Predictive Modeling
Predictive modeling encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Predictive analytics adds great value to a businesses decision making capabilities by allowing it to formulate smart policies on the basis of predictions of future outcomes. Multivariate Solutions offers broad range of tools and techniques for this type of analysis. Their selection is determined by the analytical sophistication of the firm as well as the nature of the problem being solved. Multivariate Solutions offers three main types of predictive analytics:
  • Predictive models
  • Descriptive models
  • Decision models
Predictive Analytics is often applied to any of the following projects:
  • Analytical Customer Relationship Management (CRM):
  • Data Mining
  • Direct marketing
  • Customer retention
  • Collection analytics
  • Product or economy level prediction
  • Sales or penetration forecasting
  • Loyalty Analytics
  • Predicative Modeling
  • Voter Identification
Predictive Modeling Articles

Combining predictive analytics with traditional employee surveys can lead to a significant decrease in employee churn. This represents a significant opportunity for market researchers to demonstrate value to corporate management, as described in this Greenbook Blog post, Boosting Employee Retention with Predictive Analytics.

Regression Analysis Positioning uses the most common predictive analytic method to distinguish between two types of customer and maximizes how to communicate to each.