Deriving the Pricing Power of Product Features by Mining Consumer Reviews

Management Science, Forthcoming

NET Institute Working Paper No. 07-36

36 Pages Posted: 27 Oct 2007 Last revised: 6 May 2011

Nikolay Archak

New York University (NYU) - Leonard N. Stern School of Business

Anindya Ghose

New York University (NYU) - Leonard N. Stern School of Business

Panagiotis G. Ipeirotis

New York University - Leonard N. Stern School of Business

Date Written: February 10, 2010

Abstract

Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted and hence, the textual content of product reviews is an important determinant of consumers’ choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique dataset from Amazon, containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers’ relative preferences for different product features and, also, how text can be used for predictive modeling of future changes in sales.

Keywords: user-generated content, consumer reviews, e-commerce, econometrics, electronic markets, sentiment analysis, text mining

Suggested Citation

Archak, Nikolay and Ghose, Anindya and Ipeirotis, Panagiotis G., Deriving the Pricing Power of Product Features by Mining Consumer Reviews (February 10, 2010). Management Science, Forthcoming; NET Institute Working Paper No. 07-36. Available at SSRN: https://ssrn.com/abstract=1024903

Nikolay Archak

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
New York, NY NY 10012
United States

Anindya Ghose (Contact Author)

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
New York, NY NY 10012
United States

Panagiotis G. Ipeirotis

New York University - Leonard N. Stern School of Business ( email )

44 West Fourth Street
Ste 8-84
New York, NY 10012
United States
+1-212-998-0803 (Phone)

HOME PAGE: http://www.stern.nyu.edu/~panos

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