linked objectives: CRM and e-crm as part of the information network

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1 linked objectives: CRM and e-crm as part of the information network SeUGI 19 Florence May 29 June One of the SAS Professional Services Papers of Interest See your conference guide for more information

2 Landesbank Baden-Württemberg (e-)crm approach Customer Loop 2

3 Landesbank Baden-Württemberg 3

4 o r i g i n Landes girokasse Südwestdeutsche Landesbank Marktteil L-Bank Landesbank Baden-Württemberg 4

5 Centers to represent the local trade abroad Centers in: Amsterdam, Barcelona, Beijing, Budapest, Hanoi, Hong Kong, London, Luxemburg, Mailand, Mumbai, New York, Paris, Prag, São Paulo, Shanghai, Singapur, Warschau, Wien, Zürich Branch offices Centers 5

6 Landesbank Baden-Württemberg balance sheet total 290 Billion Euro over 9900 employees over 240 branches 6

7 What was our motivation to commence the CRM / e-crm-initiative? 7

8 Starting point Competitive pressure in banking industry is increasing worldwide Vital factors to stay competitive are: Customer proximity Flexibility and quick response (time to market) 8

9 The clock is ticking the customer life cycle: c u s t o m e r l o y a l t y customer retention wavering customer complaint management cross-selling selling customer segmentation winning back Non customer customer canvassing new customer prospective customer attracting new customer w i n n i n g n e w c u s t o m e r 9

10 The strategic objectives are imperative! effect importance achieve cutting edge over competitors easier to qualify and utilize customer potential improve competitiveness increase efficiency cost reduction Support function long term securing of profit stabilize market position competitive edge time 10

11 Marketing product-driven driven approach # of satisfied customer needs?? stocks Investment funds savings book Credit facilities life insurance credit card # of customers 11

12 One-to-One Marketing Customer-driven approach # of satisfied customer needs stocks Investment funds savings book Credit facilities life insurance credit card # of customers 12

13 How did we implement our CRM-strategy? 13

14 Customer Loop ACD Voice Mail TK- Anlagen CTI- Server CRM- Server Response A B Metadaten zentrale Front- Office DB C D Distribution channel Data Warehouse Event Customer- DB Data Zielformulierung Zielformulierung Ziel- Operationalisierung Szenario Performancefaktoren/ Leistungstreiber Data Mining Daten- Audit Repräsentativer Datenausschnitt Datenhandling Konzept Briefing Daten Qualitäts- Vertriebs- Lettershop Response- Kampagnen- - sicherung info / Versand aufnahme controlling selekt on Modellspezifische Datenanpassung Neuronale Netze CHAID Regression Methodenwahl Flatfile- Erstellung Datentransformation Data-Mining-ModellModell Aggregationen Entwicklung Simulation ökonomische Validierung Parallel Entwurf Agentur Abstimmung Produktion Auftraggeber Campaign-Mgt Mgt. Prozeßintegration Motivation Lernkurven-Effekte Implementierung & Roll-Out Feedbac k & Controll ing Data Mining / Reporting 14

15 Enterprise Warehouse -architecture Process management EWH- Administrator Line of business Metadata Query Reports Controlling Analysis Customer DB Riskmgt. credit Compliance Directbanking Compliance Marketing Camp.- Mgt. Events 1 - n OLAP Data Mining accounts Operational Data Marketing Controlling Enterprise WH Riskmanag. Data Marts Directbanking Mailing Decision MVS Infrastructure UNIX Server Fat-Client FD 3/99 15

16 How the quality rule system works Line of business Qualitylayer Customer DB rule engine Riskmgt. credit Compliance Directbanking accounts Operational Data protocol DB Quality Manager Marketing Controlling Enterprise WH error list Test results 16

17 Customer Loop ACD Voice Mail TK- Anlagen CTI- Server CRM- Server Response A B Metadaten zentrale Front- Office DB C D Distribution channel Data Warehouse Event Customer- DB Data Zielformulierung Zielformulierung Ziel- Operationalisierung Szenario Performancefaktoren/ Leistungstreiber Data Mining Daten- Audit Repräsentativer Datenausschnitt Datenhandling Konzept Briefing Daten Qualitäts- Vertriebs- Lettershop Response- Kampagnen- - sicherung info / Versand aufnahme controlling selekt on Modellspezifische Datenanpassung Neuronale Netze CHAID Regression Methodenwahl Flatfile- Erstellung Datentransformation Data-Mining-ModellModell Aggregationen Entwicklung Simulation ökonomische Validierung Parallel Entwurf Agentur Abstimmung Produktion Auftraggeber Campaign-Mgt Mgt. Prozeßintegration Motivation Lernkurven-Effekte Implementierung & Roll-Out Feedbac k & Controll ing Data Mining / Reporting 17

18 The Web-Mining-Loop Offline Online Enterprise Data Warehouse Click Stream Data Mart WWW WWW Individualized Web Mining Trackingdata / Web Logs Application Server Mining Modell rules Rule export Rule Engine 18

19 Customer Loop ACD Voice Mail TK- Anlagen CTI- Server CRM- Server Response A B Metadaten zentrale Front- Office DB C D Distribution channel Data Warehouse Event Customer- DB Data Zielformulierung Zielformulierung Ziel- Operationalisierung Szenario Performancefaktoren/ Leistungstreiber Data Mining Daten- Audit Repräsentativer Datenausschnitt Datenhandling Konzept Briefing Daten Qualitäts- Vertriebs- Lettershop Response- Kampagnen- - sicherung info / Versand aufnahme controlling selekt on Modellspezifische Datenanpassung Neuronale Netze CHAID Regression Methodenwahl Flatfile- Erstellung Datentransformation Data-Mining-ModellModell Aggregationen Entwicklung Simulation ökonomische Validierung Parallel Entwurf Agentur Abstimmung Produktion Auftraggeber Campaign-Mgt Mgt. Prozeßintegration Motivation Lernkurven-Effekte Implementierung & Roll-Out Feedbac k & Controll ing Data Mining / Reporting 19

20 the right product, at the right time for the right customer Channel-distribution at customers discretion R u l e s Web Banking campaign Call Center Knowledge Event management Mobile system Selfservice Branch workstation 20

21 Customer Loop ACD Voice Mail TK- Anlagen CTI- Server CRM- Server Response A B Metadaten zentrale Front- Office DB C D Distribution channel Data Warehouse Event Customer- DB Data Zielformulierung Zielformulierung Ziel- Operationalisierung Szenario Performancefaktoren/ Leistungstreiber Data Mining Daten- Audit Repräsentativer Datenausschnitt Datenhandling Konzept Briefing Daten Qualitäts- Vertriebs- Lettershop Response- Kampagnen- - sicherung info / Versand aufnahme controlling selekt on Modellspezifische Datenanpassung Neuronale Netze CHAID Regression Methodenwahl Flatfile- Erstellung Datentransformation Data-Mining-ModellModell Aggregationen Entwicklung Simulation ökonomische Validierung Parallel Entwurf Agentur Abstimmung Produktion Auftraggeber Campaign-Mgt Mgt. Prozeßintegration Motivation Lernkurven-Effekte Implementierung & Roll-Out Feedbac k & Controll ing Data Mining / Reporting 21

22 Multi tier architecture Presentation layer (Customer Interface) user interface for branch consultant User interface for cc-agent user interface for online channels Function layer (customer process) Advisory Initiative service Offer Contract Input / Output customer information scheduling of activities contact management Data layer (customer management) Datamining Campaign-Mgt. Quality management Individualization Enterprise Data Warehouse METADATEN Operative Systeme (OAS) 22

23 CRM has to be connected to other information sources to leverage its strategic importance 23

24 It s not just CRM Personnel Controlling Competitor Competitor Marketing Competitor Procurement Supplier Supplier Supplier 24

25 Integration if information is a critical success factor The future is and automatized integration of comprehensive information. intercompany business relations require integration of external events Synchronization of IT-Systems is imperative to implement value chains between or within companies 25

26 The future of Networking IEII Inter Enterprise Integration completeness EII Enterprise Integration connecting information within companies IEII Data Enterprise Trust W a r e h o u s e Inter Enterprise Integration connecting information wordwide and between companies EAI Enterprise Application Integration connecting applications within companies Multi tier architecture IEAI (e-) procurement Inter Enterprise Application Integration connecting applications between companies scope 26

27 Providing information ( push not only pull ) c r e a t e an engine build a warehouse with verified data generate transform information into rules automatize the usage of the rules on a rule system p u s h the information to the point of sale (deliver information) l e a r n from the response 27

28 Who benefits from these systems? 28

29 s of Networking a From the customer s point of view: customized configuration of the communication platform (Internet-Banking, desktop, etc.) Besides access to the whole range of information offered, customized information will be delivered suited to his/her needs. The customer perceives that he/she gets personal advice 29

30 s of Networking a From the bank s point of view: C u s t o m e r l o y a l t y, i. e. to reduce the customer attrition due to individualization and significantly improved service levels The individualized selection of products increases profit (efficiency) and reduces costs Quick response to market and to customer needs (time to market) One of the SAS Professional Services Papers of Interest See your conference guide for more information 30

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