Forecasting Nike’s Sales using Facebook Data

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Forecasting Nike’s Sales using Facebook Data

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dc.contributor.author Boldt, Linda Camilla
dc.contributor.author Vinayagamoorthy, Vinothan
dc.contributor.author Winder, Florian
dc.contributor.author Schnittger, Melanie
dc.contributor.author Ekram, Mats
dc.contributor.author Mukkamala, Raghava Rao
dc.contributor.author Lassen, Niels Buus
dc.contributor.author Flesch, Benjamin
dc.contributor.author Hussain, Abid
dc.contributor.author Vatrapu, Ravi
dc.date.accessioned 2017-01-24T09:23:41Z
dc.date.available 2017-01-24T09:23:41Z
dc.date.issued 2017-01-24
dc.identifier.uri http://hdl.handle.net/10398/9448
dc.description.abstract 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. en_US
dc.format.extent 10 en_US
dc.language eng en_US
dc.subject.other Predictive Analytics en_US
dc.subject.other Big Data Analytics en_US
dc.subject.other Big Social Data en_US
dc.subject.other Event Study en_US
dc.subject.other Nike en_US
dc.subject.other Facebook Data Analytics en_US
dc.title Forecasting Nike’s Sales using Facebook Data en_US
dc.type cp en_US
dc.accessionstatus modt17jan24 soma en_US
dc.contributor.corporation Copenhagen Business School. CBS en_US
dc.contributor.department Institut for IT-Ledelse en_US
dc.contributor.departmentshort ITM en_US
dc.contributor.departmentuk Department of IT Management en_US
dc.contributor.departmentukshort ITM en_US
dc.description.notes Paper presented at the 2016 IEEE International Conference on Big Data (IEEE BigData 2016). December 5-8 2016, Washington DC, USA en_US
dc.publisher.city ´Frederiksberg en_US
dc.publisher.year 2017 en_US


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