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Data Mining What is Data Mining? “Data Mining is the process of selecting, exploring and modeling large amounts of data to uncover previously unknown information for using it to make crucial business decisions.” Goal of Data Mining Simplification and automation of the overall statistical process, from data source(s) to model application DATA INFORMATION KNOWLEDGE Data Mining vs. Other analytical approach Goal Deliver -ables Output Format DATA MINING OLAP In order to solve problems, companies look into their data for scientific & logical evidence Users would like to see pre-defined business trends quickly and easily ‘If customer age is between 35 ~ 45 & product is ‘A’,’E’ & there is 30% increase in usage of ATM recently then response rate is 4 times higher” Reports in the form of ‘revenue by year/month/area ’ ‘revenue by month/area/ weekday’ . • Rule : If age in (35,45) and product (‘A’,’E’) and ATM usage > 30% then… • Score : 0.55, 0.90.. area/ weekday BK PK SM 01/M 02/T 03/W 9999 3456 4335 1234 4353 5467 3456 6578 5673 Data Mining is … Decision Trees Nearest Neighbor Classification Neural Networks Rule Induction K-Means Clustering Data Mining Algorithms Predictive use data on past process to predict future production Historical Data Predictive algorithm - neural - tree - regression Probability of Future production Descriptive use data on past process to describe current situation Historical Data Descriptive algorithm - cluster - association Description of current production Why Data Mining?—Potential Applications Data analysis and decision making support • Market analysis and management – Target marketing, customer relationship management, market basket analysis, cross selling, etc • Risk analysis and management – Forecasting, customer retention, improved underwriting, quality control, competitive analysis • Fraud detection and detection of unusual patterns (outliers) • Text mining (news group, email, documents) and Web mining • Stream data mining • Bioinformatics and bio-data analysis Market Analysis and Management • Where does the data come from?—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 • Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association • Customer profiling—What types of customers buy what products (clustering or classification) • Customer requirement analysis • Identify the best products for different groups of customers • Predict what factors will attract new customers • Provision of summary information • Multidimensional summary reports • Statistical summary information (data central tendency and variation) Corporate Analysis & Risk Management • 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 • group customers into classes and a class-based pricing procedure • set pricing strategy in a highly competitive market Fraud Detection & Mining Unusual Patterns • • Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. • Auto insurance: ring of collisions • Money laundering: suspicious monetary transactions • Medical insurance – Professional patients, ring of doctors, and ring of references – Unnecessary or correlated screening tests • Telecommunications: phone-call fraud – Phone call model: destination of the call, duration, time of day or week. • Retail industry – Analysts estimate that 38% of retail shrink is due to dishonest employees • Anti-terrorism Data Mining Process Define business problem Evaluate environment Make data available Review Mine in cycles Explore Modify Implement in production Sample Model Assess Indicative ROI Example Retention Targeting Assumptions Number of customers (in selected segment) = 300,000 Average revenue per user (ARPU)/year = THB 14,400 Annual churn rate = 30% New churn rate through targeted churn activities = 29% Annual Loss due to old churn rate = THB 1,296 million Annual Loss due to new churn rate = THB 1,252.8 million Annual Savings = THB 43.2 million Indicative ROI Example Cross selling/Up selling Assumptions Number of customers(in selected segment) = 400,000 Number of direct mail/year = 6 Variable cost per direct mail = THB 80.00 Modeling allows for elimination of lower 20% ranked direct mail list without significant loss in gross response Annual Cost without modeling = THB 192 million Annual Cost with modeling = THB 153.6 million Annual Savings = THB 38.4 million Indicative ROI Example Acquisition Targeting Assumptions Number of targeted prospects = 30 000 Number of direct marketing campaigns/year = 12 Average response rate = 2% Average revenue per user (ARPU) = THB 14,400 Improved response rate (due to market segmentation & value proposition) = 3% Annual Benefit without modeling = THB 103.6 million Annual Benefit with modeling = THB 155.5 million Annual Savings = THB 51.9 million Justifying ROI Indicative ROI Example Retention Targeting Cross selling/up selling Acquisition Targeting = = = THB 43.2 million THB 38.4 million THB 51.9 million Total Savings/Benefits = THB 133.5 million Case Study The Financial Services of La Poste “A bank like other banks, but not like other banks” Generalist Positioning 28 million people have an account with the Financial Services of La Poste 12 million have a current account at La Poste 5.6 million customers are under 25 1.2 million customers are financially insecure 500,000 own assets 500,000 are professionals and companies Multi-channel Customers 800 million incoming annual contacts with La Poste 320 million visits to Post Offices 368 million cash machine contacts 60 million Internet/Minitel contacts 40 million "incoming" telephone calls 500 million annual outgoing contacts with La Poste Very Loyal Customers Customers who have great confidence in us and who are very loyalto La Poste because they share our values….. Build an integrated CRM …. But whom we don’t know well enough and with whom we need to improve the relationship.