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The trouble with predictive analytics tools

Predictive analytics tools have been hailed as the latest cure-all for business woes. But they don't make cappuccinos, and they don't tell you how to make business decisions.

Can predictive analytics fix an erratic sales funnel?

If analytics has been the buzzword of the past few years, predictive analytics is close behind.

It's an attractive prospect. Companies want to be able to use data about their operations, products, customers and so on to predict what might happen in the future. Predictive analytic tools have helped companies cut their losses on product defects or increase revenue as they see early spikes in product demand.

But there are also false indicators that analysts may seize on erroneously. They may decide that a certain product is selling better in Miami than Toronto and reroute some of that product to warm climates out of a false belief that demand is influenced by temperature, when in fact it may be influenced by a second factor for which analysts failed to account.

Maybe that's why predicting the future has always been the province of tarot readers rather than data analysts. And that's why predictive analytics isn't just hard, but also slower to take hold than software providers might have you believe. According to the Advanced and Predictive Analytics Market Study, just 27% of respondents report that they use predictive analytics today. And a similar percentage of respondents have no plans to use these tools.

Nonetheless, companies are using predictive analytics to home in on better sales prospects and leads, to target particular segments for certain offers or messaging and so on. The goal is to use existing data based on constituents' behavior today to sell, sell faster and become more efficient. But let's remember: Predictive analytics tools don't do everything for your business.

In our latest compilation of articles, contributors explore the role of predictive analytics today and whether it can achieve these kinds of goals. First, Steve Robins discusses the role of predictive sales analytics in identifying better, more high-value leads: that is, prospects that are more likely to turn into customers faster and with more buying potential. Next, Matt James explores how predictive analytics is being used in new corners, such as hiring. Predictive analytics may help companies determine which employees are a good fit skills-wise and may also be more likely to stay. Finally, Robins explores how predictive analytics can be used to make the entire prospect-to-customer nurturing process more efficient by helping what is known as the sales funnel.

Lauren Horwitz
Executive Editor

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