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DC Field | Value | Language |
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dc.contributor.author | Aviles Gonzalez, Jonnatan Fernando | |
dc.contributor.author | Llivisaca Villazhañay, Juan Carlos | |
dc.date.accessioned | 2023-02-28T16:41:13Z | - |
dc.date.available | 2023-02-28T16:41:13Z | - |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-3-031-24985-3 | |
dc.identifier.issn | 1865-0937 | |
dc.identifier.uri | http://dspace.ucuenca.edu.ec/handle/123456789/41154 | - |
dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-031-24985-3_18 | |
dc.description.abstract | In competitive markets, customer segmentation improves customer loyalty and business performance, but in practice, these analyses are carried out using simple relationships in dashboard, or Microsoft Excel’ sheets, which do not show customer behavior. Data segmentation in the era of big data has changed this paradigm with some techniques that try to decrease bias. In this research, four segmentation techniques are tested with a large set of data from a retail store. CLARA (Clustering Large Applications Algorithm) and Random Forest algorithms both were the best. Through the RFM (Recency, Frequency, Monetary) approach, eight customer segments were found, where Champions customers spend more money and return frequently to the retail store. In addition, each segment of customer buys following a model, this was demonstrated with the a priori algorithm. Finally, some strategies are given into which products should go together and how to distribute them so that customers can find them, as well as the best-selling products. | |
dc.language.iso | es_ES | |
dc.publisher | Springer | |
dc.source | Applied Technologies. ICAT 2022. Communications in Computer and Information Science | |
dc.subject | Retail | |
dc.subject | Random forest | |
dc.subject | Clustering algorithm | |
dc.subject | Data mining | |
dc.subject | A priori | |
dc.title | Customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules | |
dc.type | ARTÍCULO DE CONFERENCIA | |
dc.description.city | Quito | |
dc.ucuenca.idautor | 0105627269 | |
dc.ucuenca.idautor | 0104803630 | |
dc.identifier.doi | 10.1007/978-3-031-24985-3_18 | |
dc.ucuenca.embargoend | 2050-12-31 | |
dc.ucuenca.version | Versión publicada | |
dc.ucuenca.embargointerno | 2050-12-31 | |
dc.ucuenca.areaconocimientounescoamplio | 07 - Ingeniería, Industria y Construcción | |
dc.ucuenca.afiliacion | Aviles, J., Universidad del Azuay, Cuenca, Ecuador | |
dc.ucuenca.afiliacion | Llivisaca, J., Universidad de Cuenca, Facultad de Ciencias Químicas, Cuenca, Ecuador; Llivisaca, J., Universidad Politécnica Estatal del Carchi (UPEC), Carchi, Ecuador | |
dc.ucuenca.correspondencia | Llivisaca Villazhañay, Juan Carlos, juan.llivisaca@ucuenca.edu.ec | |
dc.ucuenca.volumen | Volumen 1755 | |
dc.ucuenca.indicebibliografico | SCOPUS | |
dc.ucuenca.factorimpacto | 0.21 | |
dc.ucuenca.cuartil | Q4 | |
dc.ucuenca.numerocitaciones | 0 | |
dc.ucuenca.areaconocimientofrascatiamplio | 2. Ingeniería y Tecnología | |
dc.ucuenca.pais | ECUADOR | |
dc.ucuenca.conferencia | 4th International Conference, ICAT 2022 | |
dc.ucuenca.areaconocimientofrascatiespecifico | 2.11 Otras Ingenierias y Tecnologías | |
dc.ucuenca.areaconocimientofrascatidetallado | 2.11.2 Otras Ingenierias y Tecnologías | |
dc.ucuenca.areaconocimientounescoespecifico | 071 - Ingeniería y Profesiones Afines | |
dc.ucuenca.areaconocimientounescodetallado | 0711 - Ingeniería y Procesos Químicos | |
dc.ucuenca.fechainicioconferencia | 2022-11-23 | |
dc.ucuenca.fechafinconferencia | 2022-11-25 | |
dc.ucuenca.organizadorconferencia | Universidad de las Fuerzas Armadas “ESPE” | |
dc.ucuenca.comiteorganizadorconferencia | Universidad de las Fuerzas Armadas “ESPE” | |
dc.ucuenca.urifuente | https://www.springer.com/series/7899 | |
dc.contributor.ponente | Llivisaca Villazhañay, Juan Carlos | |
Appears in Collections: | Artículos |
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File | Size | Format | |
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documento.pdf Until 2050-12-31 | 314.5 kB | Adobe PDF | View/Open Request a copy |
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