Application Of Association Rule Learning In Customer Relationship Management
The main purpose of this study is the application of association rule learning using data mining techniques in customer relationship management of a diagnostics centres. Clustering customers is needed to find unsatisfied need, promote services packages and create new service packages. The proposed system diagnostics data mining system (DDMS) consists of three components; pre-processing, clustering and post processing. The data collected is for a period of four month for 6700 transaction. Three data sets are constructed from the original data set by dividing the whole data into 90%, 85% and 80% for training and 10%, 15% and 20% for testing respectively. Three K-means model are used with k=10, 15 and 18 cluster and each data set is used to calibrate and test the model for a total of nine ones. It is found that the best model is the one with 15 clusters. The clustering results are represented to a health and diagnostics personnel who found that some results are reasonable and others go along with the policy guiding customer relationship management in the centers.
1.1 Data Mining
Data mining is the process that uses a variety of data analysis and modelling techniques to discover patterns and relationships in data that may be used to make accurate predictions (Guarav andAggraval, 2012).
It’s described as the process of extracting knowledge data discovery of valid, authentic and actionable information from large data bases. It is also used to derive patterns and trends that exist in the collected data ( Masheswari et al, 2014).
Data mining is a continuous iterative process that is the very core of business intelligence. It involves the use of data mining software, sound methodology and human creativity to achieve new insight through the exploration of data to uncover patterns, relationships, anomalies and dependencies (PuneetShukla, 2015).According to (PuneetShukla, 2015) the process of data mining consists of three stages which are the Initial exploration, Model building or pattern identification with validation/verification, Deployment (i.e. the application of the model to new data in order to generate predictions).
Data mining consists of five major elements which includes extracting, transform and load data onto data warehouse systems, Storing and manage data , provide data access to business analysts and information technology professionals, analyse the data by application software and present the datain a useful format such as a graph or table.
Data mining involves six common classes of tasks which are;
1.2 Customer Relationship Management
It helps business to gain insight into the behaviour of customers and their value so that the company can increase their profit by acting according to the customer characteristics. Customer relationship management technology is a mediator between customer management activities in all stages of a relationship (initiation, maintenance and termination) and business performance. It consists of customer identification, customer attraction, customer retention and customer development (Dhandayudam andKrishnamurthi, 2013).Customer relationship management is a set of process which enables the business strategy to build long term and profitable relationship with the customers (Masheswari, 2014).
Customer relationship management refers to the methodologies and tools used to help businesses manage customer relationships in an organized way. CRM simply means managing all customer interactions which requires using information about your customers and prospects to more effectively interact with your customers in all stages of your relationship with them(Gupta and Aggraval, 2012). There are three components of CRM which are customer, relationship, and management. Four basic tasks are used to achieve the basic goals in CRM
- Customer identification: Identify the customers through web site marketing.
- Customer differentiation: Every customer has their own lifetime value from the company’s point of view.
- Customer interaction: Customer demands changes every time. There are four stages of customer life cycle which are the initiation, integration, intelligence and value creation.
- Customization: Treat the customers uniquely through the entire CRM process.
1.3 Problem Statement
Companies and organizations should have more awareness of their type of customers. For example, how managers can have an effective sale to irritable customers. Customer relationship management (CRM) usually involves the need of IT professionals to implement the methodologies involved to carry out effective management of customers. The issue of not having a suitable commercial brand(Dr Javad Khalatbari, 2011).
There is a strong requirement for data integration before data mining which involves getting data from different sources and integrate them before actual data exploration can begin. Companies usually make the mistake of gaining the technology needed and then applying it to discover it is not actually solving the main problem.
The ability to know which category of customers to channel their effort to which are more likely to remain.
To develop a predictive model that will be used for more accurate predictions of customer acquisition and also retention.
- Selection of right customers from a large set of potential customers.
- Develop and Simulate the model.
1.6 Research Methodology
To achieve the objectives stated above, the following methods would be adopted
- Proper literature review on journals relating with this project topic.
- Gathering necessary information and required data from related personnel concerned.
- Using association rule technique to the gathered data to make prediction.
1.7 Scope of Study
The use of information technology allows the process of data extraction that helps in getting interesting facts to enable the effective prediction of customer behaviour.
1.8 Significance of Study
When this research is implemented there are foreseeable benefits which includes;
- Enable the prediction of customer behaviour
- To enable organizations have a proper view of the type of customers they would have and how to solve irregularities.
Application Of Association Rule Learning In Customer Relationship Management
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