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A comparison between choice experiments and prediction markets for collecting preference data in conjoint analysis
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A comparison between choice experiments and prediction markets for collecting preference data in conjoint analysis

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A comparison between choice experiments and prediction markets for collecting preference data in conjoint analysis A comparison between choice experiments and prediction markets for collecting preference data in conjoint analysis Presentation Transcript

  • © Hajime MizuyamaA comparison between choice experimentsand prediction markets for collectingpreference data in conjoint analysisHajime MizuyamaDept. of Industrial & Systems Engineering,Aoyama Gakuin Universitymizuyama [at] ise.aoyama.ac.jpISOMS 2013 @ Osaka 2/June/2013
  • © Hajime Mizuyama• In todays changing market, developing a new product is vital for a companyto survive and sustain.• The product should be made different from competing ones and attractive totarget customers by introducing new attributes.• Candidate attributes should be devised and properly evaluated, and the bothtasks can be supported by crowdsourced knowledge and intelligence.• This talk focuses on the conjoint analysis, which has been widely used forsupporting the evaluation task.• It evaluates the partial utility of each candidate attribute based on preferencedata collected from potential customers through virtual choice experiments.Research background
  • © Hajime Mizuyama• Conducting choice experiments on crowds is tedious and costly, and it is noteasy to properly motivate them to express their true preference.• A potentially effective solution to this issue is to gamify the process ofcollecting preference data, and it can be realized through prediction markets.• To present how to utilize prediction markets for collecting preference dataand how to derive the partial utilities of candidate attributes from the data.• To actually conduct conjoint analysis on a simple example problem usingchoice experiment approach and prediction market approach respectively.• To confirm that the two approaches lead to similar conclusions and henceprediction markets can be substituted for choice experiments.Research objective
  • © Hajime Mizuyama• Research background and objective• Choice experiment approach• Prediction market approach• Comparison between the approaches: A simple case• ConclusionsAgenda
  • © Hajime Mizuyama• There are N candidate attributes, B1, B2, …, BN, which can be incorporated intothe new product under development.• The concept of a new product can be expressed as a bundle of attributes.Thus, any possible subset of the candidate attributes represents a newproduct concept.• The empty set corresponds to the product concept which does not includeany newly proposed attributes and its attractiveness is treated as the baseline.• Each possible product concept can be denoted by x = (x1, x2, …, xN)T,where xn = 1 represents that the attribute Bn is included in the conceptand xn = 0 indicates that it is not.Product concepts and attributes
  • © Hajime Mizuyama• Various small subsets of possible product concepts x1, x2, … to be comparedare created through the design of experiments technique*.• For each of the subset, virtual choice experiments are conducted on somepotential customers.• That is, the customers are asked to choose a product to buy from the subset,assuming a shopping occasion where only the products in the subset areavailable.• The choice data collected in this manner are then used to statisticallyestimate the attribute utilities according to the logit choice model.Choice experiment approach: How to derive partial utilities* E.g., Design and Analysis of Choice Experiments Using R: A Brief IntroductionH. Aizaki and K. NishimuraAgricaltural Information Research, vol.17, no.2, pp. 86-94 (2008)
  • © Hajime MizuyamaOverview of choice experiment approachAttribute B1Attribute B2Attribute B3Subset 1Subset 2Subset 3CombineAttributeutilitiesa1, a2, …DeriveCollectingpreferenceson conceptsthroughchoiceexperimentsChoicedataEvaluate
  • © Hajime Mizuyama• The more attractive the product concept x is, the higher the probability acustomer chooses it from among alternatives.• The logit choice model defines the probability SA(x) as:where A(x) is the attractiveness of the product concept x, defined as:The attractiveness of the baseline concept is taken as a unit, an represents thepartial utility added by including the attribute Bn, and a = (a1, a2, …, aN)T.Logit choice model
  • © Hajime Mizuyama• Research background and objective• Choice experiment approach• Prediction market approach• Comparison between the approaches: A simple case• ConclusionsAgenda
  • © Hajime Mizuyama• Some subsets of possible product concepts x1, x2, … to be compared arecreated through the design of experiments technique.• For each of the subset, likely values of their relative market shares p1, p2, …are evaluated as a set through a prediction market*.• That is, some potential customers etc. are asked to trade prediction securitiescorresponding to the products in the subset, whose payoffs are proportionalto the relative market share values.• The relative market share values p1, p2, … are then used to statisticallyestimate the attribute utilities according to the logit market share model.Prediction market approach: How to derive partial utilities*A Knowledge-Driven Approach Using Prediction Markets for Planning and Marketing of a New ProductH.MizuyamaProc. of the 4th World Conference on Production and Operations Management, July (2012)
  • © Hajime MizuyamaOverview of prediction market approachAttribute B1Attribute B2Attribute B3Attributeutilitiesa1, a2, …DeriveSubset 1Subset 2Subset 3CombineCollectingpreferenceson conceptsthroughpredictionmarketsRelativemarket sharesp1, p2, …Evaluate
  • © Hajime MizuyamaHow to use a prediction marketA market for EMSPScontrolled by a CMMBid & ask offersMarket prices= EstimatedsharesRelativemarket sharepredictionsecurity(RMSPS)The designer, some other employees,loyal customers, etc. of the company.Conceptx1Conceptx2Conceptx3Payoffs proportionalto the sharesestimated bythe whole results
  • © Hajime Mizuyama• The more attractive the product concept x is, the higher the market share itwill achieve if actually launched.• The logit market share model defines the market share SA(x) as:where A(x) is the attractiveness of the product concept x, defined as:The attractiveness of the baseline concept is taken as a unit, an represents thepartial utility added by including the attribute Bn, and a = (a1, a2, …, aN)T.• The model can be linearlized as:Logit market share model
  • © Hajime Mizuyama• Research background and objective• Choice experiment approach• Prediction market approach• Comparison between the approaches: A simple case• ConclusionsAgendaDo these approaches leadto similar conclusions?
  • © Hajime MizuyamaBaseline design:• Service facility (a Mahjong parlor targeting college students),which is more than 10 minutes away from a station, located on the groundfloor of a building, playing pop music for BGM, about 10 people capacity, andprohibiting smoking.Candidate attributes:B1: Within 5min. B2: Within 10 min.B3: Underground floor B4: Upper floorB5: Classical music B6: JazzB7: 30 people cap. B8: 50 people cap.B9: Smoking separated B10: Smoking allowedCase description
  • © Hajime MizuyamaHow comparison is madeChoice experiments:• 6 students at Aoyama GakuinUniversity• 27 choice experiments:A choice is made from among 3candidate shop profiles.• Partial utilities are derived from thepreference data (choice data)based on the logit choice model.• Relative market shares areestimated using the model.Prediction markets:• 6 students at Aoyama GakuinUniversity• 9 market sessions (LMSR):6 candidate shop profiles arecompared.• Relative market shares areevaluated according to the marketprices.• Partial utilities are derived from therelative market shares based onthe logit market share model.
  • © Hajime Mizuyama0.10 0.15 0.20 0.250.00.10.20.30.4Shares estimated by choice experimentsSharesestimatedbypredictionmarketsEstimated market sharesCorrelationcoefficient= 0.69Relative shares estimated by choice experimentsRelativesharesestimatedbypredictionmarkets
  • © Hajime MizuyamaB1 B2 B3 B4 B5 B6 B7 B8 B9 B10-0.50.00.51.01.5B1 B2 B3 B4 B5 B6 B7 B8 B9 B10-0.20.20.40.6Estimated partial utilitiesPartial utilities estimated by choice experimentsPartial utilities estimated by prediction marketsCorrelationcoefficient= 0.90
  • © Hajime Mizuyama• Research background and objective• Choice experiment approach• Prediction market approach• Comparison between the approaches: A simple case• ConclusionsAgenda
  • © Hajime Mizuyama• A prediction market approach to collecting preference data from the crowdfor conjoint analysis is proposed.• It is confirmed with an example conjoint analysis problem that the proposedapproach and a conventional approach using choice experiments lead tosimilar conclusions.• Thus, it seem that the proposed prediction market approach can besubstituted for tedious choice experiments.• However, this is a tentative conclusion based only on a single case. Furtherresearch is needed by piling up case studies.Conclusions
  • Thank you for your kind attention!Questions and comments are welcome.mizuyama [at] ise.aoyama.ac.jp