Leveraging the Three Stages of Analytics

“With predictive analytics alone, you have the risk of overspending, throwing money in the form of a rebate or coupon at a problem where a friendly email would have been enough,” Noels explains.

Prescriptive analytics, on the other hand, can be used to determine the best possible action to achieve a business goal—in the case of customer satisfaction, the action would be designed to optimize satisfaction, loyalty, or lifetime value. Prescriptive analytics “accounts for a greater perspective, and sometimes favors less immediate benefits over more instant gratification,” Noels says.

He adds that prescriptive analytics will typically test multiple scenarios, evaluating each of them to determine which course of action will yield the most desirable outcome—a process that often involves machine learning.

BETTER WITH MACHINE LEARNING

The rise of machine learning is expanding the applicability and capabilities of both predictive and prescriptive analytics, according to Puget. He cites “reinforcement learning”—the ability of a system to learn how to react in a given scenario—as a particularly powerful development. Machine learning, he adds, “is blurring the difference between predictive and prescriptive analytics.”

“Machine learning has enhanced the effectiveness and value of prescriptive analytics through continuous collection of data and recording actions,” says Greg Layok, senior director and leader of the Technology and Advanced Analytics practices at West Monroe Partners. “This heightened level of interactivity with your customer provides the ability to prescribe products or offers that are custom-designed for an individual. This is as prescriptive as you can get today.”

Predictive and prescriptive analytics will also become more powerful due to the ever-increasing amount of available data from connected devices, social media channels, and other sources. “With the amount of data available now, it is no longer trivial for someone to come up with insights,” Cao says. “Having the science and technology available to analyze the data and deliver the solution to customer-facing representatives is now possible, and every business could benefit from using them.”

Using a medical scenario, Nevala illustrates why companies need to not only invest in predictive and prescriptive analytics but also follow through on the insights they gain from both: While a medical professional might be able to detect that a patient is at risk of developing a chronic disease and prescribe an appropriate treatment plan, value is not derived if the patient does not take the medicine or make the requisite lifestyle changes. Similarly, organizations must not only develop their analytic skills but also the initiative to put the insights they uncover into action, she explains.

“As customers have come to expect highly contextual, personalized communication from brands across nearly every industry, including financial services, telecommunications, retail, gaming, and more, the companies that are able to leverage predictive and prescriptive analytics have a distinct competitive advantage,” Downs says.

Layok agrees, noting that “predictive and prescriptive analytics work together and are critical in today’s highly competitive, data-driven business world. Not only will leveraging these strategic tools give companies an advantage over the competition, but customers are also quickly beginning to expect it.”

Layok further adds that customers “leverage this technology every day through e-commerce, music listening, and audio books”; soon, they will look for personalization from all the organizations with which they interact. Companies that don’t adopt predictive and prescriptive analytics to deliver these experiences will find themselves left behind, he cautions.

But for businesses, adoption of all three forms of analytics should occur in stages. To develop an analytics strategy that successfully encompasses all three elements, businesses need to ensure that they are beginning with a solid foundation—namely, that their data is trustworthy and well-organized. Without a curated selection of data with which to work, any efforts at predicting trends or prescribing actions will be for naught. For this reason, organizations need to be particularly attentive to their descriptive analytics. Using a data management tool is crucial, as is installing a process for determining the validity and relevance of the data, according to experts.

Then, once businesses have laid the groundwork of their strategy with descriptive analytics, they can begin to build out predictions from this data. Because they have already determined that the data they are using for these predictions is accurate and up to date, they can rest assured that the predictions made will have a reasonably high likelihood of occurring and are worth further examination.

Finally, once they have determined which events are likely to occur next, businesses can begin to develop responses to them. In this stage, they can look into investments in machine learning platforms and other technologies that use simulations to determine the best course of action given a set of circumstances.

Moving through the analytics cycle is definitely a crawl-walk-run process, experts agree.

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Assistant Editor Sam Del Rowe can be reached at sdelrowe@infotoday.com.

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