1. MKT 700 Business Intelligence and Decision Models Week 6: Segmentation and Cluster Analysis

2. What have we seen so far? Overview:Analytical CRM Overview:CRISP-DM Methodology Data Preparation Legacy Approach: RFM Customer Value

3. Where are we going from now? Classification: Clustering Classification +: Profiling Predictive Modeling: Response Probability

4. Outline for Today Clustering: Clustering and Segmentation B2C and B2B Clustering theory Lab

5. Typical Classroom Segmentation (Beer) Strong Light Local Import Blue Collar Office Worker Foodies Selective Maple Leaf Fans Occasional

7. Clusters and Segments Differences between clusters and segments Learning segmentation Dynamic segmentation

8. Consumer Segmentation Taxonomy Product usage/loyalty Buying behaviour Preferred channel Family life cycle (stage in life) Lifestyle (personal values)

9. Status Levels and Segments (needs + treatment)

10. Data Sources for Segmentation Internal Transactions Surveys & Customer Service External (Data overlays) Lists Census Taxfiler Geocoding

11. Geo-Segmentation in CDA Birds of a feather f___k together… Environics (Prizm) http://www.environicsanalytics.ca/prizm-c2-cluster- lookup http://www.environicsanalytics.ca/prizm-c2-cluster- lookup Generation5 (Mosaic) http://www.generation5.ca Manifold: http://www.manifolddatamining.com/html/lifestyle/lifes tyle171.htm http://www.manifolddatamining.com/html/lifestyle/lifes tyle171.htm Pitney-Bowes (Mapinfo) http://www.utahbluemedia.com/pbbi/psyte/psyteCanad a.html http://www.utahbluemedia.com/pbbi/psyte/psyteCanad a.html

12. B2B Segmentation Taxonomy Firm size (employees, sales) Industry (SIC, NAICS) Buying process Value within finished product Usage (Production/Maintenance) Order size and Frequency Expectations

15. Clustering Measuring distances (differences) or proximities (similarities) between subjects

16. BI Modeling Techniques No Target (No dependent variable, unsupervised learning) RFM Cluster Analysis Target (Dependent variable, supervised learning) Regression Analysis Decision Trees Neural Net Analysis

17. 17 Measuring distances (two dimensions, x and y) A B C Pythagoras

18. 18 Measuring distances (two dimensions) A B C D(b,a) D(a,c) D(b,c)

19. 19 Measuring distances (two dimensions) A C d ac 2 = (d x 2 + d y 2 ) d ac 2 =  (d i ) 2 d ac = [  (d i ) 2 ] 1/2 B Euclid

20. Distances between US cities ATLCHIDENHOULAMIANYSFSEADC Atlanta05871212701193660474821392182543 Chicago58709209401745118871318581737597 Denver121292008798311726163194910211494 Houston701940879013749681420164518911220 Los_Angeles1936174583113740233924513479592300 Miami6041188172696823390109225942734923 New_York7487131631142024511092025712408205 San_Francisco2139185894916453472594257106782442 Seattle21821737102118919592734240867802329 Washington_DC543597149412202300923205244223290

21. Cluster Analysis Techniques Hierarchical Clustering Metric, small datasets

22. SPSS Hierarchical Clusters Dendogram

23. SPSS Multidimensional Scaling (Euclidean Distance) 1 2 1. Atlanta.9575 -.1905 2. Chicago.5090.4541 3. Denver -.6416.0337 4. Houston.2151-.7631 5. Los_Angeles -1.6036 -.5197 6. Miami 1.5101-.7752 7. New_York 1.4284.6914 8. San_Francisco -1.8925 -.1500 9. Seattle -1.7875.7723 10. Washington 1.3051.4469

24. Euclidean distance mapping

26. Cluster Analysis Techniques Hierarchical Clustering Metric variables, small datasets K-mean Clustering Metric, large datasets Two-Step Clustering Metric/non-metric, large datasets, optimal clustering

27. Cluster Analysis Techniques See Chapter 23, SPSS Base Statistics for description of methods

28. Two-Step Cluster Tutorials SPSS, Direct Marketing, Chapter 3 and 9  Help  Case Studies  Direct Marketing  Cluster Analysis  File to be used: dmdata.sav SPSS, Base Statistics, Chapter 24  Analyze  Classifiy  Two-Step Cluster  File to be used: Car_Sales.sav  Help: “Show me”