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

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

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

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