Navigating Customer Lifetime Value Conundrum

For Customer Intelligence Professionals

June 3, 2011
Navigating The Customer Lifetime value
Conundrum
A Decision-Making Framework For Lifetime value Analysis
by srividya sridharan
with Suresh vittal and Allison Smith

ExECUT I v E S U M MA Ry
Customer lifetime value (CLV), a forward-looking indicator of customer profitability, became common
parlance as relationship marketing gained momentum. However, calculating CLV remains a significant
iterative process within organizations and is often fraught with challenges in each step. At best,
organizations arrive at close estimates based on how they choose to design the model. But the process
remains expensive, error-prone, and often lacks real-world application. The effort required to prepare for
and progress through model development is where customer intelligence (CI) professionals need to do
the heavy lifting. To help, Forrester recommends a three-phased approach to determine lifetime value.

dispel mytHs tHat undermine lifetime value analysis
Forrester defines customer lifetime value as a customer’s potential monetary worth through the course
of his or her relationship with a business.1 CLV is a powerful metric that potentially affects marketing
execution across the entire life cycle — customer acquisition, targeting, and retention — and ultimately
drives firm equity and shareholder value. It enables marketers to make resource allocation decisions with
greater certainty and forces differential treatment of customers based on profitability. Given the broad
applicability, it’s not surprising that marketers struggle with many misconceptions when it comes to CLV:

· CLV derived from calculators is adequate. Ready-made CLV calculators do not capture the
complexities in lifetime value analysis but are a good beginning, especially for organizations without
analytical support. But firms that rely only on the output of a CLV calculator to design marketing
programs compromise the accurate assessment of customer profitability and, in turn, are prone to
mistaken treatment of customers based on value.

· Customer value only means financial value. To compute CLV, it is vital to determine the value-
generating activities of the customer. In its most basic form, a value-generating activity is when
order becomes cash (i.e., when a customer transacts with the firm). But often other customer
behaviors, such as social media activity, also generate value for firms although, in the short term,
these may not always translate into dollar terms. For example, customers generate social value based
on the strength of their influence, reach, and the value of their networks in social spaces.2 Firms that
ignore nonmonetary value risk miscalculating the real value that customers bring to the firm.

Headquarters
Forrester Research, Inc., 400 Technology Square, Cambridge, MA 02139 USA
Tel: +1 617.613.6000 • Fax: +1 617.613.5000 • www.forrester.com

Navigating The Customer Lifetime value Conundrum 2

· Average is “good enough” to estimate inputs. The use of averages to determine important
inputs into lifetime value analysis, such as revenue per customer, acquisition cost, and
relationship duration, leads to gross generalizations. For instance, combining all customer
segments into a single bucket for analysis to compute average revenue per customer is
detrimental to devising CLV-based strategies for the firm’s high-value customers. Marketers
who do this risk devaluing their best customers.

· Cost attribution is the domain of finance. While marketing manages customer activities that
drive revenue and profitability, finance and accounting manage the costing processes. The type
of costing approach (full or marginal costing) has a dramatic impact on the decision to nurture
or reject a group of customers based on profitability.3 Cost attribution at the customer level calls
for a combined effort between marketing and finance; without this, the relationship between
CLV and the firm’s financial performance is harder to establish.

follow a regimented approacH to clv
CLV is one of the few metrics that reflect marketing’s financial outcomes with dependencies on
multiple aspects of the business outside of marketing.4 As a result, the process of calculating CLV is
confusing and resource-intensive — not suitable for marketers to venture into without a clear grasp
on its intended applicability. To help marketers navigate and assess readiness to go through this
complex process, we have devised a framework comprising of three phases — define, develop, and
deploy (see Figure 1).

June 3, 2011 © 2011, Forrester Research, Inc. Reproduction Prohibited


figure 1 Customer Lifetime value Decision-Making Framework

Define
START

Do I
Define No know why
objective I need
CLV?

Yes
• Customer How are you • Transactional
Is it No
• Product defining the units • Marketing
of analysis? usable?
• Cost • Cost data
• Profit • Social data Yes
What data do you
have at your
disposal?
• Get IT help
• Get help from
analytical
Develop service
What are the model providers
• Value drivers
assumptions?
• Duration of customer
relationship
Customer Attrition/churn
• Cost and revenue What other
attribution customer response Propensity to buy
• Discount rate models exist? models Survival
• Competitive factors
• Share of wallet • RFM Don’t know
What type of • Probability which one
methodology should to use and
I use for CLV modeling? • Econometric,
etc. why?
Recalibrate
model
Deploy
How do I test my model?

How do I integrate my model
into live environments?
CLV ($$$) CLV
(score/index)
What do I do with
the output?

Resource Retention/ Segmen-
winback Loyalty Cross-sell Forecasting
allocation tation

REFINE

59245 Source: Forrester Research, Inc.



phase 1: define the parameters of lifetime value analysis
The first stage in lifetime value analysis is to define the various parameters that feed into the CLV
model. CI professionals must:

· Determine the intent of the CLV exercise. Let’s get one thing clear from the outset: Developing
a CLV model is expensive and complicated. Marketers who are unclear about the intended use
should stay away. For instance, if the intention is to use CLV for singular initiatives like marketing
channel optimization, marketers are better served by relying on aggregate channel-focused
performance measures, such as lift and ROI, rather than on customer-level measures like CLV.

· Settle on a common unit of analysis. Before jumping into data gathering, CI professionals
should define the important analysis units, such as customer definitions, profit calculations, and
lifetime definitions. As the head of analytics for a retailer put it, “A common understanding of
key terminology is important to avoid confusion in the process” (see Figure 2).

· Audit and evaluate available data. Lifetime value analysis involves large volumes of data, as
the level of analysis is usually at the individual or segment level. CI professionals must audit,
prepare, and cleanse the available data from transactional databases (purchase behavior);
accounting systems; marketing databases (customer segment information); and social data
(social influence and engagement) to feed the CLV model (see Figure 3).

figure 2 Define Data Specifications

Data category Description Example
Level of aggregation The granularity in customer-level Individual or segment/
data household?
Customers
Customer state The status of customers Active/inactive?
considered for analysis
Product unit The types of products/services Product lines/sub-brands?
Products/ considered
services
Level of aggregation The granularity in product data Regional/national?
The length of the time period
Time horizon used Present/future?
Proﬁtability
Level of aggregation The granularity in proﬁt data Business unit/lines of business?
Costs to customer Assignment of variable costs to Variable costs/customer service
the customer costs?
Cost
Acquisition costs Assignment of marketing-related Channel costs/media costs?
costs to the customer




figure 3 Sample Data Audit Framework

CUSTOMER LIFETIME VALUE: DATA AUDIT

DATA_SOURCE
DATA_TYPE
NUMBER_OF_RECORDS
PRE-AUDIT_ACCURACY
POST-AUDIT_ACCURACY

Steps in D
DATA_ D
DATA_ DATA_ DATA_ DATA_
data audit Description ASS
ASSET_1 AS
ASSET_2 ASSE
ASSET_3 ASSET_4 ASSET_5
1 CODING iables
Inclusion of coding variables
2 DE-DUPING ntries
Removal of duplicate entries
3 SORTING Arranging data items in a
pattern for categori
categorization
purposes
4 NEW_VARIABLE_ Creation of any new variables
CREATION
5 NORMALIZATION Organizing da to minimi
rganizing data minimize
dundancy
redundancy
E_
6 MISSING_VALUE_ usion valu
Inclusion of missing values
GENERATION
DIZA
7 STANDARDIZATION pmen
Development and
imple ntati of technical
implementation
data sta dard and formats
standards
8 ATTRIBUTE_ tion
Creation of variable and
MAPPING ribut
attribute relationship maps
9 AGGREGATION Comb
Combination of data from
d
diﬀerent data sources
10 DE-DUPING Removal of duplicate entries


phase 2: develop lifetime value in conjunction with response models
In the development phase, CI professionals must enlist support from analytical service providers
specializing in customer modeling techniques.5 CI professionals must:

· Build data-driven assumptions. After settling on a common understanding of the units of
analysis, build the assumptions around value-drivers of the customer relationship, duration of
the relationship, cost and revenue attribution rules, and the discount rate. A leading European
insurance company built a blueprint of assumptions upfront as a result of the CLV project
kickoff process, which considerably helped reduce the project delivery time.



· Examine existing customer response models. These models should yield accurate or field-
tested measures of key inputs like churn, retention rate, or loyalty that will influence CLV model
development. For example, a telecom company uses its attrition model to understand the
drivers of churn, which in turn feeds into its customer lifetime model.

· Agree on suitable modeling methodology. The most analytically intense step in the CLV
process is to determine the modeling technique — choices range from simple recency-
frequency-monetary (RFM) models to more complex econometric models, each with significant
benefits and limitations. For example, the probability-based Pareto/negative binomial
distribution (NBD) model is more appropriate in noncontractual business settings, where a
customer transaction occurs at any time, while an RFM model is a scoring model, not suitable
when a dollar amount for CLV is required.

phase 3: deploy clv into a practical decision-support environment
After CLV is computed, either as a score or a multiyear dollar value, organizations must determine
how to use it in the marketing planning process. In this phase, CI professionals must:

· Test, learn, and adapt the model. In this step, the model is not only tested for its statistical
stability but also vetted for business relevance and applicability. For instance, based on the initial
findings of CLV, marketers should pilot a cross-sell program targeted at a low-CLV segment.
The response and resulting profit generated from converting customers acts as a strong example
to the practical use of CLV.

· Integrate the model into relevant decision engines. Once the model is stable, embed it
into decision systems. For example, after the customer lifetime value score is computed, CI
professionals should score the customer database and then segment customers based on their
current and future value. This makes CLV operational and usable for future marketing activities.

· Apply CLV to everyday marketing. The most common applications of CLV are marketing
resource allocation, designing retention and win-back programs, creating value-based
segmentation, and optimizing marketing execution. Prioritizing where to apply the results of CLV
depends on the business problem the organization is trying to address. For example, Farmers
Insurance Group applied its CLV model to its direct marketing efforts and increased ROI by 14%.6

use clv to manage customer value creation across tHe life cycle
CLV stands out as a predictive approach because it brings profitability into the mix. Customers with
a high propensity to buy or with high revenue-generating potential may not always generate the
highest profit and vice versa. Revenue-based metrics mislead marketing efforts because they focus
on growth in isolation. Contribution-based or margin-based models like CLV help marketers create
value across the customer life cycle in the following ways (see Figure 4):



· Target customers based on value potential. Marketers save considerable resources by
segmenting and targeting customers based on their value potential. A large airline company,
for example, used its value-based customer model to identify high-value frequent flyers and to
design retention programs based on the potential lifetime value of these flyers.

· Manage acquisition and recurring costs. CI professionals should use CLV to identify
marketing activities that drive up costs and reduce profit. The refrain “it costs the average
business five to 10 times more to acquire a new customer than to retain and service an existing
one” still holds true. For instance, one retail bank found that by streamlining logistics and
improving efficiency of marketing operations, it was able to reduce the cost of service by 20%.

· Drive value on the retention front. As retention is an important indicator of the source of
volatility in a customer relationship, its direct relationship to CLV is evident. Case in point:
The head of analytics for a credit card firm told us that a 5% reduction in customer defection
increases profits by 25% to 80%. By designing programs that reduce attrition, marketers can
drive significant lift in the bottom line.

figure 4 Levers Of CLv Across The Life Cycle

Acquisition costs
Discover
ver Response rate
Disco
Customer lifetime value
Exp

Trial rate
lor

Explore
Distribution costs
e

Revenue or sales
Buy Cross-sell/upsell
En
ga

Recurring costs
ge

Churn or defection
Buy Engage Service levels
Repurchase rate




R E C O M M E N D AT I O N S

lifetime value underpins customer intelligence success
To leverage the benefits of calculating and using CLv models in customer value management,
customer intelligence professionals must use customer lifetime value as a means to:

· champion the cause of customer-centricity. The impact of CLv cascades into other
customer-centric business indicators, such as loyalty, satisfaction, and overall brand
health. As a result, CI professionals must use CLv to broaden the scope of customer value
management, elevating their role from model-building and campaign-planning to devising
customer-centric CLv strategies.
· encourage commonality in measurement objectives. There are many metrics that are
relevant and critical for marketing, but CLv is one such metric that aligns multiple facets
of marketing. CI professionals must use CLv as a shared goal across conflicting marketing
objectives, which requires establishing clear linkages between marketing inputs and
customer value.
· infuse predictive thinking in ci practices. There is merit in reporting about what happened
and why, which is the most prevalent form of measurement, but to answer the “what next?”
question, CI professionals need CLv in short-term and long-term marketing planning.

endnotes
1
The most important aspect of CLV analysis is that it focuses on multiple interactions between customers
and the business over time, as opposed to one-time sales. See the March 11, 2009, “Executive Q&A:
Customer Lifetime Value” report.
2
Although there is no magical group of consumers driving everyone else’s decisions, some consumers
are potentially more “socially valuable” than others. See the February 27, 2008, “Redefining High-Value
Customers” report.
3
Full costing is a conventional method of cost accounting that includes manufacturing costs — including
direct costs, labor, and both fixed and variable overheads. Marginal costing includes only variable
manufacturing costs, excluding fixed costs.
4
The customer life cycle and the metrics you can develop from the framework are just tools. The challenge
here is to shift an ingrained, inward-looking point of view to an external one centered on the customer. See
the February 8, 2011, “Customer Life-Cycle Marketing Demands New Metrics” report.
5
Given the arduous struggle to find, hire, and retain marketing analysts, marketers should look outside their
organizations for support. See the July 11, 2008, “Six Success Factors For Outsourcing Customer Analytics”
report.
6
Source: SAS, “Farmers Insurance analyzes customer lifetime value with SAS,” (http://www.sas.com/success/
farmers.html).

Forrester Research, Inc. (Nasdaq: FORR) is an independent research company that provides pragmatic and forward-thinking advice to global leaders in business
and technology. Forrester works with professionals in 19 key roles at major companies providing proprietary research, customer insight, consulting, events, and
peer-to-peer executive programs. For more than 27 years, Forrester has been making IT, marketing, and technology industry leaders successful every day. For
more information, visit www.forrester.com.
© 2011 Forrester Research, Inc. All rights reserved. Forrester, Forrester Wave, RoleView, Technographics, TechRankings, and Total Economic Impact are trademarks
of Forrester Research, Inc. All other trademarks are the property of their respective owners. Reproduction or sharing of this content in any form without prior
written permission is strictly prohibited. To purchase reprints of this document, please email clientsupport@forrester.com. For additional reproduction and usage
information, see Forrester’s Citation Policy located at www.forrester.com. Information is based on best available resources. Opinions reflect judgment at the time
and are subject to change. 59245

Navigating Customer Lifetime Value Conundrum

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Navigating Customer Lifetime Value Conundrum

Similar to Navigating Customer Lifetime Value Conundrum (20)

More from gregmgaffney

More from gregmgaffney (7)

Navigating Customer Lifetime Value Conundrum