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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    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.
    OriginalsprogEngelsk
    TitelProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
    RedaktørerRonay 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
    Antal sider10
    Udgivelses stedPiscataway, NJ
    ForlagIEEE
    Publikationsdato2016
    Sider2447-2456
    Artikelnummer7840881
    ISBN (Trykt)9781467390057
    ISBN (Elektronisk)9781467390040
    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 R. Ak, G. Karypis, Y. Xia, X. T. Hu, P. S. Yu, J. Joshi, L. Ungar, L. Liu, A-H. Sato, T. Suzumura, S. Rachuri, R. Govindaraju, ... W. Xu (red.), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (s. 2447-2456). [7840881] 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 - 2016 IEEE International Conference on Big Data, Big Data 2016. red. / Ronay 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. 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 = "Ronay Ak and George Karypis and Yinglong Xia and Hu, {Xiaohua Tony} and Yu, {Philip S.} and James Joshi and Lyle Ungar and Ling Liu and Aki-Hiro Sato and Toyotaro Suzumura and Sudarsan Rachuri and Rama Govindaraju and Weijia Xu",
    booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
    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 R Ak, G Karypis, Y Xia, XT Hu, PS Yu, J Joshi, L Ungar, L Liu, A-H Sato, T Suzumura, S Rachuri, R Govindaraju & W Xu (red), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840881, 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 - 2016 IEEE International Conference on Big Data, Big Data 2016. red. / Ronay 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. Piscataway, NJ : IEEE, 2016. s. 2447-2456 7840881.

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

    TY - GEN

    T1 - Forecasting Nike’s Sales using Facebook Data

    AU - Boldt, Linda Camilla

    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

    Y1 - 2016

    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.

    KW - Predictive analytics

    KW - Big data analytics

    KW - Big social data

    KW - Event study

    KW - Nike

    KW - Facebook data analytics

    KW - Predictive analytics

    KW - Big data analytics

    KW - Big social data

    KW - Event study

    KW - Nike

    KW - Facebook data analytics

    M3 - Article in proceedings

    SN - 9781467390057

    SP - 2447

    EP - 2456

    BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

    A2 - Ak, Ronay

    A2 - Karypis, George

    A2 - Xia, Yinglong

    A2 - Hu, Xiaohua Tony

    A2 - Yu, Philip S.

    A2 - Joshi, James

    A2 - Ungar, Lyle

    A2 - Liu, Ling

    A2 - Sato, Aki-Hiro

    A2 - Suzumura, Toyotaro

    A2 - Rachuri, Sudarsan

    A2 - Govindaraju, Rama

    A2 - Xu, Weijia

    PB - IEEE

    CY - Piscataway, NJ

    ER -

    Boldt LC, Vinayagamoorthy V, Winder F, Melanie S, Ekram M, Mukkamala RR et al. Forecasting Nike’s Sales using Facebook Data. I Ak R, Karypis G, Xia Y, Hu XT, Yu PS, Joshi J, Ungar L, Liu L, Sato A-H, Suzumura T, Rachuri S, Govindaraju R, Xu W, red., Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Piscataway, NJ: IEEE. 2016. s. 2447-2456. 7840881