DATA MINING IN THE FINANCIAL SERVICES INDUSTRY

Transcription

1 DATA MINING IN THE FINANCIAL SERVICES INDUSTRY PRESENTATION TO KNOWLEDGE DISCOVERY CENTRE (15 FEBRUARY 2001) Steven Parker Head CRM Consumer Banking Standard Chartered 1

2 STANDARD CHARTERED World s leading emerging markets bank - Asia, Sub- Continent, Africa, the Middle East and Latin America 740+ offices (55 countries); US$90bn in assets Key business lines: Consumer Banking - deposits, mutual funds, mortgages, credit cards, personal loans Commercial Banking - cash management, trade finance, treasury, custody services Long-term commitment to Hong Kong e.g. note issuer, #1 consumer credit card etc. 2

3 KNOWLEDGE - WHY? THE NEW IMPERATIVE Identify Opportunity and Key Success Factors GOAL Focus on a Precise Proposition Business Plan METHOD Build World-Class Talent Gather Purpose-Appropriate Infrastructure + Amass Actionable Information Database Database Deliver Best-of-Breed Solutions Business Plan RESULT Earn Customer Loyalty I would like to buy... Earn Superior Returns Source: Corporate Executive Board 3

4 KNOWLEDGE - WHY? Revenue Years of Relationship Cost It is the customers usage of the product over time which has the potential to create profit And our ability to cross-sell & up-sell other products which are also used profitably 4

5 KNOWLEDGE - HOW? Retention Acquisition New Customer Management Customer Relationship Management Re-pricing time (months) Acquisition Model Customer Segmentation Retention Model 5

6 KNOWLEDGE - HOW? KNOWLEDGE OF CUSTOMERS Purchase Behaviour Service Needs Demographics Product Usage Relationship Price Sensitivity etc. TAILORED PROGRAMMES AND COMMUNICATION TAILORED CUSTOMER SERVICE Customer Retention + Cross-sell/Utilisation + Pricing Optimisation + Customer De-marketing + Product Re-design + Channel Management + = HIGHER PROFIT 6

7 KNOWLEDGE - HOW? Learning Test & Learn Cycle Definitive Results Proprietary Knowledge = Competitive Advantage Today - some learning based on assumptions about what worked Time 7

8 DATA MINING INTO PRACTICE Data Warehouse Customer Profitability/ Segmentation Campaign Management Contact Management 8

9 DATA MINING INTO PRACTICE Taiwan DW India DW Hong Kong DW Campaign Contact Thailand DW Philippines DW Malaysia DW Campaign Contact Singapore DW Campaign Contact Brunei DW Indonesia DW 9

10 CHALLENGES Changing the culture - data-driven, product to customer, volume to value Shortage of skills - analytical, technical, marketing Immature market e.g. vendor networks, public data, lists etc. 10

11 SUCCESSES Building on early wins Learn from developed markets - faster cycle time No public data = proprietary data even more valuable Ability to combine emerging markets channels with information capabilities 11

12 EXAMPLE: RETENTION 10.0% Attrition Cycle 9.0% 8.0% 7.0% 6.0% Predict Attrition Win-Back 5.0% 4.0% 3.0% 2.0% 1.0% 0.0% Length Of Relationship Data Warehouse Customer Profitability Campaign Management Contact Management 12

13 EXAMPLE: RETENTION No Pre-Attrition No Yes Post Attrition Yes Retained Yes Attrition Gating No Yes No High Profit Post Post Attrition High Profit No Low Potential Attritors Low Potential 13

14 EXAMPLE: MIGRATION Customer Segmentation 7.6% 7.6% 10.0% 9.9% % % % 11.4% 9.7% Data Warehouse Customer Profitability Campaign Management Contact Management 14

15 EXAMPLE: MIGRATION Segment Focus Maintain Retain / Grow 1 2 ( ) Migration and Relationship Packages Re-Package Mass Market Up-sell Mid Market Affluent Strategic Importance Profitability/Balance Sheet/Potential Channel Usage Mix Branch Self- service Branch Self- service 15

16 EXAMPLE: CROSS-SELL Depth of relationship (no. of products held) Intensity of relationship (no. of transactions) Data Warehouse Customer Profitability Campaign Management 16

17 EXAMPLE: CROSS-SELL Business Value Centre Wealth Secured Un-Secured BFS Customer Segments Segment 1 Segment 2 Segment 3 Segment 4 Segment 5 Segment 6 BUILD CUSTOMER VALUE BUILD CUSTOMER VALUE BUILD CUSTOMER VALUE BUILD CUSTOMER VALUE BUILD CUSTOMER VALUE BUILD CUSTOMER VALUE Segment 7 BUILD CUSTOMER VALUE 17

18 EXAMPLE: CROSS-SELL Established Loyals Developing Loyals I Share of customers 3% Share of customers 9% Share of profits 8% Share of profits 44% Multiple account holding is common Long relationship time High transaction activities High phone banking usage Highest asset balance across segments 25% of segment has high bank assets Liabilities low Developing Loyals II Borrowing Potentials Share of customers Share of profits 12% 13% Share of customers Share of profits 10% 12% Card bill Highest level of multiple deposit account holding Average account balance very high Mean age is 45 All hold credit cards Most have loans in small amounts Deposit balance low 18

19 EXAMPLE: CROSS-SELL New Savers Share of customers Share of profits 12% 3% Dominated by single deposit account holders Short relationship time Open accounts in response to promotions Low Value Savers Low Activity Savers z z z Share of customers 10% Share of customers 15% Share of profits 0% Share of profits 2% Single deposit account holders mainly saving accounts Longest relationship time with SCB Mean age is 50 Over-represented by females Mostly customers with one deposit account Dormant customers over-represented Highest proportion of static balance 19

20 TARGET BENEFIT - QUICK WINS RETENTION High MIGRATION Medium/high CROSS-SELL SELL Medium/low 20

21 FACING THE CHALLENGES Building competencies Integrating value concepts into all points of customer contact Re-engineering processes in marketing, sales and service Continuing technology investment 21