Navigating Customer Lifetime Value Conundrum

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For Customer Intelligence Professionals

ExECUTIvE SUMMARyCustomer 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.

June 3, 2011

Navigating The Customer Lifetime value ConundrumA Decision-Making Framework For Lifetime value Analysisby srividya sridharanwith Suresh vittal and Allison Smith

© 2011, Forrester Research, Inc. Reproduction ProhibitedJune 3, 2011

2Navigating The Customer Lifetime value Conundrum For Customer Intelligence Professionals

· 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).



figure 1 Customer Lifetime value Decision-Making Framework

Source: Forrester Research, Inc.59245

Deploy

START

No

Yes

Do Iknow why

I needCLV?

How are youdefining the units

of analysis?

• Customer• Product• Cost• Profit

• Value drivers• Duration of customer

relationship• Cost and revenue

attribution• Discount rate• Competitive factors• Share of wallet • RFM

• Probability• Econometric,

etc.

• Transactional• Marketing• Cost data• Social data

• Get IT help• Get help from

analyticalserviceproviders

What data do youhave at your

disposal?

Defineobjective

Is itusable?

What are the modelassumptions?

What othercustomer

models exist?

What type ofmethodology should

I use for CLV modeling?Recalibrate

model

Customerresponsemodels

Attrition/churn

Propensity to buySurvival

Don’t knowwhich oneto use and

why?

REFINE

How do I test my model?

How do I integrate my modelinto live environments?

What do I do withthe output?

CLV ($$$) CLV(score/index)

Resourceallocation

Retention/winback Loyalty Cross-sell Segmen-

tation Forecasting

Define

Develop

No

Yes



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

CustomersCustomer state The status of customers

considered for analysisActive/inactive?

Products/services

Product unit The types of products/servicesconsidered

Product lines/sub-brands?

Level of aggregation The granularity in product data Regional/national?

Level of aggregation The granularity in customer-leveldata

Individual or segment/household?

ProfitabilityLevel of aggregation The granularity in profit data Business unit/lines of business?

Time horizon The length of the time periodused Present/future?

Cost

Costs to customer Assignment of variable costs tothe customer

Variable costs/customer servicecosts?

Acquisition costs Assignment of marketing-relatedcosts to the customer

Channel costs/media costs?



figure 3 Sample Data Audit Framework


Steps indata audit

DATA_ASSET_1

DATA_ASSET_2

DATA_ASSET_3

DATA_ASSET_4

DATA_ASSET_5

1

2

3

4

5

6

7

8

9

10

CODING

DE-DUPING

SORTING

NEW_VARIABLE_CREATION

NORMALIZATION

MISSING_VALUE_GENERATION

STANDARDIZATION

ATTRIBUTE_MAPPING

AGGREGATION

DE-DUPING

Description

Inclusion of coding variables

Removal of duplicate entries

Arranging data items in apattern for categorizationpurposes

Creation of any new variables

Organizing data to minimizeredundancy

Inclusion of missing values

Development andimplementation of technicaldata standards and formats

Creation of variable andattribute relationship maps

Combination of data fromdifferent data sources

Removal of duplicate entries

CUSTOMER LIFETIME VALUE: DATA AUDIT

E_

DIZAimpledata sta

tionribut

Combd

rganizing dadundancy

usion of missing valu

pmenntatidard

iables

ntries

categori

new variables

minimi

DASS

DAS

DATA_ASSE

DATA_SOURCE

DATA_TYPE

NUMBER_OF_RECORDS

PRE-AUDIT_ACCURACY

POST-AUDIT_ACCURACY

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


Cu

sto

mer

life

tim

e va

lueExplore

Buy

Engage

Discover Discover

Explore

Buy

Engage

Response rate

Trial rate

Revenue or salesCross-sell/upsell

Churn or defection

Recurring costs

Distribution costs

Acquisition costs

Service levelsRepurchase rate


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 [email protected]. 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

R E C O M M E N D A T 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.

endnotes1 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).

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