Microsoft SQL Data Mining Algorithms

The following table describes the different business use-cases that drive data mining adoption, and the relevant SQL Server Analytics Services (SSAS) algorithms:
Business Purpose
 Description
 Algorithm(s)
      
Market Basket Analysis Discover items that are most frequently purchased together to optimize product bundles and placement
  • Association
  • Decision Trees
Churn Analysis Anticipate customers who may be considering canceling their service and target communication/services to improve customer retention
  • Decision Trees
  • Linear Regression
  • Logistic Regression
Market Analysis Define market segments by automatically grouping similar customers together and use segmentation to target the most profitable customers
  • Clustering
  • Sequence Clustering
Sales Forecasting Predict sales and inventory amounts and learn how they are interrelated to foresee bottlenecks and improve performance
  • Decision Trees
  • Time Series
Data Exploration Analyze profitability across customers, or compare customers that prefer different brands of the same product to discover new opportunities
  • Neural Network
Unsupervised Learning Identify previously unknown relationships between various elements of your business to inform your decisions
  • Neural Network
Web Site Analysis Understand how people use your Web site and group similar usage patterns to offer a better experience
  • Sequence Clustering
Campaign Analysis Spend marketing funds more effectively by targeting the customers most likely to respond to a promotion
  • Decision Trees
  • Naïve Bayes
  • Clustering
Information Quality Identify and handle anomalies during data entry or data loading to improve the quality of information
  • Linear Regression
  • Logistic Regression
Text Analysis Analyze feedback to find common themes and trends that concern your customers or employees, informing decisions with unstructured input
  • Text Mining