Mobile games success and failure: mining the hidden factors

Creative Commons License

Kerim A., GENÇ B.

NEURAL COMPUTING & APPLICATIONS, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00521-022-07154-z
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Anahtar Kelimeler: Data mining, Mobile games, Game features, Machine learning, ANN, FEATURE-EXTRACTION, DETERMINANTS, ICON
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released each day. However, a few of them succeed while the majority fail. Toward the goal of investigating the potential correlation between the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousand games were considered for that reason. We show that IAPs (In-App Purchases), genre, number of supported languages, developer profile, and release month have a clear effect on the success of a mobile game. We also develop a novel success score reflecting multiple objectives. Furthermore, we show that game icons with certain visual characteristics tend to be associated with more rating counts. We employ different machine learning models to predict a novel success score metric of a mobile game given its attributes. The trained models were able to predict this score, as well as the expected rating average and rating count for a mobile game with 70% accuracy.