SlideShare a Scribd company logo
1 of 33
Class Outline

• Conjoint Demonstration
• Conjoint Analysis using Excel
• Needs-Based Market Segmentation
Conjoint Demonstration

http://www.dobney.com/Conjoint/CnjtDemo.htm
More examples
• 1. Rank-based conjoint
– http://www.sawtoothsoftware.com/products/ssiweb/ssiwebsamples

• 2. ACA Choice Based conjoint
– http://www.sawtoothsoftware.com/products/ssiweb/ssiwebsamples

• 3. Pair-wise “choices”
– http://www.dobney.com/Conjoint/CnjtDemo.htm

Conjoint Demos
Conjoint Analysis using Excel
Group Exercise – Rating Based Conjoint
• 10=Most Preferred; 1=Least Preferred
Brand

Memory

Price

Rating

Apple

20

150

10

Apple

10

150

8

Apple

20

200

6

Samsung

20

150

7

Apple

10

200

4

Samsung

10

150

5

Samsung

20

200

3

Samsung

10

200

1
Group Exercise #1 - WTP
• $50 = ( 4 ) units
• Apple > Samsung

( 3 ) units = $( 37.5 )
• 20GB > 10GB

( 2 ) units = $( 25 )
• Most important attribute: ( Price )
• Least important attribute: ( Memory )
Group Exercise #2 - WTP
• 10=Most Preferred; 1=Least Preferred
Brand

Camera

Price

Rating

Samsung

Yes

150

10

Samsung

Yes

200

6

Samsung

No

150

7

Samsung

No

200

3

LG

Yes

150

8

LG

Yes

200

4

LG

No

150

5

LG

No

200

1
Group Exercise #2 - Question
• $50 = (
• Brand: (

) Rating
)>(

)

( ) Util. = $( )
• Camera: (

)> (

)

( ) Util. = $(
• Most important attribute: (
• Least important attribute: (

)
)
)
Group Exercise #2 - Solution
• $50 = ( 4
) Rating
• Brand: (Samsung) > (LG)
( 2 ) Util. = $( 25 )
• Camera: ( Yes )> ( No )
( 3 ) Util. = $(37.5)
• Most important attribute: (Price)
• Least important attribute: (Brand)
Worth of
Smartphone

Brand
Memory
Price

Other factors
Respondent1’s Rating
10
8
6
7
4
5
3
1

Dependent
Variable

Brand
Apple
Apple
Apple
Samsung
Apple
Samsung
Samsung
Samsung

Memory
20
10
20
20
10
10
20
10

Discrete
Independent
Variables

Price
150
150
200
150
200
150
200
200
Brand
DumApple Memory
Apple
1
20
Apple
1
10
Apple
1
20
Samsung
0
20
Apple
1
10
Samsung
0
10
Samsung
0
20
Samsung
0
10

DumM20
1
0
1
1
0
0
1
0

• DumApple = 1, Brand=Apple
= 0, otherwise
• DumM20 = 1, Memory = 20
= 0, otherwise
• DumP150 = 1, Price = 150
= 0, otherwise

Price
150
150
200
150
200
150
200
200

DumP150
1
1
0
1
0
1
0
0

Baseline category: Samsung

Baseline category: 10 GB
Baseline category: $200
Coeff Standard Error t Stat
Intercept
1
1.677E-16 5.964E+15
DumApple
3
1.677E-16 1.789E+16
DumM20
2
1.677E-16 1.193E+16
DumP150
4
1.677E-16 2.385E+16

P-value L95% U95%
4.744E-63
1
1
5.857E-65
3
3
2.965E-64
2
2
1.853E-65
4
4

Rating = 1 + 3 * DumApple + 2 * DumM20 + 4 * DumP150
Respondent1’s Rating
10
8
6
7
4
5
3
1

Brand
Apple
Apple
Apple
Samsung
Apple
Samsung
Samsung
Samsung

Memory
20
10
20
20
10
10
20
10

Price
150
150
200
150
200
150
200
200
Estimating Regression Coefficients Using
“linest” function

1. Select 4 cells
Estimating Regression Coefficients Using
“linest” function

2. =transpose((linest(B3:B10,C3:E10,1,0))
Dependent Independent
Variable
Variable

3. Ctrl + Shift + Enter (array formula)
Estimating Regression Coefficients Using
“linest” function

DumP150
DumM20
DumApple
Intercept
Group Exercise #3 - Estimation
• Download the excel file: “Conjoint1.xlsx”
• Q1. Based on Brand, Memory, Price descriptions,
finish dummy coding on “Sheet1.”
• Q2. On “Sheet2”, estimate partworths of
respondent 1 and respondent 2.
• Q3. Make partworth tables for respondent 1 and 2.

• Q4. Describe how these two individuals are
different in their preference.
Group Exercise #3 - Solution
• Q1. Refer to the coding in “Sheet2”

• Q2. Partworth estimation
Variable

Respondent 1

Variable

Respondent 1

DumP150

3.75

DumP150

2.00

DumM20

0.75

DumM20

2.00

DumApple

3.25

DumApple

2.00

Intercept

0.00

Intercept

0.00
Group Exercise #3 - Solution
• Q3. Partworth tables
Respondent 1)
Brand

Memory

Price

Apple

3.25

10 GB

0.0

$200

0.0

Samsung

0.0

20 GB

0.75

$150

3.75

Respondent 2)
Brand

Memory

Price

Apple

10 GB

0.0

$200

0.0

Samsung

•

2.00
0.0

20 GB

2.00

$150

2.00

Q4)
• Respondent 1: Price-sensitive, brand-loyal, does not value memory
• Respondent 2: equally values three attributes; Less brand loyal than
respondent 1, Values memory more than respondent 1
Group Exercise #4: Partworth Table Interpretation
• From regression analysis of rating-based conjoint, we get a “partworth” for
each level of each attribute:
User interface

Brand

Price

Touch screen

9.2

Apple

59.5

CAD199

40.1

Keyboard

0.0

Blackberry

20.5

CAD299

30.1

Samsung

30.5

CAD399

20.1

Motorola

0.0

CAD499

0.0

• Q1) Calculate utility of the following profile.
User interface
Profile A

Brand

Price

Utility

Touch screen

Blackberry

CAD299

?
Group Exercise #4: Partworth Table Interpretation
• From regression analysis of rating-based conjoint, we get a “partworth” for
each level of each attribute:
User interface

Brand

Price

Touch screen

9.2

Apple

59.5

CAD199

40.1

Keyboard

0.0

Blackberry

20.5

CAD299

30.1

Samsung

30.5

CAD399

20.1

Motorola

0.0

CAD499

0.0

• Q1) Calculate utility of the following profile.
User interface
Profile A

Brand

Price

Utility

Touch screen

Blackberry

CAD299

?
Group Exercise #4 – Q1 Solution
•

“Utility” is a measure of preference. Higher the utility, greater the likelihood of
purchase.

•

Given a product “profile” described in terms of the chosen attributes and attribute
levels, we can compute its utility simply by adding up the partworths for the
various elements of the profile

•

Solution
User interface
Profile A

Brand

Price

Utility

Touch screen

Blackberry

CAD299

?

– Utility of Profile A = 9.2 + 20.5 + 30.1 = 59.8 utils
Group Exercise #4: Q2 Choice Prediction
• From regression analysis of rating-based conjoint, we get a “partworth” for
each level of each attribute:
User interface

Brand

Price

Touch screen

Apple

59.5

CAD199

40.1

Keyboard

0.0

Blackberry

20.5

CAD299

30.1

Samsung

30.5

CAD399

20.1

Motorola
•

9.2

0.0

CAD499

0.0

Q2) Suppose you are given the following three profiles. Given the three
alternatives, which product is the consumer most likely to choose?
User interface

Brand

Price

Profile C

Touch screen

Blackberry

CAD299

Profile D

Keyboard

Apple

CAD399

Profile E

Touch screen

Samsung

CAD199

Utility
Group Exercise #4: Q2 Solution
•

Suppose you are given the following three profiles. Given the three alternatives,
which product is the consumer most likely to choose?
User interface

Price

Utility

Profile C

Touch screen

Blackberry

CAD299

59.8

Profile D

Keyboard

Apple

CAD399

79.6

Profile E

•

Brand

Touch screen

Samsung

CAD199

79.8

Profile E has the highest utility and so is the most likely to be chosen. Profile D is
the next most likely and Profile C is the least likely.
Group Exercise #4: Q3 Willingness-to-pay
• From regression analysis of rating-based conjoint, we get a “partworth” for
each level of each attribute:
User interface

Brand

Price

Touch screen

9.2

Apple

59.5

CAD199

40.1

Keyboard

0.0

Blackberry

20.5

CAD299

30.1

Samsung

30.5

CAD399

20.1

Motorola

0.0

CAD499

0.0

•

The WTP for a certain attribute value change (within a certain attribute) is defined
as the maximum dollar amount that the customer will pay for the change (provided
the change represents an improvement).

•

Q3) what is the WTP for the change from Brand=Samsung to Brand=Apple?
Group Exercise #4: Q3 Solution
•

The change represents an incremental value of 59.5-30.5=29.0 utils
WTP = 29 x Dollar value of 1 util

•

The dollar value per util can be inferred by looking at the extremes of the partworths for the price
attribute:
(40.1 – 0.0) utils = $499 - $199
1 util = $300/40.1
= $7.481297

•

Putting it all together
WTP = 29 x Dollar value of 1 util
= 29 x $7.481297
= $216.9576

•

If the profile with the more preferred attribute level is priced exactly WTP amount dollars higher,
then the customer is exactly indifferent about the change in that attribute level.
Group Exercise #4: Q4 Assessing Attribute Importance
• From regression analysis of rating-based conjoint, we get a “partworth” for
each level of each attribute:
User interface

Brand

Price

Touch screen

9.2

Apple

59.5

CAD199

40.1

Keyboard

0.0

Blackberry

20.5

CAD299

30.1

Samsung

30.5

CAD399

20.1

Motorola

0.0

CAD499

0.0

• Q4) Calculate relative importance of each attribute in %.
Group Exercise #4: Q4 Solution

•

In conjoint analysis, the “importance” of each attribute is computed via the
following 2 steps.

•

Step 1: For each attribute, look at the partworths for all the attribute levels within
that attribute and compute the difference between the largest and the smallest.
This difference is the “partworth range” for that attribute.

•

In our example, the partworth ranges are:
Attribute

Partworth Range

User interface

9.2 – 0.0 = 9.2

Brand

59.5 – 0.0 = 59.5

Price

40.1 – 0.0 = 40.1
Group Exercise #4: Q4 Solution

•

Step 2: The importance (weights) are just partworth ranges normalized to add up to
1. To normalize, compute the sum of the partworth ranges. Divide each partworth
ranges by the sum.

•

In our example, the sum of the partworth ranges is: 9.2 + 59.5 + 40.1 = 108.8.

•

So the importance for each attribute is:
Attribute

Importance

User interface

9.2/108.8 =0.0846

Brand

59.5/108.8 =0.5469

Price

40.1/108.8 =0.3685
Segmentation Using Conjoint Analysis
Segmentation Using Conjoint Analysis
• A market segment is a sub-set of a market
made up of people with one or more
characteristics that cause them to demand
similar product or services
• Why?
Consumer A
• $50 = 4 Units
 $12.5 = 1 Unit
• Apple > Samsung
4 Units = $50
• 20GB > 10GB
1 Units = $12.5

Consumer
Consumer E F
Consumer
Consumer C D
Consumer B = 2 Units
• $502 Units
• $502 Units
• $502 Units
= =
• $50 =Units
• $50 = 2  $251 Unit
$251 Unit Unit
= =1
$251 Unit
= 1 Unit
$25 Samsung > Applg
= =
 $25 Samsung > Apple
• •
• SamsungApple
• SamsungApple
> > Applg
• Samsung >
0.5 Units$12.0
0.5 Units$12.0
= = $12.2
0.5 Units$12.5
0.5 Units$12.510GB
= =
• 20GB10GB
0.5 Units = > >
• 20GB10GB
• 20GB10GB
• 20GB10GB
> >
• 20GB >
4 Units$100
4 Units$100$100
= =
4 Units$100
4 Units$100
= =
4 Units =
$ Value of Apple

Segment 1

Segment 2

$ Value of Memory

More Related Content

What's hot

Ch 2 Marketing research
Ch 2 Marketing researchCh 2 Marketing research
Ch 2 Marketing research
Ritesh Kumar
 
Customer value and Satisfaction
Customer value and SatisfactionCustomer value and Satisfaction
Customer value and Satisfaction
Kiran Prasad Naik
 
Market Research Guide - What Can Market Research Do
Market Research Guide - What Can Market Research DoMarket Research Guide - What Can Market Research Do
Market Research Guide - What Can Market Research Do
Ilya Bilbao
 
Chp 15 Time and Territory
Chp 15 Time and TerritoryChp 15 Time and Territory
Chp 15 Time and Territory
swhitman1
 

What's hot (20)

Philip Kotler Chapter 1
Philip Kotler Chapter 1Philip Kotler Chapter 1
Philip Kotler Chapter 1
 
Retail Merchandising 1
Retail Merchandising 1Retail Merchandising 1
Retail Merchandising 1
 
Ch 2 Marketing research
Ch 2 Marketing researchCh 2 Marketing research
Ch 2 Marketing research
 
Market Segmentation Targeting And Positioning
Market Segmentation Targeting And PositioningMarket Segmentation Targeting And Positioning
Market Segmentation Targeting And Positioning
 
Conjoint analysis
Conjoint analysisConjoint analysis
Conjoint analysis
 
Product
ProductProduct
Product
 
BRAND POSITIONING & VALUES
 BRAND POSITIONING & VALUES BRAND POSITIONING & VALUES
BRAND POSITIONING & VALUES
 
Introduction to marketing management
Introduction to marketing managementIntroduction to marketing management
Introduction to marketing management
 
Channel Management
Channel ManagementChannel Management
Channel Management
 
Identifying market segments and targets
Identifying market segments and targetsIdentifying market segments and targets
Identifying market segments and targets
 
Category Management
Category Management Category Management
Category Management
 
Evaluation and Control of Sales Performance
Evaluation and Control of Sales PerformanceEvaluation and Control of Sales Performance
Evaluation and Control of Sales Performance
 
Customer value and Satisfaction
Customer value and SatisfactionCustomer value and Satisfaction
Customer value and Satisfaction
 
How to Run Discrete Choice Conjoint Analysis
How to Run Discrete Choice Conjoint AnalysisHow to Run Discrete Choice Conjoint Analysis
How to Run Discrete Choice Conjoint Analysis
 
Segmentation, targeting and positioning
Segmentation, targeting and positioningSegmentation, targeting and positioning
Segmentation, targeting and positioning
 
Market Research Guide - What Can Market Research Do
Market Research Guide - What Can Market Research DoMarket Research Guide - What Can Market Research Do
Market Research Guide - What Can Market Research Do
 
SEGMENTATION, TARGETING and POSITIONING
SEGMENTATION, TARGETING and POSITIONINGSEGMENTATION, TARGETING and POSITIONING
SEGMENTATION, TARGETING and POSITIONING
 
Introduction to marketing research
Introduction to marketing researchIntroduction to marketing research
Introduction to marketing research
 
Philip Kotler Marketing
Philip Kotler MarketingPhilip Kotler Marketing
Philip Kotler Marketing
 
Chp 15 Time and Territory
Chp 15 Time and TerritoryChp 15 Time and Territory
Chp 15 Time and Territory
 

Similar to Conjoint Analysis - Part 2/3

Real-time Face Recognition & Detection Systems 1
Real-time Face Recognition & Detection Systems 1Real-time Face Recognition & Detection Systems 1
Real-time Face Recognition & Detection Systems 1
Suvadip Shome
 
Case Study Analysis 2The Cholesterol.xls records cholesterol lev.docx
Case Study Analysis 2The Cholesterol.xls records cholesterol lev.docxCase Study Analysis 2The Cholesterol.xls records cholesterol lev.docx
Case Study Analysis 2The Cholesterol.xls records cholesterol lev.docx
wendolynhalbert
 
Cis 355 ilab 4 of 6
Cis 355 ilab 4 of 6Cis 355 ilab 4 of 6
Cis 355 ilab 4 of 6
comp274
 
Track 2 session 4 db2 for z os optimizer- what’s new in db2 11 and exploiti...
Track 2 session 4   db2 for z os optimizer- what’s new in db2 11 and exploiti...Track 2 session 4   db2 for z os optimizer- what’s new in db2 11 and exploiti...
Track 2 session 4 db2 for z os optimizer- what’s new in db2 11 and exploiti...
IBMSystemzEvents
 

Similar to Conjoint Analysis - Part 2/3 (20)

Applied Machine Learning for Ranking Products in an Ecommerce Setting
Applied Machine Learning for Ranking Products in an Ecommerce SettingApplied Machine Learning for Ranking Products in an Ecommerce Setting
Applied Machine Learning for Ranking Products in an Ecommerce Setting
 
OR Ndejje Univ.pptx
OR Ndejje Univ.pptxOR Ndejje Univ.pptx
OR Ndejje Univ.pptx
 
OR Ndejje Univ (1).pptx
OR Ndejje Univ (1).pptxOR Ndejje Univ (1).pptx
OR Ndejje Univ (1).pptx
 
Conjoint Analysis.pptx
Conjoint Analysis.pptxConjoint Analysis.pptx
Conjoint Analysis.pptx
 
Hair Dryer
Hair DryerHair Dryer
Hair Dryer
 
Cis 115 Education Redefined-snaptutorial.com
Cis 115 Education Redefined-snaptutorial.comCis 115 Education Redefined-snaptutorial.com
Cis 115 Education Redefined-snaptutorial.com
 
Real-time Face Recognition & Detection Systems 1
Real-time Face Recognition & Detection Systems 1Real-time Face Recognition & Detection Systems 1
Real-time Face Recognition & Detection Systems 1
 
Case Study Analysis 2The Cholesterol.xls records cholesterol lev.docx
Case Study Analysis 2The Cholesterol.xls records cholesterol lev.docxCase Study Analysis 2The Cholesterol.xls records cholesterol lev.docx
Case Study Analysis 2The Cholesterol.xls records cholesterol lev.docx
 
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML ToolkitAugmenting Machine Learning with Databricks Labs AutoML Toolkit
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
 
COM 211 PRESENTATION.pptx
COM 211 PRESENTATION.pptxCOM 211 PRESENTATION.pptx
COM 211 PRESENTATION.pptx
 
Lo 09
Lo 09Lo 09
Lo 09
 
Week1 programming challenges
Week1 programming challengesWeek1 programming challenges
Week1 programming challenges
 
Software estimation models ii lec .05
Software estimation models ii lec .05Software estimation models ii lec .05
Software estimation models ii lec .05
 
Unit Testing a Primer
Unit Testing a PrimerUnit Testing a Primer
Unit Testing a Primer
 
problem solving and design By ZAK
problem solving and design By ZAKproblem solving and design By ZAK
problem solving and design By ZAK
 
Cis 355 ilab 4 of 6
Cis 355 ilab 4 of 6Cis 355 ilab 4 of 6
Cis 355 ilab 4 of 6
 
IBM Cognos 10 Framework Manager Metadata Modeling: Tips and Tricks
IBM Cognos 10 Framework Manager Metadata Modeling: Tips and TricksIBM Cognos 10 Framework Manager Metadata Modeling: Tips and Tricks
IBM Cognos 10 Framework Manager Metadata Modeling: Tips and Tricks
 
Track 2 session 4 db2 for z os optimizer- what’s new in db2 11 and exploiti...
Track 2 session 4   db2 for z os optimizer- what’s new in db2 11 and exploiti...Track 2 session 4   db2 for z os optimizer- what’s new in db2 11 and exploiti...
Track 2 session 4 db2 for z os optimizer- what’s new in db2 11 and exploiti...
 
Automated product categorization
Automated product categorizationAutomated product categorization
Automated product categorization
 
Automated product categorization
Automated product categorization   Automated product categorization
Automated product categorization
 

More from Minha Hwang

More from Minha Hwang (12)

Marketing Experiment - Part II: Analysis
Marketing Experiment - Part II: Analysis Marketing Experiment - Part II: Analysis
Marketing Experiment - Part II: Analysis
 
Marketing Experimentation - Part I
Marketing Experimentation - Part IMarketing Experimentation - Part I
Marketing Experimentation - Part I
 
Introduction to Recommendation System
Introduction to Recommendation SystemIntroduction to Recommendation System
Introduction to Recommendation System
 
Promotion Analytics - Module 2: Model and Estimation
Promotion Analytics - Module 2: Model and EstimationPromotion Analytics - Module 2: Model and Estimation
Promotion Analytics - Module 2: Model and Estimation
 
Promotion Analytics in Consumer Electronics - Module 1: Data
Promotion Analytics in Consumer Electronics - Module 1: DataPromotion Analytics in Consumer Electronics - Module 1: Data
Promotion Analytics in Consumer Electronics - Module 1: Data
 
Dummy Variable Regression Analysis
Dummy Variable Regression AnalysisDummy Variable Regression Analysis
Dummy Variable Regression Analysis
 
Multiple Regression Analysis
Multiple Regression AnalysisMultiple Regression Analysis
Multiple Regression Analysis
 
Introduction to Regression Analysis
Introduction to Regression AnalysisIntroduction to Regression Analysis
Introduction to Regression Analysis
 
Introduction to Text Mining
Introduction to Text MiningIntroduction to Text Mining
Introduction to Text Mining
 
Marketing Research - Perceptual Map
Marketing Research - Perceptual MapMarketing Research - Perceptual Map
Marketing Research - Perceptual Map
 
Channel capabilities, product characteristics, and impacts of mobile channel ...
Channel capabilities, product characteristics, and impacts of mobile channel ...Channel capabilities, product characteristics, and impacts of mobile channel ...
Channel capabilities, product characteristics, and impacts of mobile channel ...
 
From Online to Mobile - Impact of Consumers' Online Purchase Behaviors on Mob...
From Online to Mobile - Impact of Consumers' Online Purchase Behaviors on Mob...From Online to Mobile - Impact of Consumers' Online Purchase Behaviors on Mob...
From Online to Mobile - Impact of Consumers' Online Purchase Behaviors on Mob...
 

Recently uploaded

4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN
4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN
4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN
Cara Menggugurkan Kandungan 087776558899
 
FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756
dollysharma2066
 

Recently uploaded (20)

Busty Desi⚡Call Girls in Sector 49 Noida Escorts >༒8448380779 Escort Service
Busty Desi⚡Call Girls in Sector 49 Noida Escorts >༒8448380779 Escort ServiceBusty Desi⚡Call Girls in Sector 49 Noida Escorts >༒8448380779 Escort Service
Busty Desi⚡Call Girls in Sector 49 Noida Escorts >༒8448380779 Escort Service
 
What is Google Search Console and What is it provide?
What is Google Search Console and What is it provide?What is Google Search Console and What is it provide?
What is Google Search Console and What is it provide?
 
Best 5 Graphics Designing Course In Chandigarh
Best 5 Graphics Designing Course In ChandigarhBest 5 Graphics Designing Course In Chandigarh
Best 5 Graphics Designing Course In Chandigarh
 
TAM_AdEx-Cross_Media_Report-Banking_Finance_Investment_(BFSI)_2023.pdf
TAM_AdEx-Cross_Media_Report-Banking_Finance_Investment_(BFSI)_2023.pdfTAM_AdEx-Cross_Media_Report-Banking_Finance_Investment_(BFSI)_2023.pdf
TAM_AdEx-Cross_Media_Report-Banking_Finance_Investment_(BFSI)_2023.pdf
 
20180928 Hofstede Insights Conference Milan The Power of Culture Led Brands.pptx
20180928 Hofstede Insights Conference Milan The Power of Culture Led Brands.pptx20180928 Hofstede Insights Conference Milan The Power of Culture Led Brands.pptx
20180928 Hofstede Insights Conference Milan The Power of Culture Led Brands.pptx
 
Unlocking the Mystery of the Voynich Manuscript
Unlocking the Mystery of the Voynich ManuscriptUnlocking the Mystery of the Voynich Manuscript
Unlocking the Mystery of the Voynich Manuscript
 
Digital-Marketing-Into-by-Zoraiz-Ahmad.pptx
Digital-Marketing-Into-by-Zoraiz-Ahmad.pptxDigital-Marketing-Into-by-Zoraiz-Ahmad.pptx
Digital-Marketing-Into-by-Zoraiz-Ahmad.pptx
 
Major SEO Trends in 2024 - Banyanbrain Digital
Major SEO Trends in 2024 - Banyanbrain DigitalMajor SEO Trends in 2024 - Banyanbrain Digital
Major SEO Trends in 2024 - Banyanbrain Digital
 
Choosing the Right White Label SEO Services to Boost Your Agency's Growth.pdf
Choosing the Right White Label SEO Services to Boost Your Agency's Growth.pdfChoosing the Right White Label SEO Services to Boost Your Agency's Growth.pdf
Choosing the Right White Label SEO Services to Boost Your Agency's Growth.pdf
 
Elevating Your Digital Presence by Evitha.pdf
Elevating Your Digital Presence by Evitha.pdfElevating Your Digital Presence by Evitha.pdf
Elevating Your Digital Presence by Evitha.pdf
 
How consumers use technology and the impacts on their lives
How consumers use technology and the impacts on their livesHow consumers use technology and the impacts on their lives
How consumers use technology and the impacts on their lives
 
personal branding kit for music business
personal branding kit for music businesspersonal branding kit for music business
personal branding kit for music business
 
Enhancing Business Visibility PR Firms in San Francisco
Enhancing Business Visibility PR Firms in San FranciscoEnhancing Business Visibility PR Firms in San Francisco
Enhancing Business Visibility PR Firms in San Francisco
 
2024 Social Trends Report V4 from Later.com
2024 Social Trends Report V4 from Later.com2024 Social Trends Report V4 from Later.com
2024 Social Trends Report V4 from Later.com
 
The Science of Landing Page Messaging.pdf
The Science of Landing Page Messaging.pdfThe Science of Landing Page Messaging.pdf
The Science of Landing Page Messaging.pdf
 
Five Essential Tools for International SEO - Natalia Witczyk - SearchNorwich 15
Five Essential Tools for International SEO - Natalia Witczyk - SearchNorwich 15Five Essential Tools for International SEO - Natalia Witczyk - SearchNorwich 15
Five Essential Tools for International SEO - Natalia Witczyk - SearchNorwich 15
 
4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN
4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN
4 TRIK CARA MENGGUGURKAN JANIN ATAU ABORSI KANDUNGAN
 
The+State+of+Careers+In+Retention+Marketing-2.pdf
The+State+of+Careers+In+Retention+Marketing-2.pdfThe+State+of+Careers+In+Retention+Marketing-2.pdf
The+State+of+Careers+In+Retention+Marketing-2.pdf
 
FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu.Ka.Tilla Delhi Contact Us 8377877756
 
Social Media Marketing Portfolio - Maharsh Benday
Social Media Marketing Portfolio - Maharsh BendaySocial Media Marketing Portfolio - Maharsh Benday
Social Media Marketing Portfolio - Maharsh Benday
 

Conjoint Analysis - Part 2/3

  • 1. Class Outline • Conjoint Demonstration • Conjoint Analysis using Excel • Needs-Based Market Segmentation
  • 3. More examples • 1. Rank-based conjoint – http://www.sawtoothsoftware.com/products/ssiweb/ssiwebsamples • 2. ACA Choice Based conjoint – http://www.sawtoothsoftware.com/products/ssiweb/ssiwebsamples • 3. Pair-wise “choices” – http://www.dobney.com/Conjoint/CnjtDemo.htm Conjoint Demos
  • 5. Group Exercise – Rating Based Conjoint • 10=Most Preferred; 1=Least Preferred Brand Memory Price Rating Apple 20 150 10 Apple 10 150 8 Apple 20 200 6 Samsung 20 150 7 Apple 10 200 4 Samsung 10 150 5 Samsung 20 200 3 Samsung 10 200 1
  • 6. Group Exercise #1 - WTP • $50 = ( 4 ) units • Apple > Samsung ( 3 ) units = $( 37.5 ) • 20GB > 10GB ( 2 ) units = $( 25 ) • Most important attribute: ( Price ) • Least important attribute: ( Memory )
  • 7. Group Exercise #2 - WTP • 10=Most Preferred; 1=Least Preferred Brand Camera Price Rating Samsung Yes 150 10 Samsung Yes 200 6 Samsung No 150 7 Samsung No 200 3 LG Yes 150 8 LG Yes 200 4 LG No 150 5 LG No 200 1
  • 8. Group Exercise #2 - Question • $50 = ( • Brand: ( ) Rating )>( ) ( ) Util. = $( ) • Camera: ( )> ( ) ( ) Util. = $( • Most important attribute: ( • Least important attribute: ( ) ) )
  • 9. Group Exercise #2 - Solution • $50 = ( 4 ) Rating • Brand: (Samsung) > (LG) ( 2 ) Util. = $( 25 ) • Camera: ( Yes )> ( No ) ( 3 ) Util. = $(37.5) • Most important attribute: (Price) • Least important attribute: (Brand)
  • 12. Brand DumApple Memory Apple 1 20 Apple 1 10 Apple 1 20 Samsung 0 20 Apple 1 10 Samsung 0 10 Samsung 0 20 Samsung 0 10 DumM20 1 0 1 1 0 0 1 0 • DumApple = 1, Brand=Apple = 0, otherwise • DumM20 = 1, Memory = 20 = 0, otherwise • DumP150 = 1, Price = 150 = 0, otherwise Price 150 150 200 150 200 150 200 200 DumP150 1 1 0 1 0 1 0 0 Baseline category: Samsung Baseline category: 10 GB Baseline category: $200
  • 13. Coeff Standard Error t Stat Intercept 1 1.677E-16 5.964E+15 DumApple 3 1.677E-16 1.789E+16 DumM20 2 1.677E-16 1.193E+16 DumP150 4 1.677E-16 2.385E+16 P-value L95% U95% 4.744E-63 1 1 5.857E-65 3 3 2.965E-64 2 2 1.853E-65 4 4 Rating = 1 + 3 * DumApple + 2 * DumM20 + 4 * DumP150 Respondent1’s Rating 10 8 6 7 4 5 3 1 Brand Apple Apple Apple Samsung Apple Samsung Samsung Samsung Memory 20 10 20 20 10 10 20 10 Price 150 150 200 150 200 150 200 200
  • 14. Estimating Regression Coefficients Using “linest” function 1. Select 4 cells
  • 15. Estimating Regression Coefficients Using “linest” function 2. =transpose((linest(B3:B10,C3:E10,1,0)) Dependent Independent Variable Variable 3. Ctrl + Shift + Enter (array formula)
  • 16. Estimating Regression Coefficients Using “linest” function DumP150 DumM20 DumApple Intercept
  • 17. Group Exercise #3 - Estimation • Download the excel file: “Conjoint1.xlsx” • Q1. Based on Brand, Memory, Price descriptions, finish dummy coding on “Sheet1.” • Q2. On “Sheet2”, estimate partworths of respondent 1 and respondent 2. • Q3. Make partworth tables for respondent 1 and 2. • Q4. Describe how these two individuals are different in their preference.
  • 18. Group Exercise #3 - Solution • Q1. Refer to the coding in “Sheet2” • Q2. Partworth estimation Variable Respondent 1 Variable Respondent 1 DumP150 3.75 DumP150 2.00 DumM20 0.75 DumM20 2.00 DumApple 3.25 DumApple 2.00 Intercept 0.00 Intercept 0.00
  • 19. Group Exercise #3 - Solution • Q3. Partworth tables Respondent 1) Brand Memory Price Apple 3.25 10 GB 0.0 $200 0.0 Samsung 0.0 20 GB 0.75 $150 3.75 Respondent 2) Brand Memory Price Apple 10 GB 0.0 $200 0.0 Samsung • 2.00 0.0 20 GB 2.00 $150 2.00 Q4) • Respondent 1: Price-sensitive, brand-loyal, does not value memory • Respondent 2: equally values three attributes; Less brand loyal than respondent 1, Values memory more than respondent 1
  • 20. Group Exercise #4: Partworth Table Interpretation • From regression analysis of rating-based conjoint, we get a “partworth” for each level of each attribute: User interface Brand Price Touch screen 9.2 Apple 59.5 CAD199 40.1 Keyboard 0.0 Blackberry 20.5 CAD299 30.1 Samsung 30.5 CAD399 20.1 Motorola 0.0 CAD499 0.0 • Q1) Calculate utility of the following profile. User interface Profile A Brand Price Utility Touch screen Blackberry CAD299 ?
  • 21. Group Exercise #4: Partworth Table Interpretation • From regression analysis of rating-based conjoint, we get a “partworth” for each level of each attribute: User interface Brand Price Touch screen 9.2 Apple 59.5 CAD199 40.1 Keyboard 0.0 Blackberry 20.5 CAD299 30.1 Samsung 30.5 CAD399 20.1 Motorola 0.0 CAD499 0.0 • Q1) Calculate utility of the following profile. User interface Profile A Brand Price Utility Touch screen Blackberry CAD299 ?
  • 22. Group Exercise #4 – Q1 Solution • “Utility” is a measure of preference. Higher the utility, greater the likelihood of purchase. • Given a product “profile” described in terms of the chosen attributes and attribute levels, we can compute its utility simply by adding up the partworths for the various elements of the profile • Solution User interface Profile A Brand Price Utility Touch screen Blackberry CAD299 ? – Utility of Profile A = 9.2 + 20.5 + 30.1 = 59.8 utils
  • 23. Group Exercise #4: Q2 Choice Prediction • From regression analysis of rating-based conjoint, we get a “partworth” for each level of each attribute: User interface Brand Price Touch screen Apple 59.5 CAD199 40.1 Keyboard 0.0 Blackberry 20.5 CAD299 30.1 Samsung 30.5 CAD399 20.1 Motorola • 9.2 0.0 CAD499 0.0 Q2) Suppose you are given the following three profiles. Given the three alternatives, which product is the consumer most likely to choose? User interface Brand Price Profile C Touch screen Blackberry CAD299 Profile D Keyboard Apple CAD399 Profile E Touch screen Samsung CAD199 Utility
  • 24. Group Exercise #4: Q2 Solution • Suppose you are given the following three profiles. Given the three alternatives, which product is the consumer most likely to choose? User interface Price Utility Profile C Touch screen Blackberry CAD299 59.8 Profile D Keyboard Apple CAD399 79.6 Profile E • Brand Touch screen Samsung CAD199 79.8 Profile E has the highest utility and so is the most likely to be chosen. Profile D is the next most likely and Profile C is the least likely.
  • 25. Group Exercise #4: Q3 Willingness-to-pay • From regression analysis of rating-based conjoint, we get a “partworth” for each level of each attribute: User interface Brand Price Touch screen 9.2 Apple 59.5 CAD199 40.1 Keyboard 0.0 Blackberry 20.5 CAD299 30.1 Samsung 30.5 CAD399 20.1 Motorola 0.0 CAD499 0.0 • The WTP for a certain attribute value change (within a certain attribute) is defined as the maximum dollar amount that the customer will pay for the change (provided the change represents an improvement). • Q3) what is the WTP for the change from Brand=Samsung to Brand=Apple?
  • 26. Group Exercise #4: Q3 Solution • The change represents an incremental value of 59.5-30.5=29.0 utils WTP = 29 x Dollar value of 1 util • The dollar value per util can be inferred by looking at the extremes of the partworths for the price attribute: (40.1 – 0.0) utils = $499 - $199 1 util = $300/40.1 = $7.481297 • Putting it all together WTP = 29 x Dollar value of 1 util = 29 x $7.481297 = $216.9576 • If the profile with the more preferred attribute level is priced exactly WTP amount dollars higher, then the customer is exactly indifferent about the change in that attribute level.
  • 27. Group Exercise #4: Q4 Assessing Attribute Importance • From regression analysis of rating-based conjoint, we get a “partworth” for each level of each attribute: User interface Brand Price Touch screen 9.2 Apple 59.5 CAD199 40.1 Keyboard 0.0 Blackberry 20.5 CAD299 30.1 Samsung 30.5 CAD399 20.1 Motorola 0.0 CAD499 0.0 • Q4) Calculate relative importance of each attribute in %.
  • 28. Group Exercise #4: Q4 Solution • In conjoint analysis, the “importance” of each attribute is computed via the following 2 steps. • Step 1: For each attribute, look at the partworths for all the attribute levels within that attribute and compute the difference between the largest and the smallest. This difference is the “partworth range” for that attribute. • In our example, the partworth ranges are: Attribute Partworth Range User interface 9.2 – 0.0 = 9.2 Brand 59.5 – 0.0 = 59.5 Price 40.1 – 0.0 = 40.1
  • 29. Group Exercise #4: Q4 Solution • Step 2: The importance (weights) are just partworth ranges normalized to add up to 1. To normalize, compute the sum of the partworth ranges. Divide each partworth ranges by the sum. • In our example, the sum of the partworth ranges is: 9.2 + 59.5 + 40.1 = 108.8. • So the importance for each attribute is: Attribute Importance User interface 9.2/108.8 =0.0846 Brand 59.5/108.8 =0.5469 Price 40.1/108.8 =0.3685
  • 31. Segmentation Using Conjoint Analysis • A market segment is a sub-set of a market made up of people with one or more characteristics that cause them to demand similar product or services • Why?
  • 32. Consumer A • $50 = 4 Units  $12.5 = 1 Unit • Apple > Samsung 4 Units = $50 • 20GB > 10GB 1 Units = $12.5 Consumer Consumer E F Consumer Consumer C D Consumer B = 2 Units • $502 Units • $502 Units • $502 Units = = • $50 =Units • $50 = 2  $251 Unit $251 Unit Unit = =1 $251 Unit = 1 Unit $25 Samsung > Applg = =  $25 Samsung > Apple • • • SamsungApple • SamsungApple > > Applg • Samsung > 0.5 Units$12.0 0.5 Units$12.0 = = $12.2 0.5 Units$12.5 0.5 Units$12.510GB = = • 20GB10GB 0.5 Units = > > • 20GB10GB • 20GB10GB • 20GB10GB > > • 20GB > 4 Units$100 4 Units$100$100 = = 4 Units$100 4 Units$100 = = 4 Units =
  • 33. $ Value of Apple Segment 1 Segment 2 $ Value of Memory