مطالب مرتبط با کلیدواژه

Customer clustering


۱.

Designing an Ethical Targeted Marketing Model by Identifying Factors Affecting Customer Clustering(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Ethics and Society, Vol ۳, No ۳, (۱۲-۲۰۲۱) 61-70

کلیدواژه‌ها: Ethical targeted marketing Entropy VIKOR Customer clustering Internet of Things

تعداد بازدید : ۷۳۲ تعداد دانلود : ۴۰۲
Background: The purpose of this study is to investigate the role of customer clustering in the design of an ethical and targeted marketing model in Internet of Things (IOT) Technology Services Companies. Method : The research method is applied, exploratory and mixed (qualitative-quantitative). Qualitative section: 15 people of sales and marketing managers of IOT companies, were selected for in-depth interviews by targeted and snowballs methods. In this phase used entropy and VIKOR decision-making techniques. The software used in this phase was Max QDE. Quantitative section : In this phase, all the customers of the studied companies were included, and due to the unlimited nature of the society with Morgan's table, 384 people were selected as the sample size by non-random and available methods. The data collection tool in this section was questionnaire, which used Cronbach's alpha to examine the validity of the questionnaire. In order to analyze the data, the exploratory factor analysis method and Partial least squares structural equation modeling (PLS-SEM) with Smart-PLS2 and Liserl software were used. Results : Qualitative section: In this phase, four clusters were identified: communication factors, ethical and behavioral factors, individual factors and economic factors Quantitative section : In this phase, the model obtained in the first phase was quantitatively examined and validated and approved . Conclusion : The results showed that four main clusters: communication factors, ethical and behavioral factors, individual factors and economic factors are dimensions of targeted marketing and customers are classified according to their characteristics. So, these companies should pay attention to these items as methods for sales promotion.
۲.

Application of Clustering and Classification Algorithms in Analyzing Customer Behavior in Data-Driven Marketing: A Case Study of Amazon Customers(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of System Management, Volume ۱۲, Issue ۱, Winter ۲۰۲۶ 67-89

کلیدواژه‌ها: Data-Driven Marketing Machine Learning Customer clustering K-means Clustering Customer Classification

حوزه‌های تخصصی:
تعداد بازدید : ۱۴ تعداد دانلود : ۱۲
In data-driven marketing, customer behavior analysis plays a crucial role in developing targeted marketing strategies aimed at increasing return on investment, enhancing profitability, and gaining a larger market share. In this study, four clustering methods- including K-means, density-based clustering, principal component analysis, and hierarchical clustering- as well as four classification methods- including Support Vector Machine, XGBoost, Random Forest, and Gradient Boosting- are examined for customer behavior analysis. The data for this study was extracted from the "Amazon Customer Behavior Survey" dataset, which includes 23 features from 602 customers. Initially, the data was preprocessed, and then, using clustering methods, customers were divided into different groups. The performance of these methods was evaluated based on criteria such as the silhouette index, and ultimately, appropriate marketing strategies for each cluster were proposed. Additionally, to examine the possibility of predicting customer membership in the extracted clusters, the aforementioned classification models were implemented and compared. The results indicate that the K-means method performed the best in clustering, while the XGBoost model performed the best in classification. The innovation of this research lies in combining clustering and classification methods to provide targeted marketing strategies and comprehensively comparing these methods on real customer data. This study demonstrates that combining clustering and classification methods can help businesses better understand customer behavior and make more optimal marketing decisions.