Deriving the Pricing Power of Product Features by Mining Consumer Reviews

1. Deriving the Pricing Power of Product Features by Mining Consumer Reviews Nikolay Archak, Anindya Ghose, Panagiotis Ipeirotis New York University Stern School of Business Information Systems Group, IOMS department

2. Word of “Mouse” • Consumer reviews • Derived from user experience • Describe different product features • Provide subjective evaluations of product features I love virtually everything about this camera....except the lousy picture quality. The camera looks great, feels nice, is easy to use, starts up quickly, and is of course waterproof. It fits easily in a pocket and the battery lasts for a reasonably long period of time. Comment | Was this review helpful to you?  (Report this) (Report this)

3. Research Questions • Are product reviews important? • How consumers use information contained in product reviews? • Can we learn consumer preferences from product reviews and sales data? • How important is each product feature to customers? • What is the pragmatic opinion of customers for each feature?

4. Hedonic goods and hedonic regressions • Not the first to measure weights of product features • Hedonic goods [Rosen, 1974]: • Each good is characterized by the set of its objectively measured features • Preferences of consumers are solely determined by features of available goods • Are all goods hedonic?

5. Research Challenges • Hedonic models cannot deal with imperfectly observable features (important in online shopping environment) • Hedonic models cannot capture beliefs held by consumers • Hedonic models are static. They don’t capture changes in the sales data.

6. Our extension of the hedonic model • Products are modeled as lotteries (subjective probability distributions over features of the product being purchased) • Consumers perform Bayesian learning of product attributes using information from product reviews • Consumers pick the product that maximizes their expected utility (use quadratic utility to accommodate mean and variance)

7. Online shopping is all about beliefs   “excellent image quality” “fantastic image quality” “superb image quality” “great image quality” “fantastic image quality” “superb image quality” Belief for Image Quality Updated Belief for Image Quality Updated Belief for Image Quality Consumers pick the product that maximizes their utility (MNL model)

8. Challenges and Approaches • What are the important product features? • Process the reviews and identify frequent nouns • Quality • Battery • … • How are features evaluated? • Research in linguistics identify adjectives as evaluations • Great quality • Battery lasts for long

9. Quantifying Opinions “excellent image quality” “fantastic image quality” “superb image quality” “great image quality” “fantastic image quality” “superb image quality” Belief for Image Quality Updated Belief for Image Quality Updated Belief for Image Quality • Assume each adjective assigns an (unknown) score • Excellent image quality = 0.9 • Great image quality = 0.8 • … • How do we discover these “scores”?

10. Basic Idea • Examine differences in product demand before and after product review to learn consumer preferences. “excellent lenses” “excellent photos” +3% +6% “poor lenses” “poor photos” -1% -2% • Feature “photos” is twice more important than “lenses” • “Excellent” is positive, “poor” is negative • “Excellent” is three times stronger than “poor”

11. Controlling for endogenous shocks • We control for changes in the product age, number of reviews, average review rating, fraction of positive/negative reviews, price, season (using monthly dummies) etc • We use IV estimator with lag of price as an instrument (precedes review effects) [Villas-Boas and Winer, 1999] • We control for brand-specific shocks by using brand search volume from Google Trends

12. Amazon.com Dataset

13. Some results for Digital Cameras

14. Results: Further Discussion • “Good” is often not good enough • Consumer expectations matter • Quadratic terms are also significant • Consumers show signs of risk aversion

15. Conclusions • Simple economic model of consumer choice in presence of product reviews • Economic interpretation of textual review content • Using text data for learning consumers’ preferences • Our results indicate that text of consumer reviews have significant predictive power for consumer behavior

16. Thank you! • Comments? Questions?

17. Overflow slides

18. Some theoretical results

19. Our Model

20. Using semi-automated review mining • Amazon Mechanical Turk was used for feature and opinion extraction • Opinions were reported in free text format, preserving the original wording • Two workers were assigned to each review, inter-rater agreement score was 34.27%

21. Relevant text mining work • Identifying product features • Hu, Liu (AAAI, 2004) • Ghani, Probst, Liu, Krema, Fano (KDD, 2006) • Scaffidi (2006) • Sentiment classification • Das, Chen (2001) • Turney, Littman (ACL, 2003) • Dave, Lawrence, Pennock (WWW, 2003) • Hu, Liu (KDD, 2004) • Popescu, Etzioni, (EMNLP, 2005) • Opinion Analysis • Hu, Liu, Cheng (WWW, 2005)