a simple tutorial on conjoint and cluster analysis

Marketing ResearchRags Srinivasan

Customer Segmentation and Market Share Estimation With Conjoint Analysis

Marketing is about segmentation and targeting

Rags Srinivasan IterativePath.com

Cannot treat the whole market as one

Nothing more strategic than segmentation

Value proposition is different for each segment

Target them differently – SKUs, messaging

What defines a segment?

Internally homogenous, externally heterogeneous

Is your segmentation valid?

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Not too small, Not too large

Meaningful, relevant and intuitively identified by constituent variables

Conjoint analysis helps you with the clustering

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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

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Price: $2499Screen: 50”Display: LCD

Price: $799Screen: 42”Display: Plasma

Price: $1999Screen: 42”Display: LCD

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

Conjoint analysis helps identify clusters

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Brand conscious

Price Sensitive

Screen size

Display type

… and relative importance of attributes

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

But you cannot ask customers about every combination

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Use commercial software to generate a manageable set of profiles

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 levels: $275, $250

Extras for Baggage, Pillows and Soft-drinks

Created 8 Profiles For Measuring Customer Utility

Brand: 3 levelsPrice: 2 levelsBaggage Fees: 2 levelsPillow Fees: 2 levelsDrink Fee: 2 levels

SoftwareA manageable set of 8 profiles that stand-in for all variable combinations

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 0AA is implicitly defined when both JetBlue and Delta are 0

Price$275 = 1 means price is $275 , if it is 0 the price is $250So 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

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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

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If we did not cluster

U = 8.36 + 0.88 * JetBlue – 0.06 * Delta – 1.9 * Price$275 – 2.41 * BaggageFee$20 – 0.83 * PillowFee$4 – 0.79 * DrinkFee$2

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

Cluster 2 U = 8.6 + 0.4 * JetBlue + 0.17 * Delta – 1.24 * Price$275 – 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

Feb 11, 2009

Compute market share from the utility values of the brands

Utilityof Product

i

Market Share

of Product i

321 UUU

UMS i

i

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