Predictive and Prescriptive Analytics Peek into the Future

talking year, five-year, or ten-year commitments, and the word predictive takes on a different meaning. Plus, they have to regularly deliver the right experiences to those very long-term customers. It's imperative to the overall success and longevity of their business," Keenan explains.

But Keenan also argues that these long-term, strategic predictive insights eventually feed into prescriptive analytics as well, creating an analytics cycle rather than a linear pattern. As relationships progress and predictions either come to fruition or don't, the results of each customer interaction continue to drive the prescriptive analytics funnel and deliver more immediately executable insights. "Prescriptive analytics are where businesses should be focusing," Keenan says. "They've got the more immediate pay, and the most immediate ability to enhance customer-facing business processes to boost customer loyalty and engagement. But in the long run, you have to keep those predictive analytics running as well. That way you're looking at macro and micro opportunities, and the predictive and prescriptive mechanisms are in a feedback loop. This is the crux of business intelligence right now," he adds.

Most vendors that offer both predictive and prescriptive analytics solutions design them to complement each other and be in constant conversation, regardless of their order along the data processing funnel. Furthermore, Bates and Keenan agree that both routes have merit, and order doesn't matter as long as the business has a clearly outlined analytics vision and "game plan," Keenan says. "Sometimes we can break out prescriptive tools or predictive tools separately depending on what the business need is," Bates says. "The idea is flexibility. These processes are never set in stone. It's more about finding the right tool for the right task," he adds. But there's a little more to it than that—to implement a seamless and effective analytics workflow, companies must ensure that they find the right tools for the right users.

Analytics from the Ground Up

Both Bates and Keenan are firm on this point, emphasizing the need for a separation of labor when it comes to analyzing the data and making data-driven decisions. Prescriptive analytics insights, for example, are much more "on the edge," where customers are already interacting with a brand's product or content, Bates says. "This is where real time is critical, and where customer experience professionals are needed. These are the front-line marketers responsible for making sure those customer touch points are firing on all cylinders at all times," he adds. And the same logic applies to sales and customer service departments—prescriptive analytics are designed for immediately executable insight and are most effective when they are used by customer-facing employees including on-the-ground salespeople and customer service representatives. "That's not to say that prescriptive analytics can't add something to other areas of the business, but their greatest potential lies in customer-facing experiences," Keenan agrees.

Predictive analytics, on the other hand, become increasingly crucial for users "higher up along the corporate ladder with a higher order of focus," Keenan says. Because predictive analytics are indispensable for strategic planning and achieving long-term goals, chief marketing officers and other top executives should make familiarizing themselves with predictive insight a priority. "But just like there's a constant dialogue between the solutions, there must be a constant dialogue between the people that work with them," Keenan adds.

It's no longer solely the responsibility of data scientists to closely monitor and analyze data, but is now the responsibility of employees across the enterprise. By 2018, International Data Corp. predicts that 30 percent of CIOs will roll out a pan-enterprise data and analytics strategy, and with predictive and prescriptive analytics becoming more accurate, automated, and easy to use, that number will increase drastically.

"For predictive and prescriptive analytics to really take off, it's all a matter of accuracy and scale. Can vendors make solutions work as accurately as they say they will without turning them into massive processing hogs? Can they scale that effectiveness up and make the solutions easy for anyone to use? Those are the questions that are still weighing down on the space," Fluss says. Yet both vendors and analysts are optimistic.

"Prescriptive and predictive tools are constantly getting smarter. Algorithms are improving; processing capabilities are getting stronger. Solutions are constantly evolving, and best practices or processes are evolving too," Keenan says. "It's up to companies and their solution providers to find out what works on a case-by-case basis, but when they discover their secret sauce," Fluss says, "the result could be fantastic."

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Associate Editor Maria Minsker can be reached at mminsker@infotoday.com.

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