ABSTRACT
With mobile shopping surging in popularity, people are spending ever more money on digital purchases through their mobile devices and phones. However, few large-scale studies of mobile shopping exist. In this paper we analyze a large data set consisting of more than 776M digital purchases made on Apple mobile devices that include songs, apps, and in-app purchases. We find that 61% of all the spending is on in-app purchases and that the top 1% of users are responsible for 59% of all the spending. These big spenders are more likely to be male and older, and less likely to be from the US. We study how they adopt and abandon individual app, and find that, after an initial phase of increased daily spending, users gradually lose interest: the delay between their purchases increases and the spending decreases with a sharp drop toward the end. Finally, we model the in-app purchasing behavior in multiple steps: 1) we model the time between purchases; 2) we train a classifier to predict whether the user will make a purchase from a new app or continue purchasing from the existing app; and 3) based on the outcome of the previous step, we attempt to predict the exact app, new or existing, from which the next purchase will come. The results yield new insights into spending habits in the mobile digital marketplace.
- Annual apple app store revenue in 2013 and 2015 (in billion u.s. dollars). http://www.statista.com/statistics/296226/annual-apple-app-store-revenue/. Accessed: 2016-07--11.Google Scholar
- Apple's cut of 2015 app store revenue tops$6b. http://www.computerworld.com/article/3019716/apple-ios/apples-cut-of-2015-app-store-revenue-tops-6b.html. Accessed: 2016-07--11.Google Scholar
- Online shopping is still a pathetically tiny fraction of all shopping. http://goo.gl/ecTKxT. Accessed: 2016-07--11.Google Scholar
- People spent an insane amount of money on apps this year. http://time.com/4169153/apple-app-store-stats-2015. Accessed: 2016-07--11.Google Scholar
- Personal consumption expenditures. https://research.stlouisfed.org/fred2/series/PCE/. Accessed: 2016-07--11.Google Scholar
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6):734--749, 2005. Google ScholarDigital Library
- H. Akaike. Information theory and an extension of the maximum likelihood principle. In Selected Papers of Hirotugu Akaike, pages 199--213. Springer, 1998. Google ScholarCross Ref
- A. Anderson, R. Kumar, A. Tomkins, and S. Vassilvitskii. The dynamics of repeat consumption. In Proceedings of the 23rd international conference on World wide web, pages 419--430. ACM, 2014. Google ScholarDigital Library
- R. Baeza-Yates, D. Jiang, F. Silvestri, and B. Harrison. Predicting the next app that you are going to use. In WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pages 285--294, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
- A. R. Benson, R. Kumar, and A. Tomkins. Modeling user consumption sequences. In Proceedings of the 25th International Conference on World Wide Web, pages 519--529. International World Wide Web Conferences Steering Committee, 2016. Google ScholarDigital Library
- A. Bhatnagar, S. Misra, and H. R. Rao. On risk, convenience, and internet shopping behavior. Commun. ACM, 43(11):98--105, Nov. 2000. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. In The Journal of machine Learning research, 2003.Google Scholar
- J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. Recommender systems survey. Knowledge-Based Systems, 46:109--132, 2013. Google ScholarDigital Library
- M. Braun and D. A. Schweidel. Modeling customer lifetimes with multiple causes of churn. Marketing Science, 30(5):881--902, 2011. Google ScholarDigital Library
- S. Farag, T. Schwanen, M. Dijst, and J. Faber. Shopping online and/or in-store? a structural equation model of the relationships between e-shopping and in-store shopping. Transportation Research Part A: Policy and Practice, 41(2):125--141, 2007. Google ScholarCross Ref
- L. Hong and B. D. Davison. Empirical study of topic modeling in twitter. In In Proceedings of the First Workshop on Social Media Analytics, 2010. Google ScholarDigital Library
- I. Kloumann, L. Adamic, J. Kleinberg, and S. Wu. The lifecycles of apps in a social ecosystem. In Proceedings of the 24th International Conference on World Wide Web, pages 581--591. ACM, 2015. Google ScholarDigital Library
- F. Kooti, K. Lerman, L. M. Aiello, M. Grbovic, N. Djuric, and V. Radosavljevic. Portrait of an online shopper: Understanding and predicting consumer behavior. In 9th ACM International Conference on Web Search and Data Mining (WSDM'16), 2015.Google Scholar
- T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, pages 3111--3119, 2013.Google ScholarDigital Library
- R. Quinlan. Data mining tools see5 and c5. 0. 2004.Google Scholar
- F. Rechinhheld and W. Sasser. Zero defections: Quality comes to service. Harvard Business Review, 68(5):105--111, 1990.Google Scholar
- Y. Richter, E. Yom-Tov, and N. Slonim. Predicting customer churn in mobile networks through analysis of social groups. In SDM, volume 2010, pages 732--741. SIAM, 2010. Google ScholarCross Ref
- J. Runge, P. Gao, F. Garcin, and B. Faltings. Churn prediction for high-value players in casual social games. In 2014 IEEE Conference on Computational Intelligence and Games, pages 1--8. IEEE, 2014. Google ScholarCross Ref
- C. Schoger. How the most successful apps monetize globally. Retrieved January, 15:2015, 2014.Google Scholar
- R. Sifa, F. Hadiji, J. Runge, A. Drachen, K. Kersting, and C. Bauckhage. Predicting purchase decisions in mobile free-to-play games. In Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference, 2015.Google Scholar
- W. R. Swinyard and S. M. Smith. Why people (don't) shop online: A lifestyle study of the internet consumer. Psychology & Marketing, 20(7):567, 2003. Google ScholarCross Ref
- W. R. Swinyard and S. M. Smith. Activities, interests, and opinions of online shoppers and non-shoppers. International Business and Economics Research Journal, 3(4):37--48, 2011. Google ScholarCross Ref
- M. Zaman and M. Y. W. Meng. Internet shopping adoption: A comparative study on city and regional consumers. In ANZMAC 2002, pages 2421--2428. Deakin University, 2002.Google Scholar
Index Terms
- iPhone's Digital Marketplace: Characterizing the Big Spenders
Recommendations
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data MiningConsumer spending accounts for a large fraction of economic footprint of modern countries. Increasingly, consumer activity is moving to the web, where digital receipts of online purchases provide valuable data sources detailing consumer behavior. We ...
Exploring iPhone usage: the influence of socioeconomic differences on smartphone adoption, usage and usability
MobileHCI '12: Proceedings of the 14th international conference on Human-computer interaction with mobile devices and servicesPrevious studies have found that smartphone users differ by orders of magnitude. We explore this variability to understand how users install and use native applications in ecologically-valid environments. A quasi-experimental approach is applied to ...
A Large-Scale Study of iPhone App Launch Behaviour
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing SystemsThere have been many large-scale investigations of users' mobile app launch behaviour, but all have been conducted on Android, even though recent reports suggest iPhones account for a third of all smartphones in use. We report on the first large-scale ...
Comments