Definition of Predictive Analytics
Predictive analytics involves extracting data from existing data sets with the goal of identifying trends and patterns. These trends and patterns are then used to predict future outcomes and trends. While it’s not an absolute science, predictive analytics does provide companies with the ability to reliably forecast future trends and behaviors.
Gartner offers a predictive analytics definition describing the concept as any approach to data mining that contains the following key elements:
Predictive analytics emerged from a desire to turn raw data into informative insights that can be used not merely to understand past patterns and trends, but provide a model for accurately predicting future outcomes.
How Predictive Analytics Differs from Other Analytics Models
Gartner visualizes the various types of analytics as being on a spectrum, with each more advanced method of analysis being more difficult, but offering increased value. Descriptive analytics are at the low end of the spectrum, with a primary focus on information. Diagnostic analytics is the next level of analysis, providing insights on the motivations and causes driving trends and behaviors.
Diagnostic analytics is followed by predictive analytics, or the ability to forecast what is likely to happen. At the top of the spectrum is prescriptive analytics, providing foresight and the knowledge required to create desired outcomes.
Predictive Analytics Methods
Predictive analytics is primarily concerned with analyzing data and manipulating variables in order to glean forecasting capabilities from existing data. Predictive analytics techniques rely on measurable variables, manipulating metrics to predict future behavior or outcomes given various measurable approaches.
Predictive analytics models combine multiple predictors, or measurable variables, into a predictive model. This approach allows for the collection of data and subsequent formulation of a statistical model, to which additional data can be added as it becomes available.
The addition of higher volumes of data as it becomes available creates a smart predictive model, relying on larger and larger data sets which produces more reliable predictions based on the volume of data analyzed. Additionally, relying on real-time data to fuel predictive analytics models results in greater accuracy of forecasting.
Uses for Predictive Analytics in Marketing
Predictive analytics is a valuable tool in marketing, allowing marketers to make accurate predictions of the most likely behaviors of consumers. These forecasts are used to formulate the most effective marketing approaches offering the greatest likelihood of achieving desired outcomes.
Other predictive analytics examples include:
Of course, in addition to forecasting opportunities, predictive analytics is often used in analyzing risk. Whether a consumer is likely to default on a payment plan, for example, is one of many ways predictive analytics is used in business to analyze and mitigate risk accompanying high-volume and high-cost consumer relationships. Likewise, predictive analytics is a valuable tool for forecasting substantial market changes. At one time, unexpected shifts in demand could be devastating for businesses financially. But with predictive analytics, companies can stay ahead of the curve and adapt in real-time with products and services that are perfectly in-tune with customer expectations.
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