3. Presenters
VIVEK BHASKARAN
Founder & CEO
SurveyAnalytics /
QuestionPro
ROB ASERON
Online Research Guru
(Former Director, Zynga)
NICO PERUZZI, Ph.D.
Partner
Outsource Research
Consulting
4. AGENDA
What is Conjoint Analysis?
How Gaming Giant, Zynga, Won
Introducing an All-in-One Solution
Questions and Answer Session
5. THE BASICS
Conjoint
Discrete Choice
Choice-Based ConjointDCM
CBC
Adaptive Conjoint
ACBC
ACA
Adaptive
Choice-Based
Conjoint
MaxDiff
MaxDiff
Scaling
MBC
Best-Worst
Scaling
Menu-Based
Choice
All these methods involve making trade-offs, and can be classified under
Choice Research
Bottom Line
8. THE BASICS
Choice Example: Your Job Offers
Salary
(x current)
Travel
(%)
Vacation
(weeks)
Bonus
(%)
Position A 1.2 0 2 10
Position B 1.2 25 3 20
Position C 1.2 75 4 30
Position D 1.5 25 2 30
Position E 1.5 75 3 10
Position F 1.5 0 4 20
Position G 1.8 75 2 20
Position H 1.8 25 2 30
Position I 1.8 25 4 10
9. Salary
(x current)
Travel
(%)
Vacation
(weeks)
Bonus
(%)
Position A 1.2 0 2 10
Position B 1.2 25 3 20
Position C 1.2 75 4 30
Position D 1.5 25 2 30
Position E 1.5 75 3 10
Position F 1.5 0 4 20
Position G 1.8 75 2 20
Position H 1.8 25 2 30
Position I 1.8 25 4 10
THE BASICS
Your Choice
Why did you choose?
By what process did you choose?
10. THE BASICS
Why Choice Research Adds More Value
LITTLE DIFFERENTIATION
Asking buyers to rate the importance of, or preference for, a
bunch of features/attributes often gives...
11. THE BASICS
Why Choice Research Adds More Value
Buyers have to make difficult trade-
offs and concessions in real life.
We have a way to force respondents to
make trade-offs, just like real-world
buyers have to do.
13. THE BASICS
How many times have you seen this type of question?
Naturally we want it all.
How do you interpret the difference between a mean of 4.2 and 4.4 on a 5-point rating scale?
You know the futility of that exercise.
14. THE BASICS
…Or these types of questions?
How much would you be willing to pay for this?
At what price would you consider X “inexpensive,” “expensive,” “too expensive,”
and “too cheap?”
But, these methods:
• Invite bargaining; can’t trust results; need to follow-up research
• Are overused and over-trusted; they feign precision
For a new product, with no idea of “what the market will bear,” this can get you near the ballpark.
15. THE BASICS
When we force respondents to make trade-offs, we get a
much better picture of the values of the product
attributes; we get better discrimination.
“What if... ?”
We also gain the opportunity to
simulate various product
configurations to figure out which
can lead to the best uptake.
We get to ask,
16. THE BASICS
Help Me Figure Out
The features that
will best optimize
my product
How many
customers will
pay for a new
features or
product
My brand
equity
Whether a new
product will
cannibalize
existing products
How much
demand will
change as we
change price
How will our product
will compete in the
current (and future)
competitive landscape
17. THE BASICS
Terminology
Attributes – the features of your product or service (e.g., price, color,
size, amount of storage, etc.)
Levels – the type or amount of an attribute (e.g., blue, red, or green
color, or 64 GB, 128 GB, or 256 GB)
Importance Scores – the maximum impact an attribute can have on
product choice
Utilities – the preference for a level of an attribute (technically called a
part-worth)
Share of Preference – interest in a product concept captured
through a market simulation
None Parameter – captures respondent propensity to not purchase
any of the choices in a simulation
18. THE BASICS
The Do’s and Don’ts
Don’t think importance scores will tell you what is “good”—they
only tell you where respondents’ attention was most focused.
Don’t compare utility scores across attributes—only consider
them relative gauges of level preference within each attribute
Do use simulations to answer your business questions
Do use a competitive set and a none parameter to provide
realism to your simulations
19. THE BASICS
The Big Difference
MaxDiff
deals with a single list of
attributes (brands, features,
messages, benefits, images,
copy, claims, etc.).
Conjoint
deals with attributes that have
various “levels” (degrees), and
that very often include price in a
product profile context.
20. THE BASICS
Bringing it All Together
MaxDiff
Conjoint
Preference or
Importance of Items
No Pricing
Feature Configuration
Product Optimization
Competitive Scenarios
Price Sensitivity
Market Scenarios
Product Demand
Cannibalization
Product Mix
Both MaxDiff and Conjoint can be used to feed Segmentation Analysis
21. CASE STUDY
How a Gaming Giant Won Using Max Diff & Conjoint
ROB ASERON
Online Research Guru
(Former Director, Zynga)
22. Product Naming Questions*
With Broad Applicability
1. What name will get a user to pause in the App Store environment?
2. Can we leverage brand halo and frame this new product as an extension of the
previous, related product?
3. What naming presentation appeals most to current user base?
4. What naming presentation has greatest attraction for expanding the user base?
5. Where will cross-promotion efforts have best traction?
* Example questions chosen because of relevance to many product types, not for representativeness to Zynga research concerns.
26. Naming the Game*
Question 3 – Under the Radar
* Hypothetical game name situation – never did ‘AntVille’ at Zynga.
27. Max Diff Fits a Start Up’s Approach
Lightweight Implementation
– Immediately-understood response format -
complex judgment yet low cognitive load
– Minimally intrusive (inline responses)
– Appropriate for micro-rewards (panel)
Data, Data, Data
• Cut by play characteristics
• Cut by demographics
Efficient
– Respondent makes 2 judgments
• A is Most Preferred
• E Least Preferred
– We collect 7 data points (of possible10 )
• A > B, A > C, A > D, A > E
• B > E, C > E, D > E
– Concurrent consideration of pos/neg
Fast
– Overnight data-based directional read
– Iterate and re-field until finalized
28. ALL-IN-ONE SOLUTION
One Platform For All Your Conjoint Analysis Needs
VIVEK BHASKARAN
Founder & CEO
SurveyAnalytics /
QuestionPro
Choice-Based Conjoint
MaxDiff