Stated preference methods and analysis
•
4 likes•3,300 views
Choice based conjoint, Max-Diff, best Worst scaling
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Stated preference methods and analysis
Similar to Stated preference methods and analysis (20)
Recently uploaded
Recently uploaded (20)
Stated preference methods and analysis
- 1. DATA MINING AND STATISTICAL ANALYSIS SOLUTIONS Stated Preference methods and analysis
- 2. Preferences Datamotus LLC 2 Revealed Preference vs. Stated Preference
- 3. Revealed Preference “ Revealed preference theory, pioneered by American economist Paul Samuelson, is a method of analyzing choices made by individuals, mostly used for comparing the influence of policies on consumer behavior. These models assume that the preferences of consumers can be revealed by their purchasing habits. ” Datamotus LLC 3
- 4. Stated Preference “ Stated Preference methods are a family of survey methods measuring people’s preferences based on decision-making in hypothetical choice situations. “ Datamotus LLC 4
- 5. Major types of Stated preference methods Datamotus LLC 5 Contingent valuation Best-worst Scaling Discrete Choice Experiments (Conjoint analysis)
- 6. Contingent Valuation (Willingness to pay studies) Datamotus LLC 6
- 7. Contingent valuation Contingent valuation is a survey-based economic technique for the valuation of non-market resources` (Willingness-to-pay) • Benefits/values of reforms • Non-material actives, such as theaters, museums, historical places, etc
- 9. Why ? 1-Very unimportant 2 3 4 5-Very Important Price On Net minutes Off net minutes Free device Unlimited Data Lets say you want to understand what feature of the telco plan customers value the most. You ask the following questions: What is wrong with this?
- 10. Why ? When you ask the buyers/respondents to rate the importance or preference for a bunch of features/attributes, often you don get enough differentiation…. As buyers wants the best with the lowest price. On a scale from 1 to 5, where 1=very unimportant and 5=very important, how important are each of the following features when you chose a mobile plan? 1-Very unimportant 2 3 4 5-Very Important Price On Net minutes Off net minutes Free device Unlimited Data
- 12. What to do ? Datamotus LLC 12 Problem: • We have a bunch of attributes/features that need to be rated by their importance Solution: • Create sets of attributes and ask the respondent to chose best and worst, or Most and Least Important attributes
- 14. Example Datamotus LLC 14 Nine major public policy issues: Need to understand public sector spending priority Source: Louviere, Jordan J., Terry N. Flynn, and A. A. J. Marley. Best-worst scaling theory, methods and applications. Cambridge: Cambridge U Press, 2015. Print.
- 15. Best-worst scaling (Case 1) Steps: 1. Choice set Design 2. Data Collection 3. Data Analysis Datamotus LLC 15
- 16. Design Datamotus LLC 16 Source: Louviere et al.
- 17. Best-worst scenarios Datamotus LLC 17 Source: Louviere et al.
- 18. Simple Analysis Datamotus LLC 18 Source: Louviere et al.
- 20. Conjoint Datamotus LLC 20 Approximates the real life situation
- 21. How it works Datamotus LLC 21
- 22. Inputs Datamotus LLC 22 Data Free SMS Free Calls Monthly $ Brand 1 GB 100 On net $50 Carrier 1 2 GB 300 All Net $60 Carrier 2 3 GB 400 $70 Carrier 3 4 GB 500 $80 5 GB 600
- 23. Inputs Datamotus LLC 23 Data Free SMS Free Calls Monthly $ Brand 1 GB 100 On net $50 Carrier 1 2 GB 300 All Net $60 Carrier 2 3 GB 400 $70 Carrier 3 4 GB 500 $80 5 GB 600 Attributes
- 24. Inputs Datamotus LLC 24 Data Free SMS Free Calls Monthly $ Brand 1 GB 100 On net $50 Carrier 1 2 GB 300 All Net $60 Carrier 2 3 GB 400 $70 Carrier 3 4 GB 500 $80 5 GB 600 Levels
- 25. Design of experiment • Depends on number of attributes and levels • Depends on the effects that need to be estimated • Main effects • Interaction terms Datamotus LLC 25
- 26. Design of experiment • Lets say we have 5 attributes with 4 level each. • Total number of all alternatives is 1024 • We can decrease this number using experimental design • Fractional Factorial Design • Balanced Incomplete Block design • Orthogonal Main Effect Designs, etc Datamotus LLC 26
- 27. How long will it take? Datamotus LLC 27 Source: Sawtoothsoftware research papers
- 28. Data Collection • Online survey, tools such as Qualtrics, Survey Monkey, Sawthooth, etc Datamotus LLC 28
- 29. General outcome of the Conjoint Analysis • Determine which product people prefer. • Market share simulation for the given product bundle • Look at the tradeoffs among different possible features. • Determine the ranking of attributes in determining choice (Attribute importance). • Compute willingness to pay for feature upgrades (How much are customers willing to pay to go from 2GB to 3GB?). • Compute brand equity (Will the change in price make people chose another brand?) Datamotus LLC 29
- 30. Decision Support System Datamotus LLC 30
- 34. Needs-Based Segmentation • Groups survey respondents into like groups based on their similar needs (utilities). • Customers are grouped into segments that place similar value on the product features included in the study. • Helps to understand what different segments need from your product or service, recommendations on which segments you should target, and implications for your portfolio. There’s no requirement to collect additional data, but there is a need for additional analysis. Datamotus LLC 34
- 35. Resources • The only online course: Predict Consumer Decision with Choice-Based Conjoint • Sawtooth Software is the leader in the market. You can find a lot of research papers and technical documentation on their website. • Aizaki, H., Nakatani, T., & Sato, K. (2015). Stated preference methods using R. Boca Raton, Fla: CRC Press, Taylor & Francis. • Louviere, J. J., Flynn, T. N., & Marley, A. A. (2015). Best-worst scaling theory, methods and applications. Cambridge: Cambridge University Press. Software and Services • Sawtooth • Qualtrics • Latent Gold Analysis • R ! Datamotus LLC 35
- 36. Datamotus LLC 36 Datamotus LLC, Marshal Baghramyan 40, room 317W Tel: 060 612 699 Email: habet@datamotus.com
- 37. Technical Note Phase 1: Alternative set design Alternative design options depend on the number of attributes, levels and the effects that need to be estimated (main effects, two- way interactions, higher order interactions). Options for the alternatives design: OMED (Orthogonal Main effect Design), Orthogonal design, full-factorial design, fractional factorial design, etc. The best design is usually found using Fedorov’s algorithm by optimizing D-Efficiency coefficient. Phase 2: Choice Set design Choice set design is done either with LMA (labeled conjoint), mix- and-match or rotation design (unlabeled conjoint) Datamotus LLC 37
- 38. Technical Note • Phase 4: Data Collection • Phase 5: Data Analysis, this includes • Using conditional logit or multinomial logit if aggregated utility levels are required • Hierarchical Bayesian estimate – used to calculate individual level utilities (usually gives better results than aggregated analysis). HB allows to use demographic variables in estimation as well • Latent Class Analysis: Divides respondents into distinct segments based on choice preference data. This is done based on Hierarchical Bayes. Datamotus LLC 38
- 39. Technical Note • Phase 6: Deliverables • Utilities for each attribute/level for each respondent (individual level) • Utilities for each attribute/level (aggregated from Hierarchical Bayesian method) • Importance of each attribute for each distinct class/segment • Marginal willingness to pay • Market share simulation. Datamotus LLC 39