How data is driving smarter software design for manufacturing
In today’s manufacturing world, data is not just a by-product; it is the engine of innovation. Every interaction, each workflow pause, and every overlooked feature tells a story. By harnessing product analytics and customer feedback, good solution providers ensure their development roadmaps reflect genuine user needs. The outcome: a software experience shaped by real usage, enabling manufacturers to deliver faster, cleaner, and more efficient work.
Real usage patterns illuminate priorities
Intelligent products increasingly rely on analytics to capture how shops move through critical workflows. They track not just which features are opened, but what options operators enabled (or disabled) during key steps, such as importing a CAD file or posting a program to the machine.
These choices reveal valuable insights. If a feature is used frequently, it may indicate that users are trying to gain more efficiency or avoid a downstream machine issue. If an option is rarely turned on, it may mean users are unaware it exists or need more training to understand its value. In either case, the data helps providers decide whether to expand functionality, simplify configuration, or provide better guidance.
This approach reflects how leading technology companies operate. Google describes its product management practices as a "relentless focus on user needs" and being "data-driven" in decision-making (cloud.google.com). It is precisely this evidence-based strategy that guides how improvements are prioritized: concentrating on areas where users derive value and reducing friction where challenges persist.
Finding and removing workflow friction
Analytics also highlight when users spend more time than expected on routine steps. If operators add multiple extra clicks to accomplish a task, it suggests the workflow could be simplified. Smart apps respond by streamlining steps, offering automation, or providing predictive suggestions to guide users toward faster outcomes.
For manufacturers, the impact is immediate. A job that once required fifteen separate actions can be cut down to five. Across dozens of programs and thousands of parts, these efficiencies translate directly into more throughput, less downtime, and a reduced chance of errors.
Bringing hidden features into the light
Another frequent finding is that customers sometimes request a feature that already exists. The real challenge is not building something new, but helping them discover and apply what is already in the software.
By pairing analytics with conversations from customer success teams, providers can see where adoption gaps exist. Maybe a checkbox is rarely enabled during program posting, even though it provides material savings. Or a reporting function is left unused because users are unaware of its impact on scheduling decisions. In these cases, training and awareness can unlock immediate benefits without waiting for a new release.
This reflects insights from broader product analytics best practices: many organizations gain more value from better awareness of existing features than from building entirely new tools (smartdatacollective.com).
Beyond CAM: the Connected Shop
The story does not stop with CAM. Intelligent products and smart apps act as new data capture and generation points throughout the entire shop. Many solution providers call this the Connected Shop. The data insight goes well beyond what the CAM application itself is producing.
There is a gold mine of information in the CRM or business system. Equally valuable are logistical data points on the floor: receiving components and materials, loading material on the machine, offloading parts to the next operation, pallet locations and queues for forklifts or cranes, and secondary processes like painting, welding, and bending.
By connecting these data points, shops gain a full picture of their operations, not just isolated snapshots. This holistic view helps identify bottlenecks, track efficiency across departments, and optimize the entire production cycle. It also feeds back into future product design, helping providers build tools that bridge gaps between engineering, planning, logistics, and production.
SimTrans transaction totals
Data meets dialogue to drive the roadmap
Strong roadmaps are shaped by two sources: numbers and narratives. Analytics show what features are being used, which options are toggled on or off, and how long certain workflows take. Conversations with customers explain the motivations: curiosity, efficiency, machine safety, or lack of awareness.
When combined with data from across a Connected Shop, this input ensures providers focus development resources where they make the most difference. They can enhance popular features, streamline high-friction processes, and shine a light on overlooked capabilities that already exist, while also building integrations that support the entire shop ecosystem.
Ribbon to Analytics
Why it matters
The global product analytics market was valued at $15.1 billion in 2023 and is projected to grow to $65.4 billion by 2031, at a compound annual growth rate of 20 percent. This reflects how critical data-driven design has become across industries (cloud.google.com, smartdatacollective.com). Tech giants like Google and Meta rely on product analytics to continuously refine user experiences. The same principles apply in manufacturing, but with a sharper focus on workflows, materials, and production efficiency.
The vision
Smarter software is not just about adding new features. It is about adapting to actual user behavior, removing inefficiencies, and ensuring customers discover and benefit from everything the software can already do. Intelligent products need to extend this vision beyond CAM to the full manufacturing process, connecting people, machines, and data. By blending analytics with customer conversations, solution providers deliver tools that evolve with real-world usage, helping manufacturers cut smarter, move faster, and achieve more with every release.
Managing and incorporating strong data practices also lays the foundation for next-generation technologies. Tools like machine learning and large language models (LLMs) thrive on an abundance of well-structured data, allowing them to augment user capabilities and provide smarter recommendations. In manufacturing, this means predictive insights, faster decision-making, and even more efficiency gains. With solid data in place, the opportunities to positively influence business results expand dramatically.