Bibliometric Analysis of Scientific Studies Published on Game Customer Churn Analysis Between 2008 and 2022

Yıl 2022, Cilt: 5 Sayı: 1, 55 - 75, 30.06.2022

Kaan Arık , Murat Gezer , Seda Tolun Tayalı

Öz

Churn prediction has become a key part of many modern businesses because of the performance advantages it brings. It may assist businesses in determining measures such as customer retention and revenue production. Business intelligence (BI) is the process of transforming data into actionable insights that help a company make better choicesThis research includes a bibliometric analysis of works on game analytics, particularly customer churn in gaming business. Our research is mostly focused on identifying the content and goals of studies that have already been published. In game analytics, classification, grouping, and statistical analysis approaches are utilized to acquire insight via the study of studies. Keywords were analyzed in articles, papers, books, and research materials on gaming analytics and customer churn analysis. Then there's a look at which measures are most important in game analytics, as well as some of the most often utilized artificial intelligence systems. These difficulties are examined in the assessment section, where performance evaluation measures are more significant in this sector. For bibliometric analysis, the keyword "game churn analysis" was used. Data from Google Scholar, as well as Publish or Perish and Zotero. Year requirements for bibliometric analysis were set between 2008 and 2022. The study data analysis tools include Excel 2019 and Vosviewer. Finally, we analyzed databases, widely used data sets, game titles, metric kinds, indexing databases, countries where studies were published, categories of scientific study, word and author bibliometric maps, and algorithms.

Anahtar Kelimeler

bibliometric analysis, churn analysis, game analytics, game data mining, literature review

2008-2022 Yılları Arasında Oyun Müşteri Kaybı Analizi Üzerine Yayımlanan Bilimsel Çalışmaların Bibliyometrik Analizi

Öz

Kayıp tahmini, getirdiği performans avantajları nedeniyle birçok modern işletmenin önemli bir parçası haline gelmiştir. İşletmelere müşteriyi elde tutma ve gelir üretimi gibi önlemleri belirlemede yardımcı olmaktadır. İş zekası (BI), bir şirketin daha iyi seçimler yapmasına yardımcı olan verileri eyleme dönüştürülebilir içgörülere dönüştürme sürecidir. Araştırmamız çoğunlukla daha önce yayınlanmış çalışmaların içeriğini ve hedeflerini belirlemeye odaklanmıştır. Oyun içi analitik, sınıflandırma, gruplama ve istatistiksel analiz yaklaşımları, çalışmaların incelenmesi yoluyla içgörü elde edilmeye çalışılmıştır. Anahtar kelimeler, oyun analizi ve müşteri kaybı analizi üzerine makaleler, makaleler, kitaplar ve araştırma materyallerinde analiz edilmiştir. Ardından, oyun analitiğinde hangi önlemlerin en önemli olduğuna ve ayrıca en sık kullanılan yapay zeka sistemlerinden bazılarına bir bakış açısı aranmıştır. Bu zorluklar, bu sektörde performans değerlendirme ölçütlerinin daha önemli olduğu değerlendirme bölümünde incelenmiştir. Bibliyometrik analiz için "oyun kayıp analizi" anahtar kelimesi kullanılmıştır. Google Akademik'in yanı sıra Publish or Perish ve Zotero'dan gelen veriler. Bibliyometrik analiz için yıl aralığı 2008 ile 2022 arasında belirlendi. Çalışma verisi analizi için Excel 2019 ve Vosviewer'ı araçları kullanıldı. Son olarak, veri tabanları, yaygın olarak kullanılan veri kümeleri, oyun başlıkları, metrik türleri, indeksleme veritabanları, çalışmaların yayınlandığı ülkeler, bilimsel çalışma kategorileri, kelime ve yazar bibliyometrik haritaları ve algoritmaları analiz edilmiş ve sonuç kısmında tartışılmıştır.

Anahtar Kelimeler

bibliyometrik analiz, kayıp analizi, oyun analitiği, oyun veri madenciliği, literatür taraması

Kaynakça

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