A Simple Tutorial on Conjoint and Cluster Analysis

Marketing Research
Rags Srinivasan

Customer Segmentation and
Market Share Estimation With
Conjoint Analysis

Marketing is about segmentation and
targeting
Nothing more strategic than segmentation

Cannot treat the Value proposition is Target them
differently – SKUs,
whole market as different for each
one segment messaging

Rags Srinivasan IterativePath.com

What defines a segment?
Internally homogenous, externally
heterogeneous

Is your segmentation valid?

Meaningful, relevant and
Not too small, Not too
intuitively identified by
large
constituent variables


Conjoint analysis helps you with the
clustering

Premise: The whole is the sum of its parts.
We can infer the relative importance of
parts from the customer preference of the
whole.


For Example
Assign a value between 1 and 100 to these options. 100 means most
likeable and 1 means least likeable

Price: $2499 Price: $799 Price: $1999
Screen: 50” Screen: 42” Screen: 42”
Display: LCD Display: Plasma Display: LCD


Conjoint analysis helps identify clusters

Brand conscious

Price Sensitive

Screen size

Display type


… and relative importance of attributes

What is the utility value a customer assigns to each
attribute?


But you cannot ask customers about every
combination

Use commercial software to generate a manageable set of profiles

Conjoin
Manageabl
t
Attributes Levels e set of Survey
Softwar
profiles
e


Let Us Walk Through An Example: My Work
On Airline Unbundled Pricing
Questions: How much do airline customers value services like free-
baggage, free drinks etc? Are airlines better off increasing ticket price
instead of unbundling pricing?

SFO JFK

With Following Options …

3 Airlines 2 Price Extras for Baggage, Pillows and Soft-
levels: $275, drinks
$250

Created 8 Profiles For Measuring Customer
Utility

Brand: 3 levels A manageable set of 8
Price: 2 levels profiles that stand-in for
Software
Baggage Fees: 2 levels all variable
Pillow Fees: 2 levels combinations
Drink Fee: 2 levels

Survey customers to find their utility value
for each profile
Rate your likelihood of choosing the option on a scale of 1 – 10 ( 8 profiles)

Model: Utility = f(Brand, Price,Fees)
Write customer utility (their likelihood of picking the airline) as a linear
function of these variables

U = Constant + b1 * JetBlue + b2* Delta + b3* Price$275 +
b4* BaggageFee$20 + b5 * PillowFee$4 +b6 * DrinkFee$2

JetBlue and Delta are mutually exclusive – 1 or 0
AA is implicitly defined when both JetBlue and Delta are 0

Price$275 = 1 means price is $275 , if it is 0 the price is $250
So on and so forth

b1, b2, … are the regression coefficients that are the relative
utilities of attributes that we seek to find

Use SPSS to indentify clusters

This margin is too narrow to contain it. Stay tuned I will add a
Camtasia demo of using SPSS to do Cluster analysis and
Regression.


Run multiple regression for each cluster to
find the coffecients

U = 8.36 + 0.88 * JetBlue – 0.06 * Delta – 1.9 * Price$275 –
If we did not
2.41 * BaggageFee$20 – 0.83 * PillowFee$4 – 0.79 *
cluster
DrinkFee$2

U = 7.9 + 1.28 * JetBlue – 0.16 * Delta – 2.34 * Price$275 –
Cluster 1
3.14 * BaggageFee$20 – 0.92* PillowFee$4 – 0.87 *
DrinkFee$2

U = 8.6 + 0.4 * JetBlue + 0.17 * Delta – 1.24 * Price$275 –
Cluster 2
1.68 * BaggageFee$20 – 0.63* PillowFee$4 – 0.58 *
DrinkFee$2


You can see the difference between two
clusters
JetBlue, $250, Baggage Fee $20, Pillow Fee $4, Drink Fee $2

Cluster 1 Cluster 2
JetBlue 9.18 9
$250 0 0
Baggage Fee $20 -3.14 -1.68
Pillow Fee $4 -0.92 -0.63
Drink Fee $2 -0.87 -0.58
Total Utility 4.25 6.11

Compute market share from the
utility values of the brands

Market
Utility
Share
of Product i
of Product i


Ui
MS i    
U1 U 2 U 3

Feb 11, 2009

The net of this is

When you want to segment customers and target them with
multiple SKUs you need to do cluster analysis

Conjoint analysis gets you there and more

A Simple Tutorial on Conjoint and Cluster Analysis

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A Simple Tutorial on Conjoint and Cluster Analysis