1. Business Intelligence /Decision Models Dr. Richard Michon TRSM 1-040Ext. 7454 rmichon@ryerson.ca www.ryerson.ca/~rmichon/mkt700

2. Leaving Traces all over the Place

3. Social medias, SMS, emails, Google, Web browsing e-Commerce, shopping baskets, basket abandonment, showrooming Payment, CC, PayPal, banking, paying bills, AirMiles Delivery Support registration and guarantees

4. Small Stores Recognition Service Friendship Information

5. Small Stores Notice Remember Learn Act

6. Larger Organizations Notice: Transaction files Remember: Data warehousing Learn: Data mining Act: CRM

7. OLTP and Warehousing

8. 8 Analytical and Operational CRM

9. 9 CRM Architecture

10. Online Analytical Process

11. Data Mining vs. OLAP OLAP Deliver key facts based on hist. data: KPIs Core Business Metrics Factual Reporting Visualization driven analysis – reporting bases See what happened in the past Data Mining Deliver the reasons or drivers of those facts Goal driven analysis Predict what’s going to happen in the future

12. Data Mining and Statistics Approach Data Mining Data Driven No assumption required If it works and makes some sense, let’s use it Focus on data exploration Can find patterns in very large amounts of data Focus on Deploying Results Requires understanding of data and business problem Statistical Analysis Tests for statistical correctness of models Are statistical assumptions of models correct? Hypothesis testing Is the relationship significant? Tends to rely on sampling Techniques are not optimized for large amounts of data Requires strong statistical skills

13. Analytics Usage

14. Analytics Challenges

15. Retail

16. Long Tail Effect AB C Vol. & Demand Assortment

17. Services Get rid of intermediaries, keep control and reduce costs

18. Media Alternate business channels and reduce costs

19. Non-Profits Nothing like trying!

20. Data Mining: Set of Techniques Classification: Customer type, No fly list, Risk, Fraud, Terrorism Estimation: Household income, Lifetime Value, Life style Clustering: RFM, Typology, Segmentation Profiling: Decision Trees Prediction: Behavioral probability (0/1)

21. Data Mining Applied to CRM Prospecting: Prospects, Channels, Message Interactive mkt: Response models, Optimizing budgets and ROI Segmentation: Cultivation: Cross-sell, Upsell, Churn Reduction, Loyalty CLV: Credit Risk: Testing:

23. MKT 700 Course Specifics Business Intelligence and Decision Modeling

24. Analytics Usage

25. Analytics Challenges

27. IT Multidisciplinary Domain Data Scientists MKT

28. Course Material Reading posted on course website SPSS Tutorials SPSS and Excel Datasets

29. Course Evaluation Percent Midterm exam 33% Final exam* 33% Labs ** 33% Total100% * Must pass ** No makeups

30. Grading Z Score = (X – Mean) / SD 0 +1 +2 -2 F D- D D+ C- C C+ B- B B+ A- A A+* * Approximate Scale

31. Course Logic and Structure Logic 1. DBMKT, DM, CRM 2. RDBMS - SQL 3. CRISP - Data Preparation 4. CLV 5. RFM 6. Classification 7. Profiling 8. Predictive Modeling Actual 1. DBMKT, DM, CRM 2. RFM 3. RFM2, CRISP, Data Preparation 4. CLV 5. CLV2, RDBMS 6. Classification 1&2 7. Profiling 1 & 2 8. Predictive Modeling 1 & 2

33. Next Week RFM (Recency, Frequency, Monetary) SPSS RFM Tutorial