Data Mining for Customer Relationship Management


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Data Mining for Customer Relationship Management

  1. 1. Data Mining for Customer Relationship Management 127 Data Mining for Customer Relationship Management Vikas Kharbanda* & Parthasarathi Dasgupta** This paper is the outcome of an analysis of the different areas in Customer Relationship Management where Data Mining can be applied, to create value for both the customer and the organization. With increasing global competition, companies have to make use of the advanced Technical and Analytical methodologies to attract and Introduction retain customers in their products and services. Advances in Data Manage- ment Techniques play a vital role in With increasing global competition in all enabling this effort. With an increasing convergence of CRM, e-Commerce and industries, shortened product life cycles and ERP systems on the Internet, it has become imperative for organizations to decreasing customer loyalty, companies are achieve a totally different view of the customer to create their greater shifting to advanced Technical and Analytical responsiveness and satisfaction. Better management of customer-centric data methodologies to create greater customer collected by organization is now more important. Companies now extract stickiness in their products and services. previously unknown information about their customers from this data bank Advances in technologies and Data collected over long duration of their operations to better predict customer Management Techniques have been important requirements and trends. This ensures better services for the customers needs. enablers in this effort, which have been made This is where Data Mining practices can create a lot of value for more pertinent with an equivalent decrease in organizations by extracting the required information from the mammoth data costs of both processing power and data storage. stores. This paper looks at the different application designs and the underlying technology for Data Mining applica- tions, and also describes the diffeent With decreasing product differentiation, architecture details of Data Mining decreasing customer attention spans for brands application and the underlying processes that classify any analytical and increasing information availability with process carried out on data collection as a Data Mining Process. * PGDCM (6th Batch), Indian Institute of Management, Calcutta. ** MIS Group, Indian Institute of Management, Calcutta. The paper was a result of a study carried out by the first author under the guidance of Prof. Parthasarathi Dasgupta, MIS Group, IIM Calcutta.
  2. 2. Data Mining for Customer Relationship Management 128 the customer, organizations are pushing hard for developing holistic 360-degree views of their customers to bring about greater responsiveness and satisfaction for the customer. CRM, especially e-CRM, is helping companies generate data centers regarding customer buying history, preferences, complaints, purchase pattern etc. so that the customer’s expectations and future requirements can be anticipated. Data from Marketing, Sales, Service, Complaint Center etc. is collated into a single source and is made available to the entire organization so that any member of the organization dealing with the customer, at the click of a button, gets the entire history of the customer’s interaction with the organization. This allows the services and the interaction with the customer to be better tailored according to the customer perception and anticipations. Data Mining as a technology, is finding increasing application in the area of “sifting” through these mammoth customer-centric databases to bring out logical patterns which might be of use to organizations in predicting customer requirements and behaviour. Some of the observations that Data Mining systems are able to unearth in these data centers would not be available to the organization intuitively. What is Data Mining ? Data mining is a new methodology for improving the quality and effectiveness of the business and scientific decision making process. It complements, and can often replace, other business decision assistance tools, such as statistical analysis, computer reporting and querying. Data mining can achieve high return on investment decisions by exploiting one of an enterprise’s database systems and data records. The objective of data mining is to extract valuable information from existing data. Data Mining also helps in converting “Data” to valuable “Information” by ferreting out patterns in large systems of Organizational data. Data Mining is in no ways dependant on Data Warehousing and may be implemented by any organization that does not implement any sort of a Data Warehouse. Data Decision, Vol. 28, No. 2, July – December, 2001
  3. 3. Data Mining for Customer Relationship Management 129 Mining may also be defined as “An automated process of data analysis in which the system itself finds information patterns from within data”. What is meant by automated pattern seek in the data is the fact that the pattern search starts without any pre-supposed hypothesis regarding what the patterns might be. In fact, the system finds what the interesting patterns are without the user thinking of any questions to start off with. Data Mining – A Formal Definition Data Mining is “a process of extracting previously unknown, valid and actionable information from large databases and then using the information to make crucial business decisions”. The basic definition of the kind of information created through Data Mining presents the crucial difference between the information gathered from more traditional forms of data analysis like Query Processing and Reporting and OLAP (On Line Analytical Processing). In essence, Data Mining is distinguished by the fact that it is aimed at information discovery without a previously formulated hypothesis. Difference Between OLAP and Data Mining The key words unknown, valid and actionable indicate the difference between Data Mining and OLAP applications. Whereas information collected from OLAP is based on known business parameters, the main aim of Data Mining applications is to find information that is not known before. The information collected from Data Mining applications should have been previously unknown. The supposition here is that the information that a data miner looks for in data mining applications is not intuitive and sometimes even counter-intuitive. Data Mining uncovers information in Data that could not even have been hypothesized in earlier scenarios. Second, the information collected from Data Mining applications should be valid. If Data Miners look hard enough in the data pool, they are bound to see some pattern in the data sooner or later. The possibility of spurious results
  4. 4. Data Mining for Customer Relationship Management 130 applies to all data mining applications and highlights the need for post mining validation and sanity checking. Third, and most critically, the new information must be actionable, that is, it must be possible to translate this information into some business advantage. This is not an easy criterion for the organizations to fulfill. Data Mining Methodologies Statistically, Data Analysis can be broken down into two main board categories — Definitive Analysis and Explorative Analysis. Definitive Analysis pertains to starting with a hypothesis and using data analysis to either prove or refute the hypothesis. Exploratory Analysis, on the other hand, deals with finding a hypothesis that would best explain trends observed in the data set. Unlike Definitive Analysis, where the system models based on the starting hypothesis, in Exploratory Analysis, the system creates a hypothesis as part of the analysis itself. Data Mining as a concept, explores on the Exploratory Analysis by starting with no pre-conceived hypothesis. The hypothesis forms a component of the final result of the Data analysis methodology. There are three main Data Mining Activities • Discovery • Predictive Modeling • Forensic Analysis Discovery encompasses the process of simply finding persistent patterns in the data set. This process involves analysis of the data records on different dimensions on which they might be represented. The system simply tries to find interesting patterns between different record sets based on values for a particular record field. Predictive Modeling is a methodology that tries to find patterns in the data sets and then tries to forecast values based on the patterns observed in the Decision, Vol. 28, No. 2, July – December, 2001
  5. 5. Data Mining for Customer Relationship Management 131 system. For each data record fed to the system, the system could forecast some of the missing values for particular fields left empty in the new record based on the previously observed patterns in the historical data records. Forensic Analysis is a methodology that aims at finding patterns in the data records and then use these patterns to mark out anomalous records or records with abnormal deviations from the observed patterns. Some of the common applications for the three main Data Mining Activities may be seen as : Activity Application Area Discovery Target Marketing, Market Basket Analysis, Cross Selling, Market Segmentation Predictive Modeling Forecasting, Customer Retention, Improved Underwriting, Quality Control, Competitive Analysis Forensic Analysis Fraud Detection Introduction to Customer Relationship Management (CRM) The idea behind CRM is to have a single, enterprise view of the customer for the purpose of cultivating a high value relationship with the customer that leads to increased loyalty and profit. These means being able to identify all the products, services and intermediate relationships which customers have with an organization. It means being able to maintain a “consistency of experience” for the customer through all forms of interaction with the organization, whether order, sales, inquiry or service CRM is, in essence, a broad cluster of strategic imperatives to allow organizations to find, retain, and build long lasting partnerships with strategically important customers.
  6. 6. Data Mining for Customer Relationship Management 132 CRM Processes Three main process areas for CRM applications have been identified as • Sales Force Automation • Marketing Automation • Customer Service Sales Force Automation allows business operatives to identify and construct customer profiles in little or no time based on the customer related data within the organization. The data could either be primary data about the customer (records of customers previous interactions with the organizations including past sales, service transcripts or customer complaints recorded) or secondary data (based on the customer demographics, psychographics etc.). This information needs to be accessible to all sales representatives of organizations in all forms when interacting with the customer. This information processing and presentation, needs to be automated so as to allow customers to self- service in different areas like order management and tracking, order lead time identification etc. Marketing Automation allows organizations to construct better marketing campaigns, business policies and customer centric strategies for efficient and personalized marketing efforts (1-to-1 marketing, direct and cross selling) along with web driven marketing executions and marketing oriented analysis. Customer Service allows organizations to focus primarily on post-sales activities. Automated services for frequent product or service complaint, service schedules and other service activities fall under this head. Decision, Vol. 28, No. 2, July – December, 2001
  7. 7. Data Mining for Customer Relationship Management 133 Business Benefits of CRM In addition to the cultivation of loyal customers who display the profitability profile of the business seek, CRM brings other benefits to the organization. An improved and detailed analysis of the customers, their needs and expectations, and how the company interacts with them is emerging as an important feature for Supply chain Management and Electronic Commerce. For example, a successful CRM initiative shall provide a better ability to model and classify various market segments, leading to better Business to Customer (B2C) e-commerce performance. Alternatively, real time order processing, whether to meet periodic or aperiodic customer needs, will need integration with SCM systems. Other benefits of CRM include improved ability to : • Adapt to the effects of globalization and deregulation • Position offerings that hold up to increased customer scrutiny (for example, web-enabled comparative analysis and research) • Control costs associated with customer acquisition and retention • Sustain competitive differentiation Building a CRM Process Chain Organizations need to develop a clear and an organization wide framework for developing and implementing their CRM Strategies. These operational framework affect not only the Marketing Department, but also, Sales, Operations, Finance, Customer Service and Information Systems. For successful implementation, CRM Operational Framework need to be used as the building blocks around which all organizational processes are developed and maintained. CRM has to be seen as a cross-functional customer focused business strategy for any success to be leveraged by the organization.
  8. 8. Data Mining for Customer Relationship Management 134 Most of the operations within any organization for CRM implementation need to focus on the following areas : • Identification and Classification of Customers • Creating a Value Proposition for the Customer • Delivering Value to the Customer Data Management Process for CRM For successful operational CRM, organizations need to maintain a strict data flow framework within their organization. A broad framework for building data flow framework within any organization would consist of the following processes : Identification of the objective : To identify the core areas in which organization would need to implement effective customer centric views. Data Preparation : To identify the different sources of customer related data from within the organization or outside. The most common sources of data for organizations is from their transactional systems, marketing data, sales records and market research data. Secondary data sources from outside the organization could also be used. Data Preprocessing : For Analysis, data from different sources needs to be collected and pooled together into a single repository. However, before any organization can do this, the data collected from different sources has to be made consistent since the data could be collected from systems that are running on different platforms, architectures or application systems. Data Analysis : Data which has been collected may then be subjected to different data mining applications depending on the business requirements as identified in the first stage itself. Decision, Vol. 28, No. 2, July – December, 2001
  9. 9. Data Mining for Customer Relationship Management 135 Assimilation of Results : Results obtained from the analysis could then be analyzed for effectiveness or applicability in the business process. Systems working without preconceived hypotheses, as in Data Mining Applications, could throw up results that might not be of direct relevance to organization process. All results have to be analyzed to identify those that may be converted into strategic knowledge for the business objective. This is the stage at which organizations would be able to formulate different value propositions for their customers. Business Decision Formulation : Depending on the result obtained from the Analysis state, a business decision might be taken. This could be an automated business decision for real-time application system, or a long-term strategic decision for any organization depending on the requirement. This stage ideally represents the selection of a value proposition or a set of value propositions that the organization might want to present to their customers. Internal Networks One crucial aspect that organizations need to recognize when creating internal systems for CRM system implementation is the creation of internal networks within the organization that allow common repository of data to be created. These networks integrate all business functions and their processes and extract required data records to assimilate into a single source. This means the integration of all back office and front office application systems into an organization-wide application network including the organization’s Transactional Systems and Analytical Systems. This requires information pertaining to and including : • Customer Contact History • Profitability Measures • Customer Research Information • Descriptive Information
  10. 10. Data Mining for Customer Relationship Management 136 Data Mining Application Areas for CRM There are several stages in the CRM Process Framework where data mining techniques can be developed and implemented. Customer Analysis is an area where Database segmentation techniques (Discovery) can be used to identify the heterogeneous sets of customers, which can be targeted together for being a SSC (Strategically Significant Customer). This technique could also help organizations build both broad level and low level classifications of their customers to allow development and analysis of specific product/service sets for each category of users. Furthermore, organizations could also use this classification to identify new customers and to allocate these new customers to the already created customer classifications based on business criteria. Customer Response is an area where organizations are increasingly using Predictive Modeling to predict the customer demand and the best value offers for the customer. Based on the patterns and preferences observed in the buying behavior of the customer, options can be developed by the organization. This could include differentiated pricing methodologies, product combinations or new product features for increased customer service levels. Predictive modeling can also enhance Network Development. It can generate fair estimates regarding the offers acceptable to the different value generating parties in the organization’s value chain. Relationship Management is the area that has found the maximum amount of application for data mining operations. Service records maintained by organization can be used in Predictive modeling operations to generate maximum value for the customer. This area would also employ deviation detection to estimate the value cost of the customer for the organization. Valuable customers generated in this step then form the starting part of the regeneration of the Value Chain. Organizations could also find the reaction Decision, Vol. 28, No. 2, July – December, 2001
  11. 11. Data Mining for Customer Relationship Management 137 patterns to changing product features or prices and integrating these observations with the cost, organizations could arrive at a suitable product/ service form better suited to the customer demand. Forensic analysis has helped organizations analyze results pertaining to payment methodologies and preferences for their customers. This could help organizations develop better and more efficient financial options for the customer. The possibilities for application areas for Data Mining operations within organizations for better CRM practices are immense. But what organizations need to be careful about when implementing such solutions is the broad level integration of different business functions without which most CRM efforts fail. Organizations need to start with a clear-cut-strategy for implementation of these practices and should make sure that the effort percolates from the top positions in the organization to the bottom most rank. CRM needs to be adopted not as another business process but as a way of doing business. Conclusion This paper attempts to explain some of the key features of Data Mining, an emerging set of techniques for exploring huge data, and Customer Relationship Management. The latter is also a new concept which is nurtured by present organizations to primarily increase their effectiveness in handling the various needs and types of customers. The paper finally attempts to discuss some of the key areas of CRM where Data Mining can be used with great satisfaction. References 1. Turkey J. (1973), Exploratory Data Analysis, McMillan Publishers. 2. Parsaye K. (February, 1997), OLAP and Data Mining : Bridging the Gap, Database Programming & Design.
  12. 12. Data Mining for Customer Relationship Management 138 3. Gary Saarenvirta, A characterization of Data Mining Technologies and processing, Information Discovery Inc. 4. Frank Teklitz & Robert L, McCarthy, Siebel Corp. Analytical Customer Relationship Management. 5. Parsaye, K. and M.H. Chignell (1993), Intelligent Database Tools and Applications, John Wiley and Sons. 6. Role of Database Servers in CRM applications, Microsoft Press. 7. Andy F Hawley, CEO Xchange Inc., Evolving to eCRM : How to optimize interactions between you and your customers. 10. Parsaye, K (December 1996), Rules Are Much More Than Decision Trees. The Journal of Data Warehousing 11. Data Mining for unusual Events, IBM corp. 12. Justin Ketelyn, Extracting Fact from Fiction. 13. Parsaye K. (September 1997), Machine Man Interaction. D M Review. 14. Jim Berkowitz, The Defining Business Initiative of the New Millennium. 15. CRM : High-Availability Networks Enable Business-to-Customer E-Business, 3Com, Technical Paper. Decision, Vol. 28, No. 2, July – December, 2001