| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Papakyriakopoulos, Dimitrios | - |
| dc.contributor.author | Bardaki, Cleopatra | - |
| dc.contributor.author | Griva, Anastasia | - |
| dc.contributor.author | Pramatari, Katerina | - |
| dc.date.accessioned | 2024-04-19T10:11:48Z | - |
| dc.date.available | 2024-04-19T10:11:48Z | - |
| dc.date.issued | 2018-06-15 | - |
| dc.identifier | scopus-85041435692 | - |
| dc.identifier.issn | 0957-4174 | - |
| dc.identifier.other | 85041435692 | - |
| dc.identifier.uri | https://uniwacris.uniwa.gr/handle/3000/2156 | - |
| dc.description.abstract | Basket analytics is a powerful tool in the retail context for acquiring knowledge about consumer shopping habits and preferences. In this paper, we propose a business analytics approach that mines customer visit segments from basket sales data. We characterize a customer visit by the purchased product categories in the basket and identify the shopping intention or mission behind the visit e.g. a ‘breakfast’ visit to purchase cereal, milk, bread, cheese etc. We also suggest a semi-supervised feature selection approach that uses the product taxonomy as input and suggests customized categories as output. This approach is utilized to balance the product taxonomy tree that has a significant effect on the data mining results. We demonstrate the utility of our approach by applying it to a real case of a major European fast-moving consumer goods (FMCG) retailer. Apart from its theoretical contribution, the proposed approach extracts knowledge that may support several decisions ranging from marketing campaigns per customer segment, redesign of a store's layout to product recommendations. | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Expert Systems with Applications | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Customer visit segmentation | en_US |
| dc.subject | Data mining | en_US |
| dc.subject | Retail business analytics | en_US |
| dc.subject | Shopper behavior | en_US |
| dc.title | Retail business analytics: customer visit segmentation using market basket data | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.1016/j.eswa.2018.01.029 | en_US |
| dc.identifier.scopus | 2-s2.0-85041435692 | - |
| dcterms.accessRights | 0 | en_US |
| dc.relation.dept | Department of Business Administration | en_US |
| dc.relation.faculty | School of Administrative, Economics and Social Sciences | en_US |
| dc.relation.volume | 100 | en_US |
| dc.identifier.spage | 1 | en_US |
| dc.identifier.epage | 16 | en_US |
| dc.collaboration | University of West Attica (UNIWA) | en_US |
| dc.subject.field | Engineering and Technology | en_US |
| dc.journals | Subscription | en_US |
| dc.publication | Peer Reviewed | en_US |
| dc.country | Greece | en_US |
| local.metadatastatus | verified | en_US |
| item.fulltext | No Fulltext | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| item.cerifentitytype | Publications | - |
| item.openairetype | Article | - |
| item.languageiso639-1 | en | - |
| item.grantfulltext | none | - |
| crisitem.author.dept | Department of Business Administration | - |
| crisitem.author.faculty | School of Administrative, Economics and Social Sciences | - |
| crisitem.author.orcid | 0000-0001-7033-1890 | - |
| crisitem.author.parentorg | School of Administrative, Economics and Social Sciences | - |
| Appears in Collections: | Articles / Άρθρα | |
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