International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013 509
ISSN 2229-5518
R.Senkamalavalli, Research Scholar, SCSVMV University, Enathur, Kanchipuram
Dr.T.Bhuvaneswari, L.N.Govt College,Ponneri,Chennai
Abstract: The way in which companies interact with their customers has changed dramatically over the past few years. A customer’s containing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made ahas been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires. In this paper we are going to discuss the Data Mining techniques used in Customer Relationship Management.
Data Mining is the non-trivial extraction of novel, implicit and actionable knowledge from large data sets. It is technology to enable data exploration, data analysis and data visualization of very large databases at a high level of abstraction, without a specific hypothesis in mind. Data Mining is a process that uses a variety of data analysis and modeling techniques to discover patterns and relationships in data that may be used to make accurate predictions. It helps you to select the right prospects on whom to focus, offer the right additional products to your existing customers and identify good customers who may be about to leave you. The result is improved revenue because of a greatly improved ability to each individual contact in the best way.
More than 1,000,000 entries/records/rows From 10 to 10,000 fields/attributes/variables Gigabytes and terabytes
Databases are growing at an unprecedented rate
Decisions must be made rapidly
Decisions must be made with maximum knowledge
Customer Relationship Management (CRM) is the practice of intelligently finding, marketing to, selling to and servicing customers. CRM is a broadly used term that covers concepts used by companies, NGO’s and public institutions to manage their relationships with customers and state holders. Technologies that support this
High
Machine
Learning
Database
Data Mining
Visualization
Applied
business purpose include the capture, storage and
analysis of customer, vendor, partner and internal
process information.
There are three aspects of CRM, which can be
Performance
Parallel
Algorithms
Pattern
Recognition
Statistics
implemented in isolation from each other:
Operational – automation of customer processes that offers support to a company’s sales or service representative.
Fig.1 Data Mining in Multi Disciplinary Field
Changes in the business environment
Customers becoming more demanding
Markets are saturated
Databases now a days are huge
Collaborative - the program communicates to customers without a company’s sales or service representative (Self-service)
Analytical – analysis of customer information for multiple purposes.
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International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013 510
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The technological requirements of a CRM strategy can be complex and for reaching the basic building blocks include:
A database for customer information operational
CRM requires customer agent support software.
Collaborative CRM requires an interactive system, eg. An interactive web site automated phone systems etc.
Analytical CRM requires statistical analysis software as well as software that manages any specific marketing companies.
Each of these can be implemented in a basic manner or in a high-end complex installation.
Data Mining helps to
Determine the behavior surrounding a particular life cycle event.
Find other people in similar life stages and
determine which customers are following similar behavior patterns.
Data Ware Customer Data
Customer Life Cycle
Information
Campaign
Management
Fig.2 Data Mining in CRM
Customer life cycle
The stages in the relationship between a customer and a business.
Prospects: People who are not yet customers, but are in the target market.
Responders: Prospects who show an interest in a product or service
Active Customers: People who are currently using the product or service.
Former Customers may be ‘bad’ customers who did not pay their bills or who incurred high costs.
Up sell
Cross sell
Keeping the customers for a longer period of time.
Solution: Applying Data Mining to CRM
Basic steps of data mining for effective CRM are
1. Define business problem
2. Build marketing database
3. Explore data
4. Prepare data for modeling
5. Build model
Building profitable Customer Relationships
Evaluate model
Deploy model and results
Each CRM application has one or more business objective for which you need to build the appropriate model. Depending on your specific goal, such as increasing the response rate or increasing the value of a response, you build a very different model. An effective statement of the problem includes a way to measure the results of your CRM project.
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International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013 511
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This step constitutes the core of the data preparation. Data preparation steps may take 50 to
90 percent of the time and effort for the entire data mining process. If you want good models you must have clean data. The data you need may reside in multiple databases such as the customer database, product database and transaction databases. This means you need to integrate and consolidate the data into a single marketing database and reconcile differences in data values from the various sources.
Before you can build good predictive models, you must understand your data. Start by gathering a variety of numerical summaries (including descriptive statistics such as averages, standard deviations and so forth) and looking at the distribution of the data. You may want to produce cross-tabulations (picot tables) for multi- dimensional data.
Graphing and visualization tools are a vital aid in data preparation and their importance for effective data analysis cannot be overemphasized.
This is the final data preparation step before building models and the step where the most “art” comes in. There are four main parts to this step:
First, you want to select the variables on which to build the model. Ideally, you take all the variables you have, feed them to the data- mining tool and let the data mining tool find those that are the best predictors.
The next step is to construct new predictors derived from the raw data. For example, forecasting credit risk using a debt-to-income ratio.
Next, you may decide to select a subset or sample of your data on which to build models. If you have a lot of a data, however, using all your data may take too long or require buying a bigger computer than you’d like. Working with a properly selected random sample usually results in no loss of information for most CRM problems.
The most important thing to remember about model building is that it is an iterative process. You need to explore alternative models to find the one that is most useful in solving your business problem.
The most overrated metric for evaluating your results is accuracy.
In building a CRM application, data mining is often a small, albeit critical, part of the final product. For example predictive patterns through data mining may be combined with the knowledge of domain experts and incorporated in a large application used by many different kinds of people. The way data mining is actually built into the application is determined by the nature of your customer interaction.
Customer relationship management is essential to compete effectively in today’s marketplace. The more effectively you can use information about your customers to meet their needs, the more profitable you will be. Operational CRM needs analytical CRM with predictive data mining models at its core. The route to a successful business requires that your understand your customers and their requirements, and data mining is the essential guide.
1. Davenport T. and Prusak L. (1998) Working Knowledge. How organizations manage what they know. Harved Business School Press. Boston, M.A.
2. Delmater R. and Handcock M. (2001) Data Mining Explaind : A Manager’s Guide to Customer-Centric Business Intelligence. Digital Press. Boston, M.A.
3. Luan, J. (2001) Data Mining Applications in Higher Education. A chapter in the upcoming New Directions for Institutional Research. Serban. A., and Luan, J. Editors. Josse-Bass. San Francisco.
4. Arun K Pujari, Data Mining Technique
R.Senkamalavalli,BE, MBA, Mphil, MTech, Currently Pursuing Ph.D, + 91 97429 93444 ; sengu_cool@yahoo.com Dr.T.Bhuvaneswari, Professor, +91 98407 07238; t_bhuvaneswari@yahoo.com
IJSER © 2013 http://www.ijser.org
International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
512
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International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013
ISSN 2229-5518
513
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