Infographic: Today’s products are being #BornDigital. Are yours?
April 9, 2019
Everyday items such as a bottle of wine, a T-shirt or a box of cereal can be enhanced with web connectivity and a digital identity, making them smart, trackable and interactive. Digitized products create new data streams at every point along the supply chain from manufacturing to post-purchase customer experiences.
The rise of smart products has created a new concept: The Digital Product Lifecycle. The Digital Product Lifecycle weaves together the disparate stages of supply chain management and customer relationship management in a connected, cyclical way, powered by real-time data.
This infographic shows how new data streams from #BornDigital products enable insights and applications through the product’s lifecycle. Get all the details with The Digital Product Lifecycle E-Book.
Machine Learning, Connected Devices, and Drinking too much Coffee
May 24, 2018
We’re excited to report on a machine learning project that we’ve recently completed. The goal of our project was to build an interactive tool that would allow our sales team to engage customers with an interactive experience of EVRYTHNG’s machine learning capabilities. Perhaps more important was what we learned about developing user centric machine learning-based products with the EVRYTHNG platform.
The first blog post in our machine learning series was about how we built a machine learning component on top of the EVRYTHNG platform to help our customers detect gray market issues with a 94% accuracy. This blog post is about the human factor of machine learning. After all, the purpose of any machine learning project is to work with people and understand their requirements and needs, then build a model that makes useful predictions.
Let’s look at a use case to better understand the context and how the tool we built fits into the EVRYTHNG proposition.
Use Case: An online insurance company for home appliances
Imagine the following use case: you had the brilliant idea to start an online insurance company for home appliances.
The premium is based on the types of appliances included in the policy. Sounds simple enough, but consider some of the complications. How would a customer get a quote — would you be required to dispatch agents to each customer’s home? That defeats the purpose of online insurance. You could ask customers to self-declare their appliances online, but the drawback here is that going through a massive catalogue of appliances will scare away all but the bravest customers.
Rather, what if each appliance could identify itself, based on observable features that are emitted when they run?
The images above show multiple patterns over time, attributed to two different types of coffee machines.
Observe an appliance long enough and a unique pattern emerges. This is what machine learning is all about: recognizing patterns to transform data into logic — in this case a model that maps vibrations to appliances.
To accomplish this, we can use cheap sensors to take measurements of the appliances’ vibrations, then send the measurements to a service in the cloud. This service will classify the appliances based on the measurements received and return a personalized insurance policy. Now we have the business model and technology solution for our online insurance company!
Figure 1: A model, supported by the EVRYTHNG Platform, that shows how vibrations are mapped to appliances.
Let’s put it all together: A customer, after registering for an insurance policy, orders a WiFi-enabled multi-sensor device for each of their appliances. Once a multi-sensor has been attached to each appliance, it will begin to send measurements to the EVRYTHNG platform. Next, a machine learning component is used to predict the type of appliance based on the measurements received from a Pycom IoT device. Then, the artificial intelligence component creates an action containing the type of appliance. In turn, this action triggers one or more Reactor™ rules on the EVRYTHNG Platform, such as a rule to generate a personalized insurance policy and another rule to send a text message noting the new appliance that was detected.
We built the demo tool using an IoT sensor running our agent and placed on a coffee machine.
It’s easy to forget that machine learning, and artificial intelligence in general, was beyond the reach of most people until very recently. We’ve all been users, directly or indirectly, of specialized AI applications such as spam filters or autopilots. But we’re now witnessing the dawn of the democratization of AI.
It’s imperative that end users understand what machine learning is and the types of problems it can solve. We found that users tend to get super excited at first and then a bit disappointed when reality doesn’t meet expectations. A number of machine learning algorithms are essentially glorified pattern matchers. Artificial intelligence today can generalize locally and do specific tasks very well, but it can’t think outside its very narrow box.
It’s also important to use the right tools and methods for a given task. Take neural networks for example. Neural networks are very well suited for supervised learning problems and dealing with huge datasets. But they are not necessarily the best tool for typical IoT examples, such as anomaly detection, which would require unsupervised learning techniques. Since we were familiar with neural networks and Keras, a popular deep learning framework, we decided not to do anomaly detection for the first release.
Show me the data
Traditional software engineering is about writing algorithms that precisely state what a machine does. With machine learning, it’s the data, not the program, that does the heavy lifting.
If you want to detect a new type of appliance all you need is to measure its vibration patterns, for example, and update the model. You won’t need to write an additional line of code. As machine learning pioneer Pedro Domingos puts it: “[machine] learners turn data into algorithms”. But there is a catch — machine learning algorithms require a lot of data to elicit patterns. And in the case of supervised learning, someone will have to painstakingly label the training data.
Had we wanted to recognize different spin cycles of a washing machine, we would have had to run every cycle several times, then manually label the sensor data with the name of the cycle that it measured. This had profound implications on what we could realistically deliver, which is why we settled for classifying appliances by type — we could simply place a few IoT sensors on our appliances at our London office, and wait for the data to be collected by coffee drinking colleagues!
Consider the why, where, when and how of end users
Unlike sensor networks that are out of reach of people (inside jet engines, on wind turbines, etc), we collected data in an open environment. Inevitably we had to deal with “noise”, that included doors being slammed, curious colleagues playing with the IoT devices, vibrations from nearby appliances being picked-up, and more. We had to come up with strategies to deal with this noise, otherwise the model would be less accurate.
One solution we used was to collect a lot of data, to drown out the noise. To that end, we drank up to ten cups of coffee a day. In addition, we also ignored events below a certain threshold — we were mainly dealing with coffee machines, so we had a rough idea of the time it takes to make a cup of coffee.
Getting good training data is really important, but you should also ask yourself how the tool will be used (for us it’s a way for our sales team to demo the capabilities of machine learning with the EVRYTHNG platform). Understand who the end users are, where they will use the tool, and what they will try to accomplish with the tool.
The first time our tool was shown to customers, the demo almost failed. We hadn’t anticipated that the sensor device would be handed around and examined before using it on the coffee machine. The device picked up vibrations from being handed around and sent a message informing that the washing machine was working (probably because the washing machine life cycle covered so many different sub patterns); we hadn’t trained the model to recognize human activities! Luckily the second event contained the measurements from the coffee machine in action, which the model correctly predicted.
Machine learning should be the least of your concerns
One reason machine learning is such a hot topic is because a number of frameworks are freely available and allow software engineers, without a formal background in artificial intelligence, to build powerful machine learning solutions.
Take another look at the workflow in Figure 1 of this blog. Only one step is about artificial intelligence. We built our model using Keras, which means we could have easily deployed the same model on AWS or Microsoft Azure. Even if we wanted to move to another deep learning framework, APIs are similar enough that we could probably create a comparable model with a different framework relatively easy. From a technical perspective, the tricky part is getting the data to the model and managing the entire experience end to end in one seamless workflow.
For the Internet of Things, the EVRYTHNG platform is clearly worth considering. Our model provides a high-level taxonomy for Internet of Things resources without being too dogmatic. We found this a healthy trade-off that facilitated data collection and transformation. The EVRYTHNG platform also uses open Web protocol, which means that most devices can talk to the EVRYTHNG platform “out of the box”.
You can read more about our API on our developer portal. Or if you seek a deeper understanding of the Internet of Things, the book “Building the Web of Things”, which was written by two of our cofounders, is really insightful.
We wanted to share our journey towards a more intelligent, user-centric Internet of Things. Hopefully this blog will help you avoid some of the challenges we encountered and give you a better understanding of the types of IoT problems that are present for machine learning. If you’d like to use EVRYTHNG to build your next machine learning and IoT project, check our detailed tutorial or contact us. We look forward to hearing about your projects!
At this point in the evolution of IoT and the smart home, it has become abundantly clear that when it comes to managing our smart devices, we have too many apps—way too many. As any semi-serious enthusiast can tell you, once you start down the smart home path, your smartphone real estate will quickly fill up with discrete apps for managing each of the devices you adopt. While a few of these apps are truly useful, we’ve really lost the forest for the trees (as so often happens with the introduction of new technology). In an effort to provide convenience and utility, we are achieving quite the opposite by inundating consumers with an avalanche of bewildering apps that have left many feeling cold toward the smart home. So, with the exception of a few apps that have added value to our daily lives, our new mantra is, there’s NO app for that.
To appreciate the practical applicability of this, we need to parse the smart device landscape. Devices fall into one of two categories: those that sense and those that actuate. Sensing devices detect things like motion in your home, the state of physical things such as doors and windows and the presence of environmental conditions such as water, smoke or mold. Actuating devices perform a function: lock a door, sound an alarm, close a valve. (For completeness, some devices combine both capabilities, e.g. a motion sensor with a built-in alarm.)
If the point of having a smart home is convenience, efficiency, safety and well being, then it’s a pretty quick conclusion that the propensity of devices will lean heavily toward sensors rather than actuators. Sensors monitor our complex environment and tell us when something’s not as it should be. As such, most of the time, they have absolutely nothing to report—there’s value in not hearing from them. We predict that of the dozens—and maybe eventually hundreds—of devices in one’s home, 90 percent will be sensors, for which a mobile app is likely overkill. It’s fine to get a simple text message that you left your garage door open; you don’t need three years of analytics on what time you opened and closed it every day.
Actuating devices are another matter. If you’re initiating an action like unlocking your door or turning on your lights, then presumably you need an app of some sort. Of course, emerging voice services like Alexa and Google Home are going after the same problem. That said, it’s hard to escape the feeling that even voice is a stopgap for many situations. Shouldn’t a truly smart home anticipate your desires and carry them out automatically, thereby further weaning you from your phone? For example, many of us now manage our home’s environment with smart thermostats that learn our preferences and set them automatically with no additional direct input.
If we could synthesize this perspective into some advice for product manufacturers, it would be this: Remember that, as cool and useful as your device or app may be, it doesn’t exist in a silo. It is, in fact, part of a system of devices called the smart home. If that system is unusable, then your product will suffer commensurately; if a rising tide floats all boats, then an ebbing one has the opposite effect. So for your next product, start by considering how your device fits into your customer’s overall smart home and how much value your device can deliver sans mobile app. Or perhaps make the app an opt-in for a higher grade of features. Sometimes, less really is more.
Lessons Learned from the Pointless $400 Connected Juicer
May 1, 2017
What do you get when you cross a juicer and an internet connection? A complete and utter disaster. At least, that’s what can be said for startup Juicero, the makers of a connected juicing machine that’s recently made a splash in the press for its rather pointless capabilities.
The premise of Juicero’s $400 device of the same name is that it squeezes pre-ordered packets of fruit and vegetables into a liquid and connects to the internet to provide its users with a replenishment service model. However, as Bloomberg pointed out, it’s possible to squeeze the pre-ordered packets with your hands, rendering the device itself useless.
As a result, Juicero became a laughingstock on Twitter, leaving a bitter taste in the mouths of anyone even considering buying one. It was a stereotypical Silicon Valley disaster: a startup trying to cash in on the juicing craze by raising $120 million from investors and then selling an expensive product that no one needed. And while it’s laudable to get into the subscription space, there’s a right way and a wrong way to go about a replenishment service.
Juicero made two fundamental business mistakes in the launch of its connected juicer: 1) It was a superfluous device that didn’t add value, and 2) It was just far too expensive.
On the first point, it should go without saying that a connected device should always add value to a consumer’s life. Otherwise, it’s simply technology for technology’s sake. What’s more, Juicero failed to pick up on the lesson Keurig learned two years ago: preventing customers from using third-party juice packs and not letting customers buy its own juice packs without first buying the machine. Breaking this fundamental “business law” (if not an actual law), Juicero underscored just how not useful its device is by forcing customers to buy it.
The company’s second misstep was not adopting a long-tail business model, meaning that even if it lost money on the device, it would more than make up for it through subscriptions. It’s an age-old model that razor companies have been using for decades: you essentially “give away” the razor, while making money off the razor blades. By charging an exorbitant amount for the useless juicer, it’s clear that Juicero was trying to have its cake and eat it too.
Yet perhaps the worst sin Juicero committed was giving people every reason to knock smart home devices for being nothing more than dumb novelties. As we know, connected devices have the potential to drive great value for both businesses and end-consumers–including by making possible a smarter product-as-a-service model called Just-in-Time Replenishment.
On a positive note, this pointlessly connected juicer now serves as an example to other businesses looking to understand how best to deploy connectivity in everyday items. The key takeaway: make it genuinely valuable to both you and your customers.
We’re really excited to tell you about some new features and improvements we’ve added to the EVRYTHNG IoT Smart Products Platform.
Our R&D has a relentless focus on three areas: Innovation, Usability and Robustness. This means we’re making sure we continuously leverage the latest technologies in our platform, and do this in a way that makes it as accessible and usable by our enterprise customers, while at the same time ensuring high performance and reliability.
So, what’s new?
Customizable Data Visualizations
First, from the usability perspective, we wanted to make it faster and simpler for our customers to access the critical KPIs for their smart products. We’ve re-engineered our administration and analytics dashboard, given it a new look and feel and made it fully customizable. Now any user, say Business Analysts or Brand Manager, can set up the data visualizations they want and filter on only the products or events they care about. For example, you can see the number of replenishment orders from customers scanning your product and how that compares to last week. Or check out the top performing stores or cities where your product is most popular.
Enhanced Product Recognition
Now onto the innovation. How do apparel and CPG products like bottles, jackets or boxes of cereal get connected to the Web? The answer is SCANTHNG, our mobile product recognition toolkit. It’s software that can be run in the mobile browser (so no app or download required), to scan and identify any physical product, powered by our powerful digital trigger and image recognition engine.
The range of on-pack technology is broad and evolving fast, with anything from proprietary codes to invisible inks to printed electronics. Crucially, it also differs depending on the type of products, packaging material and industry. Responding to this complexity, we’ve enhanced SCANTHNG to offer the industry’s widest possible range of identification technologies, including Image Recognition, Optical Character Recognition, 1D and 2D barcodes and Watermarking, giving brands the most flexible mobile capabilities for interacting with digitized products.
Continued Platform Expansion
And finally, robustness. With over half a billion smart products now being managed by the EVRYTHNG platform, and our enterprise customer deployments continuing to ramp up, we’ve introduced platform architecture changes to handle bigger and bigger loads. We’ve also rolled out a new platform in Europe to cater for growing global demand. Our infrastructure team runs frequent scalability load tests to ensure the platform is optimized for predicted future growth, vital to support recent packaging partnerships (Avery Dennison, WestRock and Crown), where scale to hundreds of billions of items is needed.
Among the other enhancements, we’ve added:
Single Sign-On (SSO): Improved security with out-of-the-box support for SSO integration with Active Directory using SAML 2.0.
Physical Web and Eddystone Support: This allows, for example, brands to create contextual consumer experiences in-store, even customized per unique product.
There have been many other improvements, and there’s plenty more in the pipeline. This week’s announcement of a new round of investment in the company signals we’re moving into a new exciting phase of growth. Expect the platform to do the same!
On February 3, a series of limited edition #AlwaysOn Midnighter top handle handbags will be sold exclusively at the Rebecca Minkoff popup shop at The Grove in Los Angeles. Each bag comes with a tag that, when scanned by a smartphone, will automatically qualify the customer for a special loyalty program and unlock a VIP invitation to the SS17 show on February 4.
Each smart bag will also be able to unlock exclusive offers and experiences from Rebecca Minkoff, including e-commerce services, private styling sessions with Rebecca, style recommendations, and video content, as well as elite experiences with lifestyle partners.
The limited edition #AlwaysOn Midnighter bag is only the beginning of our exciting new, multi-year partnership. By Summer 2017, all Rebecca Minkoff bags will be smart.
This collaboration with Rebecca Minkoff is the latest example of how #BornDigital™ apparel products can differentiate brands, create personalized consumer experiences, and make the products themselves stickier, more desirable and more valuable to their customers and companies.
When #BornDigital™ products are created with individual digital identities and web-connected smart labels, they enter the world with an invisible layer of data intelligence in the cloud. This opens up a brand new world of interactive product applications and analytic insights that changes the nature of what a fashion product is and how it can become stitched into your digital life.
To consumers, products like the #AlwaysOn Midnighter bag effortlessly offer personalized content, VIP service, and truly unique experiences. To fashion brands, it allows them to forge direct consumer relationships like never before and to better understand their customers through insights they couldn’t access before.
In short: Your bag is no longer just a style accessory. It’s a piece of connected, personal technology.
At EVRYTHNG we’ve been giving a unique URL and API to Billions of physical products (aka. THNGs) since 2011. Hence, when Google announced the Physical Web project in 2014 we were excited and amongst the first IoT platforms to integrate with the project.
The basic idea of the Physical Web is to tag the real world. It is implemented with devices called beacons that broadcast small identifiers such as short URLs to everyone listening as defined by a standard protocol called Eddystone. These beacons use the Bluetooth Low Energy (aka BLE, part of the Bluetooth 4.0 standard) protocol which means that they can operate for quite a while even if running on battery power (right now about a year).
Typical listeners of the broadcasted messages are mobile phones and there are two many ways for users to interact with beacons. First, passively: when a beacon is detected nearby mobile phones will show a message, notifying the user of something interesting offered by the nearby real world! These messages usually appear in the notification bar of the mobile OS, just like when you get a text message or a new mail. If the user click on the message then the corresponding Web page is loaded.
Second, actively: an app can actively search for beacons nearby. For example you might use a search app such as Google Search to find a service nearby, the search app can search for beacons to give you hyper local results together with the traditional indexed results.
Beacons are still relatively expensive (5-10$) so it is unlikely to find them on every single instance of a product in the near future, however they are perfect to represent a product class or a series of products and can be attached to places, in-store displays, etc. As said before, the project has been around for 2 years already but back from Google I/O, the Physical Web seem to be one step closer to reality thanks to its increasing support on Android (through the Google Play Store and Google Search) but also on iOS (through Chrome for iOS and other apps) and hence is likely to hit prime time in a near future!
This is why we are really excited to announce full Physical Web and Eddystone support in the EVRYTHNG Platform. In fact, we have been supporting the Physical Web even before it existed as the EVRYTHNG managed products (or THNGs) get a unique identity in the form of a secure URL which is one way to implement the Physical Web. As a consequence, connecting a Physical Web beacon to EVRYTHNG THNGS and Products is a seamless process that we described in a step-by-step walkthrough.
As you’ll see, the EVRYTHNG Platform does not only enable basic support for the Physical Web and beacons, but it also supports complex rules that dynamically decide what experience to serve depending on a lot of context parameters (who is the user? where is she? what time is it? what other products did he interact with? in which country? etc.) thanks to our unique REDIRECTOR component. As an example with this component you could implement an interactive treasure hunt for your brand in a store or make sure the offers you broadcast to your users are really relevant to their profiles. All in all this let’s you create very interactive beacon experiences for your products to make them even smarter.
EVRYTHNG Opens up the Smart Home for the Property Insurance Sector
September 29, 2016
EVRYTHNG, the IoT Smart Products Platform, announced today the availability of its IoT Insurance solution to enable insurers to design and deploy new smart home insurance propositions. Built upon an ecosystem of pre-integrated devices, including high-profile partners such as Jasco, iHome and First Alert, it enables insurers to bypass the complexity associated with multi-vendor smart home environments.
With the solution, insurance companies can offer services under their own brand and use any combination of manufacturer-branded or white-labeled devices. And, unlike most ‘affinity’ or co-marketing programs, insurance companies have complete access to all real-time data, control over the device ecosystem, and a customer-facing, branded mobile app.
Available now, it comes with a structured Pilot Kit program including full customization services and ongoing operational support to help Insurers get to market and validate new value propositions quickly.
Niall Murphy, CEO and Co-Founder at EVRYTHNG, said: “The disruptive potential of the Internet of Things is already transforming the automotive and health insurance sectors. Now, home and property insurance is benefiting from the same opportunities with data from smart devices. Insurers can help consumers access the benefits of smart home safety and security, apply sensory data to offer proactive, personalized services and response, manage risk more effectively, and ease administration. EVRYTHNG gives insurance service providers an easy way to access a rich ecosystem of devices in the home to both create attractive value propositions for their customers and work at scale with data from these devices and their own systems with security and analytical intelligence.”
Curt Schacker, EVP for Connected Products at EVRYTHNG, added: “This is about providing insurance companies all the components they need to bring their own brand-led value proposition to market, including a scalable cloud platform, a curated ecosystem of interoperable devices, mobile app, analytics dashboards, and system interconnections. Insurers can now get going quickly and easily with a staged program to pilot value propositions with customers with a specific set of devices, service, and experience, and then move to scale over time.”
Ecosystem connectivity is at the core of the EVRYTHNG Platform-as-a-Service. It has pre-built connectors for home automation platforms such as SmartThings and Wink, and a Works with Nest-certified SDK for the Nest Cam Indoor and Nest Cam Outdoor security cameras, a global-first unveiled at Google I/O 2016. Additional platform and device partnerships will follow over the coming months to extend the ecosystem for insurers even further.
Gary Schultz, Business & Product Development Director at iHome, said: “With the broad iHome Control smart home product range available within the EVRYTHNG ecosystem, Insurers can bundle iHome’s award winning Smart Monitor, Smart Plugs and Sensors into their services and access critical real-time data with features such as motion, temperature, energy consumption, humidity and appliance and lighting controls anywhere in or around the subscriber’s home.”
Keith Lashley, VP of Products at Jasco added: “Through our participation in the EVRYTHNG ecosystem, the broad range of sensing and control devices we produce under the GE brand are now available to insurers to monitor and respond to virtually any threat or event which can impact a home.”
Mark Devine, Senior Vice President of Marketing for First Alert stated: “First Alert, the most trusted name in home safety, is synonymous with residential fire protection, so it’s natural to integrate our Onelink by First Alert Smoke + Carbon Monoxide alarms into the EVRYTHNG platform.”
“What’s most impressive about EVRYTHNG’s new approach,” said Lee Gruenfeld, Vice President of Strategic Initiatives at Support.com, “is that they’ve thought beyond just the devices and infrastructure, all the way to the full customer experience. Including Support.com’s advanced customer support software and services as a strategic part of this rich ecosystem allows EVRYTHNG to give insurers a crucial advantage in delivering customer satisfaction through better support and reduced customer effort.”
The IoT Insurance solution and Pilot Kit are available to order today. To find out more, go to https://evrythng.com/industries/insurance/
EVRYTHNG is the Internet of Things Smart Products Platform connecting consumer products to the Web, and managing real-time data to drive applications. The world’s leading consumer product manufacturers work with EVRYTHNG to manage billions of intelligent online identities in the cloud for their products, deliver real-time interactive experiences and support services to consumers, and connect with the ecosystem of other applications and products in their digital lives. To find out more about how EVRYTHNG’s award-winning IoT cloud platform delivers better consumer-product experiences and smarter product operations, please visit evrythng.com and follow @EVRYTHNG.
For further information please contact:
Neil Robertson FieldHouse Associates neil(at)fieldhouseassociates(dot)com +1 646 233 1150 @neil_robertson
Iain Alexander FieldHouse Associates Iain(at)fieldhouseassociates(dot)com @iaingalexander
IoT is currently disrupting the property insurance market, just as it has done in the automotive and health industries. To help insurance companies capitalize on this opportunity, EVRYTHNG today announced our new IoT Insurance solution. Read our press release here.
We give insurers a cutting-edge market advantage by using the data generated from connected devices to better risk-profile those insured and provide consumers with the customized service they want. With our solution, insurance companies are able to offer this service under their own brand identity, using any combination of manufactured-branded or white-labeled devices they feel is suitable for their company. Insurance companies are given complete access to real-time data, a control over the device’s ecosystem, and a branded mobile app for customers.
And to help insurers road-test new propositions fast, get consumer feedback and then launch their services before the competition, we’re making available a Pilot Kit too. You can read more about the Pilot Kit and how EVRYTHNG is disrupting the insurance industry here.
As part of the launch, our SVP of Connected Products, Curt Schacker, participated in a webinar with Parks Associates yesterday focusing on how IoT is revolutionizing Insurance companies, and gave a keynote presentation at the Internet of Insurance event in New York City.
EVRYTHNG is offering insurance companies a cutting edge, cost saving advantage to minimize claims from those they protect. To learn more about how EVRYTHNG is disrupting the insurance industry with smart home devices. Click Here
EVRYTHNG is heavily involved in creating IoT smart home and Insurance provider solutions. As a result of this innovative work, Curt Schacker, our Senior Vice President of Connected Products, has been invited to participate in Park Associates’ Insurance Webinar panel discussion on September 27th. Curt will be discussing the current impact IoT has on insurance companies in the U.S and Europe.
Curt’s 25 years of experience in the Tech Industry in many senior level positions means he’s had the pleasure of speaking on various panels worldwide, sharing information and understanding into leading edge embedded and communications software topics. He is passionate for the progression of Web-connected products and sharing this information.
IoT has been disrupting and revolutionizing insurance companies by giving users real-time sensory data from (and about) insured assets. EVRYTHNG offers insurance companies a cutting edge, cost saving advantage to minimize claims. Not only do these solutions save money for insurance companies, smart home solutions give customers peace of mind that their home is better protected from damage and loss.
Register for this free webcast today (http://buff.ly/2cIhHDI) to secure your spot in gaining insight on the business models and relevance IoT has on the insurance industry. This webcast will feature the impact IoT has on insurance corporations, new business cases, applications under development, and strategies for insurance companies to leverage data to provide new services.
This complimentary webcast is set to air live Tuesday, September 27 10 AM CT (11 AM ET), and you don’t want to miss it.