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MKT 700 Business Intelligence and Decision Models Week 6: Segmentation and Cluster Analysis What have we seen so far? Overview:Analytical CRM Overview:CRISP-DM Methodology Data Preparation Legacy Approach: RFM Customer Value Where are we going from now? Classification: Clustering Classification +: Profiling Predictive Modeling: Response Probability Outline for Today Clustering: Clustering and Segmentation B2C and B2B Clustering theory Lab Typical Classroom Segmentation (Beer) Local Office Worker Maple Leaf Fans Blue Collar Light Strong Occasional Selective Foodies Import Clusters and Segments Differences between clusters and segments Learning segmentation Dynamic segmentation Consumer Segmentation Taxonomy Product usage/loyalty Buying behaviour Preferred channel Family life cycle (stage in life) Lifestyle (personal values) Status Levels and Segments (needs + treatment) Status Levels Marketing Segments Business Customers Gold Affluent Retired Silver Young Singles Families With Kids Customer Bronze Bargain Shoppers Occasional Buyers Marketing Staff Data Sources for Segmentation Internal Transactions Surveys & Customer Service External (Data overlays) Lists Census Taxfiler Geocoding Geo-Segmentation in CDA Birds of a feather f___k together… Environics (Prizm) Generation5 (Mosaic) http://www.generation5.ca Manifold: http://www.environicsanalytics.ca/prizm-c2-clusterlookup http://www.manifolddatamining.com/html/lifestyle/lifes tyle171.htm Pitney-Bowes (Mapinfo) http://www.utahbluemedia.com/pbbi/psyte/psyteCanad a.html B2B Segmentation Taxonomy Firm size (employees, sales) Industry (SIC, NAICS) Buying process Value within finished product Usage (Production/Maintenance) Order size and Frequency Expectations Clustering Measuring distances (differences) or proximities (similarities) between subjects 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 Measuring distances (two dimensions, x and y) A B C Pythagoras 17 Measuring distances (two dimensions) D(b,a) A B D(a,c) D(b,c) C 18 Measuring distances (two dimensions) dac2 = (dx2 + dy2) A B C Euclid dac2 = (di)2 dac = [(di)2]1/2 19 Distances between US cities ATL CHI DEN HOU LA MIA NY SF SEA DC 0 587 1212 701 1936 604 748 2139 2182 543 Chicago 587 0 920 940 1745 1188 713 1858 1737 597 Denver 1212 920 0 879 831 1726 1631 949 1021 1494 701 940 879 0 1374 968 1420 1645 1891 1220 1936 1745 831 1374 0 2339 2451 347 959 2300 Miami 604 1188 1726 968 2339 0 1092 2594 2734 923 New_York 748 713 1631 1420 2451 1092 0 2571 2408 205 2139 2182 543 1858 1737 597 949 1021 1494 1645 1891 1220 347 959 2300 2594 2734 923 2571 2408 205 0 678 2442 678 0 2329 2442 2329 0 Atlanta Houston Los_Angeles San_Francisco Seattle Washington_DC Cluster Analysis Techniques Hierarchical Clustering Metric, small datasets SPSS Hierarchical Clusters Dendogram SPSS Multidimensional Scaling (Euclidean Distance) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Atlanta Chicago Denver Houston Los_Angeles Miami New_York San_Francisco Seattle Washington 1 2 .9575 .5090 -.6416 .2151 -1.6036 1.5101 1.4284 -1.8925 -1.7875 1.3051 -.1905 .4541 .0337 -.7631 -.5197 -.7752 .6914 -.1500 .7723 .4469 Euclidean distance mapping Cluster Analysis Techniques Hierarchical Clustering K-mean Clustering Metric variables, small datasets Metric, large datasets Two-Step Clustering Metric/non-metric, large datasets, optimal clustering Cluster Analysis Techniques See Chapter 23, SPSS Base Statistics for description of methods 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”