Why Data Mining?- Potential Applications 6 Database analysis and decision support Market analysis and management target marketing customer relation management, market basket analysis, cross selling, market segmentation Risk analysis and management Forecasting, customer retention improved underwriting quality control, competitive analysis Fraud detection and management ◆ Other Applications Text mining (news group email, documents )and Web analysis Intelligent query answering 16
16 Why Data Mining? — Potential Applications Database analysis and decision support ◼ Market analysis and management target marketing, customer relation management, market basket analysis, cross selling, market segmentation ◼ Risk analysis and management Forecasting, customer retention, improved underwriting, quality control, competitive analysis ◼ Fraud detection and management Other Applications ◼ Text mining (news group, email, documents) and Web analysis. ◼ Intelligent query answering
Market Analysis and Management(1) WWhere are the data sources for analysis? Credit card transactions loyalty cards discount coupons customer complaint calls, plus(public) lifestyle studies ◆ Target marketing Find clusters of" customers who share the same characteristics: interest, income level, spending habits, etc Determine customer purchasing patterns over time Conversion of single to a joint bank account: marriage, etc o Cross-market analysis Associations/ Co-relations between product sales Prediction based on the association information
17 Market Analysis and Management (1) Where are the data sources for analysis? ◼ Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies Target marketing ◼ Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. Determine customer purchasing patterns over time ◼ Conversion of single to a joint bank account: marriage, etc. Cross-market analysis ◼ Associations/ co-relations between product sales ◼ Prediction based on the association information
Market Analysis and Management(2) ◆ Customer profiling data mining can tell you what types of customers buy what products(clustering or classification) o Identifying customer requirements h identifying the best products for different customers use prediction to find what factors will attract new customers o Provides summary information a various multidimensional summary reports statistical summary information(data central tendency and variation) 18
18 Market Analysis and Management (2) Customer profiling ◼ data mining can tell you what types of customers buy what products (clustering or classification) Identifying customer requirements ◼ identifying the best products for different customers ◼ use prediction to find what factors will attract new customers Provides summary information ◼ various multidimensional summary reports ◼ statistical summary information (data central tendency and variation)
Corporate analysis and risk Management k Finance planning and asset evaluation cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis(financial- ratio, trend analysis, etc.) ◆ Resource planning: summarize and compare the resources and spending ◆ Competition: monitor competitors and market directions L LI group customers into classes and a class ase pricing procedure set pricing strategy in a highly competitive market 19
19 Corporate Analysis and Risk Management Finance planning and asset evaluation ◼ cash flow analysis and prediction ◼ contingent claim analysis to evaluate assets ◼ cross-sectional and time series analysis (financialratio, trend analysis, etc.) Resource planning: ◼ summarize and compare the resources and spending Competition: ◼ monitor competitors and market directions ◼ group customers into classes and a class-based pricing procedure ◼ set pricing strategy in a highly competitive market
Fraud Detection and Management(1) ◆App| ications widely used in health care, retail, credit card services, telecommunications(phone card fraud), etc ◆ Approach use historical data to build models of fraudulent behavior and use data mining to help identify similar instances
20 Fraud Detection and Management (1) Applications ◼ widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. Approach ◼ use historical data to build models of fraudulent behavior and use data mining to help identify similar instances