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The Biggest Thing You're Missing in Your IoT Strategy

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I love morning routines. Since joining Very in July 2020, I’ve noticed a common theme in all of our morning routines during our early video calls. Almost everyone’s routine involves one common item: the EmberⓇ Mug, an IoT mug that allows you to set and maintain a preferred temperature for your coffee or tea with a simple app on your phone.

Then, I started asking around and realized just how much everyone at Very believes in IoT devices. Not only do we develop them, but everyone at Very uses IoT technologies extensively in their everyday lives. Which, since we’ve been remote-first since the beginning (by design, not by COVID), means that these IoT devices impact our workday.

All of this rabbit-holing into our personal IoT usage coupled with the team’s deep experience launching IoT devices led me to ask one question: with all of these devices, how do we take IoT to the next level? Although the IoT industry is growing at a fast clip, the field is still nascent. The industry at large is missing a cohesive IoT business strategy that connects better experiences for consumers with more repeatable revenue for businesses.

From Predictive to Prescriptive Analytics

prescriptive-analyticsIt’s true that businesses are using IoT devices in strategic ways, but there’s room for substantial growth beyond what’s currently being done. Take predictive maintenance, for example. If you can use machine learning and IoT to determine when a machine will break before it does, you will save millions of dollars in productivity gains alone, not to mention the services and parts costs.

But if you stop there, you’re still missing out on a huge piece of value that could be derived from this AI and IoT solution. What if you could go beyond predictive maintenance and adopt what the industry describes as prescriptive analytics?

With prescriptive analytics, you could recommend certain products or services from the behavioral data that you’re collecting that would reduce service calls in the future. You could save your customers money by upgrading or downgrading them to a different service tier based on their usage, creating more long-term revenue and loyalty.

By tying into a larger strategy and consistently evaluating the data that your devices are constantly collecting, you can build a long-term success strategy that is personalized, but automated, for each customer.

This goes beyond predictive maintenance, which is often utilized in an industrial context. We see this in general consumer devices as well. A company produces a smart device that’s slick or cool because it’s connected to the internet. But that novelty will wear off as IoT devices become more ubiquitous. At the end of the day, without a plan to create additional value for your consumers over time, your warming mug that’s connected to your phone is, well, just a mug.

The biggest benefit of creating a prescriptive analytics strategy for IoT is that you create more long-term recurring value. This allows you to charge recurring revenue for continued service, or potentially increase prices. By offering continual “success” strategies to the customer, you are extending the relationship and value beyond the initial sale. The only limit to this is the quality of your data science operation and the creativity of your team.

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Operations for a Prescriptive Analytics IoT Strategy

The operation of a prescriptive analytics strategy is usually one limiting factor because it revolves around two core principles — that customer data is reasonably accessible and that you have access to the data science talent needed to turn that data into insight. 

B2B IoT — Siloed Data

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It is amazing how many companies, both large and small, either have no central place where customer data is stored or have multiple siloed spots where that data exists. Most business-to-business providers (IIoT and commercial IoT) that we encounter have many points of interacting with customer data. There’s the CRM, the ERP, the billing system, an archaic database of serial numbers, and the list goes on.

Often when we dig in to build a prescriptive analytics strategy in IIoT or commercial implementations, there is little to no integration between the data sources, and different departments own each system. Typically deep inside the organization, you’ll find a BI tool that joins some of the data, but it fails to centralize everything in one place.

B2C IoT — Huge Numbers of Consumers

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In business-to-consumer IoT, we see a different issue that is no less problematic. Because there are so many individual consumers and so many data points, building meaningful machine learning models requires extraordinarily talented data scientists and software engineers, working together to tailor the models to individuals. 

How IoT and Data Science Provide the Solution

What people often forget when creating an IoT product is that they are sitting on a trove of customer data inside the ecosystem they have created. With a little bit of customization when developing the application, you can collect and synchronize customer data, thus solving both the siloed data problem and the no-system-of-record problem.

I’d still recommend choosing a system to house your customer data that is not the IoT cloud, like a CRM. The right choice can vary based on the organization that you’re in.

As mentioned above when talking about B2C specifically, this is all easier said than done. If you go this route, you’re going to be dealing with an unholy amount of data. Every single action from devices at the edge can be tracked and processed. In theory, it sounds like a great idea to just collect all of this and figure things out later, but this can be both expensive and headache-inducing down the line when you want to evaluate the data for other trends.

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The right move is to engage with a data science team early in the architecture of your data collection. This group of people will give you both an initial model to evaluate as well as a future-proofed schema by which to evaluate changes needed later.

The other problem we see here is sending constant streams of data directly into the system of record. Unless you want your SaaS expenses to skyrocket, this is a failing strategy. Store the data in the cheapest place possible, and have your data science team build algorithms to send only select information to that system of record.

Finally, you’ll want to get in a cadence with data science to evaluate your algorithms and ensure they’re sending up the right notifications, and there are not more that you’re missing out on.

What Do You Do With All That Data? 

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Yes, you need your data science team to look at these algorithms on a rhythm, but the next step is how you use that data. This is the million-dollar problem to solve. Don’t be afraid to be creative with solutions to patterns your machines and algorithms see in the data. Engage with a variety of people in your organization. Interview customers about the patterns you’re seeing. Ask probing questions. And then work with your product team to develop innovative solutions to these problems.

An Important Word About Privacy

Prescriptive analytics strategies can provide highly scalable ROI for companies looking to go to market with an IoT device. However, you do not want to get carried away with the behavioral and demographic data you are collecting on customers. Not only is there a question of ethics, but there are also laws, most notably GDPR, that protect consumers against unwanted tracking and data collection.

This means you need to provide a reason and value for collecting and analyzing data. While it might be a headache to some for all the opt-ins they need to collect, this is good for the consumer.

The moral here is: when creating a prescriptive analytics strategy, consult both a lawyer and your conscience. Make sure that the data you’re collecting and the algorithms you’re producing are providing actual value to the consumer of the device, not just so you can sell more stuff.

How Do You Get Started?

IoT devices still feel shiny and new in many markets, but ubiquity is coming. Once everyone has multiple IoT devices at home and at work, the competitive edge will be in the residual value that you can provide to consumers with your prescriptive analytics strategy. 

And what I’m truly hoping for? Someone using prescriptive analytics to improve my morning routine. 

But I doubt you’ll be able to change my craft-roasted Ethiopian coffee.

Looking to work with a top IoT development company that understands how your product fits with your overall business strategy? Drop a line to our team today. 

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