Turning decades of customer insight into measurable product decisions
The challenge
A product shaped over 35 years, rich in capability, and complex in real-world use
For most NedGraphics customers, the software is the surface where an entire workday happens. Designers rely on it to move from concept to production-ready artwork with precision and efficiency. Over decades, customer interviews, feature requests, and hands-on collaboration helped shape a platform capable of supporting a wide variety of professional workflows.
But that same growth brought complexity. As new industries, use cases, and specialized processes were added, understanding usage through conversation alone became increasingly difficult. Individual customer insights were valuable, but they couldn't represent the full picture of how thousands of users interacted with such a broad toolset every day.
The product roadmap decisions reflected years of customer input, support interactions, and deep industry knowledge. However, the team lacked a reliable way to measure how that feedback translated into real behavior across the product. Without behavioral visibility, it was hard to answer fundamental questions with confidence:
- Which features were central to daily work?
- Which workflows mattered most across different verticals?
- Which requests reflected widespread needs versus highly specific scenarios?
The discovery
Many workflows, one shared outcome
When NedGraphics began looking at behavioral data, the most important insight wasn't where users failed - it was how differently they succeeded.
Customers across fashion, home textiles, flooring, and automotive industries were all seeking the same outcome: production-ready designs. But the paths they took varied significantly. Teams adapted the platform to fit their own operational rhythms, combining features in ways that weren't always visible through interviews or support conversations.
This reframed how the team thought about the product. There was no single "correct" workflow to optimize. Success meant supporting multiple valid usage patterns at once and recognizing that feature value depended heavily on context.
The solution
Connecting product usage, customer context, and performance in one place
By introducing Countly, NedGraphics added more than analytics to its product practice. The team gained a shared view of how customers used the platform across features, workflows, industries, and customer segments.
This gave product, design, and commercial teams a common source of evidence. Instead of relying on isolated feedback or individual customer requests, they could validate product hypotheses against real usage patterns, see which capabilities supported daily work, and identify where workflows slowed down or created friction.
Countly also helped connect different types of product signals in one place. Feature adoption, workflow behavior, customer context, and performance issues could be reviewed together, making it easier to understand not only what users were doing, but where the product experience could be improved.
Customer conversations still mattered. Countly made them sharper. Feedback and feature requests could now be evaluated alongside measurable behavior, giving NedGraphics a stronger foundation for prioritization, roadmap planning, and cross-team alignment.
The outcome
Product decisions made measurable
With Countly, NedGraphics could pair what customers were saying with what the product data showed. Usage patterns across workflows, segments, and features gave teams the evidence they needed to validate priorities, reduce guesswork, and make roadmap decisions with greater confidence.
Insights grounded in evidence
Adoption visible at scale
Friction made explicit
Proactive issue detection
The takeaway
Mature products need measurable insight
After 35 years of product evolution, the challenge for NedGraphics wasn't a lack of customer insight. It was the scale and complexity of understanding usage across such a rich platform. Behavioral data didn't change the team's values or customer focus - it gave them the missing lens to see how those values played out in practice.
Their advice to other mature software teams is shaped by that experience: