Please use this identifier to cite or link to this item:
Full metadata record
|dc.contributor.author||Alomari, Khaled Mohammad||-|
|dc.identifier.citation||Alomari K.M., Ncube C., Shaalan K. (2018) Predicting Success of a Mobile Game: A Proposed Data Analytics-Based Prediction Model. In: Mouhoub M., Sadaoui S., Ait Mohamed O., Ali M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science, vol 10868. Springer, Cham||en_US|
|dc.identifier.isbn||978-3-319-92057-3 (print) 978-3-319-92058-0 (online)||-|
|dc.description.abstract||Even though billions of dollars in revenue have been generated from mobile game apps, there is still a knowledge gap with regard to mobile game user behavior and methodologies for predicting the likely success of mobile game apps during the development phase. This paper analyses game features and (Acquisition, Retention and Monetization) ARM strategies as primary drivers of mobile game application success. This study addresses these challenges through data driven research of the mobile gaming application market, mobile gaming application features, user acquisition and retention trends, and monetization strategies using the CRISP-DM model for data mining in order to prove a successful method for predictions of mobile game application success. A prediction model is developed then applied to 50 games. The prediction of successful mobile game application from a sample of 50 games is achieved by running a batch prediction for the game features dataset and a separate batch prediction for the user behavior dataset. The model produced a total of 9 titles from the sample with the highest probability of success. The significant outcomes for the comparisons included the predominance of the Social Networking Features, Offers, and (In App Purchase) IAP 90% to 100% of the sample. A model of mobile game app success prediction based upon the game features values that are created is proposed.||en_US|
|dc.title||Predicting Success of a Mobile Game: A Proposed Data Analytics-Based Prediction Model||en_US|
|Appears in Collections:||Ncube, Cornelius|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.