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

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Presentation transcript:

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) Strong Light Local Import Blue Collar Office Worker Foodies Selective Maple Leaf Fans Occasional

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)

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 Target (Dependent variable, supervised learning) Regression Analysis Decision Trees Neural Net Analysis

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

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

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

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”