10 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 KNOWLEDGE INFORMATION DATA

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 Goal ‘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’. Deliver -ables Output Format Rule : If age in (35,45) and product (‘A’,’E’) and ATM usage > 30% then… Score : 0.55, area/ weekday BK PK SM 01/M /T /W OLAPOLAP DATA MINING Data Mining vs. Other analytical approach

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 Descriptive use data on past process to describe current situation Probability of Future production Historical Data Predictive algorithm - neural - tree - regression Description of current production Historical Data Descriptive algorithm - cluster - association

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, , 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

Define business problem Make data available Sample Explore Modify Model Assess Mine in cycles Review Implement in production Evaluate environment Data Mining Process

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 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 million  Annual Savings = THB 38.4 million Indicative ROI Example

Acquisition Targeting Assumptions Number of targeted prospects = 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 million  Annual Benefit with modeling = THB million  Annual Savings = THB 51.9 million Indicative ROI Example

Retention Targeting=THB 43.2 million Cross selling/up selling =THB 38.4 million Acquisition Targeting=THB 51.9 million Total Savings/Benefits=THB million Justifying ROI

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 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….. …. But whom we don’t know well enough and with whom we need to improve the relationship. Build an integrated CRM