driving intelligent growth with customer value maximization

8/6/2019 Driving Intelligent Growth With Customer Value Maximization

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EMEA Banking Practice

Driving intelligent growth withCustomer Value Maximization

How banks should go beyond CRM


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EMEA Banking PracticeDriving intelligent growth with Customer Value Maximization

Following the intense uncertainty ushered in by the recent financial crisis, many bankers

are confronting huge future ambiguity about markets, risk, and demand. The questions

that will shape the future of banking appear, in many regions of the world, to hinge

around macroeconomics and regulation. A large part of the new normal to which

the banks are returning is the dilemma of how to deliver further growth when demand

appears to be stagnant or even declining. Is there any way to fuel revenues and profits

despite the aftermath of the downturn? There are certain ly better ways in the retail

market segment at least than focusing on new client acquisition, where economics

show that acquisition costs often exceed more than one years client profitability. Toborrow a catchphrase: the answer lies within.

Despite the vagaries of external circumstances, banks still have vast largely untapped

potential at their fingertips: their existing customer base. Banks can enhance their

revenues while maximizing the value of this base using next-generation one-to-one

banking similar to the customer lifecycle management approach now being applied

by leading telcos. In-depth analysis of each customers profile and preferences using

state-of-the-art data mining techniques an approach we have termed Customer Value

Maximization (CVM) can yield very positive results surprisingly fast. Banks have seen

with this methodology a shif t from fewer than two products per client to over six. Others

have been able to increase profitability in their retail branch network by some 20 percent

by combining tailormade pricing and cross-selling initiatives. CVM does, however,

require a fundamental shift in frontline mindset and capabilities. This article begins by

examining the differences between banks traditional marketing approaches and CVM,

then looks at the various stages in the CVM journey, and closes with a new one-stop

shop solution for ramping up the process swiftly.

The benefits of using CVM

CVM is a proven methodology that goes far beyond basic CRM capabilities, allowing

retail banks to identify and capture full potential from their existing customers, with imme-

diate impact on their bottomline (Exhibit 1). A well-devised CVM program will make highly

differentiated offers with varying product attributes to customer microsegments, rather

than broadcasting the same product-based offering to all customers. Instead of static,backward-oriented segmentation, leading-edge techniques use predictive, self-adjusting

models to define the best next offer for each customer. This in-depth analysis based on

multiterabyte data can identify future potential by customer far more valuable than a

pure focus on current customer revenues. This data, if skillfully applied, can yield over 100

microtargeted campaigns a year with hit rates of 20 percent and above, compared to the

usual handful of broadly targeted campaigns with rates of only 3 to 5 percent.

Banks that are able to capture and sustain CVM potential achieve best-in-class perfor-

mance, incorporating lifetime value as a key indicator for customer targeting when priori-

tizing and identifying opportunities across all customer segments and levers. Their

targeted campaigns use value-based segmentation and propensity models, and imple-

ment highly coherent operating models across channels and functions.

This approach is being applied within banks across widespread geographies. Canadian

and Spanish banks are among the most advanced, consistently achieving a step change

in the effectiveness of their commercial actions, allowing them to capture initial profit

growth of at least 10 to 15 percent.

Driving intelligent growth withCustomer Value MaximizationHow banks should go beyond CRM


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Capital One reaping the rewards of full-fledged CVM

Capital One is one of the best-practice examples in the industry. Its co-founders call

it an information-based marketing company, not a credit card issuer. The company

has been following this philosophy for many years, using individual customer value as

a key indicator for its commercial initiatives. It uses customer value in its segmentation

process, for example, as well as its behavioral indicators, offering open data accessto its front line with a uniform and holistic view of its customer data. Capital One was

also one of the first players in the industry to conduct offer prioritization based on

expected increase in customer value (across value drivers), using propensity models for

cross-selling to maximize the value of customers it had acquired. It also analyzes client

purchase patterns and dates to infer its risk and propensity to buy specific products. As

a result, it is able to target tailored customer offers (over 3,000 credit card variations) to

over 100,000 segments. It also conducts highly flexible test-and-learn campaigns at a

rate of more than 65,000 tests a year. These efforts are given top-pr iority support by the

organization via elements such as a unique relational database management system,

specific customer acquisition targets for employees at all levels, and an extra bonus to

staff who sell higher-yielding products.

Banks' profits can be fueled in a downturn by maximizingthe value of their customer base

that helps identify and capture the full

potential of a bank's customer base

3 3 2

5

11100

119

Profit

year 2

Cus-

tomer

churn

Prod-

uct

churn

30

Re-

newal

Cross-

sell

30

Up-

sell

Acti-

vation

Win-

back

New

cus-

tomers

17

Profit

year 1

Value creation along value drivers

Typical impact is 10 to 15% on profits (6 to 8%

on revenues), with a significant share captured

in 4 to 6 months, thus self-funding the program

CVM is a proven methodology ...

Understand customer base:

identify the most profitable cus-

tomers as well as the "revenue

chains"; segment your commer-

cial initiatives

Go after opportunities along all

value levers (acquisition, cross-

selling, retention), especially

beyond the obvious ones (e.g.,

volume churn)

Increase the ROI of commercial

initiatives and increase their

effectiveness

Exhibit 1


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CVM in three steps

For effective CVM, it is vital that a bank first understands its customer base: it needs

to identify its most profitable customers as well as its value drains, segmenting its

commercial initiatives accordingly. It should pursue defined opportunities along all value

levers acquisition, cross-selling, and retention with a focus beyond the obvious, as

will become clear in the examples below. Finally, it needs to apply rigorous execution.

The heightened effectiveness of this microcampaigning and the ensuing commercial

initiatives can result in hit rates of over 20 percent, with returns of over 400 percent. Ofcourse, CVM implications are much broader than only personalized commercial offers.

Typically, they influence other commercial areas that could include pricing, new product

development, dedicated save-desks, or sales force incentives.

Step 1: Develop customer profiles. Creating a multichannel, multidimensional view

of the customer by leveraging advanced banking infrastructure and data is the first

step in the CVM journey. Lifetime perspective and potential should be incorporated

as a key indicator when quantifying customer value. It is also vital to use customer

value in the segmentation process, combined with behavioral indicators, and to offer

this uniform, holistic customer profile view to the front line. Some banks abandon

the initiative at this first stage or struggle to overcome some initial challenges like

lack of information, multiple data sources, or different frontline culture. It is extremely

important to focus on pragmatic solutions in customer value definition that could be

further adjusted along the process (Exhibit 2).

AnnualprofitEUR mSegment

1

1

12

14

Transactors

Investors

Mortgage

holders

Other

Savers -15

Product penetration

Percent

Deep dive into individual customer value and profiling

to understand segments and drivers of value

Mort-gage

9

11

2

14

100

8

Currentaccount

100

84

100

90

100

85

Sav-ings

20

100

21

44

17

21

Card

81

29

15

36

82

32

Mutualfund

19

19

17

34

20

59

Directinvest-ment

21

18

16

39

17

72

Insur-ance

25

12

6

14

67

8

Credit

12

2

2

6

13

8

AUM/customerEUR

49,401

85,313

54,596

827

36,171

132,581

Numberof prod-ucts

6.5

8.0

4.0

8.3

7.0

7.4

Profit/cus-tomerEUR

2,825

117

-730

86

171

415

Number ofcustomersThousands

5

9

18

14

30

TOP 30% CUSTOMERS BY AUM

SOUTH EUROPEANBANK EXAMPLE

CUSTOMER PROFILE DEVELOPMENT

Average

Key insights

At least five distinct behavioral customer segments exist each with a distinct pattern

of product holding and usage

A large segment of savers primarily hold unprofitable savings and deposit products

Still significant potential to further drive product penetration across all customer

segments, particularly for highly profitable mortgage and card products

Exhibit 2


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Step 2: Create and prioritize a heat map. For identifying and prioritizing opportunities

banks need to look across all commercial levers (acquisit ion, customer develop-

ment, and retention) and across all customer segments. Reality is typically different,

however. Our recent interviews with 20 leading European banks revealed that only

15 percent of them were measuring impact of attrition on their results, with attrition

including not only lost customers, but also product and volume churn. This prioritiza-

tion exercise has both strategic and operational implications.

The first substep is to generate a heat map with large potential pools by seg-ment/product /commercial lever. This helps banks identify areas of high impor-

tance, regardless of the scenario or strategy option, as well as those areas

relevant to a specific strategic option. Different segments and levers will be used

as a priority depending on the strategic goal the bank wishes to achieve: strategy

should be a combination of segment and commercial lever focus (Exhibit 3).

The second substep is to identify specific commercial opportunities from the

heat map by segment and to assess the relative value/importance of all key

levers (acquisition, development, retention) across all main segments (retail

mass, affluent; SMEs micro, small, medium), prioritizing commercial activities

for a given year. This will mean defin ing areas of focus and deprioritizing others.

At very advanced banks, annual commercial budgeting is verified bottom up

taking into account the additional growth target for each individual client or client

segment, and then adding up the results to achieve a figure for the entire net-

work (Exhibit 4).

Heat map analysis can be used to identify high-level CVM

opportunities by macrosegment Key valueopportunities

CEE BANK EXAMPLE

HEAT MAP CREATION

Heat map model output scenario 1

EUR millions, annualized

Total

Retail

SMEs

Mass

Affluent

Acquisition Development Retention

Subtotal

SOHO/Micro

Small

Medium

Subtotal

Segments

Key CVM value drivers

New cus-

tomers

97

110

237

30

40

140

24

33

Win-

back

39

4

33

37

0

0

2

2

Activa-

tion

123

40

23

63

21

16

23

60

Up-

selling

157

36

30

27

63

15

49

94

Cross-

selling

120

20

231

52

16

140

23

91

Renew-

als

4

0

1

1

0

0

3

3

Product

churn

196

25

30

101

126

22

18

70

Customer

churn

22

7

6

13

1

0

8

9

Total

1,009

262

321

583

166

96

164

426

Exhibit 3


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Step 3: Execute effectively across the organization. For impact on the bottom line,

defining bank priorities needs to be followed by developing specific commercial

initiatives and precise execution via the channels. Banks need to effectively manage

a bipolar world: with sophisticated analyses in the back office, being translated into

simple offers and processes for customer and frontline staff. Hence banks need

to work on accurately matching customers to attractive offers and significantly

improving campaign response rates by using enhanced customer information from

segmentation and advanced modeling techniques. It is often critical to use multilens

segmentation (for example, behavioral, needs, attitudinal, economic value, etc.),

advanced modeling (for acquisition, development, and retention), both historical and

predictive lifetime value, and descriptive statistics. Only then will a banks specif icactions for selected customer segments be sufficiently precise to have impact,

whether through best next product to buy, alternative pricing, product rebalancing,

or early warning signals of attrition. The results of these analyses need to be then

translated into specif ic offers though sales support tools for the frontline to be able to

effectively communicate with customers in this increased complexity environment.

Effective targeting of campaigns using value-based segmentation and propensity to

buy/churn models is crucial in the first instance. However, banks then need to execute

on campaigns more precisely by testing them before they go out into the field with them,

and by using optimal marketing processes to continually improve results. Best-practice

banks use multichannel, multivehicle campaign design for this as well as fractional

factorial test execution, vehicle testing frameworks, peer group models, and advanced

measurement and performance monitoring (Exhibit 5).

Identify opportunities to create value across

the customer base

-170

-50-20

020

48

105

230

8

Average profitper customer:

EUR 80

1097

-7

654321

845

Destroy 25% of total profit

Customer

deciles

Generate retention

actions and early

warning signals

Development of

deciles 2 and 3 (up-sell

volumes and usage

increase) Targeted offerfor cus-

tomers with deposits

elsewhere Optimize service

levels

Increase activation rates/activity

levels

Increase cross-selling (overall

cross-sell and selected product

penetration)

Promote sending salary to

account and contact inactive clients Introduce typical product

bundles, especially for attractive

products (e.g., current account for

every buyer of a savings account)

Modify pricing

Redesign product

Increase activation

rates/activity levels

Develop targeted

actions to develop

customers below 30 Optimize service costs

Generate 85%of total profit

Average customer profit per decile

EUR

HEAT MAP CREATION

CEE CLIENT EXAMPLE

Exhibit 4


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McKinseys Customer Solution Center

Some banks struggle when it comes to developing commercial actions based on

analyses of massive amounts of datat or to changing mindset in their distribution

channels. Migrating frontline employees from a reactive product-dispatching approach

to a highly targeted proactive client-by-client pitch as well as using reactive contact to

proactively offer best next product to buy is often a major challenge. Typical hurdles

include:

Data infrastructure issues: Relevant data is often dispersed nonhomogeneouslyacross business segments or units in various data sources across the organization,

and is hard to integrate quickly. Banks frequently struggle with insuff icient capacity

to handle very large data sets to perform analyses on their full customer data.

Limited analytical and modeling expertise: Banks all too often lack expertise in

designing high-power predictive data models at an individual customer level (for

cross-selling or assessing churn risk, for example). They also tend to have very few

tested hypotheses on key levers to pull by product and customer segment, or none

at all.

Low campaign management capacity and capability: Banks capability to handle

multiple campaigns at the same time is often low, with limited tools typically

available, especially in branches (for example, use of a separate Excel list for each

product campaign). They also do not optimize channel roles, their utilization of

branch potential is low, and they lack automated campaign report mechanisms/

tools to monitor results and quickly adjust/learn.

Descript ion Status Pre-campaign Post-campaign

Take% Costs ROI% Subs Rev Take% Costs ROI Subs Rev

BM x xx Idea

BM xxx ROI

M M x xx A pp ro ve d

M M x xx A pp ro ve d

B M xxx I n p rogr ess

M M x xx F in is he d

M M xxx E va luat ed

37

CampaignOverview

SMSStimulationResources

OfferEconomics

Campaign: SMSStimulation

Overview: CampaigndesignedtostimulateusageofSMS

Hypothesis: SubscribersthatSMSatleastoncewilllikelybeginto

useSMStosomeextent

ValueDrivers: Usage

Segment: Postpaidandprepaid

Dates: 15/1/200415/02/2004

CampaignOwner: None

TemplateOwner: T.Akgodan

Department: None

Date: 03/12/2003

ROIModel Owner: None

Department: None

Date: None

OfferExecution

Target: SubswithzeroSMSusageafterfirst2monthsof

service

Size: Target:3x10,000 Control:10,000

Businessrules: ARPU>25EURforpostpaid,Balance>5EUR,0SMSMoUinanyofprevious6months,consumer,>2monthssinceactivation,revenueaccts

Offer: 1 )$ 1 00 c o n te s t 2 )1 0 f ree S M S

3)10freevoiceMoU

Pre-CampaignEst imates:

TakeRate ARPU C hu rn R OI

10% xx Xx EURxx

Channels:

DM TM Emai l

0 % 0 %

SMS Bi l lMessagin g Bi l l Inser ts

100% 0% 0%0%O/B:

0% 0 % 0 % 0 % N/AN/AI/B:

Messages: 2differentmessagesperoffer

Takers: 1251(12.5%) Contacts:9002(90%)

Economics:

TargetT ot a l C on t ro l N e t Ch an ge

3 .6 8% - 0. 34 %3.67%Churn ($2641)

1.5%23.07xx

-2.2%0.07xx

80.8%0.33xx

-51.2%0.26xx

4.2%26.99TotalRevenue $10687

Benefit/Revenue* $8047

-CostofOffer

0.85CostofContact** $8502

TotalCosts $8502

ROI: ($455)

Notes:*xxxxx

Tobe

comp

leted

post

camp

aign

Scientific campaign design with quick tests and feedback cyclesis essential for capturing value

More, diverse ideas Opportunity trees Deep-dive facts/analyses Focus on "unknown"

assumptions to test

Campaign idea Pre-campaign ROI

Standard screening Expected return (ROI) Prioritization of ideas Accelerated approval

Test environment

execution

Fast implementation Dedicated test support Accelerated new

offers implementation Scientific setup

Post-campaign ROI

Automated evaluation Real economics of

campaign

Campaign library

Institutionalization of lessons learned

Alignment of pre- and post-campaign evaluation Objective comparison of new and existing offers Knowledge base of ideas for future offers

EXECUTION ACROSS ORGANIZATION

Clear timing, ownership,

and end products defined

across campaign process

Exhibit 5


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McKinsey has developed an answer to these obstacles. Its global Customer Solution

Center (CSC) combines deep exper tise, analytical power, and infrastructure to analyze

our clients customer base and help them prioritize actions to design highly targeted,

high-impact campaigns. McKinseys Customer Solution Center allows banks to

accelerate the identification of opportunities to generate faster impact. The facilities

available include:

McKinsey IT infrastructure: Client data is transferred onto a remote McKinsey IT

platform with 24 TB capacity, offering a fast track to eliminate IT bottlenecks, avoidinterference with regular operations, and secure, state-of-the-art hardware and

statistical software applications.

McKinsey capabilities and tools: Statistical/data mining software, tested,

ready-to-use algorithms, best-in-class tools and capabilities, tried and proven

methodologies with quantifiable results, industry-specific, proprietary models, and

knowledge-leveraging cross-industry approaches (Exhibit 6).

These facilities offer a unique opportunity to quickly create an integrated customer

view from fragmented data environments, deep-dive into the individual customer value

and profile, understand segments and drivers of value, and jointly define bank-specific

initiatives.

CSC has developed an innovative service model, where on top ofanalytical skills McKinsey also provides physical infrastructure

Action per clientClient listMr. SmithMr. ___Mr. ___Mr. ___Mr.

80,000 names

Mr. SmithMr. ___Mr. ___Mr. ___

150,000 names

Mr. ___Mr. ___Mr. SmithMr. ___Mr. ___

100,000 names

Mr. ___Mr. ___Mr. ___Mr. Smith Mr. ___Mr. ___

50,000 names

Mr. ___Mr. ___Mr. ___Mr. ___Mr. Smith

1 million names

Cross-selling

Retention

Repricing

Nonperformingloan early warning

McKINSEY CUSTOMER SOLUTION CENTER

ConfidentialityagreementMcKinsey bank

McKinseyCustomerSolution Center

Data request

Sourcesof value

Cross-sellingRetention

Repricing

Transactionmigration

Nonperformingloan early warning

EUR m

1020

30

5

25

90Total

2

53

4

1

Commercial strategy for Mr. Smith: "Increase these 2

commissions, ask him not to use branches for cashwithdrawals, offer him this deposit, and have a con-

versation on whether his wife's payroll could comeinto the accounts, too"

6

1

2 4

6

3 5

Exhibit 6


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Managing a banks existing customer base to capture higher revenues while also offer-

ing greater customer value requires vast analytical precision and reach. Clearly the

preferences and needs of each individual customer are different: the difficulty is mining

the relevant data time- and cost-eff iciently. CVM turns data into value using a lifecycle

perspective with highly effective end-to-end customer analytics. Banks lacking the IT

architecture or ski lls have the option of entrusting the entire operation to McKinseys

Customer Solution Center. The advantage is that these capabilities and techniques,

once in place, are so granular that they are extremely hard for rivals to replicate. This can

give a bank a substantial competitive advantage, translating into boosting its bottomline.


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EMEA Banking Practice

December 2010

Copyright McKinsey & Company, Inc.

Driving intelligent growthwith Customer ValueMaximization How banks should gobeyond CRM

Authors:Anna Fiorentino

Knowledge Expert

Rome Ofce

[email protected]

Magdalena Proga-Stpie

Associate PrincipalWarsaw Ofce

magdalena_proga-stepien@mckinsey. com

Radek Przyby

Principal

Warsaw Ofce

[email protected]

Carlos Trascasa

Director

Madrid Ofce

[email protected]

driving intelligent growth with customer value maximization

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