Analytical customer relationship management in insurance industry using data mining: a case study of Indian insurance company

2011 ◽  
Vol 9 (4) ◽  
pp. 331 ◽  
Author(s):  
Vishal Bhatnagar ◽  
Jayanthi Ranjan ◽  
Raghuvir Singh
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Insurance Industry ◽  
Relationship Management ◽  
Customer Relationship ◽  
Insurance Company ◽  
Using Data ◽  
Indian Insurance

Mining Customers Behavior Based on RFM Model to Improve the Customer Satisfaction

2011 ◽  
Vol 2 (3) ◽  
pp. 79-91
Author(s):  
Fatemeh Bagheri ◽  
Mohammad J. Tarokh
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Rfm Model ◽  
Management Processes ◽  
Premium Calculation ◽  
Hidden Knowledge

Organizations use data mining to improve their customer relationship management processes. Data mining is a new and well-known technique, which can be used to extract hidden knowledge and information about customers’ behaviors. In this paper, a model is proposed to enhance the premium calculation policies in an automobile insurance company. This method is based on customer clustering. K-means algorithm is used for clustering based on RFM models. Customers of the insurance company are categorized into some groups, which are ranked based on the RFM model. A number of rules are proposed to calculate the premiums and insurance charges based on the insurance manner of customers. These rules can improve the customers’ satisfaction and loyalty as well as the company profitability.

Data analytics dilemma at Alpen Hotel

2019 ◽  
Vol 9 (2) ◽  
pp. 58-63
Author(s):  
Tammy Wee ◽  
Arif Perdana ◽  
Detlev Remy
Keyword(s):  
Customer Relationship Management ◽  
Data Analytics ◽  
Data Privacy ◽  
Hospitality Industry ◽  
Service Providers ◽  
Relationship Management ◽  
Customer Relationship ◽  
Using Data ◽  
Privacy And Confidentiality

Data analytics is currently the buzzword for the hospitality industry to stay ahead of their competitors. Service providers use data analytics to ensure their brand remains relevant for customers. Using data analytics in customer relationship management is a relatively novel initiative for the hospitality industry to enhance the efforts of customer relationship management. Obtaining customers’ data (i.e. customers’ hotel stay and preferences) provides both opportunity and challenges for the hospitality industry. Data analytics helps the hospitality industry to quickly, effectively, and efficiently pursue data-driven decision-making. At the same time, acquiring relevant customers’ data is a challenge, for example, data privacy and confidentiality. This case study is based on Alpen Hotel (pseudonym), a luxury hotel in Singapore with a good standing in the hospitality industry. This case is focused on the issues they experienced in implementing data analytics as part of the hotel’s customer relationship management efforts. This case study aims to highlight data analytics dilemma at the hotel and may create an opportunity for hospitality educators to work interdisciplinary with faculties from an information systems or technology discipline. Finally, the case study may enhance knowledge and minimise the practice gap between industry and academia.

Decision Support System For A Customer Relationship Management Case Study

2014 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Özge Kart ◽  
Alp Kut ◽  
Vladimir Radevski
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Banking Sector ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Set ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Management Case Study

<span lang="EN-US">Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesian classification. The accuracy rate of the model is determined by doing cross validation. The results demonstrated the applicability and effectiveness of the proposed model. Naive Bayesian classifier reported high accuracy. So the classification rules can be used to support decision making in CRM field. The aim of this study is to apply the data mining model to the banking sector as example case study. This work also contains an example data set related with customers to predict if the client will subscribe a term deposit. The results of the implementation are available on a mobile platform. </span>

Data Mining as Support for Customer Relationship Management: A Case Study

2014 ◽  
Vol 13 (11) ◽  
pp. 5113-5120
Author(s):  
José Gonalo Dos Santos
Keyword(s):  
Data Mining ◽  
Information System ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Association Rules Discovery ◽  
Data Mining Application

This article describes the data mining application to CRM - Customer Relationship Management. The article starts with an introduction showing the importance of the CRM strategy for the company, after it’s introduced the theoretical about CRM, Knowledge Discovery Database and its stages, with emphasis to the mining stage and concludes with presentation of a case study and the conclusions. For the case study it was developed a prototype of an information system of a bookstore, it was implemented, beyond the conventional functions, the association rules discovery algorithm. The implementation of the data mining technique allowed to the system supply support so that the user knows better the client, becoming possible the application of the strategy of CRM.

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