How Analytics Can Take Wearables to the Next Level

Wearable technology, or devices with small motion sensors that can be worn by a consumer to provide data tracking insights, is an industry that has begun to boom.

According to CCS Insight’s Wearables Forecast, Worldwide, 2015 – 2019, the estimated shipment of wearable devices will reach an approximate 84 million units. By 2019, this number will reach 245 million per year.

From fitness and activity trackers to smartwatches and "smart clothing," the possibilities are practically limitless within the wearable technology industry. And this growth is enhanced by the monetary rewards, as the wearable technology industry is projected to reach $25 billion in monetary value by 2019.

But data collection and design are only a small component of the potential that wearables offer to both consumers and companies. Bringing a wearable device to the next level of usefulness requires companies to build in additional analysis features that will improve the usefulness of the wearable technology, increase engagement levels, and provide an unparalleled benefit to the consumer.

In other words, analytics can help companies effectively determine what the wearable device is actually doing for their consumers.

Collected data is only as good as the action that it propels. Analytics turn collected data into the foundation needed for actionable insights, and in doing so provides additional consumer and company benefits. For example, a sleep tracking device might collect data on how a consumer is sleeping. But without the analysis of the collected data, the device only identifies the hours that a consumer sleeps or fails to reach a REM cycle. However, what good is this knowledge to the consumer? To provide an answer, analytics need to be used to provide actionable insights.

Wearable devices offer the unique capability of anonymously collecting copious amounts of data. Through the proper type of secure analytics, this data can then be used to offer better public-facing services. Using the above example, when analytics are added to the sleep tracking device, the "crunched data" can be used to determine how to better help the majority of workers achieve better sleeping habits. The data might reveal that a later office starting time or improved public transportation services, would help the majority of individuals achieve better, longer sleep cycles without the worry of making it to work on time or experiencing public travel delays.

Just as stores use geo-locational pings within apps to send consumers hyper-targeted marketing offers, wearable devices can leverage the power of analytics to provide personalized offers to consumers. By analyzing the data captured by wearable technologies, companies can create marketing offers that are customized to each consumer. A step-tracker might reveal that a consumer takes the most steps around lunch time. A company could then leverage this information via personalized offers to encourage the consumer to take more steps throughout the day. In this way, the device not only serves its purpose of tracking fitness data, but it also provides additional analytic benefits to the consumer.

With the help of data analytics, companies will soon be able to use wearable technologies to improve employee productivity and health. Wearable devices can collect the data needed for a company to analyze the hours of the day that their employees are most productive. Additionally, the devices can monitor the health of employees, so that the company can mitigate the risks associated with high levels of stress, lack of sleep, and other health symptoms that contribute to an unhappy and ineffective workforce.

Using advanced analytics, wearable device companies can realize the potential that each device offers to its targeted consumer bases. If a device, like a smartwatch, can help a consumer to make payments on the go, serve as a personal trainer with fitness tracking, and filter notifications, while simultaneously allowing consumers to avoid opening their smartphone every few minutes, then it quickly becomes a device that won’t be left behind in the morning.

In short, the more time that companies dedicate to smart analytics and machine learning, the more sought-after a wearable device will become.