2 MKT 315 Ce Liang Dr.M.GailVermillion 5/35/2020 Assumptions in conjoint analysis There are various assumptions in a conjoint analysis. The first assumption is that any product or service is a bundle of attributes. This means that depending on the product in question, it will be defined by several attributes, including its name, outlook, reliability among many other attributes (Rao, 2014). A product is also defined by other attributes such as aesthetics, physical and psychological. Delete the yellow. all you need to say is “products are a bundle of attributes” The other assumption is that products variance is based on the attribute levels. This means that products with highly regarded attributes will have higher prices and vice versa. This depends on the value that consumers attach to the product. This assumption has nothing to do with price. All it says is “If an attribute level changes the product Changes” That is all you needed to say. Since it appears that you do not understand I am going to give you an example, This is just so you can learn – do not include this in your REDO tool summary 3. Example: If a pair of glasses are made from the attributes Color, Brand and Material. If the glasses change from being black glasses to white glasses the product changes. Do you understand? Reply in the email you send me. The last assumption is that the preferences of consumers for the bundles of attributes differ. This means that the value attributed to a certain feature by one consumer may differ with the next consumer. Therefore, this can only be established by finding out the utility levels for each attribute. Delete the yellow Steps in a conjoint analysis To perform a conjoint analysis, several key steps have to be followed. These are explained below; I do not want you to give examples I want you to tell me the steps. This is where you should be writing the things I say over and over in the video. I try to say it in one to two sentences sometimes less. I want you to watch the video again and put it on slow motion or stop it. It there is ANYTHING you do not understand I want you to pause the tape and write down your question. You can text it to me or email me. The first step is the selection of attributes and levels that a product is composed of. The focus groups, and what else is used? in this case, may include all people. These may range from aesthetic, performance, physical and psychological aspects (Rao, 2014). For instance, a house may be defined by aspects such as space, location, purpose and type. This means that we can have a three bedroomed house, located in town, suitable for young families and is a bungalow. This description will be different for different houses. The levels should also be kept as specific as possible to avoid any contradictions. Delete yellow The second step is the selection of stimulus representation. This begs the question of the choices of the seller regarding the profile to b ...

1. 2
MKT 315
Ce Liang
Dr.M.GailVermillion
5/35/2020
Assumptions in conjoint analysis
There are various assumptions in a conjoint analysis. The first
assumption is that any product or service is a bundle of
attributes. This means that depending on the product in
question, it will be defined by several attributes, including its
name, outlook, reliability among many other attributes (Rao,
2014). A product is also defined by other attributes such as
aesthetics, physical and psychological. Delete the yellow. all
you need to say is “products are a bundle of attributes”
The other assumption is that products variance is based on the
attribute levels. This means that products with highly regarded
attributes will have higher prices and vice versa. This depends
on the value that consumers attach to the product. This
assumption has nothing to do with price. All it says is “If an
attribute level changes the product Changes” That is all you
needed to say. Since it appears that you do not understand I am
going to give you an example, This is just so you can learn – do
not include this in your REDO tool summary 3. Example: If a
pair of glasses are made from the attributes Color, Brand and
Material. If the glasses change from being black glasses to
white glasses the product changes. Do you understand? Reply in
the email you send me.
The last assumption is that the preferences of consumers for the
bundles of attributes differ. This means that the value attributed
to a certain feature by one consumer may differ with the next
consumer. Therefore, this can only be established by finding out
the utility levels for each attribute. Delete the yellow

2. Steps in a conjoint analysis
To perform a conjoint analysis, several key steps have to be
followed. These are explained below; I do not want you to give
examples I want you to tell me the steps. This is where you
should be writing the things I say over and over in the video. I
try to say it in one to two sentences sometimes less. I want you
to watch the video again and put it on slow motion or stop it. It
there is ANYTHING you do not understand I want you to pause
the tape and write down your question. You can text it to me or
email me.
The first step is the selection of attributes and levels that a
product is composed of. The focus groups, and what else is
used? in this case, may include all people. These may range
from aesthetic, performance, physical and psychological aspects
(Rao, 2014). For instance, a house may be defined by aspects
such as space, location, purpose and type. This means that we
can have a three bedroomed house, located in town, suitable for
young families and is a bungalow. This description will be
different for different houses. The levels should also be kept as
specific as possible to avoid any contradictions. Delete yellow
The second step is the selection of stimulus representation. This
begs the question of the choices of the seller regarding the
profile to be exhibited to consumers. This requires one to come
up with actual products, make use of texts, pictures, prototypes
and use of pictures and texts. Delete yellow I said several times
what we would be using and gave the reason. Write what I said
in the video here.
The third step involves designing the experiment. This involves
establishing how many possible profiles can be found and
whether the profilers should rate or rank each of the profiles.
Delete yellow This requires the use of orthogonal experimental
design.
The fourth step involves the regression analysis. This is
followed by an interpretation of the conjoint output. The
interpretation is made by use of a bar chat (Rao, 2014). Once
this is done, it can enable one to understand the customer value

3. structure better. It also enables the producer to use utility to
find the best product. Besides, it is also possible to find the
total attribute importance and an estimation of the relative
market share. Delete yellow. Tell me what the three things we
look at are on the Regression output and what they mean. One
sentence for each. I will give you the first one Adjusted R2
should be at .3 or more and shows how much data we captured.
Now you do Sig F and Coefficients.
I am a little concerned that you do not care if you pass this class
because you did not even bother to email and ask for help or try
to do what you know is 60% of the assignment. Even after I
know you said you had been in contact with your group. You
are encouraged to share data with your group. So do what you
can then send your work by email to group and say “hey guys
this is what I have done so far can you help or send me in right
direction”
This is the last time I will allow a REDO on anything in the
class. SO PLEASE ask for my help.
References
Rao, V. R. (2014). Applied conjoint analysis (p. 389). New
York: Springer. Do not use references it makes you try to write
more and leads you off track
cereal
rankingPROFILERankX1X2X3X4X5X6X7X81100000000Origin
al Data from Surveys put in format to run Regression Analysis

4. in Excel - go to next
sheet250001010136001010104301000110570101100064011000
01721000100188100100109910100100110000000026000101013
90010101044010001105801011000650110000173100010018210
01001097101001001100000000240001010139001010104201000
11058010110006701100001751000100183100100109610100100
14000000002800010101390010101045010001105601011000670
11000017110001001821001001093101001001100000000280001
01013300101010420100011055010110006901100001741000100
186100100109710100100
&"Helvetica Neue,Regular"&12&K000000&P
Regression OutputSUMMARY OUTPUTRegression
StatisticsMultiple R0.7402702209R Square0.548This sheet is
the output you get after running a regression analysis for
Conjoint Analysis data.Adjusted R
Square0.4475555556Standard Error1.9407902171AdjR2 shows
how much data you captured we want it to be .30 or
aboveObservations45Significance F tell me my results are not
due to luck if the number is below
.05ANOVAdfSSMSFSignificance FCoefficients are the
UTILITY numbers that will be rescaled to fall between 0-1 -
Regression8164.420.555.45575221240.0001549253 the utility
numbers represent how happy a person is with each attribute
level. Residual36135.63.7666666667The Intercept is included
with UTILITY numbers,Total44300Take note that the chart does
not include the levels that were dropped. I will add the dropped
attribute levels back as 0 and then rescale them with the rest of
the numbers - next sheetCoefficientsStandard Errort StatP-
valueLower 95%Upper 95%Lower 95.0%Upper
95.0%Intercept1.60.86794777111.84342889430.0735111575-
0.16027966773.3602796677-0.16027966773.3602796677X1-
0.46666666670.7086763875-0.65850460790.5144036435-
1.90392899680.9705956635-
1.90392899680.9705956635X20.46666666670.70867638750.65
850460790.5144036435-0.97059566351.9039289968-

5. 0.97059566351.9039289968X34.06666666670.70867638755.73
839729710.00000155462.62940433655.50392899682.62940433
655.5039289968X43.13333333330.70867638754.42138808140.
00008666031.69607100324.57059566351.69607100324.570595
6635X51.60.70867638752.25773008410.03012282410.1627376
6983.03726233020.16273766983.0372623302X61.20.70867638
751.69329756310.0990363362-0.23726233022.6372623302-
0.23726233022.6372623302X7-0.06666666670.7086763875-
0.09407208680.9255735344-1.50392899681.3705956635-
1.50392899681.3705956635X80.26666666670.70867638750.37
628834740.7089111697-1.17059566351.7039289968-
1.17059566351.7039289968
RescaleSTEP 1: Find the biggest number under coefficients
including Intercept-sometimes the biggest number is the
InterceptStep 2: Find the smallest numberStep 3: Calculate the
Range by subtracting the smallest number from the largest
number.Step 4: Rescale using the following formula -
=(Coeffient Number - Lowest Number)/RangeStep 5: Round off
to 2 digits and then transfer to the next
pageCoefficientsRESCALED Rounded
OffIntercept1.60.45695364240.46X1-
0.46666666670.00073583520X20.46666666670.20676968360.2
1X34.06666666671.00147167031X43.13333333330.7954378219
0.79X51.60.45695364240.46X61.20.36865342160.37X7-
0.06666666670.08903605590.09X80.26666666670.1626195732
0.16soggy00.10375275940.1Bad00.10375275940.1Salty00.1037
5275940.1Grandpa00.10375275940.1Biggest
Number4.06Smallest Number-0.47Range4.53
Final Graph DataX1CrunchyX2ChewyTextureDroppedSoggy
This shows the attributes I dropped from each category-soggy ,
Bad, Salty, Grandpa
X3GoodX4EdibleTasteDroppedBadX5HealthyX6SugaryNutritio
nDroppedSaltyX7ChildrenX8CollegeAgeDroppedGrandpause
yellow box to create a column graph - only left X1-X8 so you
could see where to put data from now on simply call by the
attribute levelFROM PREVIOUS

6. SHEETX1Crunchy0CoeffiecientsRounded
OffX2Chewy0.21Intercept0.46Soggy
0.1X10X20.21X3Good1X31X4Edible0.79X40.79Bad0.1X50.46
X60.37X5Healthy0.46X70.09X6Sugary0.37X80.16Salty0.1sogg
y0.1Bad0.1X7Children0.09Salty0.1X8College0.16Grandpa0.1Gr
andpa0.1Intercept0.46
Understanding Consumer Preferences with Conjoint Analysis
Overview of Today’s ClassUnderstanding conjoint analysis
The procedure for conjoint analysis
Interpreting conjoint output
Creating and using choice simulators
Running conjoint analysis using Excel
So, What Is Conjoint Analysis?Methodology used to decompose
an individual’s value system for a product from overall
judgment of the product
Decomposition of the value system allows researcher to
understand the value/utility of each product attribute at each
attribute level. That’s right!For each attributeFor each attribute
level
When in CA Used?Very useful to make feature and feature-level
trade-offs in new product design

7. Calculate market share
Determine market entry barriers
Simulate market activity
3 Assumptions of Conjoint AnalysisEvery product/service is a
“bundle” of attributese.g. Image, brand name, reliability, etc.
For this class we will be hired by a brand so you may not use
brand as an attribute!!Physical, psychological, and aesthetic
attributes
Products differ via varying levels of attributes providede.g.
Quiet; Subdued; $1,200
3 Assumptions of Conjoint AnalysisConsumer preferences for
these “bundles” differAnd hence, overall consumer preferences
for these bundles can be decomposed into basic building
blocksUtilities for each attribute and their levels
Procedure for Conjoint Analysis
Designing and Conducting the Experiment
Selecting attributes and levels that form
the product
Choosing stimulus representation and
Response People
Interpreting conjoint output
Data Collection &

8. Data Analysis via Multiple Regression
Step 1. Selecting Attributes and Levels That Form the Product –
Focus GroupsCan include all Peoples - physical, performance,
psychological, aesthetice.g. let’s assume 4 attributes of a Car
1. Image: Family, Sporty, Prestigious
2. Sound:Quiet, Subdued, Loud
3. People: 2 people, 4 people, 5 people
4. Service: Easy, Difficult, Impossible
Keep the levels specificAvoid words like “high, low, medium”
or “average” Avoid any words that are not actionable
Step 1. Selecting Attributes and Levels That Form the
ProductKey to selecting attributes: Use focus groups, managers’
inputs, and competitive analysesMost relevant - 3 to 7
attributesMatch number of levels - 3 to 4 levels each
In our Car example: How many versions/combinations are
possible? For our example? 3x3x3x3 = 81 possible profilesDo
consumers have to rank all versions?
Step 2. Choosing Stimulus RepresentationWhat are your choices
in exhibiting the “profiles” to customers?Create actual
productsUse prototypesUse picturesUse textUse pictures and
text
Step 2. Choosing Stimulus RepresentationFull profile all
attributes included in each profile

9. Profile 1
Impossible
Quiet
Sporty
4 people
Profile 2
Service: Easy
Sound: Subdued
Image: Sporty
People: 5 people
Step 2. Choosing Response PeopleChoosing customer response
Peopleranking profiles – less than 10 profilesrating profiles –
10 OR more than 10 profileschoice based
Pause….Remember decompositional technique?What are you
“decomposing”?
Step 2. Choosing Response PeopleGenerally, ranking and rating
data provide similar results - hence choose based onnumber of
profilespotential respondent fatigue

10. Step 3. Designing the ExperimentHow many profiles
possible?Multiplicative product of number of levels across all
attributesFor our example? 3x3x3x3 = 81 possible profiles
Should respondents rank/rate each profile?Tiring! Fatigue =
Source of error
Use orthogonal experimental design
Step 3. Orthogonal Experimental DesignsLimited number of
profilesHowever, limited enough such that RELIABLE
estimation of all utilities is possible
So, how many profiles?At least = # of utilities estimated # of
utilities estimated =
Sum across all attributes (# of levels for each attribute - 1)
Step 3. Orthogonal Experimental DesignsSo for our example…#
of levels for Image = 3# of levels for Sound = 3# of levels for
People = 3# of levels of Service = 3Hence, # of utilities
estimated = (3-1) + (3-1) + (3-1) + (3-1) = 8Hence, # of profiles
= 8 = FLOORWOW! Ranking/Rating a minimum of 8 carefully
selected profiles will enable us to RELIABLY estimate utilities
for 81 possible profilesEfficient & ReliableIf Orthogonal
Design book does not have design with 8 profiles go to the next
level
Step 3. The Design Part A The Code-sheetThink multiple
regression
Y = a + b1X1 + b2X2 + b3X3 + b4 X4 + b5X5 + b6X6 + b7X7

11. + b8X8ImageX1 = 0,1 (0 = not Prestigious, 1 = Prestigious)X2
= 0,1 (0 = not Sporty, 1 = Sporty)SoundX3 = 0,1 (0= not Quiet,
1 = Quiet)X4 = 0,1 (0=not Subdued, 1 = Subdued)PeopleX5 =
0,1 (0 = not 5 people, 1 = 5 people)X6 = 0,1 (0 = not 4 people,
1 = 4 people)ServiceX7 = 0,1 (0= not Easy, 1 = Easy)X8 = 0,1
(0=not Difficult, 1 = Difficult)
What about Family, Loud, 2 people, & Impossible?
Step 3. The Design – Part BRemember, for our example we need
at least 8 profiles
KEY: Each row above is a profile to be ranked/rated Put
profiles in words – there are 9 rows, hence 9 profiles above
PROFILERankX1X2X3X4X5X6X7X8100000000200010101300
10101040100011050101100060110000171000100181001001091
0100100

12. Step 3. Conducting the Experiment
Use Design Part A and Design Part B together
Profile 1 results from row 1 in Design Part B (ignore the label
row), and from the code-sheet that you created in Design Part A
Profile 2 results from combining row 2 in Design Part B and the
code-sheet in Design Part A
And so on….
Step 3. Combining Design Parts A & B
Profile 1
Image: Family
Sound: Loud
People: 2 people
Service: Impossible
Profile 2
Image: Family

13. Sound: Subdued
People: 4 people
Brand name: Difficult
Profile 3
Image: Family
Sound: Quiet
People: 5 people
Brand name: Easy
Profile 4
Image: Sporty
Sound: Loud
People: 4 people
Service: Easy
Profile 5
Image: Sporty
Sound: Subdued
People: 5 people
Service: Impossible
Profile 6
Image: Sporty
Sound: Quiet
People: 2 people
Service: Difficult
Profile 7
Image: Prestigious
Sound: Loud
People: 5 people
Service: Difficult
Profile 8
Image: Prestigious
Sound: Subdued
People: 2 people
Service: Easy
Profile 9
Image: Prestigious
Sound: Quiet

14. People: 4 people
Service: Impossible
Step 3. Presenting the ProfilesFew Rules:Make the profiles
uncluttered – not too wordy
Mention both the feature name and the feature level in each
profile
Put a rating or a ranking option below each profiles
Let the respondents clearly know what the scale or ranking
means
Watch for signs of confusion and fatigue – pre-test, pre-test
Step 3. Presenting the Profiles – I have template for you!!!
Step 4. Obtain Rankings or RatingsHigher the ranking/rating
means higher the number9 means most preferred1 means least
preferred
RankX1X2X3X4X5X6X7X810000000020001010150010101060
10001107010110004011000019100010013100100108101001001
00000000400010101500101010201000110601011000301100001
71000100191001001081010010020000000010001010140010101
06010001105010110008011000013100010017100100109101001
00

15. Step 4. The Regression AnalysisUse Excel for analysis, multiple
regression

16. Step 4. Re-scaling Utilities - Utilities are re-scaled to fit
between 0 and 1UtilityRescaledRescale Formula= U-L/RangeX3
= Quiet2.911(2.91-(-.58))/3.493X1= Prestigious2.830.98(2.83-(-
.58))/3.493X5= 5 people2.250.81X4= Subdued2.080.76X2 =
Sporty1.660.64X6= 4 people1.250.52Intercept10.45(1-(-
.58))/3.493X9= Family00.17(0-(-.58))/3.493X10=
Loud00.17X11= 2 people00.17X12= Impossible00.17X7= Easy-
0.410.05X8 = Difficult-0.580 Range=Highest-Lowest
U=Utility Number

17. Prestigious
Sporty
Family
Loud
Subdued
Quiet
2 people
4 people
5 people
Easy
Difficult
Impossible
Intercept
Chart1Category 1Category 1Category 1Category 2Category
2Category 2Category 3Category 3Category 3Category
4Category 4Category 40.45
Step 5: Interpreting Conjoint Output
series 1
series 2
series 3
0.98
0.64
0.17
0.17
0.76
1
0.17
0.52
0.81
0.05
0

18. 0.17
Sheet1series 1series 2series 3Category 10.980.640.17Category
20.170.761Category 30.170.520.81Category 40.0500.170.45
6 Outputs of Conjoint AnalysisOnce you’ve created a bar chart
using the rescaled attribute level utilities, you can
Get a deeper understanding of customer value structure
Find the best product based on total utility
Determine overall attribute importance
Estimate relative market share
Anticipate how a change in one attribute will impact total
utility and hence market share, and what value-neutral tradeoffs
can be made – also called simulating the market
Identify the minimum acceptable product
Interpreting Output 1 – Develop Better Understanding of
Customer Value StructureMaking trade-offs between various
levels of Image, Sound, People, & Service
Understand drop in utilities between levels
Find “sweet spots” if they exist
Get a very good idea of customers’ value structure
Prestigious
Sporty
Family
Loud
Subdued
Quiet
2 people
4 people

19. 5 people
Easy
Difficult
Impossible
Intercept
Linear or Non-Linear – MUST KNOW COST TO DETERMINE
–in this class only use for price
Chart1Category 1Category 1Category 1Category 2Category
2Category 2Category 3Category 3Category 3Category
4Category 4Category 40.45
series 1
series 2
series 3
0.98
0.64
0.17
0.17
0.76
1
0.17
0.52
0.81
0.05
0
0.17
Sheet1series 1series 2series 3Category 10.980.640.17Category
20.170.761Category 30.170.520.81Category 40.0500.170.45
$500
$800
$1200
MADE UP
SWEET SPOTS these have nothing to do with previous chart

20. $500
$800
$1200
Chart1Category 1Category 1Category 1Category 2Category
2Category 2
Series 1
Series 2
Series 3
0.45
0.25
0.45
0.25
0.45
0.25
Sheet1Series 1Series 2Series 3Category 10.450.250.45Category
20.250.450.25To resize chart data range, drag lower right
corner of range.
Interpreting Output 2 – Optimal ProductHow many profiles did
customers rank for the Car example?
How many Car combinations were possible?
Can test all possible combinationsEven if customers did not see
all combinationsWHY??The efficiency and reliability of CA!!
In short, create optimal products
Interpreting Output 2 – Optimal ProductThe concept of TPU –
Total Product Utility
Best possible Car? – Look at the bar chart!!
Prestigious .98
Quiet 1.00
5 people .81

21. Impossible .17
TPU = 2.96
however, can we afford to offer this combination?
Worst possible product?2nd best?15th from the top?...and so on
Interpreting Output 2 – Optimal ProductTo create an optimal
product, a company MUSTProvide customers with the highest
possible TPUAND simultaneously make a profit!
Rank order all products by TPU and by costs – then supply the
one with the highest TPU and the maximum profit – we can’t do
in class because we do not have costs for creating each attribute
level
Interpreting Output 3:
Calculating Overall Feature ImportanceWe know what the
utility of each level of each feature is (the BAR CHART, OF
COURSE!)What about the overall features?Image? Sound?
People? Service?
Calculate range for each featureHighest utility value within a
feature minus the lowest utility value with a feature
Interpreting Output 3:
Calculating Overall Feature ImportanceLook at the bar
chart!Image Range = 0.98 – 0.17 = 0.81Sound Range = 1.00 –
0.17 = 0.83People Range = 0.81 – 0.17 = 0.64Service Range =
0.17 – 0.00 = 0.17
Sum of ranges = 0.81+0.83+0.64+0.17 = 2.45

22. Interpreting Output 3:
Calculating Overall Feature ImportanceImportance of each
feature = Feature range divided by sum of all features’
rangesImportance of Image = 0.81/2.45 = 33.06%Importance of
Sound = 0.83/2.45 = 33.87%Importance of People = 0.64/2.45 =
26.12%Importance of Service = 0.17/2.45 = 6.93%
Interpreting Output 3:
Calculating Overall Feature Importance
Chart1ImageSoundPeopleService
0.3308
0.3387
0.2612
0.0693
Sheet1ImageSoundPeopleService33.08%33.87%26.12%6.93%
Interpreting Output 3: Most Important Attributes/ “hot
buttons”So what are the “hot buttons” or important attributes in
our PC example?ImageSoundPeopleService
So this gives you an investment priority
How is this similar to perceptual mapping?
Interpreting Output 4:

23. The InterceptThe re-scaled intercept suggests a market entry
barrier as perceived by the target marketMinimum acceptable
product for it to be part of target market’s consideration setAny
product must total up to be greater than the intercept
What does a large intercept value typically indicate?
Interpreting Output 5:
Calculating Current Market ShareRemember from output 2, we
could have potentially calculated TPU values for all possible
combinations
Somewhere among those TPU values are all our competitors
Use the appropriate feature/level combinations and create the
entire market’s profiles that you compete withYou can create a
realistic marketplace10-12 competitors including yourself
Interpreting Output 5:
Calculating Current Market ShareCreate the entire market’s
profiles and calculate each profile’s utility
Market share =
exp (Utility of Us)/sum exp (Utility of Us; Utility of them)
Interpreting Output 5:Calculating
Current Market Share (Stage 1)Assume 3 product market–
Capri Prelude BMW
Family .17 Sporty .64 Prestigious .98

24. Loud.17 Subdued .76 Subdued .76
2 people .17 4 people .52 5 people .81
Easy .05 Difficult .00 Impossible .17
TPU .56 1.92 2.72
Stage 1: Current Market Share
Chart1CapriPreludeBMW
Market Share
0.073
0.287
0.64
Sheet1Market ShareCapri7.30%Prelude28.70%BMW64%To
resize chart data range, drag lower right corner of range.
Interpreting Output 5:Calculating Current Market Share (Stage
1)Market share calculations:Capri = exp(.56)/[exp(.56) +
exp(1.92) + exp(2.72)]= 1.75/(1.75 + 6.82 + 15.18)= 7.3%
Prelude = exp(1.92)/[exp(1.92) + exp(.56) + exp(2.72)]=
6.82/(6.82 + 1.75 + 15.18)= 28.7%
BMW = exp(2.72)/[exp(2.57) + exp(.56) + exp(1.92)]=
15.18/(15.18 + 1.75 + 6.82)= 64%
Interpreting Output 5:
Simulating the Market – Potential Market ShareCan test
reaction to competitors’ actions
Simulate the market
Test impact of feature changes on market shareWhat if Capri
tries to catch-up to Prelude?Loud to QuietWhat happens?

25. Interpreting Output 5:Simulating the Market – Potential Market
Share (Stage 2)Assume same 3 product market –
hypotheticalBut with the changes from the previous slide
Capri Prelude BMW
Quiet 1.00 Subdued .76 Subdued .76
Family .17 Sporty .64 Prestigious .98
2 people .17 4 people .52 5 people .81
Easy .05 Difficult .00 Impossible .17
TPU 1.39 1.92 2.72
*
Stage 2: Changed Most Important Attribute
UP 8.1%
Chart1CapriPreludeBMW
Market Share
0.154
0.262
0.584
Sheet1Market ShareCapri15.40%Prelude26%BMW58.40%To
resize chart data range, drag lower right corner of range.
Interpreting Output 5: Simulating the Market – Potential Market
Share (Stage 2)Market share calculations:Capri =
exp(1.39)/[exp(1.39) + exp(1.92) + exp(2.72)]= 4.01/(4.01+

26. 6.82 + 15.18)= 15.4% up 8.1% (WOO HOO!)
Prelude = exp(1.92)/[exp(1.92) + exp(1.39) + exp(2.72)]=
6.82/(6.82 + 4.01 + 15.18)= 26.2% down 2.5%
BMW = exp(2.72)/[exp(2.72) + 39 + exp(1.92)]= 15.18/(15.18 +
4.01 + 6.82)= 58.4% down 5.6%
Interpreting Output 5:Simulating the Market – Competition
reacts (Stage 3)
BMW will not sit still when they lose over 10% market share
Capri Prelude BMW
Quiet 1.00 Subdued .76 Subdued .76
Family .64 Sporty .64 Prestigious .98
2 people .17 4 people .52 5 people .81
Easy .05 Difficult .00 Easy .05
TPU 1.39 1.92 2.60
Stage 3: Potential Market Share
up 1.1% (WOO HOO!) from beginning up 9.2%
Chart1CapriPreludeBMW
Column1
0.165
0.28
0.555
Sheet1Column1Capri16.50%Prelude28.00%BMW56%To resize
chart data range, drag lower right corner of range.

27. Interpreting Output 5:Simulating the Market – Potential Market
Share (Stage 3)Market share calculations:Capri =
exp(1.39)/[exp(1.39) + exp(1.92) + exp(2.60)]= 4.01/(4.01 +
6.82 + 13.46)= 16.5% up 1.1% (WOO HOO!) from beginning
up 9.2%
Prelude = exp(1.92)/[exp(1.92) + exp(1.39) + exp(2.60)]=
6.82/(6.82 + 4.01 + 13.46)= 28% up 1.8%
BMW = exp(2.60)/[exp(2.60) + exp(1.39) + exp(1.92)]=
13.46/(13.46 + 4.01 + 6.82)= 55.5% down 2.9%
Interpreting Output 5:
Simulating the Market – Potential Market ShareCan test many
such rounds of our firm’s actions and competitive
reactionsDetermine the most appropriate feature and level
combination based on these simulations
SummaryThe value of conjoint analysis
Using conjoint analysis
Interpreting and leveraging conjoint analysis
7.30%28.70%64%CapriPreludeBMW
15.40%26%58.40%CapriPreludeBMW
16.50%28.00%56%CapriPreludeBMW
Tool Summary 3
1. Define CA/ Assumptions (20%)
2. EXPLAIN Steps for CA (20%)
3. Explain Example(60%)
Draw column chart
· Show linearities, non-linearities, sweet spots – only with

28. numerical values
· Best product show product and TPU Worst product show
product and TPU Is it viable? Why?
· Intercept – what does it mean? What is it in your example?
· Overall Feature Importance – Pie Chart
· Simulate Market – 3 products – 3 stages STAGE 1 :-Now
STAGE 2- I change my product HOT BUTTON BETTER
CHANGE!! ONLY ONE ATTRIBUTE CHANGES
STAGE 3-competition reacts Do not let them change a hot
button! Only ONE competitor reacts and they change only one
attribute level