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The Nine Most Common Data Mining Techniques Used in Predictive Analytics

by Laura Patterson  |  
April 27, 2010

Predictive analytics 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.

Predictive models have been used in business to assess the risk or potential associated with a particular set of conditions as a way to guide decision making. Predictive models improve marketing effectiveness by analyzing past performance to assess how likely a customer is to exhibit a specific behavior in the future.

Marketing and sales professionals are beginning to capture and analyze many different types of customer data—attitudinal, behavioral, and transactional—related to purchasing and product preferences to make predictions about future buying behavior.

Today's challenging environment is forcing more organizations to explore predictive analytics. Commonly used by market researchers when analyzing survey data, predictive analytics can also be applied in real-time scenarios, such as personalizing offers to customers or improving an online customer experience.

There are various approaches to predictive analytics, and most depend on clean databases and the ability to mine data to look for patterns or to create classifications. It is important to understand the various approaches so you know when to use which one.

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Laura Patterson is president and founder of VisionEdge Marketing. For 20+ years, she has been helping CEOs and marketing executives at companies such as Cisco, Elsevier, ING, Intel, Kennametal, and Southwest Airlines prove and improve the value of marketing. Her most recent book is Metrics in Action: Creating a Performance-Driven Marketing Organization.

Twitter: @LauraVEM

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  • by Jascha Tue Apr 27, 2010 via web

    This is a great introduction to the basics of predictive analytics! We currently use most all of those listed in our models and have some proprietary artificial neural networks (ANN) that help in our data mining and statistical analysis.

  • by Sconyers Wed May 5, 2010 via web

    StatSoft's STATISTICA Data Miner is a great tool for predictive modeling in Marketing. Their tools help you find:(

    -Customers who respond to new products
    -Customers who respond to discounts
    -Customers who buy in specific product categories
    -Your most loyal customers
    -Geographic or other differences
    -Each marketing campaign's ROI

    And much more

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