ÇEVRİMİÇİ TÜKETİCİ BÜTÜNLEŞMESİ VE ARAMA MOTORU VERİLERİ KULLANILARAK YAPAY SİNİR AĞLARI İLE OTOMOBİL SATIŞ TAHMİNİ

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.

Anahtar Kelimeler:

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

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

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,

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