Hybrid clustering based on multi-criteria segmentation for higher education marketing

Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful information about student preference. Data result of the integration will be processed using hybrid clustering using K-means and self organizing map (SOM) algorithm. The hybrid clustering conducted to get promising clustering result along with the visualization of segmentation. This study successfully produces five student segments. It received 1,386 as the Davies-Bouldin index (DBI) value and 2,752 as the quantization error (QE) value which indicates a good clustering result for market segmentation. In addition, the visualization of the clustering result can be seen in a hexagonal map

RFL-based customer segmentation using K-means algorithm

Customer segmentation has become crucial for the company’s survival and growth due to the rapid development of information technology (IT) and state-of-the-art databases that have facilitated the collection of customer data. Financial firms, particularly insurance companies, need to analyze these data using data mining techniques in order to identify the risk levels of their customer segments and revise the unproductive groups while retaining valuable ones. In this regard, firms have utilized clustering algorithms in conjunction with customer behavior-focused approaches, the most popular of which is RFM (recency, frequency, and monetary value). The shortcoming of the traditional RFM is that it provides a one-dimensional evaluation of customers that neglects the risk factor. Using data from 2586 insurance customers, we suggest a novel risk-adjusted RFM called RFL, where R stands for recency of policy renewal/purchase, F for frequency of policy renewal/purchase, and L for the loss ratio, which is the ratio of total incurred loss to the total earned premiums. Accordingly, customers are grouped based on the RFL variables employing the CRISP-DM and K-means clustering algorithm. In addition, further analyses, such as ANOVA as well as Duncan’s post hoc tests, are performed to ensure the quality of the results. According to the findings, the RFL performs better than the original RFM in customer differentiation, demonstrating the significant role of the risk factor in customer behavior evaluation and clustering in sectors that have to deal with customer risk

Understanding the attributes of digital wallet customers: Segmentation based on perceived risk during the Covid-19 pandemic

Penelitian ini menekankan pada pentingnya segmentasi pelanggan dompet digital, khususnya segmentasi yang didasarkan pada persepsi risiko pelanggan pada saat pandemi Covid-19. Kontribusi mendasar yang diharapkan dari studi ini adalah penjelasan konsep risiko dalam konteks dompet digital. Studi ini juga akan mengkonfirmasi faktor risiko dalam dompet digital dan pemetaan pengguna berdasarkan faktor persepsi resiko tersebut. Studi ini melibatkan 270 pengguna dompet digital sebagai sampel penelitian, yang di ambil menggunakan teknik purposive sampling. Keseluruhan sampel diambil di Surabaya yang dilakukan saat pandemi Covid-19 masih berlangsung. Dompet digital yang digunakan adalah layanan aplikasi OVO, Gopay dan Dana. Analisis dilakukan  menggunakan analisis faktor, kluster dan uji beda. Temuan penting studi ini adalah faktor risiko pada dompet digital teridentifikasi terdiri dari faktor risiko keamanan, finansial, sosial dan operasional. Terbentuk tiga segmen pengguna berdasarkan risiko, yaitu klaster risiko rendah, sedang, dan tinggi. Masing-masing segmen memiliki karakteristik dan implikasi manajeral yang berbeda.This research emphasizes the importance of digital wallet customer segmentation, mainly based on customer risk perceptions during the Covid-19 pandemic. The fundamental contribution of the study was to explain the concept of perceived risk in a digital wallet, also to confirm risk factors in digital wallets, and identify segments based on perceived risk factors. The respondents are 270 digital wallet users of OVO, Gopay, and Dana obtained in Surabaya, Indonesia, during the Covid-19 pandemic, which was taken using the purposive sampling technique. The multiple analysis data were carried out using factor analysis, cluster analysis, and difference test techniques. An essential finding of this study shows that perceived risk factors consist of security, financial, social, and operational risks. There are three segments based on the perceived risk: low-risk, medium-risk, and high-risk. Each segment has different characteristics and managerial implications

A multi-attribute data mining model for rule extraction and service operations benchmarking

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking. Design/methodology/approach The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns. Findings As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed. Originality/value The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry