Guest Column | June 22, 2020

Crawl, Walk, Run: How To Remove Guesswork From Product Management With Software Usage Analytics

By Vic DeMarines, Revenera


What drives successful customer experiences? How are customers using a product? What is valuable to them and what isn’t?

Product managers have to make big decisions about the future of their product, its path from being an on-premises solution to be a cloud solution, and sometimes its end of life or the support for different platforms. All too often, these decisions are made based on anecdotal information, discussions with just a few customers, or interactions with the support team. In a data-driven world, this isn’t enough anymore.

Software product management professionals who are prepared with actionable intelligence are well equipped to make informed decisions about how to develop their products. Remove the guesswork to arrive at data-driven answers that can guide you to smart, valuable decisions.

What Is Software Usage Analytics?

Research and advisory firm Gartner describes software usage analytics as “the detailed tracking and analysis of users’ interactions within a software application.” It aggregates behaviors of users—as individuals, within accounts, or overall. Software usage data draws on anonymous, comprehensive usage data regarding customers’ file types, features, system configurations, and other highly relevant aspects of their behavior.

By offering insights about feature usage and adoption, software usage analytics can help product managers create successful products on time and within budget, while also meeting customer expectations. It removes the guesswork in understanding what drives customer success; instead, usage data provides clear metrics that address critical questions. Usage data can augment (and sometimes replace) expensive customer surveys and other customer data reports.

Find—And Act—On The Information You Need

Wading through noisy, incomplete, or bad data is a waste of time. Investing time in software usage analytics can yield hard data that enhance the ability to make business decisions. This also improves the efficacy of the product team’s collaboration with the development team.

Software usage analytics tools can provide insights that address critical product management concerns, such as:

  • What users think about new product features—and why users aren’t embracing the feature that you think is “killer.”
  • How to incorporate customer feedback and anticipate customer needs as part of the software development process.
  • Ways to optimize user experience (UX) and/or user interface (UI)—without aggravating users
  • Which products your customers are using actively.
  • Whether most users possess the computing power to use a particular resource-intensive product feature.
  • Why the conversion rate from trial user to paying customer isn’t higher.
  • When support for an old software version can be dropped.

Getting a thorough understanding of this information doesn’t need to be overwhelming or happen all at once. Whether you’re already relying on software usage analytics to drive your product management decisions and evaluate customer expectations or if you’re looking for a way to start, you can step into this process at a point suited to your comfort zone, then keep moving forward.

The Software Usage Analytics Journey

Software usage analytics maturity is a step-wise process. First, identify basic metrics that help answer direct questions. Next, as you get more confident in your use of software analytics, you’ll be able to gather and analyze the increasingly complex data sets that allow you to answer more nuanced questions. Finally, with tools in place that can grow with and adapt to your needs, you’ll be positioned to layers that will allow you to leverage data, regardless of where your team is on the analytics maturity spectrum.


Start by collecting and analyzing basic metrics. These may include anonymized usage data about features, system configurations, customers’ file types, and other key metrics of user behavior. Insights from these metrics can support key data-driven roadmap decisions right away. As you move forward, they also can set you up to do more complex analyses. By starting with the basics, product managers can avoid feeling overwhelmed by data or by the sense that they don’t have the context to extract informative insights yet.

For example, before updating your product roadmap, you may want to consider the fundamental metrics of usage and engagement. Determine if customers are running the most recent version of an application; how often they engage with it; which features customers use or ignore; and the devices, hardware, and operating systems they use. Remember to consider the behavior not just of customers, but of prospects, too. A basic metric here may be how long it takes for a prospect to evaluate your product.


Once you have a good sense of basic software usage, you might be able to differentiate actionable from non-actionable (or “vanity”) metrics. At this point, you’re ready to ignore distractions and focus on tracking more relevant information. This may mean doing additional filtering and segmentation. Segmentation not only provides you with more substantial insight into users’ engagement with your product. It enables you to push contextually relevant in-app messages, helping you reach the right users at the right time, potentially influencing their behavior. You also can do beta testing with the users whose usage profile matches your testing parameters.

Here, the questions you may seek to answer will likely become more detailed. Sample considerations include how user engagement with your app differs between paid users and trial users; how long it takes for a trial user to evaluate the product and become a paying customer; or if geography plays a role in the rate of upgrades to the latest version of your app.


Finally, you’re ready to run. Your data collection processes are mature, your product roadmap is built on a data-driven approach, and you ask the right (often complex) questions that provide a detailed picture of your users. Now you’re able to match data from varied sources and evaluate not just who your customers are, but which campaigns may have influenced them. In addition to expanding your in-app messaging and segmentation to increase the adoption of underused features, you’re equipped to create campaigns (e.g., through email) to reach users who engage with the app less frequently.

To put your data to its most effective use, take action on robust questions. Is there an optimal pricing model for your product, based on user groups and usage patterns? How can you export trial usage data to better upsell users through your third-party marketing automation software? What do your power users look like and how can you collect valuable in-app feedback that may help improve the product? Consider a wide range of questions here to evaluate everything from the best times to send in-app messages to users in various time zones, how to predict the revenue impact of sunsetting a particular feature set, the user profile of those who churn from your evaluation product, and beyond.

Every product manager has plenty on their to-do list: product strategy, road mapping, go-to-market planning, and more. Playing guessing games doesn’t need to be on that list.

About The Author

Vic DeMarines, vice president of software monetization product management at Revenera (formerly known as Flexera’s Supplier Division), has more than 20 years of experience in software product management. He holds an MS in engineering management from the University of Massachusetts at Lowell.