How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

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.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Metodologia para clusterização de clientes e recomendação de produtos

Orientadora: Prof.ª Dra. Mariana KleinaCoorientador: Prof. Dr. Marcos Augusto Mendes MarquesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 25/05/2021Inclui referências: p. 133-138Resumo: O agrupamento de clientes auxilia o marketing estratégico, permitindo traçar estratégias diferenciadas a grupos específicos de clientes, com objetivo de criar relacionamento. A identificação do perfil de cada grupo associada a algoritmos de recomendação de produtos, auxilia os clientes a encontrarem os itens mais indicados às necessidades específicas. Esta facilidade pode auxiliar muito empresas que possuem uma extensa gama de produtos, pois, a tarefa exaustiva da busca de produtos por parte dos clientes pode ocasionar que ele compre da concorrência que pode conseguir fazer uma recomendação assertiva. Este trabalho se baseou nesta necessidade para demonstrar a aplicação de um método que combinou o algoritmo de clusterização e o de regras de associação, mostrando cada etapa da aplicação em uma base mista, que possuem tanto variáveis quantitativas quanto qualitativa. Os resultados mostraram que a medida de distância de Gower, utilizada para verificar a semelhança entre os clientes, gerou clusters com estrutura mais forte, de acordo com Coeficiente de Silhueta e o Índice de Davies Bouldin, se comparada a Jaccard. Para possibilitar o agrupamento empregou-se o K-Medoid, por ser mais flexível a utilização de diferentes medidas, o que propiciou a comparação e gerou onze clusters com perfis diferentes de clientes em um estudo de caso no setor de serviços. Para a recomendação de produtos foi avaliado o desempenho dos algoritmos Apriori, Filtragem Colaborativa Baseada em Clientes e Filtragem Colaborativa Baseada no Item, este último apresentou êxito nos dez primeiros clusters, analisando-se as Taxas de Recall e Precisão, e Curva ROC. Porém no cluster onze o Apriori apresentou melhores resultados. Após a identificação dos algoritmos de recomendação, visando otimizar as métricas de eficiência, foi ajustado o número de vizinhos mais próximos do algoritmo de Filtragem colaborativa e os parâmetros de suporte e confiança do Apriori, o que garantiu melhor desempenho de ambos. Palavras-chave: Clusterização. Recomendação de Produtos. K-Medoid. Filtragem Colaborativa. Apriori.Abstract: The grouping of clients assists strategic marketing, allowing the design of differentiated strategies to specific groups of clients, with the objective of creating relationships. The identification of the profile of each group associated with product recommendation algorithms, helps customers to find the most suitable items for their specific needs. This facility can help a lot of companies that have an extensive range of products, because the exhaustive task of searching for products on the part of customers can cause them to buy from the competition that may be able to make an assertive recommendation. This work was based on this need to demonstrate the application of a method that combined the clustering algorithm and that of association rules, showing each step of the application on a mixed basis, which have both quantitative and qualitative variables. The results showed that the Gower distance measure, used to verify the similarity between the clients, generated clusters with a stronger structure, according to the Silhouette Coefficient and the Davies Bouldin Index, when compared to Jaccard. To make the grouping possible, K-Medoid was used, as it is more flexible to use different measures, which enabled the comparison and generated eleven clusters with different customer profiles in a case study in the service sector. For the recommendation of products, the performance of the Apriori algorithms, Collaborative Client-Based Filtering and Item-Based Collaborative Filtering was evaluated, the latter was successful in the first ten clusters, analyzing the Recall and Precision Rates, and ROC Curve. However, in cluster eleven, Apriori presented better results. After identifying the recommendation algorithms, in order to optimize the efficiency metrics, the number of neighbors closest to the collaborative filtering algorithm and the support and trust parameters of Apriori were adjusted, which guaranteed better performances by both. Keywords: Clustering. Product Recommendation. K-Medoid. Collaborative Filtering. Apriori