MKT 700Business Intelligence and

Decision Models

Week 6:Segmentation and Cluster Analysis

What have we seen so far?

Data Architecture, CRISP and Preparation1.What is Business intelligence and database marketing2.Database infrastructure3.Data preparation and transformationCustomer Classification4.Customer lifetime value 5.RFM6.Customer Clustering

Where are we going from now?

Reading week 7.Mid-TermPredictive Modeling8. Customers’ Profiling/Decision tree9.…Decision tree (CHAID/CRT)10. Customers’ Propensity to buy11.…Logistic regression12.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

Gold

Silver

Bronze

BusinessCustomers

AffluentRetired

YoungSingles

FamiliesWith Kids

BargainShoppers

OccasionalBuyers

Status LevelsMarketing Segments

Customer Marketing Staff

Gold

Silver

Bronze

BusinessCustomers

AffluentRetired

YoungSingles

FamiliesWith Kids

BargainShoppers

OccasionalBuyers

Status LevelsMarketing Segments

Customer Marketing Staff

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)

http://www.environicsanalytics.ca/prizm-c2-cluster-lookup Generation5 (Mosaic)

• http://www.generation5.ca

Manifold: http://www.manifolddatamining.com/html/lifestyle/

lifestyle171.htmPitney-Bowes (Mapinfo)

http://www.utahbluemedia.com/pbbi/psyte/psyteCanada.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

17

Measuring distances(two dimensions, x and y)

AB

C

18

Measuring distances(two dimensions)

A

C

dac2 = (dx

2 + dy2)

dac2 = (di)2

dac = [(di)2]1/2

B

19

Measuring distances(two dimensions)

AB

C

D(b,a)

D(a,c)

D(b,c)

Distances between US citiesATL CHI DEN HOU LA MIA NY SF SEA DC

Atlanta 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

Houston 701 940 879 0 1374 968 1420 1645 1891 1220

Los_Angeles 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

San_Francisco 2139 1858 949 1645 347 2594 2571 0 678 2442Seattle 2182 1737 1021 1891 959 2734 2408 678 0 2329Washington_DC 543 597 1494 1220 2300 923 205 2442 2329 0

Cluster Analysis Techniques Hierarchical Clustering

Metric, small datasets

SPSS Hierarchical Clusters Dendogram

SPSS Multidimensional Scaling (Euclidean Distance)

1 2 1. Atlanta .9575 -.19052. Chicago .5090 .45413. Denver -.6416 .03374. Houston .2151 -.76315. Los_Angeles -1.6036 -.51976. Miami 1.5101 -.77527. New_York 1.4284 .69148. San_Francisco -1.8925 -.15009. Seattle -1.7875 .772310. Washington 1.3051 .4469

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”