Smart devices, dumb support

By Tom - June 29, 2018

Originally posted on our Medium blog.

One of the amazing things about working with IoT companies is seeing all the clever ways people are instrumenting the devices we interact with to eek out exciting new insights to make our lives better: once upon a time, you had a an electric meter that you had to go read, now you have a smart meter that can read itself; no more spider strewn adventures to the meter cupboard!

Over the last few weeks I’ve been putting together integrations between DevicePilot and the customer and business support tools our own customers use. During these sprints it’s become increasingly clear that whilst our devices are getting smarter, our tools aren’t. I’m sure later posts will discuss what it means to integrate an IoT system with specific tools like Zendesk, Intercom or Salesforce. But today I want to talk about the issue in general.

What does integrating the ‘smarts’ of your product with your support tools really mean? To me, and to the IoT companies DevicePilot supports, it means data-driven insights and automation.

For example: I once had a call to an ISP customer service line that I suspect will be familiar to most. After exchanging enough information to ensure that I wasn’t secretly trying to resolve someone else’ problems, the operator said “I can see that your router hasn’t been setup properly.” At which point it took all my willpower not to crush my phone.

If they knew there was a problem- why did I have to call up and tell them?

One of the biggest drives to smarten up your device is to improve customer satisfaction. So now we’ve all connected our products to insecure S3 buckets, let’s start to consider the journey from infuriated customers stuck on the phone, to shiny happy people raving about how good your product is…

Baseline: The device provides information to customer services

While it’s marginally better than having to negotiate confused instructions of things to do to the device to get the diagnostic information and report back; the approach is highly reactive. And for every customer who complains, they’ll be a dozen who just abandon your product and bad-mouth you on the internet.

Level 1: The device raises a support ticket on behalf of the user when it knows something is wrong

We’ll call this the in-the-loop approach. This move to a proactive response means that, while still knowing there’s a fault, the customer at least also knows you’re now aware of it. The assumption that this means you’re also working on it leaves a sentiment that can go a long way to improving satisfaction.

Level 2: The device raises a support ticket on behalf of itself and waits to be fixed, escalating to the user if required

The problem with immediately putting the user in the loop is that there’s a large class of device faults that do not need any interaction from the user to be fixed. By making your devices first class citizens on your support platform, your team will have the chance to service the devices directly; escalating the issue to the attention of the user only if necessary.

Level 3: Monitoring identifies that a device needs support, and attempts to provide it automatically before even starting the support process

To echo the previous paragraph- the problem with immediately putting your expensive support team in the loop is that there’s a large class of device faults that can be automatically fixed. If the first thing you’re going to do is try turning it off and back on again, why not have your device be remotely restarted (unless it’s a self driving car…) when there’s a problem and reporting it only if it persists?

Level 4: Your self-aware AI identifies that an issue will occur in the future and changes reality to prevent it

Despite the flippant title, predictive analytics is a thing- and for a lot of faults it’s trivial. It doesn’t take DeepMind, for example, to tell steady decrease in battery life means it’s time to send out a replacement… However, you’ll only achieve this by working your way through the previous levels, and learning exactly what the problems are in your device estate, and how to automatically fix them!

So what are you waiting for? It’s time to level up your customer support!



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Erik Fairbairn, CEO at POD Point:
Achieved 99% uptime across device estate

"We're totally data driven at POD Point, and if we can answer a question using data then we think that’s the best way - there’s no guesswork and you can use the facts.

Our DevicePilot dashboards have really let us get that actionable insight out of our devices."