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Deriving the Pricing Power of Product Features by Mining Consumer Reviews

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Abstract

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data.

Suggested Citation

  • Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
  • Handle: RePEc:net:wpaper:0736
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    References listed on IDEAS

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    1. Judith Chevalier & Austan Goolsbee, 2003. "Measuring Prices and Price Competition Online: Amazon.com and BarnesandNoble.com," Quantitative Marketing and Economics (QME), Springer, vol. 1(2), pages 203-222, June.
    2. Anindya Ghose & Arun Sundararajan, 2006. "Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges," Papers math/0609170, arXiv.org.
    3. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
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    Citations

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    Cited by:

    1. Weijia Dai & Ginger Z. Jin & Jungmin Lee & Michael Luca, 2012. "Aggregation of Consumer Ratings: An Application to Yelp.com," NBER Working Papers 18567, National Bureau of Economic Research, Inc.
    2. Yi-Fen Chen & Shi-Han Chang, 2016. "The online framing effect: the moderating role of warning, brand familiarity, and product type," Electronic Commerce Research, Springer, vol. 16(3), pages 355-374, September.
    3. Kun Chen & Peng Luo & Huaiqing Wang, 2017. "Investigating transitive influences on WOM: from the product network perspective," Electronic Commerce Research, Springer, vol. 17(1), pages 149-167, March.
    4. Joe Cox & Daniel Kaimann, 2013. "The Signaling Effect of Critics - Evidence from a Market for Experience Goods," Working Papers CIE 68, Paderborn University, CIE Center for International Economics.
    5. Daniel Kaimann & Joe Cox, 2014. "The Signaling Effect of Critics: Do Professionals outweigh Word-of-Mouth? Evidence from the Video Game Industry," Working Papers Dissertations 10, Paderborn University, Faculty of Business Administration and Economics.
    6. Daniel Kaimann, 2014. "Combining Qualitative Comparative Analysis and Shapley Value Decomposition: A Novel Approach for Modeling Complex Causal Structures in Dynamic Markets," Working Papers Dissertations 12, Paderborn University, Faculty of Business Administration and Economics.
    7. repec:spr:elcore:v:17:y:2017:i:4:d:10.1007_s10660-016-9234-7 is not listed on IDEAS
    8. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    9. Yue Ma & Guoqing Chen & Qiang Wei, 2017. "Finding users preferences from large-scale online reviews for personalized recommendation," Electronic Commerce Research, Springer, vol. 17(1), pages 3-29, March.
    10. repec:spr:infosf:v:15:y:2013:i:3:d:10.1007_s10796-012-9400-y is not listed on IDEAS
    11. Bin Guo & Shasha Zhou, 0. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 0, pages 1-24.
    12. Yili Hong & Pei-yu Chen & Lorin Hitt, 2014. "Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications," Working Papers 14-03, NET Institute.
    13. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    14. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    15. Oliver J. Rutz & Michael Trusov & Randolph E. Bucklin, 2011. "Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?," Marketing Science, INFORMS, vol. 30(4), pages 646-665, July.
    16. Philipp Herrmann, 2014. "The impact of the variance of online consumer ratings on pricing and demand – An analytical model," Working Papers Dissertations 07, Paderborn University, Faculty of Business Administration and Economics.
    17. repec:spr:elmark:v:27:y:2017:i:3:d:10.1007_s12525-017-0262-5 is not listed on IDEAS
    18. repec:ibn:ibrjnl:v:10:y:2017:i:9:p:1-16 is not listed on IDEAS
    19. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers CIE 84, Paderborn University, CIE Center for International Economics.
    20. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    21. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    22. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2013. "Platform or Wholesale? Different Implications for Retailers of Online Product," Working Papers 13-14, NET Institute.

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    Keywords

    consumer reviews; e-commerce; econometrics; electronic commerce; electronic markets; hedonic analysis; Internet; opinion mining; product review; sentiment analysis; text mining; user-generated content.;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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