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Forecasting sales of new and existing products using consumer reviews: A random projections approach

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  • Schneider, Matthew J.
  • Gupta, Sachin

Abstract

We consider the problem of predicting sales of new and existing products using both the numeric and textual data contained in consumer reviews. Many of the extant approaches require considerable manual pre-processing of the textual data, making the methods prohibitively expensive to implement and difficult to scale. In contrast, our approach uses a bag-of-words method that requires minimal pre-processing and parsing, making it efficient and scalable. However, a key implementation challenge with the bag-of-words approach is that the number of predictors can quickly outstrip the number of degrees of freedom available. Furthermore, the method can require impracticably large computational resources. We propose a random projections approach for dealing with the curse-of-dimensionality issue that afflicts bag-of-words models. The random projections approach is computationally simple, flexible and fast, and has desirable statistical properties. We apply the proposed approach to the forecasting of sales at Amazon.com using consumer reviews with an attributes-based regression model. The model is applied to produce of one-week-ahead rolling horizon sales forecasts for existing and newly-introduced tablet computers. The results show that the predictive performance of the proposed approach for both tasks is strong and significantly better than those of either models that ignore the textual content of consumer reviews, or a support vector regression machine with the textual content. Furthermore, the approach is easy to repeat across product categories, and readily scalable to much larger datasets.

Suggested Citation

  • Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
  • Handle: RePEc:eee:intfor:v:32:y:2016:i:2:p:243-256
    DOI: 10.1016/j.ijforecast.2015.08.005
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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. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    5. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    6. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    7. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    8. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    9. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    10. 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.
    11. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    12. Stephan Kolassa & Wolfgang Schütz, 2007. "Advantages of the MAD/Mean Ratio over the MAPE," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 40-43, Spring.
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