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ESTIMATION OF AUTOMOBILE SALES WITH ARTIFICIAL NEURAL NETWORK USING DATA OF ONLINE CONSUMER ENGAGEMENT AND SEARCH ENGINE

Year 2019, Volume: 9 Issue: 2, 534 - 551, 31.12.2019

İbrahim Topal

Abstract

Estimation of sales volume affects many segments of
the enterprises from raw material purchase to advertising expenses. A large
number of consumer data is needed to be used in sales forecasting. The use of
many consumer data is private, causing problems. On the other hand, data from
social networks and search engines, which are influential in the purchasing
decision process, are valuable and accessible to everyone. For businesses, it
is important to estimate sales figures accurately. For consumers, protecting
their personal data is substantial. In this study, it is aimed to estimate the
sales volume of an automobile brand by using Online Consumer Engagement and
search engine data which are effective in the purchasing decision process of
consumers and publicly available. 2267 posts, created between 2012-2017, likes,
comments, and sharing data of these posts were taken from Facebook brand page
of the business where Online Consumer Engagement is intense using Facebook
Graph API. Search engine data was obtained from Google Trends, and sales data
was obtained from Automotive Distributors Association website. Data were
normalized by Min-Max method and analyzed by feedforward artificial neural
networks and Bayesian Regulation backpropagation method. Successful sales
volume estimation, whose the correlation value is %74 and mean error value is
%1, was made with Facebook brand page and search engine data. In addition,
detailed data covering 6 years has been prepared and presented as descriptive
information. In the study, successful sales estimation was made without using
the private information of the consumers. This study contributes to the sector
and academic literature by relying on real data and using artificial neural
networks in the business administration.

Keywords

Online Consumer Engagement Search Engine Sales Estimation Artificial Neural Networks Bayesian Regulation

References

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  • Kelleher, T. (2009). Conversational voice, communicated commitment, and public relations outcomes in interactive online communication. Journal of Communication, 59(1), 172–188. doi:10.1111/j.1460-2466.2008.01410.x
  • Kim, H. W., Gupta, S. ve Koh, J. (2011). Investigating the intention to purchase digital items in social networking communities: A customer value perspective. Information and Management, 48(6), 228–234. doi:10.1016/j.im.2011.05.004
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ÇEVRİMİÇİ TÜKETİCİ BÜTÜNLEŞMESİ VE ARAMA MOTORU VERİLERİ KULLANILARAK YAPAY SİNİR AĞLARI İLE OTOMOBİL SATIŞ TAHMİNİ

Year 2019, Volume: 9 Issue: 2, 534 - 551, 31.12.2019

İbrahim Topal

Abstract

Satış miktarının tahmin
edilmesi hammadde alımından reklam giderlerinin belirlenmesine kadar
işletmelerde birçok bölüme etki etmektedir. Satış tahmininde kullanılmak üzere çok
sayıda tüketici verisine ihtiyaç duyulmaktadır. Birçok tüketici verisinin
kişisel olması nedeniyle kullanılması sorun oluşturmaktadır. Buna karşın, satın
alma karar sürecinde etkili olan sosyal ağlar ve arama motorlarına ait veriler
değerli olmanın yanında herkesin erişimine açıktır. İşletmeler için satış
rakamlarının gerçeğe yakın tahmin edilmesi ve tüketiciler için ise kişisel
verilerin korunması önemlidir. Bu bağlamda, çalışmada tüketicilerin satın alma
karar sürecinde etkili ve halka açık olan Çevrimiçi Tüketici Bütünleşme ve
arama motoru verileri kullanılarak bir otomobil markasının satış miktarının
tahmin edilmesi amaçlanmıştır. Çevrimiçi Tüketici Bütünleşmesinin yoğun
yaşandığı işletmeye ait Facebook marka sayfasından 2012-2017 yılları arasında
oluşturulan 2267 adet gönderi ve bu gönderilere ait beğenme, yorum ve paylaşma
verileri Facebook Graph API ile alınmıştır. Arama motoru verileri Google
Trends, satış verileri Otomotiv Distribütörleri Derneği web sitesinden elde
edilmiştir. Veriler Min-Max yöntemiyle normalleştirilmiş ve yapay sinir ağları,
Bayesian Regülasyon geri yayılım yöntemiyle analiz edilmiştir. Facebook marka
sayfası ve arama motoru verileriyle %74 korelasyon ve %1 ortalama hata
değeriyle başarılı satış miktarı tahmini yapılmıştır. Ayrıca 6 yılı kapsayan
detaylı veriler düzenlenerek tanımlayıcı bilgiler olarak sunulmuştur.  Çalışmada tüketicilerin özel bilgileri
kullanılmadan başarılı satış tahminlemesi yapılmıştır. Bununla birlikte, çalışma
gerçek verilere dayanması ve yapay sinir ağlarının işletme alanında
kullanımıyla sektöre ve akademik yazına katkı sağlamaktadır.

Keywords

Çevrimiçi Tüketici Bütünleşmesi Arama Motoru Satış Tahmini Yapay Sinir Ağları Bayesian Regülasyon

References

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  • Atsalakis, G. S., Atsalaki, I. G. ve Zopounidis, C. (2018). Forecasting the success of a new tourism service by a neuro-fuzzy technique. European Journal of Operational Research, 268(2), 716–727. doi:10.1016/j.ejor.2018.01.044
  • Barreira, N., Godinho, P. ve Melo, P. (2013). Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends. NETNOMICS: Economic Research and Electronic Networking, 14(3), 129–165. doi:10.1007/s11066-013-9082-8
  • Berthon, P. R., Pitt, L. F., Plangger, K. ve Shapiro, D. (2012). Marketing Meets Web 2.0, Social Media, and Creative Consumers: Implications for International Marketing Strategy. Business Horizons, 55(3), 261–271. doi:10.1016/j.bushor.2012.01.007
  • Caner, E. (2012). Türkiye’de Facebook Kullanıcı Sayısı Hangi Durumlarda Artar?
  • Cerit, I., Yildirim, A., Ucar, M. K., Demirkol, A., Cosansu, S. ve Demirkol, O. (2017). Estimation of antioxidant activity of foods using artificial neural networks. Journal of Food and Nutrition Research, 56(2), 138–148.
  • Choi, H. ve Varian, H. (2012). Predicting the Present with Google Trends. Economic Record, 88(SUPPL.1), 2–9. doi:10.1111/j.1475-4932.2012.00809.x
  • Chopra, S., Yadav, D. ve Chopra, A. N. (2019). International Journal of Swarm Intelligence and Evolutionary Computation Artificial Neural Networks Based Indian Stock Market Price Prediction : Before and After Demonetization, 8(1), 1–7. doi:10.4172/2090-4908.1000174
  • Chu, S. C. ve Kim, Y. (2011). Determinants of Consumer Engagement in Electronic Word-Of-Mouth (eWOM) in Social Networking Sites. International Journal of Advertising, 30(1). doi:10.2501/IJA-30-1-047-075
  • Çuhadar, M. ve Kayacan, C. (2005). Yapay Sinir Ağları Kullanılarak Konaklama İşletmelerinde Doluluk Oranı Tahmini : Türkiye ’ deki Konaklama İşletmeleri Üzerine Bir Deneme, 24–30.
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  • Cvijikj, I. P. ve Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network Analysis and Mining, 3(4), 843–861. doi:10.1007/s13278-013-0098-8
  • De Vries, L., Gensler, S. ve Leeflang, P. S. H. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26(2), 83–91. doi:10.1016/j.intmar.2012.01.003
  • Ding, X., Liu, T., Duan, J. ve Nie, J.-Y. (2015). Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), 2389–2395.
  • Doğru, F. (2015). Güncel Optimizasyon Yöntemleri Kullanılarak Rezidüel Gravite Anomalilerinden Parametre Kestirimi. Yerbilimleri/Hacettepe Üniversitesi Yerbilimleri Uygulama ve Araştırma Merkezi Dergisi, 36(1), 31–43. doi:10.17824/yrb.71895
  • Doorn, J. van, Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P. ve Verhoef, P. C. (2010). Customer Engagement Behavior: Theoretical Foundations and Research Directions. Journal of Service Research, 13(3), 253–266. doi:10.1177/1094670510375599
  • Ellison, N. B. ve Boyd, D. (2007). Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication, 210–230. doi:10.1111/j.1083-6101.2007.00393.x
  • Etter, M. ve Fieseler, C. (2010). On Relational Capital in Social Media. Studies in Communication Sciences, 10(2), 167–189.
  • Forrester Consulting. (2008). How Engaged are Your Customers ?, (September), 1–22. http://www.indigopacific.com/pdf/Forrester_TLP_How_Engaged_Are_Your_Customers.pdf adresinden erişildi.
  • Funk, T. (2010). Advanced social media marketing: How to lead, launch, and manage a successful social media program. Press.
  • Gordini, N., Sanpaolo, I. ve Veglio, V. (2015). Customer relationship management and data mining : A classification decision tree to predict customer purchasing behavior in global market. doi:10.4018/978-1-4666-4450-2.ch001
  • Gupta, R. ve Pathak, C. (2014). A Machine Learning Framework for Predicting Purchase by online customers based on Dynamic Pricing. Procedia - Procedia Computer Science, 36, 599–605. doi:10.1016/j.procs.2014.09.060
  • Hapsari, R., Clemes, M. D. ve Dean, D. (2017). The impact of service quality, customer engagement and selected marketing constructs on airline passenger loyalty. International Journal of Quality and Service Sciences, 9(1), 21–40. doi:10.1108/IJQSS-07-2016-0048
  • Hollebeek, L. (2011). Exploring customer brand engagement: definition and themes. Journal of Strategic Marketing, 19(7), 555–573. doi:10.1080/0965254X.2011.599493
  • Jayalakshmi, T. ve Santhakumaran, A. (2011). Statistical Normalization and Back Propagationfor Classification. International Journal of Computer Theory and Engineering, 3(1), 89–93. doi:10.7763/IJCTE.2011.V3.288
  • Ji, Y. G., Li, C., North, M. ve Liu, J. (2017). Staking Reputation on Stakeholders: How Does Stakeholders’ Facebook Engagement Help or Ruin a Company’s Reputation? Public Relations Review, 43(1), 201–210. doi:10.1016/j.pubrev.2016.12.004
  • Kelleher, T. (2009). Conversational voice, communicated commitment, and public relations outcomes in interactive online communication. Journal of Communication, 59(1), 172–188. doi:10.1111/j.1460-2466.2008.01410.x
  • Kim, H. W., Gupta, S. ve Koh, J. (2011). Investigating the intention to purchase digital items in social networking communities: A customer value perspective. Information and Management, 48(6), 228–234. doi:10.1016/j.im.2011.05.004
  • King, M. A., Abrahams, A. S. ve Ragsdale, C. T. (2014). Ensemble methods for advanced skier days prediction. Expert Systems with Applications, 41(4), 1176–1188. doi:10.1016/J.ESWA.2013.08.002
  • Kotler, P., Keller, K. L. (2012). Marketing Management 14E. New York: Pearson Education Inc.
  • Lassen, N. B., Madsen, R. ve Vatrapu, R. (2014). Predicting iPhone Sales from iPhone Tweets. Proceedings . IEEE 18th international Enterprise Distributed object computing conference, 2014–Decem(December), 81–90. doi:10.1109/EDOC.2014.20
  • MacKay, D. J. C. (1992). A Practical Bayesian Framework for Backpropagation Networks. EFSA Journal, 4, 448–472. doi:10.2903/j.efsa.2018.5430
  • McCulloch, A. (2015). Measuring the Right Social KPIs. SocialBakers. 2 Mart 2018 tarihinde https://www.socialbakers.com/blog/2384-measuring-the-right-social-kpis adresinden erişildi.
  • Mudambi, S. M. ve Schuff, D. (2010). What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly, 34(1), 185–200. doi:Article
  • ODD. (2017). 2017 Yılı (Ocak-Aralık) Perakende Satışlar (Yerli&İthal). Otomotiv Distribütörleri Derneği. 5 Ağustos 2019 tarihinde http://www.odd.org.tr/web_2837_1/sortial.aspx?linkpos=3&target=categorial1&type=36&primary_id=&detail=single&sp_table=&sp_primary=&sp_fields=&sp_language=&sp_table_extra=&extracriteria=&language_id=1&search_fields=&search_values= adresinden erişildi.
  • Park, S. ve Huh, S. (2019). A Social Network-Based Inference Model for Validating Customer Profile Data, 36(4), 1217–1237.
  • Qiu, J., Lin, Z. ve Li, Y. (2015). Predicting customer purchase behavior in the e-commerce context. Electronic Commerce Research, 15(4), 427–452. doi:10.1007/s10660-015-9191-6
  • Richter, D., Riemer, K. ve vom Brocke, J. (2011). Internet Social Networking. Wirtschaftsinformatik, 53(2), 89–103.
  • Rybalko, S. ve Seltzer, T. (2010). Dialogic Communication in 140 Characters or Less: How Fortune 500 Companies Engage Stakeholders Using Twitter. Public Relations Review, 36(4), 336–341. doi:10.1016/j.pubrev.2010.08.004
  • Sakar, C. O., Polat, S. O., Katircioglu, M. ve Kastro, Y. (2018). Real-time prediction of online shoppers ’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 0. doi:10.1007/s00521-018-3523-0
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Details

Primary Language Turkish
Journal Section DERGİNİN TAMAMI
Authors

İbrahim Topal 0000-0002-7119-9470

Publication Date December 31, 2019
Published in Issue Year 2019 Volume: 9 Issue: 2

Cite

APA Topal, İ. (2019). ÇEVRİMİÇİ TÜKETİCİ BÜTÜNLEŞMESİ VE ARAMA MOTORU VERİLERİ KULLANILARAK YAPAY SİNİR AĞLARI İLE OTOMOBİL SATIŞ TAHMİNİ. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 9(2), 534-551.