how to run discrete choice conjoint analysis
TRANSCRIPT
Andrew JeavonsEsther LaVielle
How To Run Discrete Choice Conjoint Analysis
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
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.
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
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
Wanna Buy A Puppy?
BreedDog BreederSizePriceCare NeededPersonalityLife Span
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 relativeto existing products and features presented.
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 !
Conjoint Analysis Core Concepts
1)Attributes/Feature:
Define the attributes of the products for your market. These are the properties ofyour product.
Seattle Tourism Study:
#HoursTime of DayTour Type
2) Levels: The different properties of the attributes. Define at least two levels for each of the attributes.
Seattle Tourism Study: Hours - 3 levelsTime of day - 4 levelsTour Type: 5 levels
Conjoint Analysis Core Concepts: 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 = .392-4hrs = .454-6hrs = .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
Kind of reminds me ofputting together ajigsaw puzzle…..
All the pieces in the project should fit
together before fieldingthe project!
Setting up a conjoint Analysis Project
Random Design
D-Optimal Design
Import Design
Survey Analytics offers 3 Conjoint Analysis Designs
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.
3 Conjoint Analysis Designs - Defined
# 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 forEach attributes
EXAMPLE:
Feature: HoursLevels: 1-2hr, 2-4hr
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 tochoose.
#Prohibited Pairs
Example: A Weird Seattle Tour will never be 4-6 hrs long
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.
D-Optimal Design
Click on Settings >> Design Type >> Doptimal >> Select Versions >> Start >> Save Settings
D-Optimal Design
Click on Settings >> View Options>> Make changes >> Update Design
Import Design
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’
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.
#5 Conjoint Analysis Survey Preview
#5 Conjoint Analysis Preview with Pictures
Review Data: Utility Calculation & Relative Importance
Relative Importance of attributesDisplayed as Pie chart
*Shows here that Tour TypeIs the most significant feature/attribute which determines what tour they want to take.
Relative Importance
Relative Importance and Average Utility Table
The tour type isthe most important attribute
Weird is GOOD! Chocolate is popular
Best & Worst Profile
The tour type is best liked.Weird works.
The tour type is best liked.Weird works.
Market Segmentation SimulatorUsing existing Data from Conjoint Analysis
Market Segmentation SimulatorMarket Segment Simulator gives you the ability to "predict" the market share of new products and concepts that may not exist 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.
Setting up a Simulator
1) Click on Online tools >>Name Simulator Profile>>change profiles
2) Click on to see results!
Results: Simulator Output Defined The market simulator uses utility values to project the probability of choice and hence the market share
Now that we know how to use this . .
What can we askand find out with the Market Segmentation Simulator?
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.
Tips for A Successful Conjoint Analysis Project
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
Best Practices: Where to Begin?
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.
What Sample Size To Start With?
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 attributesKeep 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 confidentialKeep survey to 15-20 minutes
Provide incentives
# 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
Esther LaVielle [email protected] http://www.surveyanalytics.comAndrew [email protected] [email protected]
Q&A
Thank you for Attending!