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Are Ratings Informative Signals? The Analysis of The Netflix Data

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    Abstract

    The aim of this research is to analyze whether and when ratings are informative signals about the quality of movies. The ratings data of Netflix is used to fit a structural Bayesian learning model. This model links revealed experience utilities of raters, previous consumers, to the product choice of the future consumers of the same good. I postulate that movies are chosen based on the prior beliefs' and signals' precisions. The extent of signals' use depends on their informativeness, that is on how many consumers revealed their preferences before. The results demonstrate that consumers learn about the quality using ratings as signals. The signal produced by one rating is very noisy and might not be taken into account. The more people rate, the better are signals' quality. Consumers are not considerably dispersed in how they value quality.

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    File URL: http://www.netinst.org/Maryanchyk_08-22.pdf
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    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 08-22.

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    Length: 35 pages
    Date of creation: Sep 2008
    Date of revision: Oct 2008
    Handle: RePEc:net:wpaper:0822

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    Keywords: rating; quality; learning; motion pictures;

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