10 data mining. what is data mining? “data mining is the process of selecting, exploring and...
TRANSCRIPT
10Data 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
KNOWLEDGEKNOWLEDGEINFORMATIONINFORMATION
DATADATA
In order to solve problems, companies look into their data for scientific & logical evidence
Users would like tosee 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
OutputFormat
• Rule : If age in (35,45) andproduct (‘A’,’E’) andATM usage > 30% then…• Score : 0.55, 0.90..
area/ weekday BK PK SM
01/M 9999 1234 3456 02/T 3456 4353 657803/W 4335 5467 5673
OLAPOLAPOLAPOLAPDATA MININGDATA MININGDATA MININGDATA 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
HistoricalData
Predictive algorithm - neural - tree - regression
Description of
currentproduction
HistoricalData
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, 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
Define business problem
Make dataavailable
Sample
Explore
Modify
ModelAssess
Mine in cycles
Review
Implement in production
Evaluate environment
Data Mining Process
Retention Targeting
AssumptionsNumber of customers (in selected segment) = 300,000Average revenue per user (ARPU)/year = THB 14,400Annual 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
AssumptionsNumber of customers(in selected segment) = 400,000Number of direct mail/year = 6Variable 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
AssumptionsNumber of targeted prospects = 30 000Number of direct marketing campaigns/year = 12Average 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
Indicative ROI Example
Indicative ROI Example
Retention Targeting = THB 43.2 millionCross selling/up selling = THB 38.4 millionAcquisition Targeting = THB 51.9 million
Total Savings/Benefits = THB 133.5 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 Poste12 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 Poste320 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