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

    Publikation: Kapitel i bog/rapport/konferenceprocesKonferencebidrag i proceedingsForskningpeer review

    Resumé

    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.
    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.
    SprogEngelsk
    TitelProceedings of the 2016 IEEE International Conference on Big Data (BigData '16)
    RedaktørerJames Joshi, George Karypis, Ling Liu
    Udgivelses stedPiscataway, NJ
    ForlagIEEE
    Dato2016
    Sider2447-2456
    ISBN (Trykt)9781467390057
    StatusUdgivet - 2016
    Begivenhed2016 IEEE International Conference on Big Data - Washington, DC, USA
    Varighed: 5 dec. 20168 dec. 2016
    http://cci.drexel.edu/bigdata/bigdata2016/

    Konference

    Konference2016 IEEE International Conference on Big Data
    LandUSA
    ByWashington, DC
    Periode05/12/201608/12/2016
    Internetadresse

    Emneord

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

    Citer dette

    Boldt, L. C., Vinayagamoorthy, V., Winder, F., Melanie, S., Ekram, M., Mukkamala, R. R., ... Vatrapu, R. (2016). Forecasting Nike’s Sales using Facebook Data. I J. Joshi, G. Karypis, & L. Liu (red.), Proceedings of the 2016 IEEE International Conference on Big Data (BigData '16) (s. 2447-2456). Piscataway, NJ: IEEE.
    Boldt, Linda Camilla ; Vinayagamoorthy, Vinothan ; Winder, Florian ; Melanie, Schnittger ; Ekram, Mats ; Mukkamala, Raghava Rao ; Buus Lassen, Niels ; Flesch, Benjamin ; Hussain, Abid ; Vatrapu, Ravi. / Forecasting Nike’s Sales using Facebook Data. Proceedings of the 2016 IEEE International Conference on Big Data (BigData '16). red. / James Joshi ; George Karypis ; Ling Liu. Piscataway, NJ : IEEE, 2016. s. 2447-2456
    @inproceedings{655b5747f8fe410ca39b5ffe0194f3c8,
    title = "Forecasting Nike’s Sales using Facebook Data",
    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.",
    keywords = "Predictive analytics, Big data analytics, Big social data, Event study, Nike, Facebook data analytics, Predictive analytics, Big data analytics, Big social data, Event study, Nike, Facebook data analytics",
    author = "Boldt, {Linda Camilla} and Vinothan Vinayagamoorthy and Florian Winder and Schnittger Melanie and Mats Ekram and Mukkamala, {Raghava Rao} and {Buus Lassen}, Niels and Benjamin Flesch and Abid Hussain and Ravi Vatrapu",
    year = "2016",
    language = "English",
    isbn = "9781467390057",
    pages = "2447--2456",
    editor = "James Joshi and George Karypis and Ling Liu",
    booktitle = "Proceedings of the 2016 IEEE International Conference on Big Data (BigData '16)",
    publisher = "IEEE",
    address = "United States",

    }

    Boldt, LC, Vinayagamoorthy, V, Winder, F, Melanie, S, Ekram, M, Mukkamala, RR, Buus Lassen, N, Flesch, B, Hussain, A & Vatrapu, R 2016, Forecasting Nike’s Sales using Facebook Data. i J Joshi, G Karypis & L Liu (red), Proceedings of the 2016 IEEE International Conference on Big Data (BigData '16). IEEE, Piscataway, NJ, s. 2447-2456, 2016 IEEE International Conference on Big Data, Washington, DC, USA, 05/12/2016.

    Forecasting Nike’s Sales using Facebook Data. / Boldt, Linda Camilla; Vinayagamoorthy, Vinothan; Winder, Florian; Melanie, Schnittger; Ekram, Mats; Mukkamala, Raghava Rao; Buus Lassen, Niels; Flesch, Benjamin; Hussain, Abid; Vatrapu, Ravi.

    Proceedings of the 2016 IEEE International Conference on Big Data (BigData '16). red. / James Joshi; George Karypis; Ling Liu. Piscataway, NJ : IEEE, 2016. s. 2447-2456.

    Publikation: Kapitel i bog/rapport/konferenceprocesKonferencebidrag i proceedingsForskningpeer review

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    AU - Vinayagamoorthy,Vinothan

    AU - Winder,Florian

    AU - Melanie,Schnittger

    AU - Ekram,Mats

    AU - Mukkamala,Raghava Rao

    AU - Buus Lassen,Niels

    AU - Flesch,Benjamin

    AU - Hussain,Abid

    AU - Vatrapu,Ravi

    PY - 2016

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    N2 - 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.

    AB - 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.

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    KW - Big social data

    KW - Event study

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    KW - Facebook data analytics

    KW - Predictive analytics

    KW - Big data analytics

    KW - Big social data

    KW - Event study

    KW - Nike

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    Boldt LC, Vinayagamoorthy V, Winder F, Melanie S, Ekram M, Mukkamala RR et al. Forecasting Nike’s Sales using Facebook Data. I Joshi J, Karypis G, Liu L, red., Proceedings of the 2016 IEEE International Conference on Big Data (BigData '16). Piscataway, NJ: IEEE. 2016. s. 2447-2456.