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Customer Centric Data Mining


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Customer Centric Data Mining

  1. 1. Customer Centric Data Mining Anjesh Dubey Fusion of BI & CRM Divya Setlur Nanda Jaiswal Rakesh Ranjan
  2. 2. Customer centric environment
  3. 3. Bottom-line questions in CRM Who are my most profitable customers? Who are my repeat website visitors? Who are my loyal customers? Who is likely increase purchase? What clients are likely defect to my rivals? Will my customer respond to the direct mail solicitation?
  4. 4. Data mining as part of CRM strategy Business that knows it’s customers best will serve them best Best spent marketing dollar is the one that retains the existing customer Business forecasting is essential A fast food chain doing hourly demand projection for its outlets Objective and quantifiable insight into customer profiling data Which of my high-profit customers are most likely to leave? Be proactive to retain them. Which of my low-profit customers are least likely to leave? Raise their price and make them more profitable.
  5. 5. Data mining is NOT magic Data mining can not ingest noisy data Data mining can not use ready-to-use business strategies based on analysis of raw data without intelligent interpretation Garbage-in garbage-out The information produced by data mining apps require human review Real data mining is methodology with technology support “Hype” data mining is mythology with marketing support
  6. 6. Data mining techniques in CRM life cycle stage CRM stage Activities Data Mining technique Discovering Lead generation Customer acquisition profiling Web data mining for prospects Targeting market Reaching Marketing programs Customer acquisition profiling Selling Contact selling Customer acquisition profiling Online shopping Scenario notification Customer-centric selling Satisfying Product performance Customer retention profiling Service performance Scenario notification customer service Customer centric selling Inquiry routing Retaining Customer retention Customer retention profiling Scenario notification Individual customer profiles
  7. 7. Data mining methodologies CRISP-DM (Cross-Industry Standard Process) methodology SEMMA (Sample, Explore, Modify, Model, Assess) methodology Other Common approaches Different tools have different way of doing the typical data mining task Data gathered -> conditioned & analyzed -> descriptive models -> predictive models
  8. 8. Data mining methods Classification & regression Association & sequencing Association rules (Market Basket Analysis) Sequential analysis Clustering Link analysis Visualization Regression Rule induction
  9. 9. The mathematics in Data mining Feature space (Euclidian space) Probability distribution Standard deviation and z-score Feature space computation Clusters Numeric coding Creating Ground truth Synthesis of features
  10. 10. Data mining techniques Neural Network Problem solving with Neural network Training and validating Decision Trees Predictive model Based on classification
  11. 11. Case Study – Loan Risk Analysis Problem definition Mortgage company ACME financial has to predict and analyze the risk associated with the Applicants before approving the loan Data Collection Loan application data Credit history and score data Data preparation (cleansing) Clean and categorize data Building the model Data mining with decision trees
  12. 12. Data Collection Ap.ID Name Address Income Company Date Hired 1 John Cook San Jose, CA $105,000.00 IBM 03/15/1999 2 Willie Chun Freemont, CA $92,000.00 Cisco 06/19/1998 3 Robbert Gillman Phoenix, AZ $28,000.00 City County 08/23/1990 4 Sam Wong Phoenix, AZ $27,000.00 Racing Co. 06/30/1995 5 Jill will Las Vegas, NV $35,000.00 Undertakers, NULL INC. 6 Rob Chung New York, NY $75,000.00 Monsters, INC. 12/14/2000 7 Amit Khare Sunnyvale CA $91,000.00 Mysql 04/01/1997 Applicant ID Company Balance 1 LTC Mortgage $400,000.00 1 Visa $15,000.00 2 Bank of America $150,000.00 2 ACME Financial $60,000.00 3 Toon Depot $45,000.00 3 Toon Bank $125,000.00 4 Master Card $54,000.00 5 Financial Aid $60,000.00 6 Toyota Credit $44,000.00 7 Financial Aid $23,000.00
  13. 13. Data Preparation Applicant ID Debt Level Income Level Job > 5 Years 1 High High No 2 High High Yes 3 High Low Yes 4 Low Low Yes 5 Low Low No 6 Low High No 7 Low High Yes
  14. 14. Demo using java predictor tool
  15. 15. Conclusion The fusion of BI and CRM is creating new opportunities as well as challenges Increasingly sophisticated consumers are creating hyper-competition for businesses Low brand loyalty among new breed of consumers highlight the importance of customer centric data mining BI and CRM together provides 360-degree view of customer data
  16. 16. References Books Data Mining Explained: A Manager's Guide to Customer-Centric Business Intelligence by Rhonda Delmater and Jr., Monte Hancock Web Articles and tutorials An Independent Study in Data Mining The Data Warehousing Information Center Test Drive Data Mining - SQL Server 2005 tutorial EventID=1032291442&EventCategory=3&culture=en- US&CountryCode=US