Transcript Car Sales.sav

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

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Decision tree
Customers’ Propensity to buy

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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
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Different responses to marketing efforts


Different treatments

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Product usage, product attributes,
communication, marketing channels
Packages, prices, copy strategy,
communication and sales channels
Basic marketing rules about segmentation
Consumer Segmentation


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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)


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Lists
Census
Taxfiler
Geocoding
Geo-Segmentation in CDA
Birds of a feather f___k together…
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Environics (Prizm)
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Generation5 (Mosaic)

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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)
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Measuring proximities (similarity
between subjects)
BI Modeling Techniques

No Target (No dependent variable,
unsupervised learning)
• RFM
• Cluster Analysis (Unsupervised learning)
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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
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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
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Hierarchical Clustering
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K-mean Clustering
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Metric variables, small datasets
Metric, large datasets
Two-Step Clustering
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Metric/non-metric, large datasets,
optimal clustering
Cluster Analysis Techniques
See Chapter 23, SPSS Base Statistics for description of methods
Two-Step Cluster Tutorials
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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
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Available
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Measurement Scale
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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.
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Segmentation requires insight, analytics, and anecdotes. Segmentation
action plan involves a road map, budget, goals, and tests.
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Segments and status levels (gold, silver) are not the same.
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Response rates can be improved by segmentation and RFM.
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Nielsen PRIZM segment codes can be profitable in deciding whom to
promote to.
Top line from Chapter 10-2
Customer Segmentation
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Demographic data can be appended to any database that has postal
addresses.
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Dynamic segmentation with direct mail is a winner.
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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.
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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.