Businesses tend to be quite savvy at harnessing analytics to improve their websites, social media campaigns, and even business workflows, but they’ve been slower to realize that the right analytics can have the same effect on their enterprise mobility efforts. This stems from the misconception that BYOD
and other mobile users will freely adopt the enterprise applications made available to them. In reality, studies show
enterprise applications are in competition with commercially available apps, and high adoption rates are by no means guaranteed.
Mobile strategists need to adopt an approach that treats employees that use enterprise app
more like customers. This means creating high quality, user-friendly applications, as well as establishing a cycle of continuous improvement whereby BYOD users have increased incentive to use and download corporate apps. Gathering mobile analytics in combination with user acceptance testing works to achieve this goal and should be an essential component of the mobile software development lifecycle.
There are two major subsets of mobile analytics that are particularly useful in the B2E app space -- operational analytics and behavioral analytics. The former focuses on the technical details of applications as well as the devices they interact with. The aim of gathering operational analytics is to make connections between the overall technical performance of the app, network conditions, and device details. This enables you to uncover potential bugs and performance bottlenecks, and when gathered during mobile app testing
, gives enterprises the opportunity to optimize B2E apps before their initial launch.
Optimizing your mobile analytics initiative means digging beneath surface-level analytics to gather actionable data. Behavioral analytics provide invaluable insight into the intangibles that development teams and business analytics can’t define in user stories and technical specifications. By observing which pathways users favor and which they avoid or ignore, development teams can make educated decisions on what to keep, what to modify, and what to scrap in subsequent app releases.
Behavioral analytics also has the ability to trigger changes in business workflows that have a direct impact on revenue. For example, by studying how top sales associates interact with mobile sales applications, managers can adjust future training efforts to reflect practices that are known to be effective and increase sales in the process.
Driving App Adoption and ROI
In today’s modern and mature app environment, simply creating B2E applications does not guarantee success. Studies show that BYOD employees won’t use enterprise apps if they under-perform. A mobile strategy that combines operational and behavioral analytics with user acceptance testing is the best way to simultaneously drive mobile app adoption
and achieve optimal ROI. Identifying correlations between performance, in-app usage, user roles, network configurations, and device details will help you to not only continuously improve individual apps, but also establish an optimal suite of applications that work to streamline work functions, save time, and increase productivity.