Driving intelligent growth with Customer Value Maximization

EMEA Banking Practice

Driving intelligent growth with Customer Value Maximization How banks should go beyond CRM

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

Driving intelligent growth with Customer Value Maximization How banks should go beyond CRM

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 certainly 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 year’s client profitability. To borrow 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 customer’s 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 shift 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 immediate 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 performance, incorporating lifetime value as a key indicator for customer targeting when prioritizing and identifying opportunities across all customer segments and levers. Their targeted campaigns use value-based segmentation and propensity models, and implement 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.

1

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

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Banks' profits can be fueled in a downturn by maximizing the value of their customer base CVM is a proven methodology ... Understand customer base: identify the most profitable customers as well as the "revenue chains"; segment your commercial initiatives Go after opportunities along all value levers (acquisition, crossselling, retention), especially beyond the obvious ones (e.g., volume churn)

… that helps identify and capture the full potential of a bank's customer base Value creation along value drivers 3 100

3

2

30

5

30 119 11

17

Increase the ROI of commercial initiatives and increase their effectiveness

Profit New Win- Acti- Upyear 1 cus- back vation sell tomers

Cross- Re- Prod- Cus- Profit sell newal uct tomer year 2 churn churn

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

Exhibit 1

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 access to 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-priority 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.

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

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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. Of course, 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. ƒƒ  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).

CUSTOMER PROFILE DEVELOPMENT SOUTH EUROPEAN Deep dive into individual customer value and profiling BANK EXAMPLE to understand segments and drivers of value TOP 30% CUSTOMERS BY AUM Product penetration Percent Profit/ Number of cusNumber Direct AUM/ customers tomer customer of prod- Mort- Sav- Mutual invest- Insur- Current gage ings fund Thousands EUR EUR ucts ment ance account Card Credit

Annual profit EUR m

Segment Mortgage holders

14

5

2,825

36,171

8.3

100

17

20

17

67

100

82

13

Investors

12

30

415

132,581

7.4

8

21

59

72

8

85

32

8

9

117

49,401

6.5

9

20

19

21

25

100

81

12

18

-730

85,313

8.0

11

100

19

18

12

84

29

2

14

86

54,596

4.0

2

21

17

16

6

100

15

2

171

827

7.0

14

44

34

39

14

90

36

6

1

Transactors Savers Other Average

-15

1

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

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

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ƒƒ Step 2: Create and prioritize a heat map. For identifying and prioritizing opportunities banks need to look across all commercial levers (acquisition, customer development, 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 prioritization exercise has both strategic and operational implications. —— The first substep is to generate a heat map with large potential pools by segment/product/commercial lever. This helps banks identify areas of high importance, 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). HEAT MAP CREATION

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

CEE BANK EXAMPLE Key value opportunities

Heat map model output – scenario 1 EUR millions, annualized Key CVM value drivers Acquisition Segments

Retail

Development

Retention

Winback

Activation

Upselling

Crossselling

Renew- Product Customer als churn churn

Total

30

4

40

36

120

0

25

7

262

Affluent

110

33

23

27

20

1

101

6

321

Subtotal

140

37

63

63

140

1

126

13

583

SOHO/Micro

40

0

21

30

52

0

22

1

166

Small

24

0

16

15

23

0

18

0

96

Medium

33

2

23

49

16

3

30

8

164

Subtotal

97

2

60

94

91

3

70

9

426

237

39

123

157

231

4

196

22

1,009

Mass

SMEs

New customers

Total

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 defining 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 network (Exhibit 4).

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

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HEAT MAP CREATION

Identify opportunities to create value across the customer base

CEE CLIENT EXAMPLE

Average customer profit per decile EUR Destroy 25% of total profit

Generate 85%of total profit 845 230 105 48

20

Average profit per customer: EUR 80

0

-7

-20

-50

7

8

9

-170

1

2

3

▪ Generate retention ▪

▪ ▪

actions and early warning signals Development of deciles 2 and 3 (up-sell volumes and usage increase) Targeted offer for customers with deposits elsewhere Optimize service levels

4

5

6

▪ 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)

10

▪ Modify pricing ▪ Redesign product ▪ Increase activation

Customer deciles

rates/activity levels

▪ Develop targeted ▪

actions to develop customers below 30 Optimize service costs

Exhibit 4 ƒƒ  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 bank’s specific actions 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 specific 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).

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

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EXECUTION ACROSS ORGANIZATION

Scientific campaign design with quick tests and feedback cycles is essential for capturing value Campaign idea

Pre-campaign ROI

Test environment execution

Post-campaign ROI

SMS Stimulation Campaign Overview

Resources

SMS Stimulation Campaign designed to stimulate usage of SMS Subscribers that SMS at least once will likely begin to use SMS to some extent Value Drivers: Usage Segment: Postpaid and prepaid Dates: 15/1/2004 – 15/02/2004 Campaign: Overview: Hypothesis:

Offer Execution Subs with zero SMS usage after first 2 months of service Size: Target: 3 x 10,000 Control: 10,000 Business rules: ARPU > 25EUR for postpaid, Balance > 5EUR, 0 SMS MoU in any of previous 6 months, consumer, >2 months since activation, revenue accts Offer: 1) $100 contest 2) 10 free SMS 3) 10 free voice MoU Target:

Pre-Campaign Estimates: Take Rate

Δ ARPU xx

Δ Churn

10% Channels: DM

TM

SMS

Email

O/B: 0% I/B: 0%

0% 0%

100% 0%

0% 0%

Messages:

None T. Akgodan None 03/12/2003

Campaign Owner: Template Owner: Department: Date:

None None None

ROI Model Owner: Department: Date:

Offer Economics

xx

ROI Xx EUR

0% N/A

1251 (12.5%)

0% N/A

Contacts: 9002 (90%) Target

Total Churn xx xx xx xx Total Revenue Benefit / Revenue*

Bill Messaging Bill Inserts

2 different messages per offer

Takers: Economics:

be Cost of Offer To Cost of Contact**

($2641)

$10687

3.68%

eted

st po

$8047 mpl

co

$8502

Total Costs

$8502

ROI:

($455)

Control Net Change 3.67%

-0.34%

0.26 26.99

-51.2% 4.2%

23.07 ign1.5% 0.07 mpa -2.2% ca 0.33 80.8%

0.85

Notes: * xxxxx

37

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

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

Fast implementation ƒ Dedicated test support ƒ Accelerated new offers implementation ƒ Scientific setup

Automated evaluation ƒ Real economics of campaign

Campaign library Description

Status

      

   Approved  Approved In progress   Finished Evaluated 

BM xxx BM xxx MM xxx MM xxx BM xxx MM xxx MM xxx

Pre-campaign Take% Costs ROI% Subs

Rev

Idea ROI

Post-campaign Take% Costs ROI

Subs

Rev

      

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

Clear timing, ownership, and end products defined across campaign process

Exhibit 5

McKinsey’s 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: ƒƒ Data infrastructure issues: Relevant data is often dispersed nonhomogeneously across business segments or units in various data sources across the organization, and is hard to integrate quickly. Banks frequently struggle with insufficient capacity to handle very large data sets to perform analyses on their full customer data. ƒƒ 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. ƒƒ 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.

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

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McKinsey has developed an answer to these obstacles. Its global Customer Solution Center (CSC) combines deep expertise, analytical power, and infrastructure to analyze our clients’ customer base and help them prioritize actions to design highly targeted, high-impact campaigns. McKinsey’s Customer Solution Center allows banks to accelerate the identification of opportunities to generate faster impact. The facilities available include: ƒƒ 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, avoid interference with regular operations, and secure, state-of-the-art hardware and statistical software applications. ƒƒ 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). McKINSEY CUSTOMER SOLUTION CENTER

CSC has developed an innovative service model, where on top of analytical skills McKinsey also provides physical infrastructure 1

Data request

3

5

McKinsey Customer Solution Center

Client list Cross-selling

80,000 names

Retention

150,000 names

Repricing

100,000 names

Nonperforming loan early warning

50,000 names

1 million names

2

4

Action per client Mr. Smith Mr. ___ Mr. ___ Mr. ___ Mr. Mr. Smith Mr. ___ Mr. ___ Mr. ___ Mr. ___ Mr. ___ Mr. Smith Mr. ___ Mr. ___ Mr. ___ Mr. ___ Mr. ___ Mr. Smith Mr. ___

Mr. ___

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

Sources of value EUR m

Confidentiality agreement McKinsey – bank

Cross-selling Retention Repricing Transaction migration Nonperforming loan early warning Total

10 20 30 5 25 90

6

Commercial strategy for Mr. Smith: "Increase these 2 commissions, ask him not to use branches for cash withdrawals, offer him this deposit, and have a conversation on whether his wife's payroll could come into the accounts, too"

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.   

EMEA Banking Practice Driving intelligent growth with Customer Value Maximization

Managing a bank’s existing customer base to capture higher revenues while also offering 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-efficiently. CVM turns data into value using a lifecycle perspective with highly effective end-to-end customer analytics. Banks lacking the IT architecture or skills have the option of entrusting the entire operation to McKinsey’s 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 bottom line.

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Driving intelligent growth with Customer Value Maximization – How banks should go beyond CRM

Authors:

Anna Fiorentino Knowledge Expert Rome Office [email protected] Magdalena Proga-Stępień Associate Principal Warsaw Office magdalena_proga-stepien@mckinsey. com Radek Przybył Principal Warsaw Office [email protected] Carlos Trascasa Director Madrid Office [email protected]

EMEA Banking Practice December 2010 Copyright © McKinsey & Company, Inc.