Data Mining Techniques for CRM

1. Data Mining Techniques for CRM Paul J.C. Chang Eneida Lau Ximena Salazar Lester Arellano José-Pablo González Edith Quispe

2. Data Mining in CRM ... “ ...through data mining – the extraction of hidden predictive information from large databases – organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions.”

3. Agenda • Introduction, Definition: Paul • The Evolution & Apps. of Data Mining: Eneida • Internal Considerations & Data mining techniques: Ximena • Data mining and CRM – relationship & customer privacy: Lester • Case Studies (Neural Networks, CHAID): JPG • CHAID vs neural nets; Conclusions: Edith

4. Introduction • Product-oriented view VS. Customer-oriented view • Design-build-sell VS. sell-build-redesign • One-on-one marketing VS. mass marketing • Goal of revolution: Establish a long term relationship with each customer • The advent of the Internet and technological tools accelerate modern CRM revolution • CRM is important for B2C or C2B, and even more crucial in B2B environments

5. Why Data Mining? • Between businesses and customers… • Collecting customer demographics and behavior data makes precision targeting possible • Helps to devise an effective promotion plan when new products developed • Creates and solidifies close customer relationships • Between businesses… • Helps to smooth transactions, communications and collaboration • Simplifies and improves logistics and procurement process

6. What is Data Mining? • “…a sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data.” • “…another way to find meaning in data.” • Data mining is part of a larger process called knowledge discovery

7. What Data Mining is ~NOT~ • Data mining software does not eliminate the need to know the business, understand the data, or be aware of general statistical methods. • DM does not find patterns or knowledge without verification • DM helps to generate hypotheses, but it does not validate the hypotheses

8. Data Collection Data Access Data Navigation Data Mining • (1960’s) • Retrospective, • static data delivery • Summations or averages • Computers, tapes, disks • IBM, CDC • (1980’s) • Retrospective, • dynamic data delivery at record level • Branch sales at specific period of time • RDBMS, SQL, ODBC • Oracle, Sybase, Informix, IBM, Microsoft • (1990’s) • Retrospective, • dynamic data delivery at multiple level • Global view or drill down • OLAP, multidimensional databases, data warehouses • Pilot, IRI, Arbor, Redbrick • (2000’s) • Retrospective, • Proactive information delivery • Online analytic tools, feedback and information exchange • Adv. Algorithms, multiprocessor, computers, massive databases • Lockheed, IBM, SGI Evolutionary Stages of Data Mining

9. Breakdown of Data Mining from a Process Orientation Data Mining Discovery Predictive Modeling Forensic Analysis • Conditional Logic • Affinities and Associations • Trends and Variations • Outcome Prediction • Forecasting • Deviation Detection • Link Analysis

10. Applications of Data Mining Retail Banking Telecommunications • Performing basket analysis • Sales forecasting • Database marketing • Merchandise planning and allocation • Card marketing • Cardholder pricing and profitability • Fraud detection • Predictive life-cycle management • Call detail record analysis • Customer loyalty

11. OTHER APPLICATIONS Customer Segmentation Manufacturing Discrete segments by adding variables Warranties Customize Products. Predict features Frequent flier incentives No. clients who will ask for claims Identify groups who can receive incentives

12. INTERNAL CONSIDERATIONS Data mining Decision-making process Skillsets and technologies must be available to integrate them • Sell to and service customers • Manage inventory • Supervise employees • Work to correct and prevent loss Knowledge gained through DM • An algorithm for scoring • A score for particular customer, employee • An action associated with a customer, employee or transaction

13. Nearest Neighbor Data Retained Case-Based Reasoning Logical Numeric and Non-numeric Cross Tabulational Non-numeric Data Data distilled Equational Numeric Data DATA MINING TECHNIQUES They are applied to tasks of predictive modeling and forensic analysis DM Approaches They extract patterns and then use for various purposes

14. CRM: Development of the offer 3 Which’s 1 - From product to customer orientation - Market Strategy from outside-in 2 • Push the development of customer orientation • Innovating value proposition CUSTOMER RELATION MANAGEMENT • Know • Target • Sell • Service Definition 2 Stage Concept

15. Internal Customer Data • Billing Records • Surveys • Web logs, Credit Card records • External data sources • Current Address, Web page viewing profiles. Customer Information Data Warehouse Analyze the Data Outside Source Data Customer Data Campaign Execution & Tracking Components of CRM Historical Data Data Mining Techniques + Customer Oriented Interactions between MKT, information, Tech and sales channels

16. Inputs What information is available Data Mining Output What is likely to be interested Data Mining & CRM • The Relationship • Customer Life Cycle • Prospects • Respondents • Active Customers • Former Customers

17. Data Mining & CRM • Inputs • Prospects Data Warehouse in other industries • Click Stream Information • Market Data Intelligence • DM can predict behavior of customer (CLC) and match it with any market event (a,i. I pod nano) • Data Mining and Customer Privacy • Privacy Bill of Rights, Independent verification of security policies. • Create an anonymous architecture for handling customer info.

18. Case Studies Neural Networks vs. CHAID

19. Case #1 Neural Networks

20. Neural Networks • The exact way in which the brain enables thought is one of the great mysteries of science

21. Neurons

22. NeoVistas Solutions’ Decision Series • For retail, insurance, telecommunications, and healthcare. • Includes discovery tools based on neural networks, clustering, genetic algorithms, and association rules

23. The problem • Large retailer • Over $1 billion in sales • Overstocked on slow-moving products • Under-stocked on most popular items at critical selling periods.

24. Solution • With Clustering and and NN: • Review point-of-sale history and equate store groupings to sales patterns. • Forecast stocking requirements on a store-by-store basis.

25. Results • Management is able to forecast seasonal trends at the store-item level. • The Decision Series tools showed that clustering similar items into actionable groups streamlined the ordering process. • Revenues increased by 11.6%

26. Case #2 CHAID

27. Applied Metrix • Uses a combination of CHAID segmentation and logistic regression response probability modeling to establish predictive models that are deployed over a proprietary Internet system

28. The problem • Home equity marketer that extended home equity lines of credit at the national level. • The client’s goal was to increase the efficiency of targeting current mortgage customers who might be interested in the client’s service.

29. The Solution • CHAID identified 16 distinct market segments. • In particular, one particular segment accounted for 65% of responses to the mailing.

30. Results • The highest-rated group from the predictive model had by far the highest response rate to the equity line of credit campaign—85% above average for the direct mailing, • The goal of the program was a 10% increase in response rate, but the actual response rate increased 30%. • The firm was able to increase profits by over one million dollars in the first year after implementation.

31. CHAID vs. Neural Networks

32. Clarity and explicability • CHAID model is understandable as a set of rules • Neural Network is obscure Implementation/integration • The CHAID model is much easier to be implemented that a Neural Network. • The risk of missing code by an IT department is slim for a CHAID model and higher for a Neural Network.

33. Data Requirements • The data for both techniques requires some pre-processing. • Neural Network require the data be transformed into binary format. Accuracy of model • Neural Networks provide more accurate models, especially for complex problems.

34. Construction of model • CHAID is easier and quicker to construct. • Neural Networks have many parameters that must be set and require more skilled manipulation. Cost • Building a Neural Network is more costly then building a CHAID model.

35. Aplications • CHAID and Neural Networks can create predictive models. • Neural Networks can handle both categorical and continuous independent variables, but these have to be transformed to 0/1 input variables. • When all or most of the independent variables are continuous, neural networks should perform better than CHAID.

36. Aplications • The Neural Networks and CHAID can be used to solve sequence prediction problems. • Neural Networks can be used to solve estimation problems. • CHAID provides good solutions to classification problems, can be used for exploratory analysis and can provide descriptive rules. • An interesting development is the combination of these two techniques to create “neural trees”.

37. CONCLUSIONS • The choice among different options is not as the choice to use data mining technologies in a CRM initiative. • Data Mining represents the link from the data stored over many years through various interactions with customers in diverse situations, and the knowledge necessary to be successful in relationship marketing concepts.

38. CONCLUSIONS • Through the full implementation of a CRM program, which must include data mining, organizations foster improved loyalty, increase the value of their customers, and attract the right customers. • As customers and businesses interact more frequently, businesses will have to leverage on CRM and related technologies to capture and analyze massive amounts of customer information.

39. CONCLUSIONS • CRM solutions focus primarily on analyzing consumer information for economic benefits, and very little touches on ensuring privacy.