CRM in the credit card area: the impact of busines intelligence

Transcription

1 CRM in the credit card area: the impact of busines intelligence Dr. Michael Flaschka Chief Operations Officer VISECA Card Services SA, Switzerland SEUGI 2002 Paris 11 June 2002

2 VISECA Card Services The Company VISECA Card Services SA is a joint venture between the Swiss cantonal banks, Raiffeisen banks, regional banks, MIGROSBANK and Bank Coop together with the private and commercial banks. Our offering encompasses:! Cards issued by the above banks! Cards issued jointly with partner firms; so-called co-branded cards! Gold and silver cards with a neutral card design With over 850,000 customers VISECA is one of the major credit card companies in the Swiss market.

3 CRM Strategy Vision Viseca a benchmark for quality and competency in cashless payments services Differentiation factors Optimal customer focus Powerful distribution platforms Professional management of partner network Technology-supported customer relationship management Success factors Customer-centric organisation Effective management of credit card use and turnover Professional risk management Broad credit card knowledge and competency Knowledge-based marketing

4 Structure of CRM Strategy CRM Strategy Analytical CRM Collaborative CRM Operative CRM Customers / Business processes Using analytical methods, customer behaviour can be analysed, classified and predicted. This knowledge of customer behaviour and needs can be used to achieve a better, more individual service across all distribution channels, resulting in enhanced customer loyalty and profitability.

5 acrm: Business Analytical CRM Environment VISECA system landscape VISECA Business Analysis Environment Customer view Profit view Account view Product view Data Mining Workbench (Enterprise Miner) Model Repository Core Rollup MIS Cockpit/BSC (Balanced Score Card) VISECA Knowledge Base (SAS Warehouse) Core Data Mart MIS Data Repository Metadata Repository DWH Archive (structured and non-restructured data) Output Layer (SQL Server 2000) Score & Campaign Data Repository Interner Integration Layer Score and selection data Response and interaction data VISECA Customer Care Center Channel integration regulations (Siebel / HiNet Xpress) CCC Contact Data Repository Webmiles Data Repository WWW Call Fax Letter Interaction channels VISECA cardholders File Transfer ServicesConnect:Direct VISECA - Integration Layer External customer data Selection, response and interaction data Customer base, TRX and billing data?? Other external data sources? Webmiles External partners Core DWH Payserv Issuing CAMS Issuing DMS Issuing BWIII Authorisation Acquiring PASS VP VP VP Acceptance points of Mastercard/VISA

6 Best Practice: Campaign Management Campaign management as a process-driven version of CRM strategy: Intelligent campaign management via analytical CRM Integrated in overall CRM strategy and architecture Designed as a communication and learning circle Dimensions of customer segmentation and action steering: Profitability Retention Applications: " Prospect management " Usage stimulation " Risk management " Retention Management Needs Development Attrition Risk

7 Retention Management: Customer Lifetime Prospect Responder Established Cardholder SURVIVAL Analysis CHURN Risk Modeling Identify characteristics of customers with high CLV Target Market Responder Identify customers with high value at risk New Spender High Value High Potential Former Cardholder Voluntary Voluntary Churn Voluntary Churn Churn Campaign histories Purchased demographics Credit checks Self-reported information Card usage Payment history Campaign responses Channel preferences Entry points for Retention Management Low Value High Risk Forced Churn Termination Reason and Date COF Risk Modeling Identify customers with high value at risk and/or default risk

8 acrm: Knowledge Generation Via Data Analysis Actionable Actionable area area for for a a proactive proactive retention retention management management Value at Risk - Customer Value Customer Value [Normiert] High CV CV10 CV9 CV8 CV7 CV6 CV5 Customer Value Decile CV4 CV3 CV2 Low CV CV1 R9 Low Risk R1 R3 R5 Attrition Risk R7 Decile High Risk The Value at Risk view reflects the loss of value caused by customer defection in relation to the likelihood of defection. " Provides an analysis basis for determining retention measures at the customer segment level.

9 Campaigning as a Communication Circle Retention Target Group Campaign Design Campaign Execution Backend Analysis socio-dem. characteristics usage behaviour risk scores customer age customer history Campaign objectives, message and mechanics Campaign structure (single vs. multi-stage) Process definition and timing Measurement method and data management Response elements and media Channel selection "Direct mail " "Telephone "... Fulfillment Response handling Instant BEA: "Sales development "Trx development "Retention analysis "Structure analysis "Response behaviour "Channel analysis "... Long-term BEA: "CLV development "Survival analysis "Risk development Success factors: # proaktive management # information flow # data cleansing / quality # integrated communication approach # optimal segment size # flexibility

10 acrm Business Case Analysis Cost Effectiveness Cumulated NPV Cost Income Does the use of warehousing technologies for VISECA make business sense? NPV Cost Income 8'000 6'000 Kumulierte Cumulated NPV Kosten Cost Income Ertrag 4'000 2' '0002'000-2'001 2'002 2'003 2'004-4'000-6'000 Reporting period MFP An analysis of the business economics aspects showed that from the net present value point of view, warehousing activities can produce positive results three to four years after they are introduced, with a very high long-term potential. high investments also have to be expected in the near term (for building the database and analysis system), which requires appropriate project management in order to ensure long-term success. in order to cover the very high initial costs for building the warehouse infrastructure and the time required to develop expertise in using datamining methods as well as to integrate them into the business processes it is necessary to tackle several concrete projects at the same time.

11 Summary Integrated marketing communication supported by a clear strategic basic philosophy Business intelligence based on knowledge and action " intelligent organisations use 'intelligent' systems Knowledge base enables effective, 'intelligent' CRM: Data " Information " Knowledge" Action" Measurement" Learning Proof of concepts enhance likelihood of success " rapid learning cycles Involvement of strong partners (IT, analysis, marketing) Challenge: bring intelligence to the sales front and the customer!

12 Conclusion The problem with intelligence is that you have to keep on learning new things. George Bernard Shaw