Tibil Capabilities

1

Building Knowledge Warehouses

2

Agenda

• Thinking Beyond Traditional Data Warehousing

• The Four Pillars of Our Practice

• Services Overview

– BI

– Predictive Modeling

• Case Studies

3

Thinking Beyond Traditional Data WarehousingDemand more from your data through analytics

4

Fewer User groups of EDWs Fewer Business Units leveraged

EDWs Limited accessibility – Computers,

IntranetExperience based Business Decision

Making

Larger User Groups of EDWs Almost all Business Units wants

to use EDWs Broader accessibility – Handhelds,

Cloud Computing Empirical Business Decision Making

Design to support operations and processes

Performance is a concern Limited support to mobile users Data Warehouse

Should also support strategic decision making

Performance is critical Comprehensive support to mobile

users Knowledge Warehouse

Past

Present

Present

Reasons

Existing Businesses should respond to the need of the hour and move towards Knowledge Warehouses

Changing Business Information Landscape

Demands Change in traditional EDWs

Future

5

The Four Pillars of Our Practice

People I Portfolio I Processes I Platforms

11

6

Our People: Empirically Driven Consultants

Our Processes : Flexible & Efficient

Our Portfolio : Full BI & Analytics-chain

Our Platforms : Technology agnostic

Led by experienced technocrats Team with deep business experience Sourcing is based on stringent 7i filters Continuous learning Development centers in Bangalore and Hyderabad

Extract – Data capture forms, Data marts & Data reconciliation

Monitor – Performance monitoring tools, Dashboards, Data cubes, Basic analytics

Predict – predictive and forecasting models for strategic planning

Ability to balance analytical prowess with pragmatic business application

Driven by customers’ business objectives Proven record of delivering step-up change in business

KPIs and P&L lines Demonstrated ability of high-octane delivery

Simple, Scalable and Robust Capable of handling offline Excel files to complex

databases Experience of working on varied source systems Our platforms integrate seamlessly with existing

infrastructure

Four Pillars

7

Our Portfolio : Full BI & Analytics-chain

11

8

Products & Solutions Services

Strategic Outsourcing

Auto

Auto

Auto

Manual

Concurrent

Layered

Personalized

Revenue Maximization

Cost Optimization

Impairment Control

Your Data feeds Our Engagement Single version of Truth Your P&L impact

• Data modeling• Create data stores• Data repair & ETL

• BusinessEye decision support portal

• CustomerEye analytical CRM

• Report automation

• Cross-sell models• Revenue & profit models• Credit risk models incl.

Basel II• Value at risk (VaR)• Loss forecasting models

• Customer segmentation• Campaign & loyalty

management• Fraud mangement• Collection & recoveries

optimization• Growth models with

ROEC / NPV triggers• Product-channel mix

optimization

Decision Support• Product/platform

selection • Product-market strategy• Portfolio management

frameworks• Retention strategy and

frameworks • Customer life-time value• Financial forecasting

frameworksData Management

Monitoring & Reporting

Business Analytics

Advanced Analytics & Strategic Consulting

BI and Analytics chain

… with constant focus on delivering a positive P&L impact

Strategic Impact

Dat

a Va

lue

Key tools : SAS, SQL, Knowledge seeker, COGNOS, InformaticaKey techniques : Regression. CHAID, Clustering, Neural networks

Data Management

BI/Reporting

Analytics

Decision Sciences

Consulting

Partial List

Our Portfolio : Across BI and Analytics Spectrum

9

Data Management

Monitoring & Reporting

Basic Analytics Decision Support

Advanced Analytics & Strategic consulting

Sales & Marketing

Portfolio Management

Customer Contact

Management

Credit & Collections

Business Finance

Business Intelligence

• Prescriptive Dashboard with a 12-16 week implementation

• Pre-packaged metrics & proprietary data models for retail banking products

• Performance management analytics

• Sales management , incentive calculation

• Basic Segmentation

Product

Product /Campaign profitability tool based on vintage engine

Campaign

• Campaign mgmt tool, with configurable rule engine

• Light software

Forecasting

• Portfolio and P&L forecasting analytics engine

Customer

• X-sell platform• Rule-based

Analytical Data Marts

Collections Engine

Decisioning tool with configurable rules

…. in addition to a host of customized services across the spectrum

Our Solutions : Effective Cross Functional Tools

10

Our Processes : Flexible and Efficient

11

11

CLIENT

Define Business

Objectives

Analytical Design & Data

Selection

Data Preparation

Modeling &Analysis

Deploy Results

Evaluate &Iterate Results

1

2

3

4

5

6

ONSITE

OFFSHORE

Cost Lever (Offshore) Requires large volume of data work

Repetitive tasks, easily productionalised

Rules based

Time Consuming (60-75% of total time spent)

Quality Lever (On-site/In-house) Requires senior analytics staff/domain

experts

Advanced education required

Ability to interact with stakeholders

Quality Lever

Cost Lever

Global Service Delivery ModelOur Delivery Model allows Clients to Flex both Quality and Cost Levers, while sourcing globally

12

‘Key Steps at Each StageService Delivery Model

• Develop clear problem statements and performance metrics

• Understand larger business and market context

• Clean and merge data using visual and statistical methods

• Create Meta data ,construct new variables• Perform high level reconciliation of the data

• Develop analytical solution and establish key hypothesis

• Identify data sources and validation sources • Establish availability of key data

• Iterate different modelling alternatives and evaluate fir vs. objectives

• Recommend one model with key assumptions

• Pressure test and validate the model• Iterate results to improve accuracy• Agree new analysis requirements / priorities• Translate model results into tangible business

impact

• Identify process changes required to implement • Implement the model/solution • Monitor for accuracy and performance ONSITE

OFFSHORE

Define Business

Objectives

1


Selection

Data Preparation

3

Modeling &Analysis

4


5

Deploy Results

6

2

CLIENT

Define Business

Objectives


Selection

Data Preparation

Modeling &Analysis

Deploy Results


1

2

3

4

5

6

Quality Lever

Cost Lever

Analytical ProcessOur analytical process is driven by customers’ business objectives

13

Services Overview

14

Data Migration Data Integration Data Architectural

Services Warehouse Design Warehouse Enhancements Performance Tuning Data Quality Audit ETL Building Knowledge Warehouses

BFSI TELECOM RETAIL HEALTHCARE

Analytical Data Marting Business Intelligence Analytics & Predictive Modelling

✔ Enterprise Dashboards✔ Alerts & Prompts ✔ Drill Downs✔ Content Management

System✔ Mobile Enablement✔ Cloud Enablement✔ Reports Factory✔ Self Service –

Automation

✔ Econometric Forecasts✔ Segmentation & Profiling✔ Profitability & ROI

Analysis✔ Lifetime Value Models✔ Scorecards✔ Optimization &

Clustering✔ Decision Trees✔ Campaign ROI Analysis

TERADATA IBM DATASTAGE ORACLE INFORMATICA

BUSINESS OBJECTS COGNOS JASPERSOFT SSRS (MSBI)

SAS SPSS R SSAS (MSBI)

Services Overview

Technology Platform

15

BI

11

16

AutoAuto Auto Manual

Analytical Data MartConsolidating & reconciling data from disparate sources and mapping onto Logical Data Model

Analytical EngineDefine business rules and triggers for monitoring and

reporting

Report Automation EngineGenerates reports basis rules set

Presentation LayerCustomizable dashboards

Privilege Manager

Administrator Control

1. High degree of user customization on the Presentation Layer permissible

2. Information privilege mirrors organization structure

3. High level of administrator rights – on rules, formats and access

4. Post implementation, our involvement needed only if data sets or rule dimensions need to be altered

5. Cost of scaling up for more data sources is marginal & proportional

Benefits

Data Source

Our BI Architecture : Modular & Scalable

17

Define Business Objectives

Analytical design & Data selection Data preparation Development & Coding Evaluate & Deploy

Onsite Offshore

…. and governed by 3 pillars of strength

Prescriptive requirements Pre-built modules Embed thru Technology

• Ability to understand business requirement and context, quickly

• Proactively think through cascading and x-functional impact

• Quick solutions to issues

• Min time from client on briefing

• Fundamental Sciences - Statistics, Econometrics, Ops Research

• Techniques - Predictive Modeling, Forecasting / Simulation, Optimization

• Pre built data adapters

• Pre-configured KPIs , dashboards & reports - rapidly customizable

• Tools & Applications to hardwire analytics in day-to-day ops

• Tools : SAS, SQL, VB, .NET, C++

• Dbases: Oracle, MS SQL, MS Access, DB2

• ETL Tools : SQL server, SAS ETL, Informatica

Our BI Delivery : Speed, Cost, & Quality Levers

18

Business Solutions

Solutions developed with business needs as focus Addresses functional issues and operational challenges

Quick Delivery

Simple and Scalable

Cuts across org. structure

One version of the TRUTH

Pre built data adapters to crunch time and cost Pre-packaged metrics & dashboard templates Well defined Requirements documents

Data format agnostic – works with data dumps from core systems & other offline sources

Light and low-cost IT infrastructure Fully customizable

Senior management has a “dashboard” view Functional executives have “drill-down” view Business analysts have a “scratch-pad” view Automated generation, transmission and distribution

Detailed reconciliation across GL, Risk and business Well defined sign off processes

Our BI Delivery

19

Analytics & Predictive Modelling

11

20

Credit Cards Retail Banking Mortgage Banking

Commercial Banking

Propensity Modeling Campaign management Marketing campaign

analytics Acquisition Analysis

Insurance

Cu

sto

mer

A

cqu

isit

ion

Risk scorecards Response scorecards Campaign management Cross Sell/ Up Sell

Analytics Acquisition Analysis

Risk scorecards Response scorecards Campaign management Lifecycle profiling

Risk scorecards Response scorecards Smart leads to offer new

lines of credit Loyalty / customer

lifetime value (CLTV) modeling

Churn prediction Renewal analytics Retention & Elasticity

modelingCu

sto

mer

R

eten

tio

n Churn prediction Credit line management delinquency forecasting

Customer profitability Loyalty programs

Product alignment / design

Surveys

Renewal strategy

Forecasting claims severity / frequency

Loss Ratio Analysis

Lo

ss M

itig

atio

n

Loss Forecasting Collections analytics Fraud prediction

Collection analytics Fraud prediction

Collection strategy Foreclosure prediction Fraud prediction

Payment risk scorecard

Automated underwriting Sales force analysis

Pro

cess

O

pti

miz

atio

n Authorization analytics Campaign management

ATM optimization Branch optimization

“Speed” underwriting Sales force analytics Approval optimization Optimizing end customer

versus intermediary interest

A/P analytics A/R analytics

Analytics & Predictive Modeling Solutions

21

Point Solutions – Banking, Financial Services

Ban

kin

g &

Fin

an

cial S

erv

ices

- Market Measurement- Response Models- Pricing - Prospect / Product

Segmentation

Acquisition Growth Profitability Retention- Customer

Segmentation- Activation Model- Spend / Usage Model- Offer Optimization- RFM Model- Up-sell / Cross-sell

Models

- CLTV- Prosperity to Revolve- Best Customer /

Upgrade Strategy

- Brand / Customer equity Analysis

- Up-sell / Cross-sell Models

- Churn / Attrition Models

- Customer Satisfaction Analysis

- Trigger Analysis- Reactivation / Silent

Attrition

Marketing Analytics

- Application Scorecards

- Risk Based Pricing- Fraud Detection

Customer Acquisition Collections & Recovery

Account Management- Behavioral Scorecards- Limit Management - Retention Analysis- Risk Profile of promotion

targets

- Collection Scorecards- Recovery Scorecards- Optimal Agency

Allocation

Risk Analytics

- Asset Allocation / Liability Modeling- Buyer / Target Lists Analysis- Investment Performance Analytics &

Analysis- Comparable Company Analysis- Company Profile Overview

Front Office Back OfficeMiddle Office- Risk Management, Identification &

Assessment- Risk Charting- Finance Flow Monitoring- Operational Risk Assessment

- Customer Segmentation & Profiling

- Redemption Rate Management- Customer Lifetime Value Modeling- Campaign Management- Data Management

Investment Banking

22

Point Solutions – Insurance

- Competitive Market Assessment

- Price Optimization- Customer Lifetime

Value- Loss Modeling

- Fraud Detection- Claims Staffing

Optimization- Loss cost driver

Analysis- Claims severity

forecasting

- Hedging Strategies- Asset liability

modeling- Yield Management

- Automated underwriting

- Straight through processing

- Selection for field underwriting

- RFM Modeling- Agency

Segmentation- Production

Forecasting- Customer

Satisfaction- Perf. Measurement

& incentive design

- Reinsurance Optimization

- Early reinsurance recoverable tagging

Attrition Models Profit Models

Customer Segmentation framework Cross-sell propensity Models

Insurance

Investment Management

Reinsurance Management

Channel & DistributionRisk Underwriting

Claims Management

Pricing

23

Case Studies

24

Dashboards Demo

http://www.fewgoodpeople.com/demos/tibil_telecom

Username: demoPassword: demo



25

• Identify the customers with the potential to be upwardly mobile (to migrate) through this segment scheme to help drive product development and portfolio actions

• The Near Term Wealth Score will assess how close a customer is to the target wealth profile within their given life stage. The higher the score, the more closely they resemble the target wealth profile.

• The Lifetime Wealth Score will assess how close a customer is to the ideal target wealth profile across all life stages. The higher the score, the more closely they resemble the target wealth profile.

Customer Lifetime Value (CLV) is long term and dynamic value that can help you optimize your decisions for long term profitability

TimeToday

AverageProfit

Optimum Short Term Strategy

Long Term Strategy

Customer A

Customer B

Today

Over a longer period customer B is more profitable than customer A

1. Wealth Modeling

0 1 2 3 4 5 6 7 8 90%

20%

40%

60%

80%

100%

120%

Random% Closed %

The models capture 40% of the potential attriters in the first two deciles.

Models that identify those customers most likely to close their accounts and Triggered Based Retention strategies

3. Retention Modelling

2. Customer Lifetime Value

Advanced Statistical Modeling

26

Results• Developed five Near Term Wealth Score models, one for each of the life

stages• Model for Life stage 1 has a maximum KS of 80%• Life stage 4 is chosen as the ideal “wealthy” profile for the entire portfolio• Model for Life stage 4 has a maximum KS of 78%• The top two deciles in Value model capture 87% of total value which

gives good separation• There are sixteen actionable segments based on Wealth score and Value

score

Approach• We developed two different scores:

1. A Near Term Wealth Score and 2. A Lifetime Wealth Score

• The Near Term Wealth Score will access how close a customer is to the target wealth profile within their given life stage. The higher the score, the more closely they resemble the target wealth profile.

• The Lifetime Wealth Score will access how close a customer is to the ideal target wealth profile across all life stages. The higher the score, the more closely they resemble the target wealth profile.

• Developed historical data model that provides monthly account level profit estimates. These estimates are then converted into Value Score

• Developed a two dimensional segmentation based on Wealth Score and Value Score

Objectives • Identify Upwardly Mobile Customers: Identify the customers

with the potential to be upwardly mobile (to migrate) through this segment scheme to help drive product development and portfolio actions

• Improve Value Understanding: To develop a value profile for the different customers and segments.

• Develop a Reusable Segmentation: Develop a segmentation that can be reused globally

Business Impact

• Targeted marketing based on Wealth profile and Value profile

• Clear strategies can be drawn to move customers from “Mass” to “Advanced” and “Premier” segments based on scores

• Plug and Play SAS codes

Wealth Model for a European Bank

27

Wea

lth

Lifestage

L1 L2 L3 L4 L5

High (Premier)

Low (Mass)

We broke down the portfolio into 5 different lifestages based on age

1

For the Near Term Wealth models, we defined target wealth customers for each of the different lifestages

2

The Near Term Wealth Scores provide a measure of how close a customer resembles the target wealth profile in their life stage

3

For the Lifetime Wealth Scores, we established the ideal target wealth profile

4

The Lifetime Wealth Scores provide a measure of how close a customer resembles the ideal target wealth profile across all lifestages

5

IllustrationDesigned an approach that will measure the wealth potential of a customer both within the lifestage

that the customer is in and across all lifestages

28

Model Variables Below 25 25-35 35-45 45-60 Above 60

Average over 6 months - ATM Transactions × × ×

Average over last 6 months - Number of TD Transactions × × × × ×Average over last 6 months - Outgoing EFT Trans Amt × × × × ×Average over last 6 months - Total CA Balance × ×Education × ×Professional Group × × × ×Residential Status × × × ×Revolver Segment × × × × ×Total # of products × × ×Transaction Band ×

• TD Transactions, EFT Transactions, and Revolver Segment are the three variables that are significant in all the Lifestage segments

• Education and Transaction Band are the least significant across all Lifestage segments

Lifestages

Scorecard Variables

29

0-0.4

B1

D1

C1

A1 A2

B2

C2

D2

A3

B3

C3

D3

A4

B4

C4

D4

% of Customers% of profitAvg. BalanceAvg. spend

7.30% -4.09%

2,032.2 2,611.3

16.63% 1.14%

1,068.1 2,264.2

53.01% 33.52% 2,186.4 4,801.6

11.51% 47.48%

9,314.5 13,337.3

0.56% -0.38%

15,035.3 16,158.0

01.65% 0.10%

2,648.2 4,171.8

2.92% 1.57%

5,516.1 10,890.0

0.85% 4.60%

23,797.9 33,074.8

0.24% -2.06%

45,737.3 49,492.7

0.43% 0.03%

12,162.9 14,375.1

1.96% 1.33%

19,850.9 25,464.4

1.79% 16.76%

106,071.5 118,127.3

0.01% -0.03%

35,271.4 35,513.1

0.05% 0.00%

1,997.9 1,997.9

0.04% 0.01%

11,105.8 11,105.8

0.00% 0.03%

163,365.1 163,365.1

8.11% -6.57%

21,564.715,066.6

18.77% 1.26%

3,307.0 4,257.6

57.93% 36.43% 6,045.6 8,707.7

14.15% 68.87%

55,780.2 58,874.2

0.79% 0.00%

18,932.5 18,603.6

0.16% 0.00%

19,108.2 20,792.6

0.07% 0.00%

52,394.9 53,633.4

0.00% 0.00%

2,143.5 2,143.5

1.03% 0.00%

36,503.8 37,380.0

Valu

e ba

nd

Life stage Probability band

89.24% 78.05% 3,909.6 6,291.9

6.16% 5.89%

12,068.9 17,959.4

4.49% 16.05%

69,323.5 77,900.2

0.11% 0.01%

36,385.8 36,440.9

100.00% 100.00%

24,744.9 25,125.2






0.4-0.7 0.7+ Missing Overall

Ove

rall

Mis

sing

500+

40-5

000-

40<=

0

Strategies developed to move customers to higher value bands

Segmentation Based on Wealth ScoreThe scorecard was used along with other criteria in creating multi dimensional segmentation. Usage

and activation strategies were based on this segmentation.

30

Results

• 95% of the value comes from two segments that have 24% of the accounts

• 9% of the accounts destroy 27.5% of the value • Two thirds of the accounts are neutral in value • The Least Value Contributor is Transactor and not

High Loss group

Approach• Forecasting assumptions used on pre-defined

segments• Forecast revenue drivers instead of actual P&L line

items• Vintage based forecasting approach• Seasonality of revenue drivers built into the

forecasting methodology• Event based cost allocation methodology• Attrition and delinquency handled using probabilistic

rates

Objectives

• The Bank was using Behavior Segments/Scores to drive the portfolio management strategy. This strategy focused on incremental lifts in response rates, with no insight/control on the profitability of the customers

• Develop algorithms & easy-to-use interfaces that calculate account level profitability based on forecasted revenue/cost drivers and use these outputs in conjunction with behavior segments/scores to drive profitable portfolio growth

Business Impact

• Detailed analyses of CLV drivers can help in designing of campaigns to maximize value

• Leverage historical value data and CLV index for balance building activities

• Leverage historical value data and CLV index for the evaluation of credit line strategies and for determining new opportunities

CLV for a European Bank

31

Revenue Drivers – Key Portfolio

parameters which drive portfolio P & L

Segmentation Framework

Forecasting Engine

for Revenue Drivers

Functional Forms from Normalized

Vintage Curves& Forecasting

assumptions for pre-defined Segments

Calculation Algorithm

for creation of account level P&L

(SAS code)

Historical account level data – Portfolio

KPIs and detailed revenue lines

CLV

Forecast Output

CLV ArchitectureVintage based forecasting engine is the cornerstone of this architecture. This methodology provides the

granularity that is required to achieve accuracy and consistency.

32

Average Revenue509

Average Balance 15732

Average Spends7955

Limit Camp Accounts27%CLV DRIVERS

CLV RESISTORS

High Value

Average Cost of Funds244

Average loss amount0.02

Average cost-20

Average CLV489 YTL

CLV RESISTORS

CLV DRIVERSAverage Revenue12

Loss Makers

Average Balance13323

Average cost of Funds209

Average Spends10,409

Average loss amount228

Average cost-17

Average CLV-235 YTL

In depth understanding of what is driving different levels of CLV on two products

Limit Camp Accounts10%

CLV Drivers and Resistors

33

-40%

-20%

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10

Top 20% contributes 97% of total profit

Bottom 40% destroys 25% of total profit

Profit Contribution by Decile (%)

Less than 20% of Accounts contribute more than 90% of total profitability

CLV Deciles

34

Value Segmentation

Low Spend(0 to <=3000)

Loss Makers

Marginal

Low

Medium

High

High Spend(>3000)

Loss Makers

Marginal

Low

Medium

High

Segmentation is built based on value and spend groups

SPEND + VALUE GROUPS

Value Segmentation

35

Distribution of Value Segments

36

• For each behavior segment, we have identified possible high level marketing strategies that address the key customer opportunities

Results

Approach

Objectives • To identify those customers that are likely to close their

cards.• Perform behaviour segmentation based on their

likeliness to attrite.• For each behavior segment, Identify high level

marketing strategies that address the key customer concerns and issues.

All Cards

All Active Cards

Bonus

Shop

&

Miles

All other products

All Inactive Cards

Activity Breakout

Product Breakout

1 2 3 4Models

Summary Gains Table

These figures show the cumulative percentage of cards. Here 36% of the attrition has been captured within the first two deciles.

Deciles

Number of Cumulative

KS

MarginalProb

(Closed)

Closed

Non Closed

ClosedNon

ClosedClosed %

Non Closed %

Closed Rate

Non Closed Rate

0.0% 0.0%0.0%

0 575 4444 575 4444 20.7% 9.4%11.3

%11.5% 88.5% 11.5%

1 416 4603 991 9047 35.7% 19.1%16.6

%8.3% 91.7% 8.3%

2 357 4662 1348 13709 48.5% 28.9%19.6

%7.1% 92.9% 7.1%

3 293 4726 1641 18435 59.1% 38.9%20.2

%5.8% 94.2% 5.8%

4 280 4739 1921 23174 69.2% 48.9%20.3

%5.6% 94.4% 5.6%

5 212 4807 2133 27981 76.8% 59.0%17.8

%4.2% 95.8% 4.2%

6 210 4809 2343 32790 84.4% 69.2%15.2

%4.2% 95.8% 4.2%

7 168 4851 2511 37641 90.4% 79.4%11.0

%3.3% 96.7% 3.3%

8 144 4875 2655 42516 95.6% 89.7%5.9%

2.9% 97.1% 2.9%

9 122 4897 2777 47413100.0

%100.0

%0.0%

2.4% 97.6% 2.4%

Retention Model

37

We started with over 300 variables in the modelling universe.

We conducted bivariate and univariate analysis for the categorical and continuous variables to make sure the trends were correct.

We reduced the variable set down to about 60 based on the bivariate analysis and the overall information value.

Then we conducted correlation analysis and eliminated any variables that were highly correlated.

We then ran stepwise regression to determine the final variables in the model.

We then validated the models based on the statistical results.

We then developed an appropriate scorecard.

Data Validation

Variable Selection

Model Building

Validation Scorecard Development

1 2 3 4 5

Retention Model Overview

The process we follow considers an exhaustive list of independent variables to make sure that predictive power of the model is maximized.

38

Customer Product Holdings

Data Used

Card Usage EFT Data Revenue Data Credit Burearu Data

Customer Demographics

Transactional Data

Authorisation Data Call Center Data

Current Month

Current Month

11 Months

11 Months

11 Months

11 Months

Current Month

11 Months

11 Months

When Pulled

• Modelling has been done at a card levelOpen Cards

55% sample

Closed Cards

85% sample

Customer Complaint Data

Available Data for Retention Modeling

39

Parameter Description of variables Bonus Shop & Miles Other

AMT_SPEND_3M_6mnths Total volume of non instalment purchases in last 3 statement periods x x

woe_age_band The age of the card x x x woe_AMT_HOUSEWARE_3_6mnths The value of transactions done in home improvement

category lst 3 months x woe_AMT_SPEND_3M_Ratio Change in spend over the past six months. xwoe_AMT_WEB_RATIO_6_3mnths Ratio of spend in Bonus network / total spend (as volume of

transactions) x x

woe_ASSETS_TOTAL_6mnths Average YTL value of all assets in bank last calendar month x woe_ASSETS_TOTAL_CURR_3mnths Total current YTL value of all assets in bank x woe_ASSETS_TOTAL_CURR_ratio Total assets change in past six months. xwoe_BHVR_SCORE_CURR_6mnths Last calculated behaviour score (scores calculated monthly) x

woe_BNS_PROM_FLAG Whether the customer has been or is enrolled in a spending commitment for Bonus card x

woe_CURR_CUST_LIMIT_A_ratio Current available customer limit xwoe_CURR_DEBT_6mnths Customer's Current outstanding balance total (of all cards) x woe_CURR_DEBT_ratio Current debt change in past six months. x woe_LAST_PUR2MAX_PUR Total new transactions in last statement / Maximum total of

new transactions in last 6 statements x xwoe_limit_band Limit of product x xwoe_mob_band The month on book group that the card is in x xwoe_multi_card_flg A flag that indicates if the owner has multiple cards with

Garanti x

woe_PAYROLL_FLAG Whether customer is payroll customer and receives salaries in current account x

woe_segmentation The business segment that the card is in. x x

Key Variables in Retention Model

40

• % of Closed CardsVIP VG1 VG2 HS Others TOTAL

High Attrition

Risk3% 8% 8% 14% 8% 8%

Med Attrition

Risk8% 4% 4% 6% 5% 5%

Low Attrition

Risk3% 3% 3% 3% 3% 3%

TOTAL 4% 5% 5% 4% 7% 6%

Number of CardsVIP VG1 VG2 HS Others TOTAL

High Attrition

Risk380%

4,93110%

3,9768%

730%

11,05822%

20,07640%

Med Attrition

Risk1630%

5,41311%

3,8448%

4231%

5,21510%

15,05830%

Low Attrition

Risk3391%

5,88712%

4,4069%

2,1044%

2,3215%

15,05730%

TOTAL 5401%

16,23132%

12,22624%

2,6005%

18,59437%

50,191100%

• 10% of the cards

• Take more urgent proactive measures to ensure that these customers are happy


• Take moderate proactive measures to reinforce use of the product


• Take moderate proactive measures to strengthen the relationship


• Stay focused on BAU activities and promoting the benefits of the product

Using the Model ScoreA heat map was created based on the scorecard. Clear actionable groups were identified and

appropriate strategies were designed.

41

Thank You