Using SAS Enterprise Miner for Analytical CRM in Finance

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1 Using SAS Enterprise Miner for Analytical CRM in Finance Sascha Schubert SAS EMEA

2 Agenda Trends in Finance Industry Analytical CRM Case Study: Customer Attrition in Banking Future Outlook

3 Trends in Finance To effectively compete against the upstart Digital 1000 and prosper, organizations must weave operational CRM systems with collaborative and analytical CRM components into a customer-centric interaction system that extends to customers via the Inter/extranet." Meta Group 2001

4 CRM is Key in Financial Services Internet-based financial services are leading the way and put pressure on traditional financial organizations Not building meaningful relationships with customers is costing commercial banks and financial institutions in the US approximately $700 million each year because of missed profit opportunities Pepper and Rogers, Jan 2001

5 Goal of CRM Build long-term and profitable relationships with chosen customers Get closer to those customers at every point of contact with them Maximize share of wallet

6 What the analysts say Three key stages of CRM implementation Operational CRM: Analytical CRM: Collaboration CRM: Track Adjust/Act Analyse Policy Hub Model

7 CRM Application Space Analytical CRM Differentiation Customization Learning Relationship Operational CRM Real Time Interactivity Feedback Loop Collaborative CRM Consistency Copyright 2001, SAS Institute Inc. All rights reserved. Meta Group

8 Analytical CRM Defined Focused analysis of data created by the operational side of CRM for the purpose of business advantage Provide new quality of information/knowledge about existing and potential customers to maximize the overall enterprise value Automate the decision process through automated Customer Behavior Modeling

9 Value of Analytical CRM Analytical CRM is part of larger process and has to play a role in that process Provide the right information to the right person in the right format at the right time To SUPPORT the decision for a certain ACTION DATA INFORMATION KNOWLEDGE

10 Business Benefits of Analytical CRM See your business from the customer perspective Create products, services that are relevant to the customer (Differentiation) Track customers across all touch-points (Consistency) Influence customers by advising pro-actively Customer-value based resource allocation Environment that enables to learn from customer

11 Link Analytical CRM to Customer Lifecycle Winback Former Customer Retention Advocate Cross- Sell Winback Established Customer Former Customer Retention Prospect Acquisition Winback New Customer Retention Cross- Sell Former Customer

12 The Journey towards Loyalty Every proposed intervention has to be crossed with value metric Is it worthwhile to act???

13 Profitability 89% Profit Maximise individual customer profitability to maximise corporate bottom line 11% Profit Top 20 Copyright 2001, SAS Institute Inc. All rights reserved. Bottom 80

14 Case Study: Credit Card Customer Attrition Customers Lost during time period t Attrition Rate (t) = Total Customer Base at the end of time period t Existing customer base: 2,000 customers Customers acquired in time period: 200 Customers lost in time period: 50 (attrition) Total customer base at end of time period: 2,150 subscribers Therefore, attrition rate is 50 / 2150 = 2.3%

15 Why is Loyalty important? CONTRIBUTION FROM AN INDIVIDUAL CUSTOMER Growth Drivers Referrals Up Selling Cross-Selling (Share of Customer) New Volume Base Sales Volume Development & Retention Costs Acquisition Cost T I M E

16 Analytical Techniques for Attrition Modeling Goals Identify business drivers for customer attrition Identify customer profiles of defectors Assign individual propensity to leave Techniques Sequencing Cluster Analysis Predictive Models

17 Sequencing Can sequences of events be identified that are more likely to lead to cancellation of products or relationship? Event-centric technique Requires transactional data with time stamp Many categories of events possible Product opening and closing Demographic changes Behavioral events, triggers

18 Sequencing

19 Descriptive Modelling / Hotspotting Cluster data to allow data to tell us of its internal structures, rather than imposing external structure Let tool search for many clusters (30-40) Once clusters have formed profile your KPI (attrition rate) Use this to identify customer groups of interest

20 Descriptive Modeling / Hot spotting

21 Propensity Modelling Traditional method when target is available Use predictive modelling techniques to calculate propensity of attrition (Trees, NN, Regression, Ensemble) Combine individual propensity with customer value Problem with data structure, need time series structure with extensive history Problems with dimensionality, there can be hundreds to thousands of attributes, especially if history is considered

22 Propensity Modelling

23 Value Based Segmentation

24 Value Based Strategies Enhance Loyalty Retention Do Nothing? Encourage to Go? Ensure Satisfaction Enhance Value Cross-Sell High Value of Customer Low Low Likelihood to Stay High

25 Today s High Value Customers are tomorrow s? High Value Customers? Churners? Low Value Customers? High Risk Customers? Defaulters? Today s Low value customers are tomorrow s?

26 P R O F I T 0 Customer Potential Current Profitability Future Potential Richard Forsyth, Sophron Partners

27 Customer Potential How much value is a customer expected to generate over a fixed period of time Need to know Expected revenue Expected cost Expected propensity to leave All components needs to be predicted Time window is industry dependent

28 Value Based Segmentation High Loyalty Low Low Potential Customer Value High Current Customer Value

29 Ways to increase Customer Potential Increase Revenue cross-sell, up-sell, stimulate product usage Decrease costs more efficient service channels more efficient sales channels less acquisition costs decrease risk through non-payment Increase Tenure higher satisfaction, higher loyalty The 3 areas are dependent on each other model effects

30 Integrated Customer Lifecycle Modeling Framework Lifetime Value Acquisition Product Propensity Product Usage Risk Management Voluntary Attrition Acquisition Prospect New Customer Established Customer Former Customer (adapted from) Gordon Linoff, Pincipal, Data Miners, 1999

31 Model Framework of Next Best Action to Capitalize Future Potential combine all models to constantly re-evaluate the situation of individual customers use outcome to choose next best action not only products but every action possible Product Service Loyalty Nothing turn the paradigm from finding customers for a specific product to finding the best next action for individual customer to maximize their lifetime value

32 Conclusions Analytical CRM adds intelligence to CRM systems Puts customer into center of business Follow stepwise process to add intelligence to CRM systems Build singular models of customer behavior Cross predicted customer behavior with current profitability Analyze every action towards the customer by the value created Predict future profitability Start building customer lifecycle management model framework Start doing it to start learning!!!

33

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