1. © 2003 Terry James. All rights reserved 1 The CRM Textbook: customer relationship management training Terry James © 2006 Chapter 12: Analytical

2. © 2006 Terry James 2 Operational vs. Analytical Operational transactions, POS point of sale answer in seconds, zero failures Analytical learning, analysis, patterns, history answer in hours or days

3. © 2006 Terry James 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 Smaller, departmental, more unique needs

4. © 2006 Terry James 4 Product Fact table Time Place Cube 2 Cube 4

5. © 2006 Terry James 5 ETL Extract Data from operational files all over, and any other useful data source Translate Standardize the data, clean it, rationalize Load Load up the data warehouse

6. © 2006 Terry James 6 Quality Major issue Plan spend 30% of your time for quality Data dictionary What is the definition, the data steward, the meaning, valid values, etc. Most common errors Missing data, invalid data, out-of-date Inconsistencies Different meanings for the same code, different codes for the same meaning, multiple data for the same data element Meta data Data about data

7. © 2006 Terry James 7 OLAP vs. data mining OLAP OnLine analytic programming You start with a question, run reports, check data, publish results Data mining Start with no question Wander across the data to uncover patterns of fraud, buying, selling, etc

8. © 2006 Terry James 8 Data Mining Techniques Correlation When prices go down, buying goes up Regression Predict the future Example: Buying = -2.4(price) + 4.1 etc. Neural network Emulates the brain (wetware) Fuzzy logic Clustering What things go together in a bundle If you are like other people who did x, they also did y Genetic algorithm Emulates nature,evolution, and mutations If random change to formula provides better predictions, keep it, otherwise retest and then loop to make new change

9. © 2006 Terry James 9 Data mining process Learn Take action New data 1.Begin with an important 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. © 2006 Terry James 10 Traps It is so cool, sexy, interesting,… Yes, but does it put cash on the table? Prove the obvious Don’t burn CPU cycles just to prove purchase patterns match marketing campaigns. Go after valuable items, not motherhood and apple pie.

11. © 2006 Terry James 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?

12. © 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