Abstract
This work was developed within the scope of industrial mathematics, in partnership with a large grocery retail company in the Portuguese market, Jerónimo Martins, in particular with Recheio, the largest chain of Cash &Carry stores in Portugal. The objective was to segment the Families (groups of products in the marketing structure) in Recheio’s HoReCa channel, into disjoint sets that can be interpreted by the business. Initially, database manipulation, preprocessing and choice of variables to use were carried out. Then followed the segmentation phase carried out in two ways: through the total HoReCa channel and through the online HoReCa channel. A clear separation of the company’s products was achieved using various Cluster Analysis methods such as K-Means, K-Medoids and Hierarchical. Different groups were obtained, based on common characteristics of the Families, with the aim of better understanding the company’s market, helping to make strategic decisions and develop more effective marketing and production plans. The work involved the use of several types of software, namely Excel, SQL, Python and R.
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References
Branco, J.A.: Uma Introdução à Análise de Clusters. Sociedade Portuguesa de Estatística (2004)
Chen, Y., Guozheng, Z., Dengfeng, H., Chua, F.: Customer segmentation based on survival character. J. Intell. Manuf. 18(4), 513–517 (2007)
Chen, N., Yang, Y.: The impact of customer experience on consumer purchase intention in cross-border E-commerce - taking network structural embeddedness as mediator variable. J. Retail. Consum. Serv. 59, 102344 (2021)
Edwin, P.C.: Beyond the Numbers: Making Sense of Statistics. National Science Teaching Association (2012)
Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Cluster Analysis, 5th edn. Wiley, Hoboken (2011)
Farahani, A., Rahiminezhed, H., Same, A., Immannezhed, K.: A comparison of partial least square (PLS) and ordinary least square (OLS) regressions in predicting of couples mental health based on their communicational patterns. Procedia Soc. Behav. Sci. 5, 1459–1463 (2010)
Grabmeier, J., Rudolph, A.: Techniques of cluster algorithms in data mining. Data Min. Knowl. Disc. 6, 303–360 (2002)
Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Kettenring, J.R.: The practice of cluster analysis. J. Classif. 23(1), 3–30 (2006)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Statistics, pp. 281–297. University of California Press, Berkeley (1967)
Malhotra, N., Birks, D., Wills, P.: Marketing Research - An Applied Approach, 5th edn. Pearson Education, London (2017)
Pestana, M.H., Gageiro, J.N.: Análise de dados para ciências sociais: a complementaridade do SPSS, 6th edn. Edições Sílabo (2014)
Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Acknowledgements
Research partially funded by FCT - Fundação para a Ciência e a Tecnologia, Portugal, under the project UIDB/00006/2020, DOI: 10.54499/UIDB/00006/2020 (CEAUL) and ISEL. The authors thank the company Recheio from the Jerónimo Martins group for authorizing the publication of the information in this paper.
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Aleixo, S.M., Pereira, B., Fernandes, T., Rodrigues, J., Pina, M. (2024). Cluster Analysis for Two HoReCa Channels of a Large Grocery Retail Company. In: Gervasi, O., Murgante, B., Garau, C., Taniar, D., C. Rocha, A.M.A., Faginas Lago, M.N. (eds) Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14815. Springer, Cham. https://doi.org/10.1007/978-3-031-65154-0_21
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