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The CRM Textbook: customer relationship management training Terry James © 2006 Chapter 12: Analytical 1 © 2003 Terry James. All rights reserved 2 Operational vs. Analytical Operational transactions, POS point of sale answer in seconds, zero failures Analytical © 2006 Terry James learning, analysis, patterns, history answer in hours or days 3 Data Warehouse vs. Data Mart More sophisticated than relational database Data warehouse Enterprise, huge, standards Level of granularity Cube – 3D Fact tables Data Mart © 2006 Terry James Smaller, departmental, more unique needs 4 Place Time Product Cube 4 © 2006 Terry James Cube 2 Fact table 5 ETL Extract Translate Data from operational files all over, and any other useful data source Standardize the data, clean it, rationalize Load © 2006 Terry James Load up the data warehouse 6 Quality Major issue Plan spend 30% of your time for quality Data dictionary Most common errors Missing data, invalid data, out-of-date Inconsistencies What is the definition, the data steward, the meaning, valid values, etc. Different meanings for the same code, different codes for the same meaning, multiple data for the same data element Meta data © 2006 Terry James Data about data 7 OLAP vs. data mining OLAP OnLine analytic programming You start with a question, run reports, check data, publish results Data mining © 2006 Terry James Start with no question Wander across the data to uncover patterns of fraud, buying, selling, etc 8 Data Mining Techniques Correlation Regression Emulates the brain (wetware) Fuzzy logic Clustering Predict the future Example: Buying = -2.4(price) + 4.1 etc. Neural network When prices go down, buying goes up What things go together in a bundle If you are like other people who did x, they also did y Genetic algorithm © 2006 Terry James Emulates nature ,evolution, and mutations If random change to formula provides better predictions, keep it, otherwise retest and then loop to make new change Data mining process 9 1.Begin with an important Learn New data © 2006 Terry James Take action company goal 2. Collect data needed 3. Data quality, ETL 4. Pick technique (genetic, neural network, …) 5. Build a model 6. Test and validate model 7. Implement model 8. Report results 9. Integrate new learning 10. Go back to step 1 10 Traps It is so cool, sexy, interesting,… Yes, but does it put cash on the table? Prove the obvious © 2006 Terry James Don’t burn CPU cycles just to prove purchase patterns match marketing campaigns. Go after valuable items, not motherhood and apple pie. 11 Validating Does the model work? Do you have a response equation to the campaign? How accurate was the model? False positive False negative Beware Bayes Theory What about the control group? © 2006 Terry James 12 Learning is a forever loop Each worthwhile analysis should be focused on action Check ahead if manager is ready for action and on what topics Take what you learn and take action Action will generate data Take data and learn Analysis and loop back to step 1 © 2006 Terry James