Why Your IoT May Not Be as Consumer-Centric as You Think

Companies are adopting IoT technologies as tools to become more consumer-centric. On the surface, the Internet of Things appears to be the perfect tool for the average consumer, regardless of their individual needs. If they can imagine it, there is an IoT device out there that can accomplish it. Still, despite everything these devices have to offer, they may not be as customer-focused as business owners might want.

What prevents your IoT from being consumer-centric, and what can you do to fix this problem and make the most of these technologies?

Why Isn’t Consumer-Centric IoT Standard?

Companies create tools for their consumers, coming up with ways to make everything from shopping to shipping easier and more efficient, but how many of those devices and technologies are designed with the customer in mind? The consumer isn’t going to benefit from an RFID tracker that maintains warehouse inventory or the programs behind it — at least not directly. It might ensure the item is in stock when the customer places their order, but beyond that, it doesn’t directly impact the consumer or their experience with the company.

On the opposite side of the coin, companies tend to think they are creating a customer-centric IoT experience, but that couldn’t be further from the truth. Upwards of 52 percent of teams publicly report that their products or feature ideas are inspired by customer feedback, but less than 10 percent capture feedback from those customers. It’s partially a case of the right hand not knowing what the left hand is doing and partially a case of teams overestimating their systems.

This isn’t to say that these IoT systems should be abandoned in favor of older techniques or technologies, but if the goal is to create a consumer-centric IoT, some changes need to be made to accomplish this task.

Shifting From Reactive to Proactive

Right now, customer service tends to be reactive. If something goes wrong, especially something that might trigger a customer complaint or a recall, all these companies can do is react to each event as it happens. At that point, they end up playing damage control. And if the incident is severe enough, it can cause irreparable damage to the company’s reputation.

Most existing systems — even those designed with IoT in mind — only collect information from about 4 percent of dissatisfied customers. The rest simply don’t complain, but 91 percent of those who don’t complain will never come back.

Combining IoT with machine learning and big data will give companies the tools to truly integrate consumer-centric IoT and use the information that they do get from consumers. A machine learning program can sort through all the data collected from consumer complaints and compliments and turn that information into actionable insights and data points that companies can use to improve their consumer experience. The more information these systems are exposed to, the smarter they become, and their actionable data points are more valuable.

One case study found that by taking a database with information about more than 33,000 members and feeding it into a machine learning program, they could parse all of that data into five unique member personas, enabling the client to create a more personalized experience for each of their members.

Over time, these machine learning systems can even make predictions by focusing on the data and sorting through it to find patterns. Amazon, one of the biggest retailers globally, is using machine learning to predict future demand for its products. Manual forecasting is traditional, but with millions of different products on the market, a single human brain can’t manage that sort of fortune-telling. It’s not magic. If Amazon could see the future, they would have foreseen the 213 percent toilet paper sale surge at the beginning of 2020. But the system couldn’t predict it because it hadn’t happened yet. Now that it is an existing data point in the system, it can react more quickly if something like that happens again.

They say that people who don’t learn from history are doomed to repeat it — in this case, learning from history gives these systems the tools to predict the future.

Measuring Consumer Experience Metrics

Consumers generate a massive amount of data every day, and companies like Google and Facebook have learned how to capitalize on that data by providing things like targeted ads. Businesses of all shapes and sizes can use the data provided by IoT devices they utilize to create a custom consumer experience for each shopper.

This can be a valuable tool for any business trying to keep up with the changing market. Creating these individualized consumer and marketing experiences doesn’t have to devour a lot of time and resources. Consumers are already providing you with everything you need to create the perfect marketing experience — most companies simply lack the tools to use it efficiently. Tweaking how the information is collected and utilized can make the most of the data these IoT systems are already collecting.

The majority of the data collected by these IoT systems is raw. It’s a mass of bits and bytes, collected and stored in the order received. It's possible to feed this data into a machine learning system to make sense of it, but it takes a lot of extra work to sort through raw data. Changing the way the data gets collected into the system can eliminate the need for those additional steps. It could be as simple as automatically sorting the information based on the user’s account, or more complex — like sorting through the data and placing it based on existing and newly discovered demographic data.

As an example, look at Amazon Alexa. Maybe a user has been using their Echo Dot for ordering household items and based on those purchases and the fact that the Echo Dot only hears one voice, it can assume a user is a single man or woman. If a second voice gets added to the mix or the ordered items change drastically — like from wine openers to diapers — it is easy to extrapolate the data to assume the household’s demographics have changed. Instead of just sorting through all the raw data, machine learning systems can automatically shift this example household from one demographic to another, ensuring the data collected is used as efficiently as possible.

Making the Most of IoT

IoT is becoming an essential part of running a business. The goal shouldn’t be to create a system that is only for the benefit of the company but rather one that can help both sides of the coin by collecting data and using that information to create actionable goals and a consumer-centric experience.