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

What have we seen so far? Data Architecture, CRISP and Preparation 1. What is Business intelligence and database marketing 2. Database infrastructure 3. Data preparation and transformation Customer Classification 4. Customer lifetime value 5. RFM 6. Customer Clustering

Where are we going from now? Reading week 7. Mid-Term Predictive Modeling 8. Customers’ Profiling/Decision tree 9. …Decision tree (CHAID/CRT) 10. Customers’ Propensity to buy 11. …Logistic regression 12. Campaign Metrics and Testing

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

Clusters and Segments (Chap 10) Differences between clusters and segments Learning segmentation Dynamic segmentation

Customers are not equal Different needs and preferences Different responses to marketing efforts Product usage, product attributes, communication, marketing channels Different marketing treatments Packages, prices, copy strategy, communication and sales channels Remember the basic marketing rules about segmentation (p. 223)

Status Levels and Segments

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

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) lookup lookup Generation5 (Mosaic) Manifold: tyle171.htm tyle171.htm Pitney-Bowes (Mapinfo) a.html 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 (Unsupervised learning) Target (Dependent variable, supervised learning) Regression Analysis Decision Trees Neural Net Analysis

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

18 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

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

Distances between US cities ATLCHIDENHOULAMIANYSFSEADC Atlanta Chicago Denver Houston Los_Angeles Miami New_York San_Francisco Seattle Washington_DC

Cluster Analysis Techniques Hierarchical Clustering Metric, small datasets

SPSS Hierarchical Clusters Dendogram

SPSS Multidimensional Scaling (Euclidean Distance) Atlanta Chicago Denver Houston Los_Angeles Miami New_York San_Francisco Seattle Washington

Euclidean distance mapping

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

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”