The Nine Most Common Data Mining Techniques Used in Predictive Analytics

The Nine Most Common Data Mining Techniques Used in Predictive Analytics : MarketingProfs.

Predictiveanalytics enables you to develop mathematical models to help you better understand the variables driving success. Predictive analytics relies on formulas that compare past successes and failures, and then uses those formulas to predict future outcomes.

Predictive analytics, pattern recognition, and classification problems are not new. Long used in the financial services and insurance industries, predictive analytics is about using statistics, data mining, and game theory to analyze current and historical facts in order to make predictions about future events.

The value of predictive analytics is obvious. The more you understand customer behavior and motivations, the more effective your marketing will be. The more you understand why some customers are loyal and how to attract and retain different customer segments, the more you can develop relevant, compelling messages and offers.

Predicting customer buying and product preferences and habits requires an analytical framework that enables you to discover meaningful patterns and relationships within customer data in order to achieve better message targeting and drive customer value and loyalty.

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