From our phones, we can turn off the lights, set a timer for the morning’s coffee, arm security systems and check locks–to name a few. IoT devices have found their way into 71% of U.S. homes, a significant presence in its own right. However, with an anticipated growth of 28.7% in the next five years, it’s clear that the Internet of Things still has yet to reach its peak.

While this explosion of new products is ripe with possibility, it also tests the limits of antique billing structures and may require a fresher approach in order for businesses to capitalize on all the unique opportunities IoT provides.

In this article, we examine five IoT billing challenges, along with solutions that may better address the particulars of this significant new technology.

The Difficulty of Maximizing Monetization

The traditional structure of one time payments between customers and providers doesn’t necessarily suit the world of IoT. While sticker price economics may work for certain devices that use self-contained features (i.e a “smart” coffee maker) it’s not as applicable for other products whose main selling point is a service.

For example, IoT-based security systems or GPS management trackers are providing a long-term service that extends well beyond the day of purchase. Consequently, companies of this nature are better served to implement a hybrid billing system, charging one price for the device itself, and then following up with subscription-based billing.

This billing method is admittedly complicated by the number of different recurring services that many vendors provide. In the case of a home security monitoring system, this might mean different charges for video recording, sensors, etc.

Providers must account for this by integrating different fees under a single plan, fluid in how it adapts to new settings and services.

Use-Based Billing

IoT-based services are beginning to adapt to a usage-based billing method. While meter monitoring for utilities and telephone use has long been established, the notion is relatively novel in the tech sector.

Fortunately, this method of billing is largely abetted by the IoT device’s natural ability to take data on such things as minutes, bandwidth, and other usage-based tick marks that clearly establish how much a device or service is being used.

Consequently, usage monitoring can be automized to establish clear billing cycles that reset on different frequencies to match the usage needs of the consumer—who is subsequently often pleased to feel as though they’re only being charged for what they use.

Keep in mind, usage-based billing methods require that the provider have the ability to pause services as it applies to the needs of unique businesses. For example, subscription-based services in the home gardening niche may be of little use in January.

The Money in Data

IoT companies should look to monetize the data they collect. In the case of some devices, IoT data can be used for direct billing purposes to call attention to a new product that the consumer might need.

For example, an IoT toothbrush might naturally intuit when the consumer needs a refill on additional products such as brush heads, toothpaste, etc. The manufacturer can then tailor that data and provide its customers with the opportunity to “subscribe” to these refillable items.

The customer is pleased because they never need to worry about running out of toothpaste, and the manufacturer benefits from a dependable monthly revenue stream.

Companies may also monetize data by tailoring their business models to the specific trends demonstrated by their customers.

In the example of an IoT toothbrush, the company might quickly learn through subscription history and use records that one brush head is substantially more popular than others. Knowing that, they may decide to pay less attention to the rest of their product line, opting instead to dote more development and marketing efforts on their star product.

The High Cost of Hardware

Though IoT devices are not as expensive as they once were, the sticker price can still be prohibitive for some businesses and individuals. Due to the sometimes high start-up cost of acquiring hardware—especially on top of service charges—some IoT businesses may need to consider monthly payments for both their products and their services. Indeed, the “layaway” approach is already common enough with computers, tablets, phones, etc.

Providing customers the opportunity to stagger their payment democratizes the technology somewhat, making it more affordable for the consumer, and ultimately, more profitable for the manufacturer.

Revenue Recognition Complexity

Accurate revenue recognition can be complicated as it relates to IoT. This is particularly true in the case of businesses that are deriving most of their income through subscriptions. Technically, revenue cannot be recognized until a service has already been rendered. In the case of a subscription service, however, this unfolds gradually over time and is not completely realized until the end of the billing cycle—a date that may or may not be different for every subscriber.

Revenue recognition is further complicated by temporary trial periods, discounts, sudden cancellations, etc.

Inaccurate revenue recognition can, at best force you to redo your reporting, and, at worst, result in legal ramifications. Fortunately, with the right subscription billing platform, your business will be able to automate and control its billing for stress-free bookkeeping.

Flexibility Wins the Day

If these considerations have a single thread tying them all together, it’s a dependence on flexibility. IoT devices are themselves extremely flexible and ask the same thing of their stewards. As the number of IoT devices in this world grows at a rate of approximately 127 units per second it seems apparent that all aspects of the industry will need to be able to adapt to the influx.

As this growth unfolds it may be the devices themselves that best serve their manufacturers. Difficult though it may be to maintain flexibility as you restructure the way you handle your billing, there is a bright side: IoT devices exist with automation and data taking in mind—abilities that are sure to assist in whatever overhauls you have in mind.

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A few years back, most people in the tech industry were talking about the “smart home.” Every device would not only be an internet of things (IoT) device, but they would also communicate to provide services. As with much new tech, that was oversold. However, there’s still the issue of the connected home. More and more devices in each home are communicating with the internet, if not with each other. Internet service providers (ISPs) are searching for way to better manage the connectivity, and artificial intelligence (AI) is one tool.

Smart phones, tablets, laptops and desktops are only the tip of the iceberg. Most televisions are web enabled. More and more homes have voice assistants. Nanny cams, security systems, climate controls, and other systems also connect. With the vast expansion of devices comes more data demanded, so ISPs need ways to better analyze usage in homes and neighborhoods in order to meet guaranteed levels of service.

In a related twist, customers will, with no surprise, first call their ISPs whenever there is any connectivity problem. In order to provide service, that means a larger call staff. However, what if the problem is a specific device? Even more complex, what if it’s a specific application being run on the phone? An ISP which can quickly identify the root cause of the issue can either fix its own issues or point the customer towards the appropriate firm to provide service. Doing that efficiently will save enormous amounts of money.

Identifying technical issues is a clear use case for AI. The question that needs to be answered is how close to the devices can an AI system run. On the ISP’s services, there’s a distance that can obscure some issues. It would be much better to run AI on an individual home’s modem or, even better, a router. The question becomes the footprint. Even runtime AI has not been known for highly efficient resource usage, and many companies have been working to address that for many IoT applications.

One such company addressing the issue for the connected home is Veego. They claim to have AI inference that runs on home routers and modems in order to identify performance issues. “The typical router has much lower performance than many connected devices,” said Denis Sirov, Co-founder and CTO, Veego. “We developed processes that provide inference on routers while utilizing less than 0.75% of CPU performance.”

One way they are able to do that is training a robust model in the datacenter, then using part of the runtime code to identify the devices in a specific household. That lets the company narrow the necessary processing to a far smaller dataset than the entire set of options.

A key component of their solution is the ability to not flood consumer or ISP with every issue identified. The context of the problem matters. If there’s a temporary glitch that doesn’t look like it’ll be repeated, that doesn’t require a flag. A small issue in buffering isn’t a problem, but something that is slowing down the presentation of a movie or the graphics in a video game should be identified so it can be corrected.

Another interesting statistic provided was that 81% of replaced routers aren’t the problem, they’re just the fastest way to diagnose a problem using current, limited technology. As mentioned above, one critical issue isn’t finding out the specific problem, but in identifying the problem’s origin. With Veego running in the customer’s location, it can more easily identify which device and which application is causing the problem. That helps in two ways. First, services calls can be diverted from the ISP to the specific provider of a device or application. The implementing ISP can choose to have customers notified by text, in real time, to contact the appropriate support when a problem impacting performance is detected. That speeds the customer’s resolution of the problem, improving satisfaction. Second, the calls to the ISP’s support now have more detailed information to more rapidly provide analysis and response to the customer.

One example of the importance of this need is the announcement that Amdocs is partnering with Veego. Amdocs has a heavy presence in the ISP sphere, and adding their skills will help the smaller company to get into the market. The product will be resold both standalone branded and as part of OEM solutions.

The connected home is becoming an increasingly complex, and sometimes overburdened, network problem. Artificial intelligence has the ability to assist the companies providing hardware and software into the home, saving all stakeholders time and money while providing an improved experience to the customer.

The post Smart Home Versus Connected Home: The Later Is Happening Today And Artificial Intelligence Can Help Manage The Complexity appeared first on Artificial Intelligence.