Modèle de présentation EDF Orange - sascommunity.org fileSAP data into gold with SAS [email protected]. May 2006 ERP et Analytical CRM 2 Electricité de France ... •Market

ERP et Analytical CRMMay 2006 1

How EDF transforms SAP data into gold with SAS

[email protected]


Electricité de FranceBusiness and Analytical CRM


EDF

Historically (until 1946)• The French public utilities responsible for

• Electricity Generation / Transportation / Distribution• Gas Importation / Transportation / Distribution

• 27 Millions “users” (domestic, small business, enterprises….)

Currently• Utilities on a competing market of energy

• Market deregulation• Company privatization

• 27 Millions French “customers”• 15 Millions “customers” abroad (mainly in Europe and Asia)

• 110 000 employees• 51,1 billions Euros of income in 2005


Customer Branch

– Role• To sell electricity, gas and associated services

– Activity• Marketing• Selling• Controlling• Information Systems (functional aspects)

– Organization• Strategic Marketing• Tier 1 customers

• High level market• Mass market customers

• Small & Medium Business (SMB) market • Household market


Analytical CRM Division

– Context

• Opening of market to competition– An entity in charge of

• Customer knowledge development• Identifying where relevant data can be found• Cleansing existing data and integrating external sources• Scores (fidelity, offers appetence, customer lifetime value, …)

loaded in CRM tools• Hypercubes (OLAP) via intranet

• Contribution to the design and IS evolutions • In order to store and deliver the relevant data• To insure that the IS tools will meet new marketing needs


Contribution of the Analytical CRM entity

– Productions

• Support to • Strategic marketing• Sale process • Controlling / finance

• By delivering• Scores , targeting• Dynamic dashboards via Intranet• Formatted lists, detailed data


A new Information System for Small & Medium Business segment

– July 2004

• The opening of the market for the SMB market segment involved a new commercial organization

• New offers• New contracts• New approach of Customer Relationship Management

• To win this challenge• A new Information System was developed• Human means were reorganized

» New selling processes» Resource affectation re-examined» Training periods


Transactional situation but KPI continuity


Legacy and new marketing strategies

– Issues

• Legacy customer management • Historical offers• Monopolistic market• Regional autonomy

• New customer management• New offers• Market open to competition• A real will to manage customer relationship

with a national coordination


Old fashioned and new approach coexistence

– Legacy customers

• Keep historical offers– New customers

• The customer can choose between historical and new offers – Mixed customer

• A legacy customer can be interested by new offers because of new needs

• Progressive migration is organized• Identification of priority segment for proposition

of migration to new offers• Launch of marketing campaigns on migration


Information analysis complexity

– Customers are spread into 2 Information Systems

• Different customer references• Different marketing approaches

– Complexity

• Metadata mapping• Management rule mapping• KPI homogeneity need


Customer data management complexity

– Customer knowledge issues

• Reconstitution of customer history • Design of new indicators for

• legacy customers• new customers• migrating customers

» IS migration customer reference mismatch» Offer changes contract modification» Moving customer new contract» Marketing segment change IS migration


KPI complexity (1/2)

– For legacy customer market• No change

– For New customer market• New KPI designed• Gap introduced by new CRM processes and new IS data model

– For legacy customer buying a new offer• Customer inserted in the new IS• Risk to count the same customer twice

• Link to build for the same customer in legacy system• Coherence issues between legacy data and new data

for the same customer (units, …)– For the whole market

• All issues above together

NOOH


KPI complexity (2/2)

– KPI definition will be different depending on 3 factors• Internal user profiles

• According to the function» Marketing» Sales» Controlling/Finance

• According to geographical localization• Customer geographical repartition

• Country, Region, City, …• Analysis Time scale

• Year• Quarter• Month• Week• Day


KPI construction

– Re-design all indicators taking into account the complexity and insure global coherence

• Renaming and formalization of management rules• Prototyping and validation• Iteration until complete validation

– Graduatly for

• Each KPI Separately• Sets of KPI for one function• All KPI together


IS infrastructure : need for reliability and performances

SAS / SAP / BULL


New Information System (1/2) Operational technical constraints

– The IS must cover the following technical constraints• Transactional and decisional • Very large amount of customer data

• today : 200 000 SMB customers, ~2 millions bills• Short term : 2 millions SMB customers• Finally : ~27 millions customers (household + SMB)

• Robustness and productivity• Scalability

– The IS must satisfy the different lines of business• Computing • Marketing and selling• Management• Analytical expertise


New Information System (2/2) Analytical Technical constraints

– Rely on SAP technical architecture

• SAP/ISU et SAP/SD for sales administration• SAP/CRM for operational CRM• BW for reporting

– Must answer to statistical analysis needs

• Compatibility with the SAS data mining tools• Access to all the detailed data• No disruption of the transactional activity


SAP connection issues

– Summary of the needs

• Ability to access to any data from SAP• SAP data dictionary • Transactional data coming from the front-office

• Feasibility to extract SAP data easily• use of filters and joins • other data processing

• Necessity for the EDF functional team to build expertise on SAS/SAP solution in order to be able to extract the necessary data

• Good performance of the solution and scalability


Architecture for data mining environment

– Architecture validated for today

• Server • Bull Escala• under AIX

• Data Warehouse • Oracle

• Statistical solution • SAS® Data Surveyor (static data and RFC)• SAS Statistical modules for data mining and CRM support

• Reporting• Oracle Express for hypercubes


Customer Growth : Infrastructure scalability (1/2)

– The transfer from the legacy IS to the new one is in progress

– Evolution to manage 2 million customers

– Benchmark carried out in Bull Benchmark Center

• To guaranty the feasibility and the performance of the targeted infrastructure

• Hardware dimensioning (processors, memory, disk space, …)

• Software tuning (SAS 9 optimization, options, …)

• With multi-disciplinary competencies of experts and consultants of EDF, Bull (SAS/SAP) and SAS

• On real size data (simulation)


Customer Growth : Infrastructure scalability (2/2)

– Meaningful results

• Extraction optimization• Configuration : 4 CPU and 16 Gb memory• SAP CRM et IS-U data : 500 Gb• Parallelization and balancing of extraction processes• Oracle and SAS parameters optimization• Performances increased of 30% (from 6 h to 4 h 1/4)

• Post-extraction treatment optimization• Volume : 100 Gb (2 tables of 175 M lines) • Parallelization of joins and aggregations• Performances increased of 60 % (from 1 h 1/2 to 1/2 h)


Conclusion

- KPI elaboration in a moving context- A global and complex analysis environment

is necessary to answer to the needs- ERP / SAS connection

- A benchmark in real size is a good investment– Key success (f)actors to transform SAP data into gold with SAS

• Collaboration between Functional and Information System experts

• SAS, SAP, Oracle, AIX, Analytical CRM• Synergy between EDF and its partners

• BULL,SAS all together

Documents

Modèle de présentation EDF Orange - sascommunity.org fileSAP data into gold with SAS [email protected]. May 2006 ERP et Analytical CRM 2 Electricité de France ... •Market