Benefits of Predictive and Anticipatory Analytics

By Ariella Brown  |  Posted 2016-08-23 Email Print this article Print
 
 
 
 
 
 
 

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Predictive and Anticipatory Analytics

Almost all large companies use predictive analytics in some form, but not all of them use anticipatory analytics, which identifies changes before they happen.

One of the key findings of Dunn & Bradstreet's "2016 Enterprise Analytics Study: Insights & Implications for Organizations Inspiring to Be Analytically Driven" was that 73 percent of analytics professionals claim to work for an analytically driven company. However, that doesn't necessarily mean that all 73 percent are using analytics in the same way, or even that the remaining 27 percent are not at all analytically driven.

Nipa Basu, chief analytics officer at Dun & Bradstreet, explained that the 73 percent are self-identified, so it represents a whole range of analytical adoption—from those using advanced analytics to those who mistakenly believe they are, as well as "a whole lot in between." The 27 percent could include professionals who do not think their company is analytically driven because they "set the bar very high for what analytics can do," and their own utilization falls short of their expectations.

There are different levels of analytics, Basu explains. The simplest level answers the question "What has happened?" That provides the information that an organization's leaders need to get a picture of where they're at in terms of the business. For example, a company that seeks to target its market would track what kind of industries, what size companies or which consumer demographics have been successful.

The next level of analytics provides the answer to "Why did this happen?" For example, if the simpler level of analytics revealed a loss of customers, you would want to run diagnostics to find out what caused the customers to leave. The next level of diagnostic determines where the company is being successful or not successful.

Prescriptive and Anticipatory Analytics

Those levels are essentially reactive—looking at the past. In contrast, prescriptive analytics shifts the view to the future, presenting a model of what is likely to happen. Beyond standard prescriptive analytics is anticipatory analytics.

Whereas almost all large companies are already using predictive analytics in some form, not all have tapped into the power of anticipatory analytics, which can identify changes in companies before they happen. Recent developments in the anticipatory analytics space fuel a business solution Dun & Bradstreet refers to as "material change"—the ability to identify "the caterpillars that will turn into butterflies," Basu says.

She explained the practical applications of higher forms of analytics this way: A business can use basic analytics to discover that large companies are its best customers and target them. However, as its competitors would also be targeting those same big companies, the business can use analytics to discover what specifically made its offerings attractive to large companies. Then it can could apply the even more advanced anticipatory analytics to find companies that are not yet large, but that are identified as likely to grow and likely to get into the segment that's attractive to the business.

This is not the type of analytics a business uses to drive sales up in the next quarter. It's for longer-term planning and draws on a lot more back-and-forth communication between the business and analytics teams on the feedback loop and adjustments to the models. It's "not as simple as using a core or model output," Basu observes. It requires very sophisticated analytics that most companies cannot do in-house.

In other words, if a company already identified a customer base, it can use predictive analytics on its own data to find out how to retain those customers and grow that base. With anticipatory analytics, though, it can even discover new markets that could work for the company based on data generated by other businesses. That way, it can discover growth possibilities that were not on the radar.

For example, Dunn & Bradstreet worked with a telecom company's analytics team to develop a solution. The company expanded efforts in identified areas in a couple of states where they tested the solution and achieved $17 million additional revenue as a result of that campaign.

Impressive as that sounds, Basu insists that it is only the tip of the analytics iceberg: "We haven't seen anything else in terms of what analytics can do," she says. "We're just on a journey, and it's going to be a very exciting journey."



 
 
 
 

Ariella Brown, a Baseline contributor, writes about analytics, marketing, branding, social media, big data, and the impact of the internet on education and society, among other topics.

 
 
 
 
 



















 
 
 
 
 
 

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