ChainsawConjoint       Andrew Jeavons     CEO, Survey Analytics         Esther LaVielleVice President, Survey Analytics
About Survey Analytics A suite of interconnected and easy-to-use information collection andanalysis tools, including onlin...
Andrew Jeavons                      Esther LaVielle   CEO of Survey Analytics        Vice President of Client Services    ...
Webinar Agenda1. What do we mean by Chainsaw Conjoint?2. The theory and logic behind discrete choice conjoint analysis3. W...
What Do We Mean by “Chainsaw” Conjoint?                     • Simple                     • Powerful                     • ...
What is Conjoint Analysis?Type of Trade-off Analysis methodologyDeveloped over the past 50 years by market researchers and ...
Statistics are...•Not accurate...•Not magic...•As good as your design...•As good as your sample...
The Kitchen Sink is Not   a Good   Idea...
Wanna Buy A Puppy?• Breed• Dog Breeder• Size• Price• Care Needed• Personality• Life Span
Theory & Logic of Conjoint AnalysisIt will help you evaluate new products or variations against an existing range of produ...
How do we come up with our numbers ?Survey Analytics uses a maximum likelihood calculation coupled witha Nelder-Mead Simpl...
Conjoint Analysis               Core Concepts1)Attributes/Feature:Define the attributes of the products for yourmarket. The...
CORE CONCEPTS Conjoint Analysis Core Concepts:3) Utility Value or Part Worth functions:These are what are produced by the ...
Setting up a Conjoint Analysis ProjectKind of reminds me of   putting together a     jigsaw puzzle…..  All the pieces in t...
Survey Analytics offers 3           Conjoint Analysis DesignsRandom DesignD-Optimal Design Import Design
3 Conjoint Analysis Designs - DefinedRandom: Random design is a purely random sample of the possible   attribute levels. Fo...
# How to Set up a Conjoint Analysis QuestionQuestion Setup for   Random, D-Optimal,   and Import Design   are the same:   ...
How to Set up Conjoint Analysis ParametersSet up Prohibited PairsThe engine will not display two levels that have been mar...
Prohibited PairsExample: A Weird Seattle Tour will never be 4-6 hrs long
Concept SimulatorThis can be used to determine what choices will be presented to the respondents when your survey is actua...
D-Optimal DesignClick on Settings >> Design Type >> Doptimal>> Select Versions >> Start >> Save Settings
D-Optimal DesignClick on Settings >> View Options>> Make changes >> Update Design
Import DesignImport Design allows designs, in the SPSS design format, to be imported and used by theSurvey Analytics DCM m...
Conjoint Analysis Survey Preview
Conjoint Analysis Preview with Pictures
  Review Data:Utility Calculation & Relative Importance
Relative ImportanceRelative Importance of attributesDisplayed as Pie chart*Shows here that Tour TypeIs the most significant...
Relative Importance and Average Utility TableThe tour type isthe most important attribute                               We...
Best & Worst ProfileThe tour type is best liked.Weird works.   The tour type is best liked.   Weird works.
Market Segmentation Simulator    Using existing Data from Conjoint Analysis
Market Segment Simulator gives youMarket Segmentation existthe ability to "predict" the market share of newproducts and co...
Setting up a Simulator1) Click on Online tools >>Name Simulator Profile>>change profiles2) Click on                   to see...
Results: Simulator Output Defined The market simulator uses utility values to project theprobability of choice and hence th...
Now that we know how to use this . . What can we askand find out with the Market Segmentation Simulator?
Segmentation SimulatorQuick Example: What happens if have a tour of 1-2 hours asopposed to 4-6 hours in the afternoon for ...
Tips for A Successful Conjoint Analysis Project
Best Practices: Where to Begin?You must use qualitative research first!What are the top attributes? What range? What langua...
What Sample Size To Start With?Sample size is a question that comes up very frequently. Richard Johnson, one of theinvento...
Practices & Tips: Surveys with Conjoint AnalysisKeep the options clear and simple as possibleNo more than 20 trade-off exer...
Survey Analytics Discrete Choice Conjoint Vs. CompetitionDiscrete Choice Conjoint AnalysisFlexible pricing availableMost u...
Thank	  You!                                  Andrew	  Jeavons,	  andrew.jeavons@surveyanaly9cs.comsales-­‐team@surveyanal...
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Chainsaw Conjoint

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Discrete Choice Conjoint that Cuts Through the Clutter

Are you sick of messing around with discrete choice conjoint software that’s too complicated?

Do you want to run conjoint without all kinds of extras you don’t need?

Are you tired of paying too much for conjoint?

Do you want to run your conjoint study without reading a manual?

In this webinar Survey Analytics CEO Andrew Jeavons and VP Esther LaVielle held a discussion of discrete choice conjoint and gave a demonstration of Survey Analytics' straightforward and powerful conjoint tool.

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Chainsaw Conjoint

  1. 1. ChainsawConjoint Andrew Jeavons CEO, Survey Analytics Esther LaVielleVice President, Survey Analytics
  2. 2. About Survey Analytics A suite of interconnected and easy-to-use information collection andanalysis tools, including online surveys, mobile data collection, advanced analytics and data visualization.  Enterprise Research Platform Mobile Visualization & Analytics   Mobile Field Data Collection Mobile Surveys and Panels Mobile Passive Data Collection
  3. 3. Andrew Jeavons Esther LaVielle CEO of Survey Analytics Vice President of Client Services at Survey Analytics25 years in the market research industry. Background in Esther is in charge of worldwide psychology and statistics, and client relations. With thecurrently focuses on innovation assistance of her colleagues, within survey research. Survey Analytics has a solid support network for clients.
  4. 4. Webinar Agenda1. What do we mean by Chainsaw Conjoint?2. The theory and logic behind discrete choice conjoint analysis3. When to use discrete choice conjoint in your research4. Specific examples of how to use discrete choice conjoint5. How to design a discrete choice conjoint project6. How to write a discrete choice conjoint questionnaire7. How to analyze the results of a discrete choice conjoint project8. Tips and Best Practices9. Q & A
  5. 5. What Do We Mean by “Chainsaw” Conjoint? • Simple • Powerful • Easy to Use • Durable • Impressive • Draws Attention • Gets the Job Done
  6. 6. What is Conjoint Analysis?Type of Trade-off Analysis methodologyDeveloped over the past 50 years by market researchers and statisticians topredict the kinds of decisions consumers will make about products by usingquestions in a survey.Conjoint analysis questions presents a series of possible products to consumers andasks 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 tothem.Survey Analytics uses Discrete Choice Conjoint Analysis which best simulates thepurchase process of consumers
  7. 7. Statistics are...•Not accurate...•Not magic...•As good as your design...•As good as your sample...
  8. 8. The Kitchen Sink is Not a Good Idea...
  9. 9. Wanna Buy A Puppy?• Breed• Dog Breeder• Size• Price• Care Needed• Personality• Life Span
  10. 10. Theory & Logic of Conjoint AnalysisIt will help you evaluate new products or variations against an existing range of productsalready offered by your company or within the marketplace.It’s much cheaper than developing new products for the marketplace with no guarantee ofsuccess.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 receivedin the marketplace.Gauge the affect on the choice/price relationship relativeto existing products and features presented.
  11. 11. How do we come up with our numbers ?Survey Analytics uses a maximum likelihood calculation coupled witha Nelder-Mead Simplex algorithm.Design options are random, D-Optimal or your own imported design. Have greater confidence in the results you receive !
  12. 12. Conjoint Analysis Core Concepts1)Attributes/Feature:Define the attributes of the products for yourmarket. These are the properties ofyour product.Seattle Tourism Study:#HoursTime of DayTour Type2) Levels: The different properties of theattributes. Define at least two levels for eachof the attributes. Hours - 3 levelsTime of day - 4 levelsTour Type: 5 levels
  13. 13. CORE CONCEPTS Conjoint Analysis Core Concepts:3) Utility Value or Part Worth functions:These are what are produced by the conjoint analysis. Thesecan then be used to determine how important an attributeis to the purchase or choice process and in “marketsimulations.”Utility Value of Hrs on Tour:1-2hrs = .392-4hrs = .454-6hrs = .324) Relative importance:How important an attribute is in the purchasing/choicedecision ? Example:Of all features to go on tour –“Time of day” determined which one most chosen
  14. 14. Setting up a Conjoint Analysis ProjectKind of reminds me of putting together a jigsaw puzzle….. All the pieces in the project should fittogether before fielding the project!
  15. 15. Survey Analytics offers 3 Conjoint Analysis DesignsRandom DesignD-Optimal Design Import Design
  16. 16. 3 Conjoint Analysis Designs - DefinedRandom: Random design is a purely random sample of the possible attribute levels. For the number of tasks per respondent SurveyAnalytics 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 SurveyAnalytics DCM module. This is useful when users want to use designs not generated by SurveyAnalytics, such as fractional factorial orthogonal designs.
  17. 17. # How to Set up a Conjoint Analysis QuestionQuestion Setup for 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
  18. 18. How to Set up Conjoint Analysis ParametersSet up Prohibited PairsThe engine will not display two levels that have been marked as"Prohibited" in the same concept (as a product) for the user tochoose.
  19. 19. Prohibited PairsExample: A Weird Seattle Tour will never be 4-6 hrs long
  20. 20. Concept SimulatorThis can be used to determine what choices will be presented to the respondents when your survey is actually deployed. Use as Guidance.
  21. 21. D-Optimal DesignClick on Settings >> Design Type >> Doptimal>> Select Versions >> Start >> Save Settings
  22. 22. D-Optimal DesignClick on Settings >> View Options>> Make changes >> Update Design
  23. 23. Import DesignImport Design allows designs, in the SPSS design format, to be imported and used by theSurvey Analytics DCM module. This is useful when users want to use designs not generatedby Survey Analytics, such as fractional factorial orthogonal designs.Step 1: Start by adding a Conjoint DCM question asis walked through above. Ensure that under Task Count and Concepts PerTask you choose the same numbers as that youhave in the Excel sheet you aregoing to importStep 2: Click on Settings.In the in-line popup in ’Design type choose Import’
  24. 24. Conjoint Analysis Survey Preview
  25. 25. Conjoint Analysis Preview with Pictures
  26. 26.   Review Data:Utility Calculation & Relative Importance
  27. 27. Relative ImportanceRelative Importance of attributesDisplayed as Pie chart*Shows here that Tour TypeIs the most significant feature/attribute which determines whattour they want to take.
  28. 28. Relative Importance and Average Utility TableThe tour type isthe most important attribute Weird is GOOD! Chocolate is popular
  29. 29. Best & Worst ProfileThe tour type is best liked.Weird works. The tour type is best liked. Weird works.
  30. 30. Market Segmentation Simulator Using existing Data from Conjoint Analysis
  31. 31. Market Segment Simulator gives youMarket Segmentation existthe ability to "predict" the market share of newproducts and concepts that may not Simulator today.Ability to measure the "Gain" or "Loss" in market share based on changes to existingproducts in the given market. Important steps in Conjoint Simulation:1- Describe/Identify the different products or concepts that you want to investigate. Wecall "Profiles".Example: Tour Type: Weird, Hours: 1-2 , Time of Day: Evening2- Find out all the existing products that are available in that market segment andsimulate 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 newproducts and configurations.
  32. 32. Setting up a Simulator1) Click on Online tools >>Name Simulator Profile>>change profiles2) Click on to see results!
  33. 33. Results: Simulator Output Defined The market simulator uses utility values to project theprobability of choice and hence the market share
  34. 34. Now that we know how to use this . . What can we askand find out with the Market Segmentation Simulator?
  35. 35. Segmentation SimulatorQuick Example: What happens if have a tour of 1-2 hours asopposed 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.
  36. 36. Tips for A Successful Conjoint Analysis Project
  37. 37. 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 studyUse Crowdsourcing tools: IdeaScale+ Other survey methods
  38. 38. What Sample Size To Start With?Sample size is a question that comes up very frequently. Richard Johnson, one of theinventors of conjoint analysis, has presented the following rule of thumb forsample size in choice based conjoint:(nta/C) > 1000Where n = the number of respondents x t= the number of tasks x a=the number ofalternatives 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 taskand the maximum number of levels on an attribute is 3 you get:(500 x 3 x 2) / 3 = 1000Generally speaking sample sizes tend to be around 200 – 1200 respondents, admittedlya wide range. 300 comes up most often for a single homogeneous group of subjects.
  39. 39. Practices & Tips: Surveys with Conjoint AnalysisKeep the options clear and simple as possibleNo more than 20 trade-off exercisesNo more than 5-6 attributesKeep the ranges simple You can ask more intimate questions of currentcustomers than potential customers, but don’t let thatstop you from trying!Follow general good online survey techniquesTest your surveyMake it clear responses are kept strictly confidentialKeep survey to 15-20 minutesProvide incentives
  40. 40. Survey Analytics Discrete Choice Conjoint Vs. CompetitionDiscrete Choice Conjoint AnalysisFlexible pricing availableMost user-friendly conjoint tool on the marketReal-time reportingPricing includes integrated research tools that would enhance efficiencies anddepth and research strategiesDedicated account management and support included
  41. 41. Thank  You! Andrew  Jeavons,  andrew.jeavons@surveyanaly9cs.comsales-­‐team@surveyanaly9cs.com Esther  LaVielle,  esther.rmah@surveyanaly9cs.com 800-­‐326-­‐5570

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