How to run conjoint analysis
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Thursday, May 28 th , 2015 WE WILL BEGIN SHORTLY questionpro.com | surveyanalytics.com Learn How to Make Better Business Decisions
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Transcript of How to run conjoint analysis
Thursday, May 28th, 2015
WE WILL BEGIN SHORTLY
questionpro.com | surveyanalytics.com
Learn How to Make Better Business Decisions
Conjoint Analysis Webinar : Will Begin Shortly….
Presenters
VIVEK BHASKARANFounder & CEO
SurveyAnalytics / QuestionPro
ROB ASERONOnline Research Guru
(Former Director, Zynga)
NICO PERUZZI, Ph.D.Partner
Outsource Research Consulting
AGENDA
What is Conjoint Analysis?
How Gaming Giant, Zynga, Won
Introducing an All-in-One
Solution
Questions and Answer Session
THE BASICS
Conjoint
Discrete Choice
Choice-Based ConjointDCM
CBC
Adaptive ConjointACBC
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
THE BASICS
Bad news:You just lost your job.
THE BASICS
Good news:You have great skills that give you options.
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
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?
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...
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.
THE BASICS
How many times have you seen this type of question?
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.
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.
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,
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
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
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
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.
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
CASE STUDY
How a Gaming Giant Won Using Max Diff & Conjoint
ROB ASERONOnline Research Guru
(Former Director, Zynga)
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.
Naming the Game*Question 1
* Hypothetical game name situation – never did ‘AntVille’ at Zynga.
Naming the Game*Question 2
* Hypothetical game name situation – never did ‘AntVille’ at Zynga.
Naming Results*Question 1
* Hypothetical game name, made up data.
Naming the Game*Question 3 – Under the Radar
* Hypothetical game name situation – never did ‘AntVille’ at Zynga.
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
ALL-IN-ONE SOLUTION
One Platform For All Your Conjoint Analysis Needs
VIVEK BHASKARANFounder & CEO
SurveyAnalytics / QuestionPro
Choice-Based Conjoint
MaxDiff
Conjoint / Max Diff Setup : 1
Max Diff : Setup
Max Diff : Settings
Max Diff : Analysis : Share of Preference
Logit Model
Max Diff : Analysis : Filtering
Max Diff : Analysis : Comparisons
Conjoint Analysis : Setup
Conjoint Analysis : Settings
Conjoint : Design Model
Randomized D-Optimal Upload
Conjoint Analysis : Data Exploration
Attribute Importance
Profile Playboard
Market Segmentation
Simulator
Brand Equity
Retail Price Equivalence
Price Elasticity & Curve
Attribute Importance
Profile Playboard
Profile Playboard
Market Segmentation : Simulator
Brand Equity / Retail Price Equivalence
Price Elasticity
QUESTIONS
1 (800) 326-5570
E-mail:sales-
Online Inquiries:www.surveyanaltics.com/
contact
Nico Peruzzi, Ph.D.
1 (408) 202-1521
E-mail:[email protected]
om
Rob AseronE-mail:
![Preference Elicitation [Conjoint Analysis]](https://static.fdocuments.in/doc/165x107/56813e5e550346895da864ca/preference-elicitation-conjoint-analysis.jpg)
