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Google searches and twitter mood: nowcasting telecom sales performance

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  • Jacques Bughin

Abstract

The web currently carries vast amounts of information as to what consumers search for, comment on, and purchase in the real economy. This paper leverages a mash-up of online Google search queries and of social media comments (from Twitter, Facebook and other blogs) to “nowcast” the product sales evolution of the major telecom companies in Belgium. A few findings stand out. With an Error Correction Mechanism (ECM) model of sales dynamics, a co-integration relationship prevails between social media valence (respectively, between search query) and telecom operators’ sales for both internet and digital television access provision (respectively, for fixed telephony provision). Elasticity estimates on sales are relatively larger for valence than for search queries. The ECM model with nowcasting variables improves telecom sales forecasts by about 25 % versus a naïve autoregressive sales model. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    • Handle: RePEc:kap:netnom:v:16:y:2015:i:1:p:87-105
    DOI: 10.1007/s11066-015-9096-5
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    1. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.
    2. Xiaozhong Lyu & Cuiqing Jiang & Yong Ding & Zhao Wang & Yao Liu, 2019. "Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions," Sustainability, MDPI, vol. 11(3), pages 1-18, February.
    3. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    4. N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.

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