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Forecasting technology comes of age

The technologies used to help companies predict things such as future customer behavior, demographics and other business intelligence (BI) metrics have matured.

The technologies used to help companies predict things such as future customer behavior, demographics and other business intelligence (BI) metrics have matured, allowing forecasts to be more accurate today than any other time in the past, according to experts.

Enterprises have a vested interest in being able to predict their futures. Accurate predictions can help companies budget their money and manage customers and employees more effectively.

Forecasting, or predictive analytics, has been around for a very long time. Companies have historically made predictions with the help of tools like spreadsheet applications or handheld calculators. But these low-tech tactics often result in predictions that are way off, especially when unexpected changes come into the fray.

"What has changed is that all of the really sophisticated tools, these data mining tools and predictive tools and so forth are much finer, much more exacting than our old Excel spreadsheets and paper and pen," said Claudia Imhoff, president of Intelligent Solutions Inc., a Boulder, Colo.-based consultancy that specializes in BI. "The tools are much more capable of granular predictions, and they're much more flexible in terms of things not going according to plans."

New predictive tools being offered by data mining and statistical analysis companies can also take the most current information into account, whereas in the past, companies had to rely on historical data.

Perhaps even more interesting than these close-to-real-time capabilities, Imhoff said, is that forecasting technologies now can go beyond the predictions and actually make suggestions about what a company can do to achieve its goals. These "guided decision making" tools suggest actions and reactions based on its rules engine and analysis of the situation.

Guided decision making applications differ from predictive analytics systems in that they proactively manage risk by allowing users to test potential actions and determine whether or not they will have the intended effect.

Imhoff explained that these more advanced predictive analytics and guided decision making systems are just now beginning to take hold, though there are some industries that use the technology more than others. Early adopters include financial institutions, retailers and insurance companies, she said. But more are sure to follow.

"It's starting to spread out through all of the traditional vertical industries quite nicely, even into supply chain management," Imhoff said.

Some vendors that offer predictive analytics and guided decision making tools include SAS Institute Inc., SPSS Inc., Teradata, SAP AG and IBM.

"The big players have all brought in the data mining types of capabilities and are now using them for more predictive functions," Imhoff said.

Raymond Pettit, a former independent marketing consultant who now works for Longwoods International in Toronto, specializes in helping companies understand the best ways to use predictive tools.

Companies that make use of the latest tools gain a big advantage over competitors that lag behind technologically, Pettit said. With these tools, companies are able to make deadly accurate budgetary forecasts and quickly respond to sudden changes in the business climate.

"Predictive analytics are really just a form of statistics or analysis that you can apply to large databases, as well as surveys," Pettit said. "It's a way to get a great handle on a lot of data that you're collecting to find the nuggets of insight that are in there, as well as predict what your customers are going to do."

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