Towards Automating the Pricing Power of Product Attributes: An Analysis of Online Product Reviews

1. Towards Automating the Pricing Power of Product Attributes: An Analysis of Online Product Reviews Nikolay Archak, Anindya Ghose, Panagiotis Ipeirotis NYU Stern, IOMS department

2. Word-of-Mouth    This camera really is a waterproof pocket camera, and it removes worries of rain, sand, mud, or other substances getting into the camera and ruining it. Its very carefree in that way. Unfortunately, it does such a lousy job of exposing, focusing, and capturing that in place of your worry about rain damage is your new worry about capturing that special moment. 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. It even comes with a nice padded belt case. But after you have seen the results, all you will be thinking about as you frame that next shot is if you'll get home and discover the picture you just took is so bad its unusable. …Comment | Was this review helpful to you?  (Report this) (Report this)

3. Was this review helpful to you? • Bickart & Schindler (2001) • Consumer reviews • User oriented (describe usage scenarios) • Always subjective and may not cover all product characteristics • More credible and trustworthy for consumers • Vendor information • Product oriented (describes technical features) • More objective and complete • Less credible and trustworthy for consumers

4. Is great better than excellent? • Can we assess strength of a consumer review opinion quantitatively? • Yes, if we can measure impact of this opinion on the product demand. • Finally, we can use consumer reviews to predictfuture product sales and improve our pricing strategy.

5. Hedonic goods and hedonic regressions • Hedonic goods: • U(good) = U(feature1, feature2,…,featuren) • Are all goods hedonic? • Hedonic regressions: • log(Demand) = const + elasticity * log(Price) + b1*feature1 + b2*feature2 + … + bn*featuren • Traditionally used by BLS for CPI calculations • Who decides which features to include and how to measure them? BLS official, not consumers.

6. Word-of-Mouth    This camera really is a waterproof pocket camera, and it removes worries of rain, sand, mud, or other substances getting into the camera and ruining it. Its very carefree in that way. Unfortunately, it does such a lousy job of exposing, focusing, and capturing that in place of your worry about rain damage is your new worry about capturing that special moment. 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. It even comes with a nice padded belt case. But after you have seen the results, all you will be thinking about as you frame that next shot is if you'll get home and discover the picture you just took is so bad its unusable. …Comment | Was this review helpful to you?  (Report this) (Report this)

7. Related Work – 1 (Evaluating Polarity of Consumer Reviews) • Looking for occurrences of sentiment phrases • Manually constructed dictionaries (Sanjiv & Chen) • WordNet (Hu & Liu) • Search engines (Turney) • Using machine learning classifiers (Pang, Lee & Vaityanathan) • Naïve Bayes • Max Entropy • SVM • Performance was not as good as in standard text classification. Main issue: consumer review heterogeneity

8. Related Work – 2 (Identifying Product Features) • Solution for heterogeneity problem: identifying product features on which consumers expressed their opinions • Feature-based summaries (Hu & Liu): • Feature: picture quality • – Positive: 97 (“It is easy to use and produces great pictures.”) • – Negative: 10 (“Indoors, this camera is horrible…”) • Feature: size • – Positive: opinion count (“sample sentence”) • – Negative: opinion count (“sample sentence”)

9. Related Work - 3 (“is great better than excellent?”) • Identifying strength of the opinion • Counting scale - Hu & Liu • Supervised learning to classify opinions by subjectivity (weak, medium or strong) – Wilson, Wiebe & Hwa • Our approach – measure impact of consumer reviews on product sales

10. Dataset

11. Extending classic model of product demand

12. Feature Selection • Use Part-Of-Speech tagger to process consumer review • Select frequent nouns and adjectives • Manually process them to select • Evaluation words: “good”, “bad”, “excellent”, “amazing”, “poor”… • Feature words: “lens”, “size”, “quality”, “lcd”…

13. Some issues of feature selection • Implicit features: • “A little noisy in low light, for example on cloudy days, grass will lack sharpness and end up looking like a big mass of green.” • Contents • “i just god married and me and my wife got this camera for our honey moon. we went to england and brugge the trip was great and so was the camera i paired it up with a 1 gig stick and took fulll rez pics the whole trip. bout 400 fit on the disk. only about 3 of the pics turned out fuzzy witch is an extreamly good ratio with the imig stabolization i thought. i came from using a crumy digatal without it will never go back. i am going to get another panasonic in the next few months for my wife to carry in her purce in replace of the one she has in ther right now. going to get one of the ultra compact ones but def going with panasonic again great product and thanks ofr the great shiping amazon”.

14. Feature Selection - Example • As far as plusses, the camera is super high quality, and is relatively easy to use. The lens is fantastic, and the rest of the camera seems to be as equal in quality. I’ve gotten used to the LCD viewfinder, and have been able to use it for some fantasticshots that I might not of otherwise been able to view with the fixed viewfinder... To summarize, this is a very highquality product, well worth the money. Nx – opinion phrase frequency Wx – opinion phrase weight s – smoothing factor

15. Space of consumer reviews • Basis: • For example, fi = ‘quality’, ej = ‘high’ • Each linear functional Ψ has basis representation: • Too many parameters: m*n • Solution: place a rank constraint • Special case (p = 1): independent features and evaluations

16. Variables • Observation • Date • Sales rank (Skt) • Price (P1kt) • Discount (P2kt) • Average consumer rating (Rkt) • Product • Consumer reviews • Date • Contents (Wkt) • Rating • Technical characteristics

17. Model Evaluation weights Feature weights Consumer review information Regularization coefficient

18. Validation (product holdout) • Take away 10% of products and train the model • Predict sales rank for removed products

19. Validation (observation holdout) • Train the model on data before 12.31.2005 • Predict sales rank for remaining dates (4 month) • Take into account autocorrelation:

20. 10-fold cross-validation (product holdout) • Average RMSE improvement 5%