13 research outputs found
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
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
Payments per claim model of outstanding claims reserve based on fuzzy linear regression
There are uncertainties in factors such as inflation. Historical data and variable values are ambiguous. They lead to ambiguity in the assessment of outstanding claims reserves. The payments per claim model can only perform point estimation. But the fuzzy linear regression is based on fuzzy theory and can directly deal with uncertainty in data. Therefore, this paper proposes a payments per claim model based on fuzzy linear regression. The linear regression method and fuzzy least square method are used to estimate the parameters of the fuzzy regression equation. And the estimated results are introduced into the payments per claim model. Then, the predicted value of each accident reserve is obtained. This result is compared with that of the traditional payments per claim model. And we find that the payments per claim model of estimating the fuzzy linear regression parameters based on the linear programming method is more effective. The model gives the width of the compensation amount for each accident year. In addition, this model solves the problem that the traditional payments per claim model cannot measure the dynamic changes in reserves
Factors Influencing Customer Satisfaction towards E-shopping in Malaysia
Online shopping or e-shopping has changed the world of business and quite a few people have
decided to work with these features. What their primary concerns precisely and the responses from
the globalisation are the competency of incorporation while doing their businesses. E-shopping has
also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce
industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction
while operating in the e-retailing environment. It is very important that customers are satisfied with
the website, or else, they would not return. Therefore, a crucial fact to look into is that companies
must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s
point of view. With is in mind, this study aimed at investigating customer satisfaction
towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students
randomly selected from various public and private universities located within Klang valley area.
Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for
further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer
satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust,
design of the website, online security and e-service quality. Finally, recommendations and future
study direction is provided.
Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia
The Impact of Entrepreneurial Orientation and Market on SMEs Performance Orientation that Influenced by External Environment and Networking Capabilities.
Entrepreneurial and market orientation can positively affect the performance of SMEs (Small
and Medium Enterprises), yet these two orientations are not enough to enable SMEs to perform
well in a dynamic and uncertain business environment. SMEs in Indonesia are both facing
challenges and opportunities from changes in the existing external environment. SMEs
must have networking capacities to access external resources. The networks are expected to
impact entrepreneurial orientation and market orientation to enable SMEs to perform better.
This study aims to empirically examine the effect of entrepreneurial and market orientations
on the performance of SMEs, influenced by external environment and networking capabilities.
This study proposes a conceptual framework that integrates external environment, the
network capabilities, entrepreneurial orientation, marketing orientation, and the performance
of SMEs in an uncertain external environment
The drivers of Corporate Social Responsibility in the supply chain. A case study.
Purpose: The paper studies the way in which a SME integrates CSR into its corporate strategy, the practices it puts in place and
how its CSR strategies reflect on its suppliers and customers relations.
Methodology/Research limitations: A qualitative case study methodology is used. The use of a single case study limits the
generalizing capacity of these findings.
Findings: The entrepreneur’s ethical beliefs and value system play a fundamental role in shaping sustainable corporate strategy.
Furthermore, the type of competitive strategy selected based on innovation, quality and responsibility clearly emerges both in
terms of well defined management procedures and supply chain relations as a whole aimed at involving partners in the process of
sustainable innovation.
Originality/value: The paper presents a SME that has devised an original innovative business model. The study pivots on the
issues of innovation and eco-sustainability in a context of drivers for CRS and business ethics. These values are considered
fundamental at International level; the United Nations has declared 2011 the “International Year of Forestry”