Forecasting Nike’s Sales using Facebook Data

Linda Camilla Boldt, Vinothan Vinayagamoorthy, Florian Winder, Schnittger Melanie, Mats Ekram, Raghava Rao Mukkamala, Niels Buus Lassen, Benjamin Flesch, Abid Hussain, Ravi Vatrapu

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    This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike’s Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire,and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike’s Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike’s financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-bycase text analysis. Our findings help assess the informational value of Big Social Data for a company’s marketing strategy, sales operations and supply chain.
    Original languageEnglish
    Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
    EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
    Number of pages10
    Place of PublicationPiscataway, NJ
    Publication date2016
    Article number7840881
    ISBN (Print)9781467390057
    ISBN (Electronic)9781467390040
    Publication statusPublished - 2016
    EventFourth IEEE International Conference on Big Data. IEEE BigData 2016 - Washington, DC, United States
    Duration: 5 Dec 20168 Dec 2016
    Conference number: 4


    ConferenceFourth IEEE International Conference on Big Data. IEEE BigData 2016
    Country/TerritoryUnited States
    CityWashington, DC
    Internet address


    • Predictive analytics
    • Big data analytics
    • Big social data
    • Event study
    • Nike
    • Facebook data analytics

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