How to run conjoint analysis
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The webinar discusses the fundamentals of conjoint analysis and its importance in making informed business decisions, featuring presenters from SurveyAnalytics and Zynga. It highlights various methods of choice research that allow respondents to make trade-offs akin to real-world scenarios, enhancing the accuracy of data collected for understanding consumer preferences. The session also covers practical applications and terminology relevant to optimizing product features and pricing strategies.
How to run conjoint analysis
- 1. Thursday, May 28th, 2015 WE WILL BEGIN SHORTLY questionpro.com | surveyanalytics.com Learn How to Make Better Business Decisions
- 2. Conjoint Analysis Webinar : Will Begin Shortly….
- 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
- 6. THE BASICS Bad news: You just lost your job.
- 7. THE BASICS Good news: You have great skills that give you options.
- 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.
- 12. THE BASICS How many times have you seen this type of question?
- 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.
- 23. Naming the Game* Question 1 * Hypothetical game name situation – never did ‘AntVille’ at Zynga.
- 24. Naming the Game* Question 2 * Hypothetical game name situation – never did ‘AntVille’ at Zynga.
- 25. Naming Results* Question 1 * Hypothetical game name, made up data.
- 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
- 29. Conjoint / Max Diff Setup : 1
- 30. Max Diff : Setup
- 31. Max Diff : Settings
- 32. Max Diff : Analysis : Share of Preference Logit Model
- 33. Max Diff : Analysis : Filtering
- 34. Max Diff : Analysis : Comparisons
- 35. Conjoint Analysis : Setup
- 36. Conjoint Analysis : Settings
- 37. Conjoint : Design Model Randomized D-Optimal Upload
- 38. Conjoint Analysis : Data Exploration Attribute Importance Profile Playboard Market Segmentation Simulator Brand Equity Retail Price Equivalence Price Elasticity & Curve
- 42. Market Segmentation : Simulator
- 43. Brand Equity / Retail Price Equivalence
- 44. Price Elasticity
- 45. QUESTIONS 1 (800) 326-5570 E-mail: sales-team@surveyanalytics.com Online Inquiries: www.surveyanaltics.com/contact Nico Peruzzi, Ph.D. 1 (408) 202-1521 E-mail: nperuzzi@orconsulting.com Rob Aseron E-mail: rob@onlineresearch.guru
Editor's Notes
- #46: Accuracy: Quality and accuracy is higher and more reliable.