PREDICTING CUSTOMER SATISFACTION REGARDING HOTELS: AN APPLICATION USING MACHINE LEARNING TECHNIQUES

Abstract

With the increasing use of artificial intelligence algorithms in many areas, applying AI in marketing studies has also begun to emerge. The aim of this study is to present an example of the use of machine learning techniques, an artificial intelligence application, in marketing. For that purpose, the accuracy rate of predicting customer satisfaction with machine learning techniques was investigated. In this research, data obtained from the Tripadvisor website, which is the largest travel platform in the world, was used. The features of 993 hotels and the satisfaction scores of these hotels were recorded by the researchers. medium level of correlation was observed with the number of amenities, number of room features, number of room types, location score, cleanliness score, value score, and number of reviews. A high level of correlation was found with the service score. The dataset was divided into 70% training data and 30% test data. Models were trained with the training data and their performance was tested with the test data. Using these variables, it was found that it is possible to predict satisfaction with accuracy rates ranging from a minimum of 71% to a maximum of 81% through various machine learning methods.

Keywords

• Customer satisfaction
• Customer reviews
• Artificial intelligence
• Machine learning

OTELLERE DAİR MÜŞTERİ TATMİNİNİN TAHMİNLENMESİ: MAKİNE ÖĞRENMESİ TEKNİKLERİ İLE BİR UYGULAMA

Abstract

Yapay zekâ algoritmalarının pek çok alanda kullanımının yaygınlaşmasıyla birlikte, pazarlama çalışmalarındaki kullanımı da gündeme gelmeye başlamıştır. Bu çalışmanın amacı bir yapay zekâ uygulaması olan makine öğrenmesi tekniklerinin pazarlamada kullanımına dair bir örnek sunmaktır. Bu amaçla müşteri tatmininin makine öğrenme teknikleri ile tahminlenmesinin hangi başarı düzeyi ile mümkün olduğu araştırılmıştır. Araştırmada, dünyadaki en büyük seyahat platformu olma özelliğini taşıyan Tripadvisor web sitesinden elde edilen veriler kullanılmıştır. 993 otele ait özellikler ve bu otellerin tatmin skorları kayıt altına alınmıştır. İşletme olanak sayısı, odalardaki özellik sayısı, oda türü sayısı, yer skoru, temizlik skoru, değer skoru, yorum sayısı ile tatmin arasında orta düzeyde ilişki olduğu, hizmet skoru ile de yüksek düzeyde ilişki olduğu sonuçları elde edilmiştir. % 70 eğitim verisi, % 30 test verisi olacak şekilde ikiye ayrılan veri seti ile önce modellerin eğitilmesi sağlanmış, sonra da model başarıları ortaya konmuştur. İlgili değişkenlere dayalı olarak en düşük % 71, en yüksek % 81 başarı oranıyla otellere dair tahminlemenin yapılmasının mümkün olduğu sonuçları elde edilmiştir.

Keywords

• Müşteri tatmini
• Müşteri değerlendirmeleri
• Yapay zekâ
• Makine öğrenmesi

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