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1、CRM in consumer banking,Dr. Yanning Zhang December, 2001,CRM Overview Bank CRM features Analytic CRM Process OLAP, Modeling and Data Mining Decision strategies Conclusion,Outline,CRM (Customer Relationship Management) is all about unified view of each customer,CRM Overview,Products,Channel,Treatment
2、s,Customer,CRM Market is still fast growing, even when overall market is slowing down. Projected CRM market will be over $10 Billion in 2003, from $3.3 Billion only in 1999. (E-commence),CRM Overview,CRM concept is not a new one, but it became a hot topic in last 5 years Accelerated consolidation in
3、 banking industry Recent major mergers expanded customer base More products available to consumers Technology allows easier collection and access of customer data China market from ERP to CRM,CRM Overview,CRM Overview Bank CRM features Analytic CRM Process OLAP, Modeling and Data Mining Decision str
4、ategies Conclusion,Outline,Bank CRM Characteristics,Banking is extensive Almost every family has a bank account Bank data is data intensive Bank data is sensitive and proprietary Bank data is multi-dimensional Bank industry is highly regulated,Personalized technologies can tailor message to individu
5、al customer Collect data as much as possible from various channels,CRM Starting point - unified customer view,Bank,Telephone Banking,ATM/Kiosk,Personal Banker,Online Banking,In-branch Teller,Bank Customer Life Cycle,Target Customer,New Customer,Initial Customer,High Potential,High Value,Low Value,In
6、voluntary Charge off,Voluntary Payoff,Prospect Prescreen,Underwriting,Bank Customer Life Cycle,Target Customer,New Customer,Initial Customer,High Potential,High Value,Low Value,Involuntary Charge off,Voluntary Payoff,Prospect Prescreen,Underwriting,Bureau data Demographic data Census data,Applicatio
7、n data Pricing data Account activation data,Behavior data Monitoring bureau data,Collection data,Data Collected and Analyzed Along the Life Cycle,CRM Overview Bank CRM features Analytic CRM Process OLAP, Modeling and Data Mining Decision strategies Conclusion,Outline,Two types of CRM Front-end custo
8、mer experience Operational CRM Back-end customer understanding Analytic CRM,Two Types of CRM,Analytic CRM,Operational CRM,Customer,Analytic CRM - Brain of customer management,Characteristics of Analytic CRM Extensive data capturing, processing and data source linking In-depth analysis and modeling I
9、ntegration with operational CRM system,Analytic CRM process is an iterative process,73% U.S. companies claim collecting information about individuals Data collection is only the first step - Important to agree on why to collect, since different part of companies have different motivation of collecti
10、ng information,Analytic CRM - Data Process,Bank data warehouse - the basic building block,Analytic CRM - Data Process,DW,Data Integration Data warehouse is the central place to have 360 degree view of customers Common mistakes in data warehouse Lack of long term vision No communication between depar
11、tments Lack of cooperation, only 37% share data with other divisions No sufficient quality control Conflict data entry point to create unnecessary conflict No sufficient documentation for data elements, especially for missing values,Analytic CRM - Analysis,Companies need to agree upon the measuremen
12、t of success Bank success measurements: Campaign response rate Annualized losses ROA (Return on Assets) ROE (Return on Equity) or ROI (Return on Investment),Analytic CRM - Metrics,CRM Overview Bank CRM features Analytic CRM Process OLAP, Modeling and Data Mining Decision strategies Conclusion,Outlin
13、e,Key components of CRM analysis,Analytic CRM - In-depth Analysis,Data Summary is the foundation In many cases, data summary is 70% of the work Common issues Confirm the data quality Verify missing values Check variable coding Continuous variable coding Missing values Special values Discrete variabl
14、e coding Avoid mis-intepret numeric values Blank may have special meaning,Analytic CRM Analysis / Data Summary,Data exploration is can be extensive, in many cases, there are over 1000 variables to explore OLAP (Online Analytic Processing) is often used in variable exploration Customer profiling is a
15、 must There are many statistical measures to deal with univariate analysis Mean (Average) Variance or Standard Deviation Medium Kurtosis Etc. In many cases, variable transformation is necessary.,Analytic CRM Analysis / OLAP and Variable Exploration,Common variable shapes,Analytic CRM Analysis / Vari
16、able Exploration,Normal,S-Shape,Linear,Exponential Decreasing,Convex (increasing),Skewed normal,Analytic CRM Analysis / Modeling,Concept of modeling mimic the future by past data. Use the data available and find patterns from the past and then apply the patterns in the model.,Past,Present,Future,Past Data Begins,Past Data Ends,Performance Period Starts,Performance Period Ends,Performance Period,Predictive Period,Analytic CRM Analysis / Modeling
