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Moving beyond fragmented data: Three usage analytics lessons for stronger software products

(Image credit: Image Credit: Bluebay / Shutterstock)

The building blocks of success come in many forms. In the construction field, they’re often discussed in the literal terms of the building trade—concrete, glass, steel. But for software products to be successful in any industry, product usage analytics are equally important building materials 

Measuring success

Solibri provides services and solutions for construction companies as they define their projects, with quality assurance (QA) tools to confirm that project quality is always at the required level and that the project complies with all construction and compliance requirements. Our model checking software for building information modeling (BIM) helps construction companies make their projects more efficient, simplifying and automating the building design and checking process, supporting building construction and management. Much of what we do focuses on strengthening our customers’ work by sharing validated data with all parties involved in construction and manufacturing. 

Yet we found ourselves struggling to get parallel data for our own use—data about how our typical users engaged with our software. How were our customers—architects, designers, and engineers; building owners and construction companies; and installers of building subsystems, including mechanical, electric, and plumbing (MEP)—actually using our product? How did their needs differ, whether across their assorted professional disciplines or across geographic boundaries?

We weren’t alone in looking for a tangible way to become a more data-driven business. Today, a declining number of companies say that their organizations are actually data-driven: 24 percent, down from 37.8 percent a year before, as reported in the Harvard Business Review. But software usage analytics can provide the actionable insights required, delivering information to help customize products and communications to meet the specific needs of varied users. 

Here, I share three lessons about how a robust software usage analytics program helped us better meet customers’ needs. These can help other product management teams tap into a continuous customer feedback loop that drives informed decisions.

1. Determine what’s being used—and what isn’t.

How are customers using our product? That’s a question better answered by data than by assumptions. 

Though we’d often collected feedback from customers during events or when they’d speak with our sales or support teams, we required more robust insights about our typical users. We needed to know things like: 

  • Metrics for particular features within a product, such as which of the model checking rules in our software customers used on a regular basis; 
  • Patterns of usage, including the in-product customer journey and drop-offs (such as whether customers experimented with some rules, but then stopped using them); 
  • Segmentation by feature usage;
  • The number of daily and monthly active users and these numbers as a ratio of total customers; and 
  • Activations, in which trial users converted to paying customers 

To better collect and analyze this product usage data, Solibri integrated Usage Intelligence from Revenera, with a dashboard that helped evaluate users’ actual engagement with our product. With this information on hand, we were able to better define our user personas, what those groups were (and weren’t) using, and identify ways to boost usage of underutilized features of our software. Taking this a step further, we’ll soon implement in-app messaging for quick “thumbs up/thumbs down” surveys—qualitative measurements of users’ feelings about particular features. 

Beyond this initial goal of evaluating how existing products were being used, our usage analytics initiative has helped us apply this data-driven approach to new product development and management. We are now able to launch products more effectively, tapping into usage patterns of existing products to help us deliver successful new products and guide users through the onboarding process. This initiative also helped us define key performance indicators (KPIs) as targets for our success metrics, such as customer retention.

2. Identify variations in usage.  

What customer segments may be using your product in unique ways? In our case, customers are spread around the globe, with professionals in 70 different countries using our model checking software. These users have localized needs. What’s happening in the UK isn’t necessarily going on in the US, for example. Our software usage analytics initiative delivered granular data about isolated usage patterns. This helped our product team incorporate customers’ needs into new product releases. We are now able to offer per-country release adoption data, helping us tailor customer communications and reminders. 

Variations may also come from unknown unknowns—the things that you hadn’t thought might occur. As we pulled together data from a variety of sources, we identified significant differences in how some customers used the 50+ checking functionalities that are built into our software. The lesson for us was in how significant the differences were between the very heavily used (e.g., 100 times a day) and the rarely used (once a day) checks. At first, the numerical disparity was confusing. Digging into the usage analytics helped us better understand why each of these checks was used differently in design projects, better informing our ongoing analysis and development of each.

3. Capture missed opportunities.  

If you’re making product decisions based on presumptions, you’re likely missing valuable business opportunities. With improved data about how customers were using our products, Solibri caught and corrected a few of our own. 

Many of our customers are architects. We had the perception that most architects using our product were working on Macs. Once we looked into the data delivered by usage analytics, we were somewhat shocked to find that only about 5 percent of them were using Macs, with the rest on PCs. With this more accurate view into customers’ hardware, our developers were able to implement new and more robust product features, tailored to the appropriate platform. 

This adaptation of our product plan helps make the overall experience better for users. We now provide in-product tutorials to help users as they adopt a product, streamlining the product usage journey, from the very first experience with it. 

A stronger foundation

Usage intelligence is certainly an essential building block for making data-driven decisions in any field. Data aggregation offers a holistic overview of product usage, while also highlighting informative metrics, such as usage shifts. As you test assumptions, you can move in new directions and better understand where R&D and marketing investments yield the most value—for you and for your customers.

Juan Rodríguez, product director, Solibri (opens in new tab)

Juan Rodríguez is the product director at Solibri. With nearly 20 years of experience in tech, Juan drives strategy to improve operations, remove silos, and promote cross-functional work.