Revulytics Enhances Usage Analytics Offering

August 09, 2018

Revulytics has added advanced feature tracking and reporting capabilities in its core analytics offering helping all customers realize additional value from their product usage analytics. Available today, the upgraded Lifetime Feature Usage analysis allows every feature that is tracked in Usage Intelligence to be filtered and segmented based on environmental properties and usage by particular audiences within an application’s install base.

“Our mission is to increase the power of product usage analytics to help our customers build better products by answering more questions about how users interact with their application. The introduction of Lifetime Feature Usage analysis extends our existing set of feature usage reports, giving our customers a deeper understanding of how specific user groups behave throughout their lifetime and reasons for churn,” said Keith Fenech, VP of Software Analytics. “Additionally, customers can better target ReachOut in-application messaging campaigns to influence that behavior and increase feature adoption and retention.”

For example, if a software application has three methods to accomplish a task, a product manager can analyze the lifetime usage of each to make data-driven roadmap decisions:

If 85 percent of users never discover “method, ” but those who have discovered it use it frequently, a change in the UI can make it more easily discoverable or a ReachOut in-app message can be sent to highlight the functionality.

If “method 2” is used only once by nearly all users, it might suggest that it is discoverable, but not useful, and that it can be removed to simplify the UI.

If “method 3” is used every runtime session by a large percentage of users, product management might test whether the approach could be utilized in other areas of the application.

Additional enhancements to Usage Intelligence include:

Sunburst Visualizations. Illustrate and analyze hierarchical or segmented data through an interactive Sunburst chart that enables visualizing multiple properties and drilldown into any property/segment by simply clicking on any chart segment to explore the relationships.

ReachOut Delivery Caps. Send an in-application promotion to a limited number of users, upgrade notifications in batches, or run A/B testing with a random user sample by setting a Maximum Delivery Cap on any ReachOut campaign to limit the number of messages delivered.

Regular Expressions (“Regex”). All filters for reporting and ReachOut now take advantage of regular expressions to provide greater flexibility and time savings; choose “Exact Match,” “Starts with,” or “Contains” to filter any string property. Like other filters, Regex-based filters can also be saved as templates and shared with a team.

Date Installed Filter. Report on usage by people who installed during a specific time period (for example, during a campaign or following an event) to easily see how many users who installed during the Q4 promo are still active and what are they doing with the software.

Date Last Seen Filter. Filter in/out users that were last seen before or after a specific date to see, for example, how many users abandoned a specific version/edition of the software in the days after an announcement that it will no longer be supported.

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