Applying Google Analytics to Understand User Behavior and Smartphone Applications Presenting Authors: Elicia C. Wartman, Jorge A. Galvez MD Co- Authors: Allan F. Simpao MD, Jody M. Richards, Justin Lockman MD, Laura Schleelein MD, Mohammed Rehman MD The Department of Anesthesiology and Critical Care Medicine, The Children s Hospital in Philadelphia, PA; Bioinformatics section Introduction: Smartphone utilization has increased dramatically across healthcare settings worldwide (1). While new Applications (Apps) have the potential to reach a large user audience quickly, a major challenge that remains is the ability to obtain feedback from users to improve design. Many aspects that are determined during development, such as user interface and usage patterns, remain unknown after application deployment. We report on our experience of developing the Pedi Crisis App, a critical event algorithm app based on the Society for Pediatric Anesthesia Critical Events checklists. While developing the Pedi Crisis application, we wanted to know how, where and to what extent people were using the application. Traditionally, developers determined software use by either directly observing users or seeking user feedback via surveys. Newer methods allow the software to automatically capture user information such as flow through screens, crashes or time spent in the App. The Google Analytics (GA) platform provides one solution to this dilemma. Designers can utilize GA to gain insight into their application s usage and traffic. GA offers several out- of- the- box features such as; audience characteristics and behavior, device and operating systems, usage statistics and application performance. Our specific questions were how to monitor usage patterns (frequency of use, length of session, flow during a session) and geographic distribution of the App over time. Methods: We gathered App usage information via Google Analytics data for the Pedi Crisis Application, from October 4, 2013 to December 11, 2013. Demographic information on App usage was obtained from the app overview screen (fig. 1.0). The screen- to- screen user flow was reported via the engagement flow section, which displayed the way users navigated the application (fig.1.3). Results: The data shows that there have been 1,252 active users (both new and returning) and 4,140 sessions (figure 1.0). Since releasing the App on October 4, 2013, it has been used in 98 countries (fig. 1.1). We found that returning users used the application longer and viewed more screens. An average of 6.46 screens were viewed per session and the average length per session was 49 seconds (fig. 1.0). The events that were accessed most frequently were
Anaphylaxis, Tachycardia and Cardiac Arrest (figure 1.4). Most users accessed the App on an iphone (81.96%), followed by ipad (17.42%) and ipod Touch 0.62% (fig. 1.0, 1.3). Discussion: Mobile Apps have the potential to reach a large audience almost instantly. We were able to apply GA as a traffic monitor for our application. Most of the information we gathered would have been inaccessible to us without a tool like GA. GA has a growing set of built- in features, requiring little configuration from the user. Monitoring software utilization can be resource intensive and time consuming.(2) GA offers the advantage of being cost- effective (free for applications that have less than 10,000,000 hits per month) and is easy to use. GA also offers visualization tools that can help designers see how users interact with the application. There are Advanced Segments options that can help differentiate between types of traffic. Social reports can help designers monitor the impact of outside events such as relevant conferences or advertisements (Fig. 1.0).Nevertheless, there are some limitations. GA only provides app download information for android based apps. However, the number of downloads is available directly from the itunes App store for IOS apps. Another limitation is that in order for GA to collect data, the application must connect to the internet; if the target user/device is offline GA will not capture the data. Figure 1.0. The red arrows in figure 1.0 show spikes in usage, that correspond with dates the Pedi Crisis App was promoted; e.g. the application was introduced at the SPA conference on October 11,2013.
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References: 1. Chu LF, Erlendson MJ, Sun JS, Alva HL, Clemenson AM. Mobile computing in medical education. Current Opinion in Anaesthesiology. 2012:25 (6) :699-718 2. Stone D, Jarrett C, Woodroffe M, Minocha S. User Interface Design and Evaluation. Morgan Kaufmann; Amsterdam, 2005.