Our April 2017 Keys to Success series deep dives into Discrete Choice Conjoint Analysis, an effective and advanced survey technique. In this training, learn to analyze how and why customers choose certain products or services over others. This training discusses research industry best practices on creating a comprehensive end-to-end conjoint analysis project, so that you can address and find solutions to even some of the most complex business objectives.

1. DISCRETE CHOICE
CONJOINT ANALYSIS 101
April 20th, 2017

2. Esther Lavielle
Implementation Manager,
QuestionPro
esther.lavielle@questionpro.com
Twitter: @esther_qpro
Welcome to the
Keys To Success Series
#K2SQP
By QuestionPro

3. Agenda
❏What is Discrete Choice Conjoint Analysis?
❏Do you Conjoint? Why You Should
❏Conjoint O’ Clock - Best Time to Run a Conjoint Study
❏QuestionPro’s 3-in-1 Design Options
❏Best Practices For Discrete Conjoint Project Design
❏Conjoint Analysis FAQs
❏Live Demo: How to Conjoint

4. Discrete Choice Conjoint Analysis
Trade-off analysis
methodology
#1 Methodology used
research technique
for customer
purchase decisions For any purchase, consumers evaluate or “trade-off”
the different characteristics of a product and decide
what is more important to them

5. Do You Conjoint? Why You Should
New Product
Research
- Evaluate new
products or
variations against
existing range of
products in the
marketplace
Budget Friendly
- Cheaper than
developing new
products for the
marketplace with no
guarantee of success
Simulates Consumer
Decision
- Captures how target
audience react to new
products/services
within marketplace
-Refine the target
audience for your study
Real Time Data
- Need this data quick?
Don’t wait. Get real-
time feedback on new
products or variations
of existing products
today
Analyze Choice vs. $
- Gauge the effect on
the choice/price
relationship relative
to existing products and
features presented

6. QuestionPro’s 3-in-1 Design Options
Random Design
- Most Popular among
QP customer
- Easy to program
- Easy to analyze
- Easy to include in
comprehensive
surveys
D-Optimal Design
- This algorithm will
produce optimal
number of tasks per
respondent based on
sample size to reach
statistical significance
Import Design
Caution*
- Available for those who are
statistically savvy. Must be able
to calculate margin of error
from your own design
-Externally create conjoint
design with custom algorithm &
Import into QuestionPro
-SPSS Design Format
-Fractional Factorial Orthogonal
Designs

7. Best Time For a
Discrete Choice
Conjoint Study
Business Objective: Market Share
Growth New Product/Services
Beginning of Innovation Process
During Product/Service Planning &
Prototyping
Cost of Overhead vs. Potential
Revenue
When you have a Statistically
Significant Target Audience

8. Best Practices
Discrete Choice Conjoint
Project Design

9. Where to Get Started
● Qualitative Research - Validate Anecdotal Info
● Identify Top Attributes & Levels for research
● Focus group or surveys with open-ended
questions will help define your top attributes
needed for your study
● Use Crowd-sourcing tools or forums: QuestionPro
Communities or IdeaScale
● Define range & language for target audience

10. Best Practices
❏ Keep options clear and simple
❏ No more than 20 trade-off exercises
❏ No more than 5-6 attributes
❏ Keep ranges simple
❏ Follow general good online survey techniques
❏ Beta test survey before launch date
❏ Exercise good privacy & confidentiality protocols
❏ Keep survey short & sweet
❏ Provide incentives or rewards $ for long surveys

11. Best Practices for Sample Size
❏ Sample size recommendation: (nta/C) > 1000
n = number of respondents x t= number of tasks x a= number of
alternatives per task / C= largest number of level for any one attribute.
❏ Example: 500 respondents, 3 tasks per respondent, 2 alternatives per task
and the maximum number of levels on an attribute is 3
(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

12. FAQ: Analytics
Discrete Choice
Conjoint
Analysis

13. 4 Discrete
Choice
Conjoint
Reporting
Options
Attribute Importance Market Share Simulation
- Shows how much
consumers are willing to
spend for each feature
they’re considering adding
to their brand
- Part worth also show if
consumers are willing to
exchange one feature for
another
- Simulate market share
potentials based on
insights collected
- Customized based on set
features & attributes

14. Profile Analysis Brand Premium
- Highlights Best vs Worst
- Customize Profile Output
- Allows you to see what
was shown to each
respondent
- Measure Potential Price
Elasticity
- Based on Features &
insights collected
4 Discrete
Choice
Conjoint
Reporting
Options

15. The Analytics
How do we come up with our numbers
for Parts Worth Calculations?
QuestionPro’s Discrete Choice Module uses
a Maximum Likelihood calculation coupled
with a Nelder-Mead Simplex algorithm
Have greater confidence in
the results you receive!

16. What’s the Market
Segment Simulator
Going to Tell Me?
Market Segment Simulator gives ability to "predict" market
share of new products and concepts that may not exist now
● Measure the "Gain" or "Loss" in market share based on
changes to existing products in the given market
Conjoint Simulation Rulebook
Identify “Profiles” - different products or concepts to investigate
Review existing products in current market segment & simulate market
share of the products to establish a baseline
Try out new services and ideas. See how the market share shifts
based on different products and configurations from data collected

17. Build it Now
How To
Conjoint

18. Recap
Ready to Conjoint? Get started Here
What Time is Conjoint O’Clock?
Pricing, preference analysis new/existing products, innovation, etc.
Build a Comprehensive Insights Project
Tap into QP’s Tools & Services
Want help?

19. Thank You!
Questions?
Want to talk Conjoint?
sales@questionpro.com
Tell me what you want to learn
esther.lavielle@questionpro.com
Twitter: @esther_qpro #K2SQP #ConjointOClockQP