As you’ve probably noticed, everything is becoming internet connected: home appliances, furniture, exercise equipment, toys. But it’s often done in a clumsy, unusable, or unnecessary way—IoT toasters, anybody? Hardware companies haven’t yet figured out where connectivity makes sense and where it doesn’t. At the same time, some great use cases are beginning to emerge: elder care, micro mobility, preventative care, energy saving, and more.

I ran into this problem as head of product at IFTTT, working with the makers of hundreds of connected devices. Unfortunately, the apps and displays that control these products tend to have terrible usability, especially for less technical people. Hardware companies don't understand how to use data to track how their products are used or where they have usability problems, let alone how to fix them. The usability methods developed at software companies haven't made it into their universe. In particular, they hardly ever use analytics tools because those require lots of configuration, and IoT PMs have more than software and UX on their plate. If they use anything, it’s usually Google Analytics because it’s pre-configured. But when they want to answer IoT specific questions (like where users get stuck when they try to connect the device), PMs need to export the data and query it outside of Google Analytics. Most of them don’t know how to do that and don’t have time.

When I was at IFTTT, I would hear from IoT PMs that even when they’d sell millions of connected devices with a recognizable brand, only a few thousand people would ever use the product as a connected device via the app. I wanted to help them get to the root of the problem: How many people tried to connect the device? How far did they get? Which connected features did users care about most and are those features easily discoverable? I was surprised that hardware companies, big and small, couldn’t answer such questions.

I’m sure some of you are thinking: do hardware companies really need more data? But often they’re stuck in the worst of both worlds: their product design enables invasive data collection, while they're not even using non-invasive anonymous events to improve the experience. That's a lose-lose situation, which can and should be turned into a win-win: less invasive analytics and better usability.

Our goal is to provide IoT companies with everything they need to make their products usable by everyone without collecting unnecessary data beyond anonymous events. I’m particularly passionate about protecting user rights. In a prior life, I drove the rollout of HTTPS by default across Wikipedia and worked with privacy at EFF. As we continue developing the product, I want to think through how to nudge the IoT industry in the right direction.

Today, we provide a pre-configured dashboard with metrics that matter for IoT experiences, like device connection success rate. The dashboard includes usability recommendations for how to improve the experience based on the data and industry benchmarks to show how the experience compares to other connected devices.

For example, our dashboard shows where users drop off when using a device for the first time. If users struggle connecting the device in the app, the PM can read about how to simplify the connection flow (like avoiding app permissions not needed to connect the device and any configuration before the device is connected). They can track whether adoption goes up after they make improvements. They can also track how frequently people use different features in the app and learn where to move features so the experience becomes more intuitive. If they need to do a drill down on something beyond our pre-configured charts, they can create a custom chart directly in the dashboard. No need to export data or write queries.

The dashboard is based on anonymized events tracked by an SDK embedded in mobile apps that control the hardware. It does not analyze events from the hardware itself, like sensor data.

We also surface feedback from app store reviews to contextualize the data. IoT PMs can analyze reviews of their own apps or other apps from their industry and get GPT-3 powered summaries of common complaints, features requests, and how users’ general sentiment changes in response to app releases. The idea behind this feature is to give PMs more product insights and identify areas for improvement without burdening users with UX surveys or waiting until things are so bad that complaints bubble up from customer support calls. (Part of this is still in development and will launch shortly.)

I’m excited to get your thoughts on what we’re building! I recorded a short video to show you how the interaction data shows up in the dashboard: https://www.youtube.com/watch?v=hndsQzowic0. If you have a connected device app to try out with the SDK, feel free to pick a free trial on the plans page. Eager to hear everyone’s comments and feedback in the thread!