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Customer RelationshipManagement (CRM)stream
Dublin, 21 june 2000
Customer RelationshipManagement (CRM)stream
Dublin, 21 june 2000
Deutsche Bank:On-line banking for profit
Resi CuypersPricewaterhouseCoopers
Deutsche Bank:On-line banking for profit
Resi CuypersPricewaterhouseCoopers
Who is Resi Cuypers?
! Since March 1998:"Data Mining expert at Analytical Intelligence Services (AIS)"AIS, experts quantitative methods and techniques inAmsterdam, NL"Responsible development I-CRM (Apply Data Mining)
! Prior:"SAS Consultant ELC"Statistical Process Engineer at ‘Heidelberger Zement’ concern
! Master degree Mathematics
Agenda
! Deutsche Bank’s business drivers forces the need for CRM
! On-line Banking Segmentation Project Objectives and Approach
! Process and Methodology Segmentation and Modelling
! Process and Methodology Customer proposition
! Key Results and Experiences
On-line Banking Service offering
! Successfully developed range of On-line Banking services(1998).On-line banking:= the delivery of banking and financial servicesthrough electronic devices such as PC’s, Mobile Phone andInteractive TV.
! Services used by > 300,000 Retail and Private customers(at beginning 1999).
! Services based on existing customer segmentation model:classification four groups based on a combination of transactionvolume, income and net wealth.
Deutsche Bank’s business drivers forcing the need for CRM
! Question: Is existing customer segmentation modelsophisticated enough for On-line Banking?
Headquarters
Telephone Infrastructure
WebInfrastructure
Retailers
Database and Data Mining
Overnight Delivery
Manufacturer
Distributors
Transportation
Direct Marketing
Suppliersand Vendors
StrategicPartners
CustomersInfomediary andOutsourced Service Products
But, Profitable Growth is More Difficult to Achieve as Businesses Face Significant Change
! New entrants(competitorsfrom 8 to 180)
! New channels
! Newtechnologies
! New markets
! Disintermediation
! Globalisation
! Deregulation
! Consolidation
! Convergence
! Market saturation
! Eroding customerloyalty
! E-mania
Deutsche Bank’s business drivers forcing the need for CRM
Deutsche Bank’s business drivers forcing the need for CRM
In the Face of these Changes, Deutsche Bank realised the Importance of Customer Centricity
! Gained competitive advantage in key area Customers & Product:
# Proactively manage customer information and relationships tomaximise lifetime customer value (incl. Recruiting)
# Differentiated management of various customer segments
# Using strength On-line Banking
CustomersProactive management of
customer information & relationships
ChannelsDevelopment & integrationof multiple sales & service
channels
CustomersProactive management of
customer information & relationships
Products/ServicesManagement of innovation
& time-to-market
CustomerRelationshipManagement
Knowing On-line Banking Customers, Market, Penetration, Competitors and taking Action
1 Development robust (static) segmentation model within:
# The German retail and private banking market# Deutsche Bank’s existing On-line Banking customer Base(map)# Deutsche Bank’s current Retail and Private Client customer base
2 Design of On-line Banking consumer propositions for the definedtarget customer segments
3 Development of high level marketing and communication plans insupport of defined consumer propositions
On-line Banking Segmentation Project Objectives and Approach
⇓⇓⇓⇓ Ultimate objective: dynamic segmentation
Project organisation PwC: November 1998 - February 1999
On-line Banking Segmentation Project Objectives and Approach
ProjectManager
Segmentationand Modeling
CustomerProposition
DynamicSegmentation
Model
Team1 Team3Team2
! Market andCustomerSegmentationexpert
! Statistician/dataminer
! Databasebuilder/modeler
! Internet Bankingmarketingexpert
! Market analyst(full time)
! MarketResearchanalyst
! 1 PwC technicalarchitect
! 1 PwC technicalanalyst
! 2 PwC databasebuilder/modellers
Example: Route map Segmentation Development Existing On- line Banking Customer Base
Start-up,Mobilisation
andRequirementsConfirmation
and
Interviews
Systems andDatabaseAnalysis
DataEvaluation
Validateexisting
Segments
BuildData
Sample
ApplyProfitability
Model2Data
MergeData
Enhancement
MarketResearch
SegmentTest
Map Segments toDBank Database
Confirm BridgingVariables for
Mapping againstexternal Market
Requires flags onDBank Database
Input of initialConsumer
Propositions
Iterative
Map against Data Models and External Data
Design and buildDynamic Segments
DesignStatic
Segments
Analysis andDesign Build and Test Implement
On-line Banking Segmentation Project Objectives and Approach
Interviews with Deutsche Bank resulted in initialising and prioritising high-level customer focus groups...
GermanResidentBanking
populationGerman ISPSubscribers
Deutsche BankCustomer Base
German InternetBanking Customers
!7: Non-Deutsche Bankcustomers in Germany withInternet banking and ISP.!8: Non-Deutsche Bankcustomers in Germany withInternet banking.!10: Non-Deutsche Bankcustomers in Germany withISP.!11: Non-Deutsche Bankcustomers in Germany.
1
2
3
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5 7
8
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10
11
Process and Methodology Segmentation and Modelling
! How easy is it for the individuals to be identified?i.e. is data available (internally or externally), would enable on-going identification of people within each segment?
! How attractive are the individual segments toDeutsche Bank?Segment of strategic importance i.e. is a specific segment likelyto grow or is it likely to contain people who aredisproportionately profitable?
! Can we identify cost effective methods ofcommunicating with the segments?i.e. through either highly targeted or broadcast media.
according to set criteria
Process and Methodology Segmentation and Modelling
Technical Environment was established for data analysis and modelling (data required not all available in DwH)
Deutsche BankLAN
Data from MainframeData Warehouse
HardwareA Client Server environment utilising existingnetwork infrastructure (40 GB), linking:! Sun Solaris 2.4 server, with
Sun OS UNIX 4.2 operating system,! Client PC, operating Microsoft NT4.
SoftwareUsed for analysis and file manipulation:! Server
SAS System license DB v6.12Enterprise Miner ™ v2.01
! ClientSAS System license DB v6.12Enterprise Miner ™ v2.01Labtam X-WinPro for X-Windows emulation and ftp
Project Client
UNIX Server
Process and Methodology Segmentation and Modelling
Our methodology comprised a number of sequential tasks
! Data Identification, Collection and Collation - INTERNAL: DwH vast and complex, knowledge very vast apart; usedDatabase Marketing data till Nov 1998 (8.21mio unique clientnumber), logfiles on-line Banking service (June-Nov 1998), Tel. banking service (Oct-Nov1998), file flag interest (June 1998)EXTERNAL: Market ‘Lifestyle’ database last 2 years, Stat. Repr. Adults, ownsegmentation system (survey quarterly: 1.1 Mio individual records, 900 var.)
! Data Preparation, Enhancement and Analysis - Eliminate poorlypopulated variables, Remove variables unlikely to influence analysis (e.g.:favourite Cigarette brand), Enhance by combining variables into scores(e.g.: score on PC Ownership sophistication - equipment and peripherals).Using Crosstabs and Descriptive Statistics.
! Data Modelling - iterative process for discriminating personnelcharacteristics; using clustering (least squares), decision trees
! Model Validation - checking with data not used in modelling phase(exclude data problems)
! Constraints - European and German Data Protection Legislation
Process and Methodology Segmentation and Modelling
Preparation process involved the merger and deduplicationof many files, prepare and enhance (Also external)
Deutsche Bank CustomerData Warehouse 18 Regional Files - All Stammnummer
(274 data items per Stammnummer)
Batch extractionprocess
On-line BankingLog files
Stratified Sample
All ‘On-line Banking’
Active ‘On-line’Banking 171,597
records
267,659records
405,839records
Process and Methodology Segmentation and Modelling
Merge and split Active and Inactive
On-line BankingUsers
Variable Reduction- exclude variables
not required foranalyses
(Approx. 70 variables eliminated)
171,597 records
96,062 records
StratifiedSample
All ‘On-lineBanking’
Active ‘On-line’Banking (Log)
405,839 records
Raw Data -Manipulation and
elimination programme External
RawMarketData
ExternalAnalysis
File
DroppedSurvey data
Variable Reduction900 to 340
Internal
Preparation Final External Market Data Files
External Market Base File
On-line Bankingnon-users
Non- PC Owners
Not PotentialPC Owners
‘Internet not importantin future’
On-line Bankingusers
PC Owners
Potential PCOwners
‘Internet importantin future’
Satisfied andDissatisfied users
Process and Methodology Segmentation and Modelling
The modelling activity was aligned with the original high-level customer groups finished for analysing
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2
3
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10
11
On-line Bankingusers
‘Internet importantin future’
PC Owners
Potential PCOwners
Technologically lessadvanced
Unlikely Remainder
Within each analysissegment it waspossible to split the‘populations’ into:
! Those whoconsideredDeutsche Bank tobe theirmain Bank.
! Those whoconsideredanother bank to betheir main bank.Primary Targets
Secondary Targets
Process and Methodology Segmentation and Modelling
The Output from our Process and Methodology...
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Product1
2
12
3
46
5 7
89
10
11
Personalised Internet Banking, incl:personalised web siteson-line advicepersonalised product offers and pricing
Broking, incl:preselected portfolio optionsstock warning systemanalysis and scenario systemslinks to other stock exchanges
Potentialsubject areasof proposition
Group 1: Online heavy users
! 20-45 years old! 6 men & 3 women! 50% high earners
Recruitment criteria
Face to facefocus groups
Targetmarket andcompetitor
analysis Developnew
propositionideas Assess
Internalscoring &
prioritisation Research
customerevaluationand ideas Evaluate
analysis and
refinement
1
2
3
4
5
Brainstorms,
workshops,
revised product
frameworks
Customer research
Score matrix
Narrower
customer group
characteristics
Macro market
Review and development
"Worked up
propositions
(Not repeated)
Process and Methodology Customer proposition
In detail...
! Estimating market opportunity and Dynamics (possible growth)
! Key Characteristics within each group were identified (resultsegmentation analysis).
! Activities, penetration Deutsche Bank’s main competitorsreviewed (result segmentation analysis).
! Created a wide range of initial propositions for existing Internetusers, starters, retention, migrate T-Online and AOL customersto the Internet - (1) based on our expertise and market knowledge (fromdocumentation, interview panels etc.) (2) based on insights of InfratestBurke (3) The categories were based upon the need to, and appetite for,Deutsche Bank to enter into partnerships with other organisations.
! Create a shortlist proposition - 46 propositions were scored throughteam Bank and score matrix decreased to 5. No conjoint analysis used.
! Qualitative Market research and Test - feedback of (pot.) custo-mers and test against published data (ICON) and Infratest Burke
Process and Methodology Customer proposition
Key Results and Experiences
We have estimated the market opportunity for On-lineBanking based on our initial customer groups...
1
2*
3*
46
5* 7**8**
9**
10
11
Red circle - 6.9m Internet users inGermany (14-59 years old).Growth 80%Growth possible?
Green circle - 2.4m on-line banking users inthe German Market. 3,5% total population[growth 49%]
Blue circle - 69m people inGermany aged over 15 years(46.4m people aged 14-59 yearsold).[growth 0,2%]
Yellow circle - 6.6mDeutsche Bank Retail andPrivate customers, of whichapproximately 67% are agedbetween 14-59 years old.9,5% total population.[growth past 3 years 2%]
* customer groups 2, 3and 5 (existing DeutscheBank on-line bankingcustomers) add up to310k retail and privatecustomers.0,4%total, 12,9% on-line bankers.[growth 42%]
** customer groups 7, 8 and 9 addup to approximately 2.1m people.
Approximately 15% of the Germanpopulation (aged 14-59 years) areinternet users. Assuming that theDeutsche Bank retail and private clientcustomer base is statistically representativeof the German population, then customergroups 5 and 6 include 990k people (agedbetween 14-59 years old).
¹ It should be noted that data relating to the German on-line banking market variesconsiderably depending upon the source.
!The respective size of key customer groups have been estimatedon the basis of available market data¹.
German ResidentBanking Population.Existing German Internet Users
Deutsche BankCustomer Base
Existing German On-lineBanking Customers
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5 78
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11
Deutsche Bank customers:1: ‘Satisfied Early Users’
2: ‘Dissatisfied Adopters’
3: ‘Potential Experimenters’
4: ‘Following the TechnologyTrend’
5: ‘Passing Interest’
Non-Deutsche Bank customers:1: ‘Satisfied Early Users’
2: ‘Dissatisfied Adopters’
3: ‘Potential Experimenters’
4: ‘Following the TechnologyTrend’
5: ‘Passing Interest’
Segmentation analysis: Six main target customer groupswere identified and ten established
Key Results and Experiences
The key characteristics of the customers within eachgroup were identified by segmentation analysis (Twoexamples)
1
2*3*
46
5* 78
9
10
11
Existing Internet Users (not DB customers)
! 31% of households with PCs are on the Internet.
! 33% of mobile phone users are on the Internet
! 44% of households with laptops at home have Internet access
! Almost 32% of Internet users are between 20 and 29 years old - withthe strongest growth rate
! More than 50% have a net household income over 5,000DM permonth
! Only 8% use an OSP for on-line services only (down from 13%)
! The slowest growth sector is those who use it for professionalreasons with ‘News’ surfers the fastest growing. Typically the ‘News’surfers are using it mainly externally (only 27% using it only at home).
! Students and Abiturienten are the fastest growing group.
! Access from work or university is increasing faster than access athome.
! Up to 53% of business heads and directors are on the Internet,suggesting a potentially high hit rate for targeted marketing.
! Self employed and freelance comprise 13% of the Internet users - anincreasing percentage.
! 79% of banking customers with Internet access could imagine usingthe Internet for part of their banking in future.
! There is strong dissatisfaction amongst Internet users who bank withother banks - over 50% of customers of the Sparkasse, Postbank andRaiffeisenbank are unhappy with their bank’s Internet offer and 20%would start a new banking relationship and use the Internet.
! 71% of T-online users use the Internet
! 47% of Internet users who also do stock and share transactionswould use the Internet to do their banking
! 53% of Internet users who also do options transactions transactionswould use the Internet to do their banking
Existing DB customers either using theInternet or Online
! Deutsche Bank already has a relationshipwith 13% of the total Internet users - but only6.8% view Deutsche Bank as their mainbank
! Internet access varied by depth ofrelationship with Deutsche Bank -24% ofthose with any relationship, 32% of thosewho regard Deutsche Bank as their mainbank, 49% of current account holders and50% of those who do wertpapiertrasnactions. Readiness to do all theirbanking by Internet does not appear to showany significant differences by productholding.
Source: PwC analysis; Claritas;GfK
Key Results and Experiences
Competitors and Proposition
! Competitors:
" Target customers of the direct banks are clearly have a muchhigher potential for On-line Banking, both in terms of PCownership at home and willingness to bank only via theInternet.
" The direct banks show a higher penetration (than Universalbanks) of key customers who will be buying new (or replacingold) PCs - potentially a driver of On-line Banking access.
! Proposition:
" Merger another bank started to have influence in the generalselection process.
" Young, well-educated women were not being targeted, in somecases neglected with respect to their banking needs andrepresented a good recruitment opportunity. It will be the nextwave of Internet users.
Key Results and Experiences
Example: One of the strongest propositions which wouldappeal to all potential customer groups was ‘1:1 banker’...
Basic proposition and features:
! Three stages of personalisation:! Personalised first page of the Internet Banking application! A personalised proposition which gives the customer tailored
products, screen, tips and advice based on their micro market,community of interest or portfolio held
! A personal one to on relationship with their adviser, through arange of e-mail and document sharing options, ultimatelyresulting in click through to on-line adviser in a ‘chat’ style basis
Value creation drivers:
! Higher share of customer wallet! Better customer information knowledge through information
provided for personalisation and self selected community ofinterest
! Better risk profile building! Customer retention in lower age group due to more
‘personalised’ image and approach from DB! Efficiencies through integrated workflow
Target market& research results:
! Young 20-35 years olds seeking more advice and support in financial needs! New markets away from traditional branches! Customers with a frequent interaction with their advisors and complex affairs! Time starved individuals; Available for all Internet customers! Research indicated high acceptance of all three stages above - as long as
proposition marketing could be selected by customer and that no ‘quasi advice’was offered on a computerised basis based on generic customer groupings
Channel integration or fit:
Internet Offline WWW
Internet WWW
Online Telefon Selbstbedienung Filiale Persönliche Beratung
! Three phases require different degree of integration with other channels. Personalised screens require little integration. Phases 2 and 3 requirefull integration with all marketing activities.
! Personal adviser fully integrated into the process of profiling for phase 2 and the e-mail options for phase 3.
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Kauf/Verkauf NOW shortcut
Kurse vom Lieblingsakte
Lieblings indices & devisen
Lieblings ‘Chart’, Veränderungen
Broking, zB:
Markt-
information, zB:‘Ticker mitKunden-ausgewälteNachrichtsthemen zB’:
Politik
Wirtschaft
Finanz
Sport
Unterhaltung
IT
1:1 Banking, zB:
‘Alert’ Nachricht vom Berater
Hotlink für E-mails zum Berater
Hotlink zum Newsletter
Personaliserte Produkt Infos
Wertpapier ‘Warnsystem’
Wertändering des Portfolios
Bestätigungen WP Geschäft
Broking Extras, zB:
Fällige Sparanlage (6m, 3m, 1m…)
Einzelne Konto oder Kreditkarte:
Kotostand
Letzte 3 transactionen
Zukunftiger Kontostand
Cashflow
Neues/Angebot des Tages….
Shortcut/alertzumImmobilienseitenSpar Alert, zB:
brick style building blocks:
Nich Finanzielle Anlagen, zB:Einzelne Edelmetal oder Diamanten Preise
Kunst und Wein Auktionen Infos
%$$ $%
Key Results and Experiences
Key messages from the qualitative research were...
!Upgrading of existing service is vital# Speed, simplicity, faster navigation and basic functionality# Wider broking functionality# Telephone banking with a human# Better pricing
!Advice on and conduct more complex transactions
!Personalisation# A facility to tailor the website screens# ‘Quasi advice’ on an automated basis would not be trusted.
!New Internet users want information and communication etc.
Source: Infratest Burke
Key Results and Experiences
Key Results and Experiences
Experience
! Data Collection and Preparation costs 90% of time.
! Comment Internal Data - AGING: (1) Majority Customer details anddemographics was collected when account were opened (2) Much datanot relevant for purpose.
! Comment External Data - AGING: (1) For this subject outdated, i.e.Most records from 1997, Internet usage related questions were notcollected in the Q1/Q2 surveys in 1998 (2) Enhancement needed forgenerating Key measurement.
! Benefit Enterprise Miner - (1) for the database marketingdepartment of Deutsche Bank lies in the extensive potential for trying outdifferent models in the shortest possible time (2) and to do this atsubstantially lower programming expense than before.
Key Results and Experiences
What is finally used from this Pilot Project?
! Some results from the static segmentation - definitions like‘satisfied early users’ etc.
! Update customer data more frequently
! Target customers changed
! Personalisation website
! No implementation segmentation model, because of merger
! Possibility Enterprise Miner
Contact Information
Resi Cuypers or Harmen Ettemamob. +31 (0)6 22 46 81 80 mob. +31 (0)6 53 56 35 50email: resi.cuypers@ email: harmen.ettema@
nl.pwcglobal.com nl.pwcglobal.com
PricewaterhouseCoopers Strawinskylaan 3127 P.O. Box 7067 1007 JB Amsterdam The Netherlands fax. +31 (0)20 568 41 54
Key Results and Experiences