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MKT 700 Business Intelligence and Decision Models Week 7: Segmentation and Cluster Analysis What have we seen so far? DB Infrastructure Relational databases, data integrity and data queries Data Preparation Data cleaning and transformation CLV (Customer Expected NPV) RFM (Classification) Where are we going from now? Customer Segmentation Customers’ Profile Decision tree Customers’ Propensity to buy Cluster analysis Logistic regression Campaign Metrics and Testing Outline for Today Clustering: Clustering and Segmentation B2C and B2B Clustering theory Lab SPSS help function (“Show Me”) Demo with Car Sales.sav Demo with DMData.sav (Lab) Customers are not all the same Different responses to marketing efforts Different treatments Product usage, product attributes, communication, marketing channels Packages, prices, copy strategy, communication and sales channels Basic marketing rules about segmentation Consumer Segmentation Family life cycle (stage in life) Lifestyle (values) Product usage/loyalty Preferred communication channel Buying behaviour Data Sources for Segmentation Internal Transaction 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/ http://www.manifolddatamining.com/html/lifestyle/lifes tyle171.htm Pitney-Bowes (Mapinfo) http://www.utahbluemedia.com/pbbi/psyte/psyteCanad a.html B2B Segmentation Firm size (employees, sales) Industry (SIC, NAICS) Buying process Value in finished product Usage (Production/Maintenance) Order size and Frequency Expectations Clustering & Segmentation Segmentation Marketing process Clustering Classification process (Topology or Taxonomy) Clustering ≠ Segmentation A cluster is not a segment But a segment is a cluster Clustering Measuring distances (differences or dissimilarities between subjects) Measuring proximities (similarity between subjects) BI Modeling Techniques No Target (No dependent variable, unsupervised learning) • RFM • Cluster Analysis (Unsupervised learning) Target (Dependent variable, supervised learning) • Regression Analysis • Decision Trees • Neural Net Analysis Measuring distances (two dimensions, x and y) A B C 16 Measuring distances (two dimensions) dac2 = (dx2 + dy2) A B C dac2 = (di)2 dac = [(di)2]1/2 17 Measuring distances (two dimensions) D(b,a) A B D(a,c) D(b,c) C 18 Cluster Analysis Techniques Hierarchical Clustering Metric, small datasets 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 SPSS Hierarchical Clusters Dendogram SPSS Multidimensional Scaling (Euclidean Distance) 1 1 2 3 4 5 6 7 8 9 10 Atlanta .9575 Chicago .5090 Denver -.6416 Houston .2151 Los_Ange Miami 1.5101 New_York San_Fran Seattle -1.7875 Washingt 2 -.1905 .4541 .0337 -.7631 -1.6036 -.5197 -.7752 1.4284 .6914 -1.8925 -.1500 .7723 1.3051 .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” Two Video Demos http://spss.co.in/video.aspx?id=62 Car Sales http://www.youtube.com/watch?v= DpucueFsigA File not available, but similar to dmdata.sav. Good demo Two-Step Clustering Available Measurement Scale Direct Marketing (Simplistic) Analyse Classify (More Advanced) Continuous Euclidian Distance Nominal Log-Likelihood Repeat for stability Explore Viewer model Top line from Chapter 10-1 Customer Segmentation Two kinds of segmentation: learning segmentation divides your customers by some criteria and sends them all the same message. See which group responds best and use that info. Dynamic segmentation: send each group different offers and text to get a better response. An ideal segment: large, well defined, can be motivated, and measured. Justifies a person’s time. Segmentation requires insight, analytics, and anecdotes. Segmentation action plan involves a road map, budget, goals, and tests. Segments and status levels (gold, silver) are not the same. Response rates can be improved by segmentation and RFM. Nielsen PRIZM segment codes can be profitable in deciding whom to promote to. Top line from Chapter 10-2 Customer Segmentation Demographic data can be appended to any database that has postal addresses. Dynamic segmentation with direct mail is a winner. Segmentation does not work for e-mail promotions if you are mailing very frequently. There is not enough time to create dynamic content. The tradeoff: with e-mails the lift from segmentation is not as great as the lift from frequent e-mails. Result: many mass e-mail marketers do not use dynamic segmentation. Good e-mails with lots of links are more complicated to create than good print copy. There are LTV charts that show the lift from dynamic segmentation. In general, e-mail marketing staff members do not have the budget or the analytic capabilities to get the resources for database marketing.