The role of shopping mission in retail customer segmentation

Sokol, Ondřej; Holý, Vladimír

doi:10.1177/1470785320921011

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…The electricity load curve of electricity customers is clustered by Pan using R -based parallelized K Means algorithm [17]. Sokol proposed a model for segmenting customers alongside the traditional methods [18]. Chan proposed a novel approach for the clustering of consumption behavior [19].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Likely Customers for Car Industries Using K-Means Clustering Compared with Logistic Regression

UpendraReddy

Thangaraj

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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This study represents a customer segmentation model which helps them to group the customers for the car industry with the same market characteristics. The study contains 2 groups i.e, the Logistic Regression model is developed in the first group and the K Means clustering model, an unsupervised machine learning algorithm is developed in the second group. Each group has a sample size of 200 and the study parameters include alpha value0.05, beta value 0.2, and the power value 0.8. The accuracies of each model are compared with others for different sample sizes. This article is an attempt to improve the accuracy of customer segmentation using the K Means clustering, an unsupervised clustering machine learning algorithm. The proposed model has improved accuracy of 87.4% with p < 0.05 in segmenting customers than the existing model of 85%. This innovative prediction model helps to know the future customers and their needs and take innovative decisions to fulfill them. The outcomes of the proposed model are compared with the Logistic Regression algorithm and the proposed model confirms to have higher accuracy than the Logistic Regression algorithm.

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Section: Discussionmentioning

confidence: 99%

Prediction of Likely Customers for Car Industries Using K-Means Clustering Compared with Logistic Regression

UpendraReddy

Thangaraj

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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“…To better leverage customer-segmentation marketing strategies, many scholars have analyzed and studied relevant sales data. Scholars such as Sokol and Holý (2021) have utilized data clustering analysis techniques to analyze customer behavior and value in the retail industry. This study obtained information on shopping proximity, frequency, and purchasing power by segmenting customers and applied data-clustering analysis to a chain pharmacy.…”

Section: Related Workmentioning

confidence: 99%

Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm

Li,

Lee

2024

Journal of Cases on Information Technology

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Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.

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“…The statement focused on the limitation of mass marketing in a period where data-driven technological possibilities arose to analyze web-users footprints and enable personalized-oriented marketing. About two decades later personalized-oriented marketing is still a key challenge that many researchers conduct in their work (Chen et al 2018;Apichottanakul et al 2021;de Marco et al 2021;Nguyen 2021;Sokol and Holy 2021). Not only has it been shown that personalized customer targeting is more profitable for companies, but also that knowledge about customer behavior is a decisive factor for success and failure (Mulhern 1999;Zeithaml et al 2001;Kumar et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

Gomes

Meisen

2023

Inf Syst E-Bus Manage

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The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

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The role of shopping mission in retail customer segmentation

Cited by 11 publications

References 39 publications

Prediction of Likely Customers for Car Industries Using K-Means Clustering Compared with Logistic Regression

Prediction of Likely Customers for Car Industries Using K-Means Clustering Compared with Logistic Regression

Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm

A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

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