Data Mining


Data mining, also known as knowledge discovery, is the science/technology of extracting implicit, unknown information and patterns from large stores of data. When combined with other programs such as customer relationship management (CRM), business intelligence (BI), and financial analysis, data mining can contribute significantly to the bottom line.

Typically, analytical approaches (like BI) involve historical data that describe where you have been and where you are, not where you should go. Data mining is a natural extension of these approaches that can provide a glimpse into the future and enables you to ask questions such as: What information could make the biggest difference for the company? And, how would this change strategies and tactics?

There are many uses for mined information that provide a rapid return on investment (ROI). Predictive analytics use historical data such as age, zip, income, and past behaviors to model future behavior. For example, prospects with a high probability to accept an offer for a product or a high likelihood to be taken by a competitor (churn) can be identified. Advance knowledge of a prospects likely behavior is a powerful strategy and tactics shaper.

Another very common use of data mining is to perform data-driven customer segmentation. Customer data are investigated to determine if naturally occurring clusters exist that would help the business design segment specific strategies for advertising, marketing, sales, etc.

When segmentation is combined with the predictive modeling process described above, a market manager could differentially treat customers, say by modifying the contact channel (call center, direct mail, web servers, email systems) and by making a product offer only to those with a high probability to buy but with a low potential to churn. Alternatively, customers with high likelihood to churn might be engaged in a retention program or made a slightly different product offer with incentives, designed to increase retention. Data mining has also been employed to identify the key drivers of a business processes and customer loyalty among other things. Once the key drivers are known, adjustments can be made internally to tweak the drivers and optimize their effect and maximize profitability.

When implemented optimally, data mining is a key differentiator for today’s businesses. learn more



  • Detect Sales Trends
  • Forecast accurately
  • Generate Customer Profiles
  • Discover Sales Opportunities (Up- & Cross-Sales)
  • Facilitate Fraud Alerts
  • Predict Customer Lifetime Value
  • Reduce Churn
  • Implement Needs Segmentation
  • Identify and Quantify Key Drivers
  • Much More



  • Telecommunications
  • Healthcare
  • Insurance
  • Automotive
  • Retail (Store and Web)
  • Pharmaceutical
  • Techniques



  • Multivariate Statistics
  • Factor Analysis
  • Discriminate Analysis
  • Structural Equation Modeling
  • Neural Networks
  • Genetic Algorithms
  • Regression
  • Descriptive and Exploratory Statistics
  • Cluster Analysis
  • Decision Tree
  • Structural Equation Analysis
  • Forecasting
  • Conjoint
  • Linear and Non-Linear Models
  • Data Sourcing
  • Data Preparation
  • Data Characterization
  • Sampling
  • Outlier Identification
  • Predictive Modeling
  • Market Basket Analysis
  • Association
  • Classification
  • Rule Induction
  • Data Visualization

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