TY - GEN
T1 - Customer Segmentation in Food Retail Sector
T2 - 4th International Conference on Applied Technologies, ICAT 2022
AU - Llivisaca, Juan
AU - Avilés-González, Jonnatan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - A priori
KW - Clustering algorithm
KW - Data mining
KW - Random forest
KW - Retail
UR - https://www.scopus.com/pages/publications/85147993669
U2 - 10.1007/978-3-031-24985-3_18
DO - 10.1007/978-3-031-24985-3_18
M3 - Contribución a la conferencia
AN - SCOPUS:85147993669
SN - 9783031249846
T3 - Communications in Computer and Information Science
SP - 240
EP - 254
BT - Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers
A2 - Botto-Tobar, Miguel
A2 - Zambrano Vizuete, Marcelo
A2 - Montes León, Sergio
A2 - Torres-Carrión, Pablo
A2 - Durakovic, Benjamin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 November 2022 through 25 November 2022
ER -