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