Using the AiSight Machine Insight Center: how we manage user feedback

The new AiSight Machine Insight Center is next-generation software for our predictive maintenance solution, developed based on feedback from the maintenance and production professionals who use our solution every day.

Thanks to that user feedback, we’ve rebuilt our software from the ground up We can now incorporate new features like machine histories, visualized alerts, more flexible organization options, and user permissions, into a faster, more intuitive, and more scalable piece of software. And we plan to continue collecting feedback in order to keep making the Machine Insight Center better.

This calls for an organized approach to collecting and managing feedback. We began structuring our feedback-management system back when we first started planning the Machine Insight Center. We needed a robust and secure system, capable of anonymously sorting and prioritizing feedback into actionable requirements. With this system in place, we can manage feedback and continue to improve the Machine Insight Center based on that feedback.

Let’s look at how we manage feedback in the Machine Insight Center.

Collecting and managing user feedback in the Machine Insight Center

To collect more user feedback on the Machine Insight Center, we've made providing feedback as easy as possible.

Whatever you love, don’t love, or would love to see happen, we want to know. So, please don’t hesitate to provide feedback! And remember, our customer success representatives are always available for a talk if you’d rather reach out to them. Whichever course you choose, your feedback will reach our software development team.

What happens when I send feedback to AiSight?

Whenever you provide feedback, our system gathers and automatically sorts that feedback based on topic. Each piece of feedback acts something like a vote. If any given topic gets a lot of feedback, it moves to the top of our to-do list and we prioritize changes accordingly.

Managing feedback for better design

By effectively managing a large volume of feedback, our system gives us the opportunity to design for outcomes—for actual added value for our customers. We hear when our customers have problems; we also hear when we successfully solve those problems.

Picking the best ways to address the pain points our users bring up in feedback is itself an act of design. By solving for outcomes, we can experiment and prototype solutions to the problems at hand.

Going into this process, we like to start with many potential solutions. Any potential output is a solution that could affect the outcome our customers want, but we want to avoid cornering ourselves or getting hung up on particular outputs. Instead, we remain open to developing the best solution to the problem.

That said, we can't prototype everything. To reduce the number of potential solutions, we need to check our assumptions about how customers use our solution. Here, we test viability, feasibility, usability, and desirability. This not only narrows the scope of what we prototype, it allows us to continually test and challenge every assumption.

Putting the best to the test

We provide our predictive maintenance solution as a service. That means that our product is never really complete—we’ll always support our customers by pushing the limits of what our solution can do. To do this effectively, we need to continuously improve our solution. And that’s only possible if we continuously discover how we can make our solution better.

Once we’ve explored every option, we test to prove the best. And this isn’t limited to options we’re interested in implementing—it extends to established functions.

In an early stage of development of the Machine Insight Center, we even tested the assumption that our customers want graphs in our software! The results of this test couldn’t have been clearer: our customers do want graphs. In fact, graphs are so useful that we’ve built a new feature into the Machine Insight Center that allows users to compare graphs. And we have plans to expand this feature further.

Feedback – the final test

Once we’ve narrowed down all potential solutions to only those that perform best in our testing for viability, feasibility, usability, and desirability, we develop full-fledged prototypes for further testing. Even here, we still want options; A/B testing is an important tool for comparing the best of the best.

Which option is the best depends, of course, on feedback from our users.

If you’re currently using AiSight’s Machine Insight Center to prevent unplanned downtime, please send us feedback whenever you’d like. We want to know what we could do better and what features you’d like to see, but also what you really like! Your feedback now will make the Machine Insight Center better tomorrow.

And if you’re not using the Machine Insight Center, we still want to hear from you!

Find out how AiSight can end unplanned downtime in your facilities.

In the next article on the Machine Insight Center, we’ll look at the features we’re currently developing. There’s a lot to get excited about, and it’s all thanks to feedback from our users.