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| Content Provider | ACM Digital Library |
|---|---|
| Author | Jain, Puneet Parate, Abhinav Kim, Kyu-Han |
| Abstract | This paper argues that there is a need to expand the capabilities of mobile app analytics beyond providing low-level insights about how efficiently a user can execute an action on a mobile app, to deeper insights about how efficiently a user can complete a task using the app (e.g., flight reservation). This paper presents RECKON, a framework for mobile app analytics, that provides insights about end-to-end user experience in completing tasks, without explicit effort from the app developers. RECKON identifies and extracts task-level information from an unlabeled data stream of user actions. Based on the extracted information, RECKON outputs various helpful metrics to the developers. We have implemented and evaluated RECKON with a popular travel assistant app with 122, 243 users for 30 days. Our evaluation results validate RECKON's performance in obtaining critical mobile app insights as well as demonstrate its wide applicability for developers. |
| Starting Page | 23 |
| Ending Page | 28 |
| Page Count | 6 |
| File Format | PDF EPUB |
| ISBN | 9781450342544 |
| DOI | 10.1145/2980147.2980154 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Task User interface App analytics Developer |
| Content Type | Text |
| Resource Type | Article |
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