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```							                 MKT 700
Decision Models

Week 7:
Segmentation and Cluster Analysis
Seen Far
   DB Infrastructure
   Relational databases, data integrity and
SQL queries
   Data Preparation
   Data cleaning and transformation
 CLV (Customer Expected NPV)
 RFM (Classification)
Outline
   Another classification technique:
 Cluster Analysis
 Theory

 SPSS help function (“Show Me”)
 Demo with Car Sales.sav
 Demo with Student2 with Scores.sav
 Demo with DMData.sav (Lab)
Clustering / Segmentation
 Segmentation, not all customers are
created equals; no one-size-fit-all
 Basic marketing rules about
segmentation
Consumer Segmentation
 Family life cycle (stage)
 Lifestyle
 Usage/Loyalty
 Preferred communication
Geo-Segmentation
   Environics
   http://www.environicsanalytics.ca/
   Pitney-Bowes
a.html
   Manifold:
   http://www.manifolddatamining.com/html/lifestyle/lifes
tyle171.htm
   Generation5
   http://www.generation5.ca
B2B Segmentation
 Firm size (employees, sales)
 Industry
 Value in finished product
 Usage (Production/Maintenance)
 Order size and Frequency
 Expectations
BI Modeling Techniques
   No Target (Dependent Variable,
Unsupervised Learning)
• RFM
• Cluster Analysis (Unsupervised learning)
   Target (Dependant Variable,
Supervised Learning)
• Regression Analysis
• Decision Trees
• Neural Net Analysis
Measuring distances
(two dimensions)

A
B

C

10
Measuring distances
(two dimensions)
dac2 = (dx2 + dy2)
A
B

C

dac2 = (di)2
dac = [(di)2]1/2
11
Measuring distances
(two dimensions)

D(b,a)
A
B                         D(a,c)
D(b,c)

C

12
Cluster Analysis Techniques
   Hierarchical Clustering
   Metric, small datasets
Cluster Analysis Techniques
   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
 File: dmdata.sav

   SPSS, Base Statistics, Chapter 24
 Files: Telco.sav and Car Sales.sav
 Help: “Show me”

   http://spss.co.in/video.aspx?id=62
 Car Sales

 File not available, but good demo
Two-Step Clustering
   Available
   Direct Marketing (Simplistic)
   Analyse  Classify (More Advanced)
   Measurement Scale
   Continuous  Euclidian Distance
   Nominal  Log-Likelihood
   Results will be different
   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

   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.

```
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