Segmentation and CrossSegmentation and Cross--SellingSellingProjectProject

AgendaAgenda

!! Corporate OverviewCorporate Overview!! Project ObjectivesProject Objectives!! MethodologyMethodology!! SegmentationSegmentation!! CrossCross--SellingSelling!! Conclusions & Next Steps Conclusions & Next Steps

Financial Highlights * Size (US$ mn) Rank

Total Assets 8,977 3Deposits 6,313 3Loans, net 3,693 1 Shareholders’ Equity 1,037 2Income from banking services 256 1

* As of December 31, 2001

Corporate OverviewCorporate Overview

!! Established in 1944, first and leading private Established in 1944, first and leading private bank in Turkeybank in Turkey

With over 7 million clients,Size* Market share*

#1 in number of credit cards 3.4 mn 19%

#1 in credit cards business volume $3.2 bn 26%

#1 in number of POS terminals 73,373 20%

#1 in POS business volume $2.1 bn 20%

#2 in number of ATMs 1,337 11%

* As of December 31, 2001

Market shares: leader and pioneer in retail banking

Corporate OverviewCorporate Overview

HEAD OFFICE YAPI KREDİYAPI KREDİ

POSTA

FEEDBACK

CRM at YKBCRM at YKB

CUSTOMER

YAPI KREDİYAPI KREDİ

POSTA

YKB Data

Warehouse

Infocenter

Campaign Management

Software

LegacySystems

Reporting / OLAP

Software

Customer Responses

Channel Integration

Channel Integration

Customer Responses

CustomerList

Analysis of customer

data

CorporateDatamart

RetailDatamart

Credit CardDatamart

CIF

DEP

KK

MN

ÖD

Data MiningDatamart

CRM at YKBCRM at YKB

Data Mining SW

MailTelefonBranch ATM Internet

EVPEVPRetail BankingRetail Banking

Individual MarketingIndividual Marketing

•• Customer Groups Customer Groups ManagementManagement

•• Campaign Design Campaign Design and Coordinationand Coordination

•• Market Research and Market Research and Data AnalysisData Analysis

CRM GROUPCRM GROUP Credit CardsCredit CardsMarketingMarketing

Data mining Data mining competencycompetency

CRM at YKBCRM at YKB

Project ScopeProject Scope

!! SegmentationSegmentation

!! CrossCross -- sellingselling!! Mutual Funds Mutual Funds

Project ObjectivesProject Objectives

!! Have a complete CRM processHave a complete CRM process!! Provide a clear understanding of the customer baseProvide a clear understanding of the customer base!! Identify homogeneous segments Identify homogeneous segments !! Keep valuable customers in handKeep valuable customers in hand!! Increase revenues with crossIncrease revenues with cross--sellingselling!! Increase operational efficiencyIncrease operational efficiency!! Provide data mining knowledge transferProvide data mining knowledge transfer

Make Data Available PhaseMake Data Available Phase

!! Data was extracted from more than 50 Data was extracted from more than 50 source system tablessource system tables

!! 20 tables were produced with 30 20 tables were produced with 30 GBsGBs of of disk space for the initial stagedisk space for the initial stage

SegmentationSegmentation

ObjectivesObjectives

!! Understand and Understand and analyseanalyse customer base better and customer base better and organize business processes accordinglyorganize business processes accordingly

!! Define profitable customers more clearlyDefine profitable customers more clearly!! Ease the new product planning and pricingEase the new product planning and pricing!! Release new product bundlesRelease new product bundles!! Improve the accuracy of the crossImprove the accuracy of the cross--selling predictionselling prediction

Success CriteriaSuccess Criteria

Segments should beSegments should be!! IdentifiableIdentifiable!! AccessibleAccessible!! Actionable Actionable !! StableStable

High Level DecisionsHigh Level Decisions

!! Customers who will be excluded from clustering Customers who will be excluded from clustering analysisanalysis

!! Defining Active VariablesDefining Active Variables!! product usageproduct usage!! demographic characteristicsdemographic characteristics

!! Transformations into Transformations into quantilesquantiles

!! Defining profiling variablesDefining profiling variables

Yapı KrediCustomer Base

Youth Banking Customers

University Banking Customers

Segmentation SchemeSegmentation SchemeLo

st C

ust

omer

sY

KB

Em

ploy

ees

Bla

ck-l

ist

Cu

stom

ers

Inac

tive

+ P

oten

tial

C

ust

omer

s

3,7 million customers

Active VariablesActive Variables

Variables related to usage of saving and investment Variables related to usage of saving and investment productsproducts

!! Average Balance on SecuritiesAverage Balance on Securities!! Average Balance on Time Deposit (TL/FX)Average Balance on Time Deposit (TL/FX)!! Average Balance on Demand Deposits (TL/FX)Average Balance on Demand Deposits (TL/FX)

Variables related to automatic payment Variables related to automatic payment behaviour behaviour !! Average Amount of PaymentsAverage Amount of Payments

Variables related to loan usageVariables related to loan usage!! Average Balance on LoansAverage Balance on Loans!! Average Balance on Credit CardsAverage Balance on Credit Cards

Active VariablesActive Variables

Variable related to Customer’s tenureVariable related to Customer’s tenure!! Maximum Tenure of Open ProductsMaximum Tenure of Open Products

Variable related to Customer’s frequency of usageVariable related to Customer’s frequency of usage!! Average number of banking transactionsAverage number of banking transactions

Variable related to Customer’s life cycleVariable related to Customer’s life cycle!! AgeAge

Profiling VariablesProfiling Variables

!! Demographic (age, gender, marital status) Demographic (age, gender, marital status)

!! Product Ownership (overall, derived, static)Product Ownership (overall, derived, static)

!! Channel usage (membership, averages, ratios, Channel usage (membership, averages, ratios, quantilesquantiles))

!! Assets (basic, Assets (basic, quantilesquantiles))

!! ProfitabilityProfitability

The Analysis CycleThe Analysis Cycle

"" More than 15 unsupervised segmentation analyses have More than 15 unsupervised segmentation analyses have been performedbeen performed

"" KK--means clustering using Active Variablesmeans clustering using Active Variables

A supervised segmentation according to customer’s Total A supervised segmentation according to customer’s Total Assets has been madeAssets has been made

!! Marketing users defined a 5Marketing users defined a 5--level scalelevel scale

Unsupervised SegmentsUnsupervised Segments

% 9

% 27

% 29

% 19

% 10% 6

Light Light UsersUsers

Young Young InvestorsInvestors

SalaSalarriediedCustomers Customers

Simply The Simply The BestBest

Credit Credit Cards OnlyCards Only

Golden Golden SeniorsSeniors

Supervised SegmentsSupervised Segments

B

C

A

D

E

Total Assets Total Assets (*)(*)

(*) Total Average Balances on Time & Demand Deposits, Securities(*) Total Average Balances on Time & Demand Deposits, Securities

38 %38 %

46 %46 %

7 %7 %

5 %5 %

4 %4 %

YKB Customer BaseYKB Customer Base

Segments by Active VariablesSegments by Active Variables

Different Methods, Same ResultsDifferent Methods, Same ResultsSegments by Total AssetsSegments by Total Assets

E

D

C

B

A

E1E2E3E4E5

A

B

C

D1D2D3D4D5

Credit Cards Only

Payroll Customers

Young Investors

Simply The Best

Golden Seniors

Simply The Best

Light Users

Young Investors

Payroll Customers

Golden Seniors

Payroll Customers

Golden Seniors

Simply The Best

Young Investors

CrossCross--Selling Mutual FundsSelling Mutual Funds

Campaign ObjectivesCampaign Objectives

!! Primary target is the customers bringing Primary target is the customers bringing more money to YKBmore money to YKB

!! It is also beneficial, if customers transfer It is also beneficial, if customers transfer their existing investments into mutual their existing investments into mutual fundsfunds

BB--type Mutual Fundstype Mutual Funds

!! Low risk investment instruments Low risk investment instruments composing of fixed income securitiescomposing of fixed income securities

!! Bought and sold easily via ATM, Internet, Bought and sold easily via ATM, Internet, and Telephoneand Telephone

ModelingModeling

!! Target variable: Balance on BTarget variable: Balance on B--type fundstype funds

6 months’ historic information of the customer ...

..to predict the buyers in the next 3 months

Month_6;Month_6; Month_5 ….Month_5 …. Month_1Month_1 Month_0; Month_1; Month_2Month_0; Month_1; Month_2

ModelingModeling

!! Five alternative models were developed. The regression Five alternative models were developed. The regression model yielding a lift value of 29 and a cumulative model yielding a lift value of 29 and a cumulative response rate of 14 % for the top percentile was response rate of 14 % for the top percentile was selectedselected

Campaign ActionsCampaign Actions

!! CrossCross--sell Bsell B--type mutual funds to targeted type mutual funds to targeted customerscustomers

!! FollowFollow--up calls to positive responders if up calls to positive responders if they haven’t purchased by the end of the they haven’t purchased by the end of the weekweek

Campaign PlanCampaign Plan

Mutual Fund Mutual Fund CampaignCampaign

Call CenterCall CenterBranchBranch

-- contact 3000 customers contact 3000 customers

-- 16 branches in Istanbul area16 branches in Istanbul area

-- contact 1200 customerscontact 1200 customers

Campaign Start Date: 4 March, 2002Campaign Start Date: 4 March, 2002Campaign Duration: 5 weeks inc. followCampaign Duration: 5 weeks inc. follow--up campaignup campaign

Campaign ResultsCampaign Results

ResponseRate(%)

Amount ofFunds Sold

(EUR)Branches 6,5 582.000

CallCenter

12,2 452.000

Total 8,2 1.034.000

Lessons LearnedLessons Learned

!! It is important to have comprehensive data about It is important to have comprehensive data about customers’ customers’ behavioursbehaviours

!! Start collecting necessary historical data before data Start collecting necessary historical data before data mining mining -- their availability will shorten project timetheir availability will shorten project time

!! Be careful with data from Source Systems Be careful with data from Source Systems -- they might they might have discrepancieshave discrepancies

!! Start integrating source system data into your Data Start integrating source system data into your Data Warehouse model Warehouse model -- Data Mining Data Mart will be easy to Data Mining Data Mart will be easy to maintainmaintain

Lessons LearnedLessons Learned

!! There are no Right’s and Wrong’s of SegmentationThere are no Right’s and Wrong’s of Segmentation

!! Different segments are obtained for different variable Different segments are obtained for different variable setssets

!! There is no end to analysis; it has got to stop when There is no end to analysis; it has got to stop when meaningful results are achievedmeaningful results are achieved

!! It is up to the Marketing Users to decide on which It is up to the Marketing Users to decide on which segmentation is best for your companysegmentation is best for your company

Next StepsNext Steps

!! Develop marketing services for target segmentsDevelop marketing services for target segments!! Product bundlesProduct bundles!! PricingPricing

!! ProductProduct--based opportunities:based opportunities:!! Cross sell credit cards, alternative channels, Cross sell credit cards, alternative channels, portfolio accounts to Segments 5 and 1portfolio accounts to Segments 5 and 1

!! Extend Mutual Fund campaign to all branchesExtend Mutual Fund campaign to all branches!! Develop next crossDevelop next cross--sell model: additional credit sell model: additional credit

cardscards

Segmentation and CrossSegmentation and Cross--SellingSellingProjectProject