target="_blank">https://dx.doi.org/10.1057/s41270-023-00235-5</a></p>2023-07-06T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1057/s41270-023-00235-5https://figshare.com/articles/journal_contribution/How_can_algorithms_help_in_segmenting_users_and_customers_A_systematic_review_and_research_agenda_for_algorithmic_customer_segmentation/26788423CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267884232023-07-06T03:00:00Z |
| spellingShingle | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation Joni Salminen (7434770) Commerce, management, tourism and services Business systems in context Information and computing sciences Artificial intelligence Data management and data science Customer segmentation Machine learning AI Algorithms |
| status_str | publishedVersion |
| title | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| title_full | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| title_fullStr | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| title_full_unstemmed | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| title_short | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| title_sort | How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation |
| topic | Commerce, management, tourism and services Business systems in context Information and computing sciences Artificial intelligence Data management and data science Customer segmentation Machine learning AI Algorithms |