When product managers were asked to name their top challenges, the difficulty of adding innovative features because the customer base demands support of older ones is very high on the list.
Determining how changes to legacy versions of your software product will affect your customer base is one of the hardest parts of the job. Balancing the needs of your oldest – and many times largest – customers, while ensuring your products stay on a path to innovation that keeps them competitive can be challenging.
But leveraging data to inform these decisions takes the guesswork out of product management, and can help all stakeholders confidently execute decisions that will benefit the base long term. Let’s take a look at actual examples of how Flexera customers are leveraging software usage analytics to better inform common software product roadmap decisions.
What effect will increasing minimum resolution display have on our current customers?
In order to deliver a more attractive, efficient, and competitive user interface, the product management team at a graphic software company wanted to increase the minimum resolution requirements from 1024x px to 1920 px. But they didn’t know if current customers were running the hardware needed to meet the new minimum requirements.
Using Usage Intelligence, the team ran comprehensive hardware architecture reports – capturing details on screen resolution, form factors, and numbers of monitors for all currently active product instances worldwide. Using custom properties, it also captured graphic card vendor and model data, which lent insight into the graphics processing power possessed by users. Beyond video, it also captured .NET framework version data to help its Windows developers assess which capabilities are available on their users’ systems.
Product management determined that only 5 percent of their customers were still using resolutions below 1920px. Based on this data, it confidently upgraded its interface for the higher resolution – knowing that it likely wouldn’t lead to a barrage of complaints or churn. In doing so, the company opened the way to additional UI/UX advances that provide a better user experience for 95 percent of the company’s customer base, and make the application far more attractive to prospects.
Is it viable to stop supporting a legacy version as we roll out a new version?
One of the biggest challenges software product managers face is deciding how to best deploy teams that are often stretched to their limits. In that light, a productivity software provider needed better data on how its legacy version was being used to inform the sunsetting of an older release, so it could move resources to developing new features and releases. It actually had data that suggested “version 1” use had dropped off significantly, but Sales was adamant customers were using the product and just never contacting support.
The company implemented Usage Intelligence, configuring its default tracking mechanism to drill deeply into how many customers were using each version of the software, how often, what features they were using, and what operating system they were running on.
Not only did product management now have detailed information on use, it had valuable information on engagement, as well as the limiting factors for upgrading to a new version.
The data supported product management’s thesis, and the team could now confidently move forward and reallocate resources to new feature development on their product roadmap. What’s more, armed with data, the company made offers to holdouts on the older version with an upgrade that they couldn’t resist to access new, higher value features. It specifically targeted users running operating systems compatible with the new version, and reassured them their existing systems could support the upgrade.
Can we eliminate features that are holding back product innovation?
Engineers at a leading practice management software provider struggled to manage a feature that was written in legacy code with obsolete tools. Whenever engineering upgraded the UI, the feature would break – costing time and resources. The team didn’t have reliable information about how many customers still used this feature, so it kept delaying the decision to abandon it – and its maintenance costs and frustrations continued to grow.
By tracking software events with Usage Intelligence, the company quantified the number of unique users who actively engaged with the legacy feature and found that only a few still did. Product management could now support a decision to abandon this feature with accurate, reliable data demonstrating that doing so would impact few customers.
In turn, using the Flexera software usage analytics reporting dashboard, the company has implemented a continuous, automated feedback loop which helps them better manage the process of eliminating old features and planning for new ones. Since they can now recognize shifting feature usage patterns more quickly and reliably, they are more effective at identifying UI design improvements that increase conversion and adoption rates.
Leveraging Usage Intelligence leads to better software product roadmaps by lending you insight on how you can best innovate for your customers while freeing up the resources to do so. For more information on how customers are leveraging software usage analytics, check out more software usage analytics use cases.