In 2017 we saw companies continue to adopt basic IoT solutions. However, many of the companies that have implemented IoT solutions are just scratching the surface with what they can accomplish with their IoT data. This is great news because once companies dip their toes in IoT, chances are they’ll see the value of how this data is helping them and start to think of ways to expand their solutions to include prescriptive real-time actions (what is prescriptive analytics you may ask? Find out more in our blog posting!). These expanded solutions will not only benefit companies, but they’ll also provide a more personal experience for customers. So what industries will probably be implementing these solutions? Here’s a list of industries where, in my opinion, we’ll see a wide adoption of cutting edge IoT solutions in 2018.
Over the past year, Amazon has caused major disruptions in the retail space. Prime Grocery, Prime Pantry and the acquisition of Whole Foods has sent the grocery sector, which notoriously has razor thin margins, into a tizzy. Also, Prime continues to plague big box retailers and small businesses alike – it’s hard to beat Amazon’s easy to access and use website and the logistics of free two day (and sometimes even two hour) shipping is something most companies cannot replicate. Because of these two things, Amazon’s competitors need to start implementing omni-channel solutions to create a more personalized experience for their customers than what Amazon provides. Omni-channel solutions take customer data from multiple sources, such as how long a customer was on a product page to how someone walked through your store to what items did a customer place in their physical or online cart, and uses that information to create an interactive and personal experience as the customer is visiting their website or walking through their physical store. For example, smaller retailers could create an app that would not only allow customers to browse their merchandise, but would also alert salespeople when customers enter the store with the app running on their phone. Based on the customer’s past purchase and browsing history, automatic recommendations for in-stock merchandise in the customers size would be generated and communicated to the sales staff. The staff would then approach the customer with the recommendations, thus increasing the likelihood of making a sale while providing stellar customer service. (For more information on omni-channel marketing, check out my recorded presentation on this topic here)
Insurance is typically a very slow industry to change, however, it is starting to warm up to the idea of using IoT data. The insurance industry is seeing that data that is collected from a customer can let them more accurately determine risks and calculate more accurate premiums. Auto insurance companies typically have set rates based on age, gender, location and car type. However, these large buckets many times don’t accurately reflect how risky a person truly is. For example, I have Mark and Bobby. Both are 21-year-old males who drive Honda Civics and live in Chicago. However, Mark typically drives 10-25 miles over the speed limit, brakes suddenly because he texts while he’s driving and drives about 50 miles every day. Bobby on the other hand, typically drives the speed limit, doesn’t slam on his brakes often and only drives about 20 miles a week. Based on demographic, car and location information Mark and Bobby would be charged the same premium. However, if before a premium was given, the data from sensors on their cars was analyzed, the insurance company would determine that they should charge Mark a higher premium and Bobby a lower one.
Also, insurance companies may start using video and image analysis to help evaluate claims. For example, instead of having an adjuster climb on a roof after a storm to see if it’s been damaged, a drone could be flown over the roof to take images of the roof. This would save time, eliminate the risk of the adjuster falling off the roof and reduce errors when determining if a roof needs to be replaced or not.
More and more devices are being connected to the internet in people’s homes. Data is not only being generated from the devices, like humidity levels and temperature, but also devices like Alexa and even smartphones are starting to listen and record what is being said around them. This information can be used and analyzed with natural language processing to discover what a certain consumer is interested in. Advertisers can use this information to deliver extremely targeted ads to customers. These ads could be on their phones, computers, tablets, streaming music services and even on their smart TVs. Think how valuable it would be to be able to sell an ad to, for example, Mercedes if the ad agency could assure them that the ad would run only for people who had mentioned the possibility of buying a new luxury vehicle in their homes. This extreme targeting would probably be much more appealing to a company than just buying a 30-second TV spot that is run to all viewers of a program, many who have no interest or means of buying a luxury car.
However, advertisers do need to be extremely careful with this. There is a fine line between personalization and intrusion. For example, if I mentioned buying a Dyson vacuum to my husband but hadn’t searched for a Dyson online and suddenly I start getting ads on my phone for a Dyson, this may actually be off-putting. Advertisers will have to find a way to balance using the information collected while respecting a consumer’s privacy.
Currently, so much information is being collected on farms – everything from moisture levels in the soil to how a tractor’s engine is performing. However, the data that is collected currently has to be sent to the cloud to be processed. This can be a challenge because many times there is limited connectivity in rural areas and it can be expensive and time consuming to upload all of the information to the cloud on a daily basis. However, edge computing is starting to change this. In the upcoming year, I expect that small processors like Raspberry Pis will start to be installed throughout fields and on tractors. These processors will analyze the data collected as its happening. The real-time insights generated from on-site edge processing will give farmers the ability to respond quickly to potentially devastating incidents that could occur in the field.
Another cool way that farmers could use IoT is to have drones or robots patrol their fields taking images or videos of the crops. The images could be processed in real-time on the vehicle and if anomalies or pests are detected employees could be sent out to take further actions. This potentially could increase the crop yields (and revenue) for the farmers by frequently monitoring the fields without having to employ additional employees to manually do this.
2017 seems to be the year of the self-driving car. Uber and Lyft are testing self-driving cars and in the next five to ten years these will probably be a reality. However, while the AI, safety issues and regulations of self-driving cars are being worked out there are many other ways that IoT will be used in 2018. Over 100 sensors are already collecting information on a car. One of the easiest ways to starting using this data is to have the car diagnosis and communicate what is wrong instead of just turning on the check engine light on. The check engine light can turn on for many reasons — some which need to be fixed at an auto shop and some that the driver can fix themselves. For example, if the gas cap is not put on properly, the check engine light may turn on. If a Siri-like program told the driver that the gas cap needed to be tightened, this would save the driver time and money from bringing the car into a shop. Taking this one step further, it wouldn’t surprise me if we see some 2019 models start to self-diagnosis and correct without even alerting the driver that there was a problem.
Also, we may start to see technology being built into high-end 2019 models that will allow cars to communicate with other cars, smart devices brought into a car and different elements of a smart city. This could include alerting drivers about accidents, sudden weather changes or if a stop light is out on a nearby street or even rerouting the driver based on this information. However, this functionality will probably not be fully useable until more vehicles and cities have smart elements in place and activated.
If you’re looking to start an IoT project in the new year, we can help! Our RTES platform is the only solution on the market that incorporates everything from machine learning to complex business rules to optimization, so our solution can grow and change with your business needs. For more information, visit our website.
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