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Exploiting Usage to Predict Instantaneous App Popularity: Trend Filters and Retention Rates

Published: 02 April 2019 Publication History

Abstract

Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app lifecycles. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users (Marginal apps). Less than 0.1% of the remaining 60% are constantly popular (Dominant apps), 1% have a quick drain of usage after an initial steep rise (Expired apps), and 6% continuously rise in popularity (Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations.

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cover image ACM Transactions on the Web
ACM Transactions on the Web  Volume 13, Issue 2
May 2019
156 pages
ISSN:1559-1131
EISSN:1559-114X
DOI:10.1145/3313948
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 April 2019
Accepted: 01 January 2019
Revised: 01 November 2018
Received: 01 October 2017
Published in TWEB Volume 13, Issue 2

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Author Tags

  1. Mobile analytics
  2. application popularity
  3. trend mining

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