IOT Evolution: Predictive Analytics & the Internet of Things

The Internet of Things and predictive analytics – these two technologies have an almost unlimited number of potential applications, and are already upending countless industries. Research and Markets estimated that the global IoT market will reach $660 billion by 2021, representing a 33 percent compound annual growth rate through that time. Similarly, the worldwide predictive analytics market is on pace to hit$9.2 billion by 2020, at a CAGR of 27.4 percent, according to Markets and Markets. As both the Internet of Things and predictive analytics continue to pick up steam, their impact is only going to widen, as well as evolve.

All of this is almost certain to have a major impact on the customer experience. We discussed some of the customer engagement aspects of the IoT-specific developments in a previous blog. Today, we’ll take a look at how these two technology trends will combine to shift the customer experience more toward self-support and personalized guidance – and how companies will need to adjust to this new landscape.

IoT and Predictive Analytics, Hand in Hand

To a significant degree, the IoT and predictive analytics are a natural match. After all, predictive analytics is all about deep data mining to find insights that can offer projections for the future, and the IoT inherently collects massive amounts of raw information on an unprecedented scale. If utilized together, organizations can learn much more about their customers’ preferences and behaviors automatically.

The CX Impact

By using this data-driven insight, organizations will be able to offer a more personalized and self-supported customer experience while maintaining or even scaling back on human involvement.

Initial examples of this trend are easy to find. For example, Target recently launched a pilot beacon program in 50 stores which sends out coupons and deals in the form of push notifications to shoppers’ mobile devices – a concept closely tied to the machine-to-machine interconnectedness of the IoT. And while it’s not clear how tailored these recommendations will be to individuals, the retailer did tell TechCrunch that it is collecting data from its mobile app to better understand its customers’ shopping behavior. Such awareness naturally lends itself to predictive analytics.

The same sort of development is occurring in the contact center. For example, Gartner’s Michael Maoz told Eptica that the IoT will allow businesses to “remotely monitor operations, statuses, and service levels – effectively by-passing the need for consumers to request repairs or servicing.”

“Customer analytics can determine which customers most likely require support.”

From the predictive side, customer analytics solutions may be able to determine which customers are most likely to require support with a specific issue or product, and then reach out to deliver that assistance proactively. This can take the form of a video, webinar or email.

Ultimately, customer engagement success in the future will depend on companies’ ability to embrace and incorporate these technologies into their customer support services throughout omnichannel environments.

How will your company use the IoT and predictive analytics in the next five years?


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