Draft materials for an e-Learning course being developed by Ray Poynter, NewMR and The Future Place.

1. Introduction to
Conjoint Analysis
Ray Poynter
NewMR
ray@new-mr.com March 2021

2. Agenda
• What sort of problems?
• Why?
• How?
• An example
• The bigger picture
• Design considerations

3. What sort of Problems?
• In designing a computer, what is the right combination of screen size,
memory, price, operating system etc?
• Looking at a financial product, what combination of features and
prices will be most successful?
• Thinking about a range of soft drinks, what sizes, containers, flavours,
sweetening should be used to create a range?
• Designing a holiday, how important is travel time, connections,
evening activities, daytime activities?
• What is the premium that a brand of tyres can charge compared with
others for a variety of options?

4. What sort of problems?
Multiple Features / Attributes
• Tangible
• E.g. Computer: Screen, Price, Brand, Size
• E.g. Car: Number of doors, Engine size, Brand, Finance option
Multiple Levels
• Tangible and discrete
• E.g. 13 inch, 15 inch, 17 inch
• E.g. 2-door versus 4-door, versus 5-door

5. Uses for Conjoint
• Modelling different product combinations
• Understanding why products are liked
• Looking at segments based on ‘needs’
• Calculating the value of attributes
• NPD and portfolio management
• B2B, consumer, expert
• Not often used with ‘emotional’ messages/issues
• Nor when attributes interact – I may like vodka, I may
like pasta, but vodka-flavoured pasta I might not like

6. Why use Conjoint?
Two key reasons
1. It allows multiple options to be tested efficiently
• If we have just 3 attributes (brand, price and colour)
• If the attributes have just 3 levels each – there are 27 possible products
• If there are 5 attributes, each with 4 levels – there are 1024 possibles
2. People can’t describe their own motivational structure
• Asking people to say how important screen size is versus memory size, versus
prices for a computer does not provide useful or predictive results
• Showing people options and asking them to make a choice is much more
predictive

7. Life is a Trade-Off
A
4 Days
$15,000
Covers main needs
B
2 Weeks
$10,000
Covers main needs
C
4 Days
$20,000
Covers all needs
D
2 Weeks
$15,000
Covers all needs
Thinking of research projects. How
do you prioritize the attributes
Speed, Price and Quality?
On a 1-10 scale, how important are:
• Fast
• Cheap
• Good
People can’t
access their own
motivations

8. Different mental models
A
4 Days
$15,000
Covers main needs
B
2 Weeks
$10,000
Covers main needs
C
4 Days
$20,000
Covers all needs
D
2 Weeks
$15,000
Covers all needs
Some people might look at the
total picture and make a
choice.
Some might filter and choose.
For example, 1st decide that it
has to be 4 days, and then pick
one of two options.
How they choose does not
impact the model or the
results.

9. Evaluating Conjoint
120GB Disk
15 inch Screen
1MB RAM
120GB Disk
17 inch Screen
2MB RAM
80GB Disk
15 inch Screen
2MB RAM
80GB Disk
17 inch Screen
1MB RAM
120GB Disk
19 inch Screen
512KB RAM
80GB Disk
19 inch Screen
512KB RAM
Card Sort
Pair-wise Comparisons
(Discrete Choice)
120GB Disk
15 inch Screen
1MB RAM
80GB Disk
17 inch Screen
2MB RAM
or
120GB Disk
15 inch Screen
1MB RAM
Not Buy Def Buy
1 2 3 4 5 6 7 8 9 10 11 Ratings Conjoint
120GB Disk
15 inch Screen
1MB RAM
100GB Disk
17 inch Screen
1MB RAM
or or
80GB Disk
15 inch Screen
2MB RAM
None
of
these
or Choice Based
(Discrete Choice)

10. More complex options are possible
If these were the only ways to get to work, which would you choose?
Car
Fuel: $1.50
per gallon
Parking Fee:
$8 per day
I’d Walk Bus
Picks up every
30 minutes
$1.00 for one-
way fare
I’d Bike
Biking lane on
80% of route I’d choose
another
method

11. Conjoint – Key Outputs
• Calculating the utility values of all the levels of all
the attributes
• We call these partworths
• For all the groups of interest (e.g. younger vs older)
• Or, for each individual participant
• Conducting ‘needs’-based segmentation
• Creating ‘What-if’ models to allow scenarios to be
investigated

12. How many tasks?
This is a task
Too few tasks makes
modelling hard
Too many tasks is difficult for
the participants
Current advice is to use
about 10 tasks

13. Not Enough Tasks
With 10 tasks per participant, there is often not enough data to
calculate all the utility values for each participant for each level of each
attribute
Options
• Build aggregate models (e.g. one for men, one for women, one for total)
• Use latent class (with choices as the dependent variable) – builds aggregate
models based on similarities in the data
• Use HB (Hierarchical Bayes) to estimate individual models

14. An Example Study
• A simple example using QuestionPro’s Conjoint option
• Use this link to take the test survey
https://www.questionpro.com/t/AGXJqZlsQY

15. Attributes and Levels
Operating System
• Apple Mac
• Windows
Memory / Ram
• 4GB
• 8GB
• 16GB
Disk Size
• 512 GB
• 1 TB
• 2 TB
Screen Size
• 13 inch
• 15 inch
• 17 inch
There are 54 possible combinations
2 Operating Systems * 3 Memory options * 3 Disk sizes * 3 Screen size options

16. Concepts Per Task
• The most typical configuration (these days) for Conjoint Analysis is 4
concepts or options, usually with a ‘None of of these’
• ‘None of these’ is a common option, but it is not a requirement
• Showing 4 concepts together fits quite badly on a mobile phone
• For this example, 2 concepts per task have been chosen
• Better for mobile
• Better for teaching

17. 6 tasks are going to be asked
If the participant chooses the
left option
Apple Mac, 4GB, 1TB & 13 inch
are all marked as ‘chosen’
Windows, 8GB, 2TB & 15 inch
are all marked as ‘rejected’
The algorithm works out the
value for each of the levels for
each of the attributes
This is DCM – discrete choice
modelling

18. The Example Data
176 completed surveys – convenience sample
57%
34%
7%
2%
Very easy
Easy
Neither difficult nor easy
Difficult
Ease of survey completion
13%
27%
36%
13%
11%
Extremely…
Very familiar
Moderately…
Slightly familiar
Not at all familiar
Familiarity with Conjoint

19. Aggregate Partworths
Memory Partworths
4GB -90
8GB 10
16GB 81
Disk Partworths
512GB -35
1TB 3
2TB 32
Operating System Partworths
Apple Mac -24
Windows 24
Screen Size Partworths
13 inch -19
15 inch 11
17 inch 8
Shows, on average, how much is each level of each attribute contributing to the total?

20. Aggregate Importances
Attribute Importance Gap (Max - Min) Importance
Memory 171 54%
Disk 67 21%
Operating System 48 15%
Screen Size 30 9%
Total 316 100%
In Conjoint, the importance of an attribute is the difference between the value of
the most preferred level minus the least preferred level.
In this example, Windows had a partworth of 24, Apple Mac had -24, so the importance is 48.
The total of all the Max – Min values was 316, so 48 is 15% of the total Aggregate importance.

21. Aggregate Importances
Attribute Importance Gap (Max - Min) Importance
Memory 171 54%
Disk 67 21%
Operating System 48 15%
Screen Size 30 9%
Total 316 100%
In Conjoint, the importance of an attribute is the difference between the value of
the most preferred level minus the least preferred level.
In this example, Windows had a partworth of 24, Apple Mac had -24, so the importance is 48.
The total of all the Max – Min values was 316. So 48 is 15% of the total Aggregate importance.

22. What-if Modelling

23. What-If Modelling
Operating System
Model
Operating
System
Memory /
Ram
Disk
Size
Screen
Size
Aggregate
Utility
Share
Estimate
Concept 1 Windows 8GB 1 TB 15 inch 48 60%
Concept 2 Apple Mac 8GB 1 TB 15 inch 0 40%
The aggregate Utility show Windows is preferred to Apple iOS.
The Aggregate Utility is the sum of all the partwords for the levels that make the Concept.
The What-if Model shows 40% of the sample prefer Apple iOS. Share is often an easier
measure to interpret, and more relevant.

24. What-If Modelling
Trading of size with specification
Model
Operating
System
Memory /
Ram
Disk
Size
Screen
Size
Aggregate
Utility
Share
Estimate
Concept 1 Windows 16GB 2 TB 17 inch 145 52%
Concept 2 Windows 8GB 1 TB 15 inch 48 34%
Concept 3 Windows 4GB 512 GB 13 inch -120 14%
The aggregate utilities suggest the high spec, larger computer is heavily preferred.
Also, that the low-spec, smaller machine is heavily disliked.
However, the modelling shows:
There are people for whom 17 inch is too negative
There are people for whom 13 inch is a strong positive

25. What else might we check?
Using sub-groups
• Do men/women, young/old, North/South etc have
different values/choices
Grouping people by their choices
• People who prefer Apple to Windows, who are they
• People who prefer a 17-inch screen, what else do they
prefer?

26. Brand and Price
The example does not include Brand or Price – for simplicity
• Brand is linked to operating systems (the Apple iOS is only available
from Apple, all the other brands would offer Windows) – this is an
interaction
• Prices would vary by country – which was unnecessary in a simple
example
This example is a measure of configuration preferences
One of the main reasons to use conjoint is to investigate
brand and price

27. Typical projects
Perhaps there is no such thing as a typical Conjoint Study, but here
is try …
• Attributes – 4 to 8
• Levels – 3 to 5 per attribute
• Options per task 4
• With a ‘None of these’
• 10 tasks per participant
• 250 to 400 participants
• Analysis either a) aggregate and sub-group,
or b) with Hierarchical Bayes

28. Hierarchical Bayes (HB)
• A very advanced statistical technique
• In general, it deals with situations where there is a lot of missing data
• In conjoint analysis it allows an approximation of a model per
participant – even when there were too few tasks per participant
• Most advanced conjoint analysis studies use HB

29. Design Considerations
• Conjoint is an advanced technique – it is best to have the support of
an expert
• At the design and analysis/interpretation stages
• Just because you can run the software, doesn’t mean you can safely and
reliably conduct a study
• It only works in cases where the appeal of a product or service can be
‘approximated’ by the sum of the appeal of the parts
• It tends to be the main focus of a study – you don’t (normally) add a
conjoint onto a study looking at other things
• The questions you ask participants should be as understandable and
as realistic as possible

30. Want to Learn More?

31. Introduction to
Conjoint Analysis
Ray Poynter
NewMR
ray@new-mr.com March 2021