This webinar provided an overview of how to conduct discrete choice conjoint analysis. It discussed the theory and logic behind conjoint analysis, when to use it, how to design a conjoint study including attributes and levels, how to write the questionnaire, how to analyze results to determine relative attribute importance and utility values, and tips for a successful project including using qualitative research first, sample size considerations, and best practices for surveys. The webinar also demonstrated how to set up a conjoint analysis question, different design types, and how to use the market simulator tool to predict how new product concepts may perform.

1. How To Run Discrete Choice Conjoint Analysis
Andrew Jeavons
Esther LaVielle

2. About Survey Analytics
Started in 2002 in Seattle, WA
Specialize in online, mobile surveys and panel, conjoint
analysis, crowdsourcing, sample, gamification and more!
#172 on Inc. 500 Fastest Growing
Private Companies
#12 on Puget Sound Journal's Top 100
in Washington
Esther LaVielle
Vice President of Client Services

3. Andrew Jeavons
President of Survey Analytics, Andrew Jeavons
25 years in the market research industry.
Background in psychology and statistics, and currently focuses on innovation within
survey research.
Studied Neuropsychology at Birkbeck College in London UK
Ten years + experience starting software companies, and in a marketing, sales and
strategic development capacity.
He has also written articles for ESOMAR, Greenbook, Research Access, and more.

4. Webinar Agenda
1- What is discrete choice conjoint analysis?
2- The theory and logic behind discrete choice conjoint analysis
3-When to use discrete choice conjoint in your research
4-Specific examples of how to use discrete choice conjoint
5-How to design a discrete choice conjoint project
6- How to write a discrete choice conjoint questionnaire
7-How to analyze the results of a discrete choice conjoint project
8- Tips and Best Practices and Q & A

5. What is Conjoint Analysis?
Type of Trade-off Analysis methodology
Developed over the past 50 years by market researchers and statisticians to
predict the kinds of decisions consumers will make about products by using questions
in a survey.
Conjoint analysis questions presents a series of possible products to consumers and
asks them to make a choice about which one they would pick.
The central idea: For any purchase decision consumers evaluate or “trade-off”
the different characteristics of a product and decide what is more important to
them.
Survey Analytics uses Discrete Choice Conjoint Analysis which
best simulates the purchase process of consumers

6. Wanna Buy A Puppy?
Breed
Dog Breeder
Size
Price
Care Needed
Personality
Life Span

7. Theory & Logic of Conjoint Analysis
It will help you evaluate new products or variations against an existing range of
products already offered by your company or within the marketplace.
It’s much cheaper than developing new products for the marketplace with no
guarantee of success.
Get real-time feedback on new products or variations of existing products.
Simulates the decisions your target consumers would make in the market place.
Gives you an idea how a new product with be received
in the marketplace.
Gauge the affect on the choice/price relationship relative
to existing products and features presented.

8. Analysis: How do we come up with our numbers ?
Survey Analytics Discrete Choice Module uses a Maximum Likelihood
calculation coupled with a Nelder-Mead Simplex algorithm.
Design options are random, D-Optimal or your own imported design.
Have greater confidence in the results you receive !

9. Conjoint Analysis
Core Concepts
1)Attributes/Feature:
Define the attributes of the products for your market. These
are the properties of
your product.
Seattle Tourism Study:
#Hours
Time of Day
Tour Type
2) Levels: The different properties of the attributes. Define
at least two levels for each
of the attributes.
Seattle Tourism Study:
Hours - 3 levels
Time of day - 4 levels
Tour Type: 5 levels

10. CORE CONCEPTS
Conjoint Analysis Core Concepts:
3) Utility Value or Part Worth functions:
These are what are produced by the conjoint analysis.
These can then be used to determine how important
an attribute is to the purchase or choice process and
in “market simulations.”
Utility Value of Hrs on Tour:
1-2hrs = .39
2-4hrs = .45
4-6hrs = .32
4) Relative importance:
How important an attribute is in the purchasing/choice
decision ?
Example: Of all features to go on tour –
“Time of day” determined which one most chosen

11. Setting up a conjoint Analysis Project
Kind of reminds me of
putting together a
jigsaw puzzle…..
All the pieces in the
project should fit
together before fielding
the project!

12. Survey Analytics offers 3
Conjoint Analysis Designs
Random Design
D-Optimal Design
Import Design

13. 3 Conjoint Analysis Designs - Defined
Random: Random design is a purely random sample of the
possible attribute levels. For the number of tasks per
respondent Survey Analytics produces a unique set of
attribute configurations to be presented to the respondent.
D-optimal: This is a design algorithm that will produce an optimal
design for the specified number of tasks per respondent and
sample size. More information on this design algorithm is
available in the D-Optimal section.
Import Design: This allows designs, in the SPSS design format,
to be imported and used by the Survey Analytics DCM
module. This is useful when users want to use designs not
generated by Survey Analytics, such as fractional factorial
orthogonal designs.

14. # How to Set up a Conjoint Analysis Question
Question Set up for
all Random,
D-Optimal, and Import
Design are the
same:
1.Set up Features/
Attributes
2. Set up Levels for
Each attributes
EXAMPLE:
Feature: Hours
Levels: 1-2hr, 2-4hr

15. How to Set up a Conjoint Analysis Parameters
Set up Prohibited Pairs
The engine will not display two levels that have been marked as
"Prohibited" in the same concept (as a product) for the user to
choose.

16. #Prohibited Pairs
Example: A Weird Seattle Tour will never be 4-6 hrs long

17. Concept Simulator
This can be used to determine what choices will
be presented to the respondents when your
survey is actually deployed. Use as Guidance.

18. D-Optimal Design
Click on Settings >> Design Type >> Doptimal
>> Select Versions >> Start >> Save Settings

19. D-Optimal Design
Click on Settings >> View Options>> Make changes >> Update Design

20. Import Design
Import Design This allows designs, in the SPSS design format, to be imported and
used by the Survey Analytics DCM module. This is useful when users want to use
designs not generated by Survey Analytics, such as fractional factorial orthogonal
designs.
Step 1:Start by adding a Conjoint DCM
question as is walked through above.
Ensure that under 'Task Count' and
'Concepts Per Task' you choose the same
numbers as that you have in the Excel
sheet you are going to import
Step 2: Click on 'Settings'.
In the in-line popup in ’
Design type' choose 'Import’

21. #5 Conjoint Analysis Survey Preview

22. #5 Conjoint Analysis Preview with Pictures

23. Review Data:
Utility Calculation & Relative Importance

24. Relative Importance
Relative Importance of attributes
Displayed as Pie chart
*Shows here that Tour Type
Is the most significant feature/
attribute which determines what
tour they want to take.

25. Relative Importance and Average Utility Table
The tour type is
the most important attribute
Weird is GOOD! Chocolate is popular

26. Best & Worst Profile
The tour type is best liked.
Weird works.
The tour type is best liked.
Weird works.

27. Market Segmentation Simulator
Using existing Data from Conjoint Analysis

28. Market Segment Simulator
Market Segmentationgivesnot exist ability to "predict" the market share of
you the
Simulator
new products and concepts that may today.
Ability to measure the "Gain" or "Loss" in market share based on changes to existing
products in the given market.
Important steps in Conjoint Simulation:
1- Describe/Identify the different products or concepts that you want to investigate.
We call "Profiles".
Example: Tour Type: Weird, Hours: 1-2 , Time of Day: Evening
2- Find out all the existing products that are available in that market segment and
simulate the market share of the products to establish a baseline.
3-Try out new services and ideas and see how the market share shifts based on
new products and configurations.

29. Setting up a Simulator
1) Click on Online tools >>Name Simulator Profile>>change
profiles
2) Click on to see results!

30. Results: Simulator Output Defined
The market simulator uses utility values to project the
probability of choice and hence the market share

31. Now that we know
how to use this . .
What can we ask
and find out with the
Market Segmentation Simulator?

32. Market Segmentation Simulator
Quick Example: What happens if have a tour of 1-2 hours
as opposed to 4-6 hours in the afternoon for “Weird Seattle”
?
Answer: We find that the 1-2 hour tour would attract about
75% of the market share.

33. Tips for A Successful Conjoint Analysis Project

34. Best Practices: Where to Begin?
You must use qualitative research first !
What are the top attributes?
What range?
What language?
A focus group or surveys with open-ended
questions will help define your top attributes
needed for your study
Use Crowd-sourcing tools: IdeaScale

35. What Sample Size To Start With?
Sample size is a question that comes up very frequently. Richard Johnson, one of the
inventors of conjoint analysis, has presented the following rule of thumb for
sample size in choice based conjoint:
(nta/C) > 1000
Where n = the number of respondents x t= the number of tasks x a=the number of
alternatives per task / C= the largest number of level for any one attribute.
So if you have 500 respondents, 3 tasks per respondent, 2 alternatives per task
and the maximum number of levels on an attribute is 3 you get:
(500 x 3 x 2) / 3 = 1000
Generally speaking sample sizes tend to be around 200 – 1200 respondents, admittedly
a wide range. 300 comes up most often for a single homogeneous group of subjects.

36. Practices & Tips: Surveys with Conjoint Analysis
Keep the options clear and simple as possible
No more than 20 trade-off exercises
No more than 5-6 attributes
Keep the ranges simple
You can ask more intimate questions of current
customers than potential customers, but don’t let
that stop you from trying!
Follow general good online survey techniques
Test your survey
Make it clear responses are kept strictly confidential
Keep survey to 15-20 minutes
Provide incentives

37. # Survey Analytics Discrete Choice Conjoint
Discrete Choice Conjoint Analysis
Flexible pricing available
Most User-friendly Conjoint Tool In The Market
Real-time Reporting
Pricing includes integrated research tools that would enhance efficiencies and
depth and research strategies
Dedicated account management and support included

38. Q&A
Thank you for Attending!
Esther LaVielle
Esther.rmah@surveyanalytics.com http://www.surveyanalytics.com
Andrew Jeavons
Andrew.jeavons@surveyanalytics.com sales-team@surveyanalytics.com