Applications of Data Mining in CRM Based on Web Log

2012 ◽  
Vol 446-449 ◽  
pp. 3762-3765
Author(s):  
Jian Ning Dang ◽  
Ai Qin Zhang ◽  
Wei Jing
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Integrated Approach ◽  
Financial Service ◽  
Customer Relationships ◽  
Customer Relationship ◽  
Web Based ◽  
Future Success ◽  
Key Factor

Nowadays, under intensified competition, winning and keeping customers is becoming more and more important. Company must focus on building long-term relationships with their customers for continuously adding market share. For defeating other financial service providers, the banks should have the ability to address their customers' preferences and priorities effectively, and should strategically use this understanding in every area to establish and strengthen long-term customer relationships. Consequently, systematic and web-based customer relationship management (CRM) will be a key factor to future success for financial service institutions. This thesis research explored advanced data mining technologies for building a best next offer predictive model, and focused on providing an integrated approach to improve performance of the prediction.

Designing a CRM Framework for online & offline sports footwear retailers

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Rodrigo Cueva ◽  
Guillem Rufian ◽  
Maria Gabriela Valdes
Keyword(s):  
Environmental Changes ◽  
Customer Relationship ◽  
Business Strategies ◽  
Economic Sectors ◽  
Running Shoes ◽  
Brick And Mortar ◽  
Key Factor ◽  
On Line ◽  
The Impact

The use of Customer Relationship Managers to foster customers loyalty has become one of the most common business strategies in the past years.  However, CRM solutions do not fill the abundance of happily ever-after relationships that business needs, and each client’s perception is different in the buying process.  Therefore, the experience must be precise, in order to extend the loyalty period of a customer as much as possible. One of the economic sectors in which CRM’s have improved this experience is retailing, where the personalized attention to the customer is a key factor.  However, brick and mortar experiences are not enough to be aware in how environmental changes could affect the industry trends in the long term.  A base unified theoretical framework must be taken into consideration, in order to develop an adaptable model for constructing or implementing CRMs into companies. Thanks to this approximation, the information is complemented, and the outcome will increment the quality in any Marketing/Sales initiative. The goal of this article is to explore the different factors grouped by three main domains within the impact of service quality, from a consumer’s perspective, in both on-line and off-line retailing sector.  Secondly, we plan to go a step further and extract base guidelines about previous analysis for designing CRM’s solutions focused on the loyalty of the customers for a specific retailing sector and its product: Sports Running Shoes.

scholarly journals The Implementation of Customer Relationship Management (CRM) Supported by SMS Gateway Technology

2019 ◽  
Vol 16 (1) ◽  
pp. 45
Author(s):  
Komang Redy Winatha
Keyword(s):  
Customer Relationship Management ◽  
Service Management ◽  
Environmental Changes ◽  
Relationship Management ◽  
Customer Relationships ◽  
Customer Relationship ◽  
Web Based ◽  
Customer Data ◽  
Gateway Technology ◽  
Web Based System

Responding to the higher restaurant industry competition, the Mailaku Roemah Nongkrong restaurant was not too flexible in facing an environmental changes. It was still using manual technology while there was an advancing technological developments. It was still applying the internal resources for business development. One way to overcome this problem is by utilizing technology and the concept of customer relationship management (CRM). CRM is a marketing strategy to create and maintain customer relationships and reduce the possibility of customers moving to other competitors. This study presented the development and implementation of CRM in a web-based system that was supported by sms gateway technology. The research methodology that will be used in this study consists of some steps, such as library study, observation, interviews, and system development which was divided into analysis, design, coding, and testing. The result was a web-based system was able to manage customer data, product promotion, and customer service management to create good relationships with customers. This system can be as an alternative for restaurants and customers in establishing practical business communication.

Customer Relationship Management and Data Mining

2016 ◽  
pp. 1362-1401
Author(s):  
Niccolò Gordini ◽  
Valerio Veglio
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Value ◽  
Relationship Management ◽  
Global Market ◽  
Customer Relationship ◽  
Digital Revolution ◽  
Marketing Activities ◽  
Potential Customers

In the global market of today, Customer Relationship Management (CRM) plays a fundamental role in market-oriented companies to understand customer behaviors, achieve and maintain a long-term relationship with them, and maximize the customer value. Moreover, the digital revolution has made information easy and fairly inexpensive to capture. Thus, companies have stored a large amount of data about their current and potential customers. However, this data is often raw and meaningless. Within the CRM framework, Data Mining (DM) is a very popular tool for extracting useful information from this data and for predicting customer behaviors in order to make profitable marketing decisions. This research aims to demonstrate the classification decision tree as one of the main computational data mining models able to forecast accurate marketing performance within global organizations. Particular attention is paid to the identification of the best marketing activities to which firms should concentrate their future marketing investments. The criteria is based on the loss functions that confirm the accuracy of this model.

An Application of Text Mining to Capture and Analyze eWOM

2016 ◽  
pp. 168-186 ◽  
Author(s):  
Taşkın Dirsehan
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Customer Relationship ◽  
Competitive Advantages ◽  
Strategic Decisions ◽  
Data Mining Tool ◽  
Mining Tool ◽  
The Moment ◽  
Mining Tools

Marketing concept has progressed through different phases of evolution in the past. At the moment, customer relationship management is considered as the last era of marketing development. The main purpose of this approach is to build long-term oriented profitable relationships with customers. So, companies should know better their customers. This knowledge can be created through a deeper analysis of companies' data with data mining tools. Companies which are able to use data mining tools will gain strong competitive advantages for their strategic decisions. Hotel industry is selected in this study, since it provides a warehouse of customer comments from which precious knowledge can be obtained if text mining as a data mining tool is used appropriately. Thus, this study attempts to explain the stages of text mining with the use of Rapidminer. As a result, different approaches according to the customer satisfaction/dissatisfaction are discussed to build competitive advantages.

Transforming Employee Engagement Into Long-Term Customer Relationships: Evidence from Information Technology Salespeople in Taiwan

2012 ◽  
Vol 40 (9) ◽  
pp. 1549-1553 ◽  
Author(s):  
Benjamin J. C. Yuan ◽  
Michael B. H. Lin ◽  
Jia-Horng Shieh ◽  
Kuang-Pin Li
Keyword(s):  
Transformational Leadership ◽  
Employee Engagement ◽  
Work Engagement ◽  
Customer Relationships ◽  
Service Performance ◽  
Relationship Development ◽  
Customer Relationship ◽  
Performance Development ◽  
Over Time

In this study, we found that when information salespeople in Taiwan perceived more transformational leadership, they were more likely to show increases in work engagement development over time. Furthermore, increases in work engagement development influenced increases in service performance development, which therefore positively predicts increases in customer relationship development over time.

scholarly journals Building a Comprehensive System of Services to Support Adults Living with Long-Term Mechanical Ventilation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
David Leasa ◽  
Stephen Elson
Keyword(s):  
Mechanical Ventilation ◽  
Operations Management ◽  
Hospital Admissions ◽  
Service Providers ◽  
Integrated Approach ◽  
System Of Care ◽  
Local Health ◽  
Care Needs ◽  
Implementation Planning

Background.Increasing numbers of individuals require long-term mechanical ventilation (LTMV) in the community. In the South West Local Health Integration Network (LHIN) in Ontario, multiple organizations have come together to design, build, and operate a system to serve adults living with LTMV.Objective.The goal was to develop an integrated approach to meet the health and supportive care needs of adults living with LTMV.Methods.The project was undertaken in three phases: System Design, Implementation Planning, and Implementation.Results.There are both qualitative and quantitative evidences that a multiorganizational system of care is now operational and functioning in a way that previously did not exist. An Oversight Committee and an Operations Management Committee currently support the system of services. A Memorandum of Understanding has been signed by the participating organizations. There is case-based evidence that hospital admissions are being avoided, transitions in care are being thoughtfully planned and executed collaboratively among service providers, and new roles and responsibilities are being accepted within the overall system of care.Conclusion.Addressing the complex and variable needs of adults living with LTMV requires a systems response involving the full continuum of care.

scholarly journals The Use of Customer value changing trends in business analysis

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Data Mining ◽  
Customer Value ◽  
Insurance Industry ◽  
Basic Structure ◽  
Rfm Model ◽  
Rapid Changes ◽  
Interactive Strategies ◽  
Key Factor ◽  
Changing Trend

With the development of competition in business and also today’s rapid changes in competitive market, understanding these changes are the key factor of being more efficient in markets. CRM which is known as basic structure for describing customer’s needs for efficiently understand customer’s behavior and finely get the maximum of market share and profit. There are major differences between B2B and B2C businesses such as long term purchase cycle, purchase interests and the amount of the transactions. These differences needs more interactive strategies. The knowledge that gets from CRM is extremely related to market changes. In recent years data mining increasingly help organizations to get and understand customer’s behavior.  But with the rapid changes in market these procedures must be change too. Change mining as the higher order of data mining tries to get knowledge by analysis patterns instead of data. In this paper we attempt to calculate customer’s value by using RFM model and K-MEANS clustering method and then analysis changes in deferent time periods. We tries to find out cluster transitions and most frequent customer value changing trend for proactive decision making. For this purpose we use customer purchase transactions in insurance industry which are gathered in 3 years.

scholarly journals Complex Needs or Simplistic Approaches? Homelessness Services and People with Complex Needs in Edinburgh

2016 ◽  
Vol 4 (4) ◽  
pp. 28-38 ◽  
Author(s):  
Manuel Macias Balda
Keyword(s):  
Social Inclusion ◽  
Service Providers ◽  
Service Users ◽  
Complex Needs ◽  
What Works ◽  
Key Factor ◽  
Interpersonal Barriers ◽  
The City ◽  
Relational Difficulties

This research addresses how homelessness services from the statutory and voluntary sector are working for people with complex needs in the City of Edinburgh. Using a qualitative approach, it analyses the service providers’ perspectives on the concept, challenges and what works when dealing with this group of people. It also explores the opinions of a sample of service users, categorised as having complex needs, regarding the accommodation and support they are receiving. After analysing the data, it is argued that homelessness agencies do not have an appropriate cognitive nor institutional framework that facilitates an effective approach to work with people with complex needs. The lack of a sophisticated understanding that recognises the relational difficulties of individuals and the presence of structural, organisational, professional and interpersonal barriers hinder the development of positive long-term relationships which is considered as the key factor of change. For this reason, it is recommended to address a set of factors that go beyond simplistic and linear approaches and move towards complex responses in order to tackle homelessness from a broader perspective and, ultimately, achieve social inclusion.

scholarly journals The Use of Customer value changing trends in business analysis

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Data Mining ◽  
Customer Value ◽  
Insurance Industry ◽  
Basic Structure ◽  
Rfm Model ◽  
Rapid Changes ◽  
Interactive Strategies ◽  
Key Factor ◽  
Changing Trend

With the development of competition in business and also today’s rapid changes in competitive market, understanding these changes are the key factor of being more efficient in markets. CRM which is known as basic structure for describing customer’s needs for efficiently understand customer’s behavior and finely get the maximum of market share and profit. There are major differences between B2B and B2C businesses such as long term purchase cycle, purchase interests and the amount of the transactions. These differences need more interactive strategies. The knowledge that gets from CRM is extremely related to market changes. In recent years’ data mining increasingly help organizations to get and understand customer’s behavior. But with the rapid changes in market these procedures must be change too. Change mining as the higher order of data mining tries to get knowledge by analysis patterns instead of data. In this paper we attempt to calculate customer’s value by using RFM model and K-MEANS clustering method and then analysis changes in deferent time periods. We try to find out cluster transitions and most frequent customer value changing trend for proactive decision making. For this purpose, we use customer purchase transactions in insurance industry which are gathered in 3 years.

scholarly journals The Use of Customer value changing trends in business analysis

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Data Mining ◽  
Customer Value ◽  
Insurance Industry ◽  
Basic Structure ◽  
Rfm Model ◽  
Rapid Changes ◽  
Interactive Strategies ◽  
Key Factor ◽  
Changing Trend

With the development of competition in business and also today’s rapid changes in competitive market, understanding these changes are the key factor of being more efficient in markets. CRM which is known as basic structure for describing customer’s needs for efficiently understand customer’s behavior and finely get the maximum of market share and profit. There are major differences between B2B and B2C businesses such as long term purchase cycle, purchase interests and the amount of the transactions. These differences needs more interactive strategies. The knowledge that gets from CRM is extremely related to market changes. In recent years data mining increasingly help organizations to get and understand customer’s behavior. But with the rapid changes in market these procedures must be change too. Change mining as the higher order of data mining tries to get knowledge by analysis patterns instead of data. In this paper we attempt to calculate customer’s value by using RFM model and K-MEANS clustering method and then analysis changes in deferent time periods. We tries to find out cluster transitions and most frequent customer value changing trend for proactive decision making. For this purpose we use customer purchase transactions in insurance industry which are gathered in 3 years.

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