Praise for Marketing Metrics“Measurement is critical to the health of any business, and Marketing Metrics highlights
key tools and techniques across many measurement landscapes—from the consumer, tothe sales force, to the ever-changing media environment. It’s a ‘must-read’ for any business leader who wants to optimize the way they measure business activities and resultsin order to grow their business.”—Kimberley B. Dedeker, Vice President, Global Consumer & Market Knowledge,
Procter & Gamble“Why read Marketing Metrics? Because better metrics lead to better decisions, whichlead to better outcomes. This book does a superb job of helping marketers, and all executives, understand which metrics to use and how to use them.”—Erv Shames, former CEO, Kraft Foods“Why was this book not written earlier? Marketing Metrics presents an excellent compendium of the metrics you really need to know, along with a structural framework thatties them together and helps you steer your business successfully.”—Dr. Hans-Willi Schroiff, Vice President, Market Research/Business Intelligence,
Henkel“Marketing is being challenged, as never before, to be accountable. This book, bydescribing metric options and their risks, will help address this challenge.”—David Aaker, author of Brand Portfolio Strategy“Measurement is central to our business discipline. What gets measured matters, andhaving the right measures is key. Marketing Metrics provides an insightful compilationof what to measure and how to measure it for today’s marketing-savvy executives.”
SM—Glenn Renwick, CEO of the Progressive Corporation (Progressive Direct
Drive® Insurance from Progressive)

and“Marketing, as a function, is under increasing pressure to develop business-oriented
metrics to justify marketing mix investments. Marketing Metrics offers clear advice on
how to develop common marketing metrics that are relevant and accessible to bothmarketing and non-marketing decision makers.”—Anil Menon, Vice President, Marketing, Systems & Technology Group, IBM

This page intentionally left blank

MARKETING METRICS

This page intentionally left blank

MARKETINGMETRICS
50+ METRICS EVERYEXECUTIVE SHOULD MASTERPaul W. Farris
Neil T. Bendle
Phillip E. PfeiferDavid J. ReibsteinVice President, Publisher: Tim Moore
Associate Publisher and Director of Marketing: Amy Neidlinger
Editor: Yoram (Jerry) Wind
Acquisitions Editor: Paula Sinnott
Editorial Assistant: Susie Abraham
Cover Designers: Chuti Prasertsith and Sandra Schroeder
Managing Editor: Gina Kanouse
Project Editor: Kayla Dugger
Copy Editor: Ben Lawson Publishing, LLC
Indexer: Lisa Stumpf
Compositor: Interactive Composition Corporation
Proofreader: Jessica McCarty
Manufacturing Buyer: Dan Uhrig© 2006 by Pearson Education, Inc.
Publishing as Prentice Hall
Upper Saddle River, New Jersey 07458
Prentice all offers excellent discounts on this book when ordered in quantity for bulk
purchases or special sales. For more information, please contact U.S. Corporate and Government
Sales, 1-800-382-3419, corpsales@pearsontechgroup.com. For sales outside the U.S., please contact
International Sales at international@pearsoned.com.
Company and product names mentioned herein are the trademarks or registered trademarks of theirrespective owners.
All rights reserved. No part of this book may be reproduced, in any form or by any means, without
permission in writing from the publisher.
Printed in the United States of America
Tenth Printing, July 2008
ISBN-10: 0-13-187370-9
ISBN-13: 978-0-13-187370-4
Pearson Education LTD.
Pearson Education Australia PTY, Limited.
Pearson Education Singapore, Pte. Ltd.
Pearson Education North Asia, Ltd.
Pearson Education Canada, Ltd.
Pearson Educatión de Mexico, S.A. de C.V.
Pearson Education—Japan
Pearson Education Malaysia, Pte. Ltd.
Library of Congress Cataloging-in-Publication Data
Marketing metrics : 50+ metrics every executive should master / Paul Farris . . . [et al.].
p. cm.
Includes bibliographical references and index.
ISBN 0-13-187370-9 (hardback : alk. paper) 1. Marketing research.
2. Marketing—Mathematical models. I. Farris, Paul.
HF5415.2.M35543 2006
658.8'3—dc22
2005031114

We dedicate this book to our students, colleagues,
and consulting clients who convinced us thata book like this would fill a real need.

This page intentionally left blankCONTENTS
Acknowledgments xi
About the Authors xiii
Foreword xv
1
INTRODUCTION 1

2
SHARE OF HEARTS, MINDS, AND MARKETS 11

3
MARGINS AND PROFITS 45

4
PRODUCT AND PORTFOLIO MANAGEMENT 89

5
CUSTOMER PROFITABILITY 129

6
SALES FORCE AND CHANNEL MANAGEMENT 157

ix

7
PRICING STRATEGY 195

8
PROMOTION 239

9ADVERTISING MEDIA AND WEB METRICS 263

10
MARKETING AND FINANCE 305

11
THE MARKETING METRICS X-RAY 323

Bibliography 335
Endnotes 339
Index 345x

MARKETING METRICSACKNOWLEDGMENTS
We hope this book will be a step, however modest, toward clarifying the language,
construction, and meaning of many of our important marketing metrics. If we havesucceeded in making such a step, we owe thanks to a number of people.
Jerry Wind reviewed our initial concept and encouraged us to set our sights higher.
Rob Northrop, Simon Bendle, and Vince Choe read early drafts and gave valuable
feedback on the most important chapters. Eric Larson, Jordan Mitchell, Tom Disantis,
and Francisco Simon helped develop material for important sections and provided theirresearch skills. Gerry Allan and Alan Rimm-Kauffman allowed us to cite liberally fromtheir materials on customers and Internet marketing.
Marc Goldstein combined business savvy with deft editing touches that improved thereadability of almost every chapter. Paula Sinnott, Tim Moore, Kayla Dugger, and theircolleagues also made significant improvements in moving from a raw manuscript to thebook in your hands.
Erv Shames, Erjen van Nierop, Peter Hedlund, Fred Telegdy, Judy Jordan, Lee Pielemier,and Richard Johnson have collaborated on our “Allocator” management simulation and“Management by the Numbers” online tutorials. That work helped us set the stage forthis volume. Finally, we thank Kate, Emily, Donna, and Karen, who graciously tolerated
the time sacrificed from home and social lives for the writing of this book.

xi

This page intentionally left blankABOUT THE AUTHORSPaul W. Farris is Landmark Communications Professor and Professor of Marketing at
The Darden Graduate Business School, University of Virginia, where he has taught since1980. Professor Farris’s research has produced award-winning articles on retail power
and the measurement of advertising effects. He has published more than 50 articles injournals such as the Harvard Business Review, Journal of Marketing, Journal ofAdvertising Research, and Marketing Science. He is currently developing improved techniques for integrating marketing and financial metrics and is co-author of several
books, including The Profit Impact of Marketing Strategy Project: Retrospect andProspects. Farris’s consulting clients range from Procter & Gamble to Apple and IBM. He
has served on boards of manufacturers, retailers, and e-Business companies. Currently,
he is a director of Sto, Inc., and GSI Group.
Neil T. Bendle is a Ph.D. student in marketing at the Carlson School of Management,
University of Minnesota. He holds an MBA from Darden, and has nearly a decade’s
experience in marketing management, consulting, business systems improvement, andfinancial management. He was responsible for measuring the success of marketing campaigns for the UK’s Labour Party.
Phillip E. Pfeifer, Alumni Research Professor of Business Administration at The Darden
Graduate Business School, currently specializes in interactive marketing. He has published a popular MBA textbook and over 25 refereed articles in journals such as the
Journal of Interactive Marketing, Journal of Database Marketing, Decision Sciences, andthe Journal of Forecasting. Pfeifer was recognized in 2004 as the Darden School’s facultyleader in external case sales. His teaching has won student awards and has been recognized in Business Week’s Guide to the Best Business Schools. His recent clients include
Circuit City, Procter & Gamble, and CarMax.
David J. Reibstein is Managing Director of CMO Partners and William Stewart
Woodside Professor of Marketing at the Wharton School. Regarded as one of the world’s
leading authorities on marketing, he served as Executive Director of the Marketing
Sciences Institute, and co-founded Wharton’s CMO Summit, which brings together
leading CMOs to address their most pressing challenges. Reibstein architected andteaches the Wharton Executive Education course on marketing metrics. He has anextensive track record consulting with leading businesses, including GE, AT&T Wireless,
Shell Oil, HP, Novartis, Johnson & Johnson, Merck, and Major League Baseball. He hasserved as Vice Dean and Director of Wharton’s Graduate Division, as visiting professor
at Stanford and INSEAD, and as faculty member at Harvard. He serves on the Board of
Directors of Shopzilla, And1, and several other organizations.

xiii

This page intentionally left blank

FOREWORD
Despite its importance, marketing is one of the least understood, least measurable functions at many companies. With sales force costs, it accounts for 10 percent or more ofoperating budgets at a wide range of public firms. Its effectiveness is fundamental tostock market valuations, which often rest upon aggressive assumptions for customer
acquisition and organic growth. Nevertheless, many corporate boards lack the understanding to evaluate marketing strategies and expenditures. Most directors—and a rising percentage of Fortune 500 CEOs—lack deep experience in this field.
Marketing executives, for their part, often fail to develop the quantitative, analyticalskills needed to manage productivity. Right-brain thinkers may devise creative campaigns to drive sales but show little interest in the wider financial impact of their work.
Frequently, they resist being held accountable even for top-line performance, assertingthat factors beyond their control—including competition—make it difficult to monitor
the results of their programs.
In this context, marketing decisions are often made without the information, expertise,and measurable feedback needed. As Procter & Gamble’s Chief Marketing Officer has
said, “Marketing is a $450 billion industry, and we are making decisions with less data
and discipline than we apply to $100,000 decisions in other aspects of our business.”
This is a troubling state of affairs. But it can change.
In a recent article in The Wall Street Journal, I called on marketing managers to take concrete steps to correct it. I urged them to gather and analyze basic market data, measure
the core factors that drive their business models, analyze the profitability of individual
customer accounts, and optimize resource allocation among increasingly fragmented
media. These are analytical, data-intensive, left-brain practices. Going forward, I believe
they’ll be crucial to the success of marketing executives and their employers. As I concluded in the Journal:“Today’s boards want chief marketing officers who can speak the language of productivity and return on investment and are willing to be held accountable. In
recent years, manufacturing, procurement and logistics have all tightened theirbelts in the cause of improved productivity. As a result, marketing expenditures
account for a larger percentage of many corporate cost structures than ever before.
Today’s boards don’t need chief marketing officers who have creative flair but nofinancial discipline. They need ambidextrous marketers who offer both.”In Marketing Metrics, Farris, Bendle, Pfeifer, and Reibstein have given us a valuablemeans toward this end. In a single volume, and with impressive clarity, they havexv

outlined the sources, strengths, and weaknesses of a broad array of marketing metrics.
They have explained how to harness those data for insight. Most importantly, they have
explained how to act on this insight—how to apply it not only in planning campaigns,
but also in measuring their impact, correcting their courses, and optimizing theirresults. In essence, Marketing Metrics is a key reference for managers who aim to become
skilled in both right- and left-brain marketing. I highly recommend it for all ambidextrous marketers.
John A. Quelch, Lincoln Filene Professor of Business Administration and Senior
Associate Dean for International Development, Harvard Business School

xvi

MARKETING METRICS

1
INTRODUCTION
In recent years, data-based marketing has swept through the business world. In its wake,measurable performance and accountability have become the keys to marketing success.
However, few managers appreciate the range of metrics by which they can evaluate
marketing strategies and dynamics. Fewer still understand the pros, cons, and nuancesof each.
In this environment, we have come to recognize that marketers, general managers, andbusiness students need a comprehensive, practical reference on the metrics used tojudge marketing programs and quantify their results. In this book, we seek to providethat reference. We wish our readers great success with it.

1.1 What Is a Metric?
A metric is a measuring system that quantifies a trend, dynamic, or characteristic.1
In virtually all disciplines, practitioners use metrics to explain phenomena, diagnosecauses, share findings, and project the results of future events. Throughout the worlds ofscience, business, and government, metrics encourage rigor and objectivity. They makeit possible to compare observations across regions and time periods. They facilitate
understanding and collaboration.

1.2 Why Do You Need Metrics?
“When you can measure what you are speaking about, and express it in numbers, youknow something about it; but when you cannot measure it, when you cannot express
it in numbers, your knowledge is of a meager and unsatisfactory kind: it may be
the beginning of knowledge, but you have scarcely, in your thoughts, advanced to thestage of science.”––William Thomson, Lord Kelvin, Popular Lectures and Addresses(1891–94)21

Lord Kelvin, a British physicist and the manager of the laying of the first successfultransatlantic cable, was one of history’s great advocates for quantitative investigation. In
his day, however, mathematical rigor had not yet spread widely beyond the worlds ofscience, engineering, and finance. Much has changed since then.
Today, numerical fluency is a crucial skill for every business leader. Managers must
quantify market opportunities and competitive threats. They must justify the financial
risks and benefits of their decisions. They must evaluate plans, explain variances, judge
performance, and identify leverage points for improvement––all in numeric terms.
These responsibilities require a strong command of measurements and of the systems
and formulas that generate them. In short, they require metrics.
Managers must select, calculate, and explain key business metrics. They must understand how each is constructed and how to use it in decision-making. Witness the following, more recent quotes from management experts:“. . . every metric, whether it is used explicitly to influence behavior, to evaluate future
strategies, or simply to take stock, will affect actions and decisions.” 3“If you can’t measure it, you can’t manage it.”4

1.3 Marketing Metrics: Opportunities, Performance,
and Accountability
Marketers are by no means immune to the drive toward quantitative planning and evaluation. Marketing may once have been regarded as more an art than a science. Executives
may once have cheerfully admitted that they knew they wasted half the money they spenton advertising, but they didn’t know which half. Those days, however, are gone.
Today, marketers must understand their addressable markets quantitatively. They mustmeasure new opportunities and the investment needed to realize them. Marketersmust quantify the value of products, customers, and distribution channels––all undervarious pricing and promotional scenarios. Increasingly, marketers are held accountable
for the financial ramifications of their decisions. Observers have noted this trend ingraphic terms:“For years, corporate marketers have walked into budget meetings like neighborhood
junkies. They couldn’t always justify how well they spent past handouts or what
difference it all made. They just wanted more money––for flashy TV ads, for big-ticket
events, for, you know, getting out the message and building up the brand. But those
heady days of blind budget increases are fast being replaced with a new mantra:
measurement and accountability.”52

MARKETING METRICS

1.4 Choosing the Right Numbers
The numeric imperative represents a challenge, however. In business and economics,
many metrics are complex and difficult to master. Some are highly specialized andbest suited to specific analyses. Many require data that may be approximate, incomplete,or unavailable.
Under these circumstances, no single metric is likely to be perfect. For this reason, we
recommend that marketers use a portfolio or “dashboard” of metrics. By doing so, theycan view market dynamics from various perspectives and arrive at “triangulated” strategies and solutions. Additionally, with multiple metrics, marketers can use each as acheck on the others. In this way, they can maximize the accuracy of their knowledge.6
They can also estimate or project one data point on the basis of others. Of course, to use
multiple metrics effectively, marketers must appreciate the relations between them andthe limitations inherent in each.
When this understanding is achieved, however, metrics can help a firm maintain aproductive focus on customers and markets. They can help managers identify thestrengths and weaknesses in both strategies and execution. Mathematically defined
and widely disseminated, metrics can become part of a precise, operational language
within a firm.

Data Availability and Globalization of Metrics
A further challenge in metrics stems from wide variations in the availability ofdata between industries and geographies. Recognizing these variations, we have tried tosuggest alternative sources and procedures for estimating some of the metrics inthis book.
Fortunately, although both the range and type of marketing metrics may vary between
countries,7 these differences are shrinking rapidly. Ambler,8 for example, reports that
performance metrics have become a common language among marketers, and that they
are now used to rally teams and benchmark efforts internationally.

1.5 Mastering Metrics
Being able to “crunch the numbers” is vital to success in marketing. Knowing which
numbers to crunch, however, is a skill that develops over time. Toward that end, managers must practice the use of metrics and learn from their mistakes. By working
through the examples in this book, we hope our readers will gain both confidence anda firm understanding of the fundamentals of data-based marketing. With time andChapter 1 Introduction

3experience, we trust that you will also develop an intuition about metrics, and learn to
dig deeper when calculations appear suspect or puzzling.
Ultimately, with regard to metrics, we believe many of our readers will require not
only familiarity but also fluency. That is, managers should be able to perform relevant
calculations on the fly––under pressure, in board meetings, and during strategic
deliberations and negotiations. Although not all readers will require that level offluency, we believe it will be increasingly expected of candidates for senior management positions, especially those with significant financial responsibility. We
anticipate that a mastery of data-based marketing will become a means for many of ourreaders to differentiate and position themselves for career advancement in an ever morechallenging environment.

Organization of the Text
This book is organized into chapters that correspond to the various roles played by marketing metrics in enterprise management. Individual chapters are dedicated to metrics
used in promotional strategy, advertising, and distribution, for example. Each chapter iscomposed of sections devoted to specific concepts and calculations.
Inevitably, we must present these metrics in a sequence that will appear somewhat
arbitrary. In organizing this text, however, we have sought to strike a balance between
two goals: (1) to establish core concepts first and build gradually toward increasing
sophistication, and (2) to group related metrics in clusters, helping our readers recognize patterns of mutual reinforcement and interdependence. In Figure 1.1, we offer agraphical presentation of this structure, demonstrating the interlocking nature of all
marketing metrics––indeed of all marketing programs––as well as the central role ofthe customer.
The central issues addressed by the metrics in this book are as follows:4■

Chapter 2––Share of Hearts, Minds, and Markets: Customer perceptions, market
share, and competitive analysis.

■

Chapter 3––Margins and Profits: Revenues, cost structures, and profitability.

■

Chapter 4––Product and Portfolio Management: The metrics behind productstrategy, including measures of trial, growth, cannibalization, and brandequity.

■Chapter 5––Customer Profitability: The value of individual customers andrelationships.

■

Chapter 6––Sales Force and Channel Management: Sales force organization,
performance, and compensation. Distribution coverage and logistics.

MARKETING METRICS

Customers and Market Research

Logistics
Operations
Product andPortfolio
Management

Customer
Profitability

Sales ForceChannelManagement
and Sales
Force

Margins andProfits
Share of Hearts, Minds,and Markets

MarketingandFinance

Pricing
Strategy

Finance
AdvertisingMedia and Web
Metrics

Promotions

The Trade
Advertising
AgencyFigure 1.1 Marketing Metrics: Marketing at the Core of the Organization■

Chapter 7––Pricing Strategy: Price sensitivity and optimization, with an eye
toward setting prices to maximize profits.

■

Chapter 8––Promotion: Temporary price promotions, coupons, rebates, andtrade allowances.

■

Chapter 9––Advertising Media and Web Metrics: The central measures of advertising coverage and effectiveness, including reach, frequency, rating points, andimpressions. Models for consumer response to advertising. Specialized metrics
for Web-based campaigns.

■

Chapter 10––Marketing and Finance: Financial evaluation of marketing programs.

■

Chapter 11––The Marketing Metrics X-Ray: The use of metrics as leading indicators of opportunities, challenges, and financial performance.

Chapter 1 Introduction

5Components of Each Chapter
As shown in Table 1.1, the chapters are composed of multiple sections, each dedicated to
specific marketing concepts or metrics. Within each section, we open with definitions,formulas, and a brief description of the metrics covered. Next, in a passage titled
Construction, we explore the issues surrounding these metrics, including their formulation, application, interpretation, and strategic ramifications. We provide examples toillustrate calculations, reinforce concepts, and help readers verify their understanding of
key formulas. That done, in a passage titled Data Sources, Complications, and Cautions,
we probe the limitations of the metrics under consideration, and potential pitfalls intheir use. Toward that end, we also examine the assumptions underlying these metrics.
Finally, we close each section with a brief survey of Related Metrics and Concepts.
In organizing the text in this way, our goal is straightforward: Most of the metrics in thisbook have broad implications and multiple layers of interpretation. Doctoral theses
could be devoted to many of them, and have been written about some. In this book,however, we want to offer an accessible, practical reference. If the devil is in the details,
we want to identify, locate, and warn readers against him, but not to elaborate his entire
demonology. Consequently, we discuss each metric in stages, working progressively
toward increasing levels of sophistication. We invite our readers to sample this information as they see fit, exploring each metric to the depth that they find most usefuland rewarding.
With an eye toward accessibility, we have also avoided advanced mathematical notation.
Most of the calculations in this book can be performed by hand, on the back of theproverbial envelope. More complex or intensive computations may require a spreadsheet. Nothing further should be needed.

Reference Materials
Throughout this text, we have highlighted formulas and definitions for easy reference.
We have also included outlines of key terms at the beginning of each chapter andsection. Within each formula, we have followed this notation to define all inputs
and outputs.
$—(Dollar Terms): A monetary value. We have used the dollar sign and “dollar
terms” for brevity, but any other currency, including the euro, yen, dinar, or yuan,would be equally appropriate.
%—(Percentage): Used as the equivalent of fractions or decimals. For readability,
we have intentionally omitted the step of multiplying decimals by 100 to obtain
percentages.

6

MARKETING METRICS

#––(Count): Used for such measures as unit sales or number of competitors.
R––(Rating): Expressed on a scale that translates qualitative judgments or preferences into numeric ratings. Example: A survey in which customers are asked toassign a rating of “1” to items that they find least satisfactory and “5” to those thatare most satisfactory. Ratings have no intrinsic meaning without reference to theirscale and context.
I––(Index): A comparative figure, often linked to or expressive of a market
average. Example: the consumer price index. Indexes are often interpreted asa percentage.

$––Dollar. %––Percentage. #––Count. R––Rating. I––Index.

References and Suggested Further Reading
Abela, Andrew, Bruce H. Clark, and Tim Ambler. “Marketing Performance Measurement,
Performance, and Learning,” working paper, September 1, 2004.
Ambler, Tim, and Chris Styles. (1995). “Brand Equity: Toward Measures That Matter,” working
paper No. 95-902, London Business School, Centre for Marketing.
Barwise, Patrick, and John U. Farley. (2003). “Which Marketing Metrics Are Used and Where?”
Marketing Science Institute, (03-111), working paper, Series issues two 03-002.
Clark, Bruce H., Andrew V. Abela, and Tim Ambler. “Return on Measurement: Relating
Marketing Metrics Practices to Strategic Performance,” working paper, January 12, 2004.
Hauser, John, and Gerald Katz. (1998). “Metrics: You Are What You Measure,” European
Management Journal, Vo. 16, No. 5, pp. 517–528.
Kaplan, R. S., and D. P. Norton. (1996). The Balanced Scorecard: Translating Strategy into Action,
Boston, MA: Harvard Business School Press.

Chapter 1 Introduction

7

Table 1.1 Major Metrics List
SectionMetricSectionMetricShare of Hearts, Minds, and Markets

3.3

Price Per Statistical Unit

2.1

Market Share

3.4

Variable and Fixed Costs

2.1

Unit Share

3.5

Marketing Spending

2.2

Relative Market Share

3.6

Contribution per Unit

2.3

Brand Development Index

3.6

Contribution Margin (%)

2.3

Category Development
Index

3.6

Break-Even Sales

3.7

Target Volume

2.4–2.6

Market Share

3.7

Target Revenues

2.4

Market Penetration

2.4

Brand Penetration

2.4

Penetration Share

2.5

Share of Requirements

2.6

Heavy Usage Index

2.7

Hierarchy of Effects

2.7

Awareness

2.7

Top of Mind

2.7

Ad Awareness

2.7

Knowledge

2.7

Beliefs

2.7

Intentions

2.7

Purchase Habits

2.7

Loyalty

2.7

Likeability

2.8

Willingness to Recommend

2.8

Customer Satisfaction

2.9

Willingness to Search

Product and Portfolio Management
4.1

Trial

4.1

Repeat Volume

4.1

Penetration

4.1

Volume Projections

4.2

Growth—Percentage

4.2

Growth––CAGR

4.3

Cannibalization Rate

4.3

Fair Share Draw Rate

4.4

Brand Equity Metrics

4.5

Conjoint Utilities andConsumer Preferences

4.6

Segment Utilities

4.7

Conjoint Utilities andVolume Projections

Customer Profitability
5.1

Customers

5.1

RecencyMargins and Profits

5.1

Retention Rate

3.1

Unit Margin

5.2

Customer Profit

3.1

Margin (%)

5.3

Customer Lifetime Value

3.2

Channel Margins

5.4

Prospect Lifetime Value

3.3

Average Price per Unit

8

MARKETING METRICS

Table 1.1 Continued
SectionMetricSectionMetric

5.5

Average Acquisition Cost

7.4

Optimal Price

5.5

Average Retention Cost

7.5

Residual Elasticity

Sales Force and Channel Management

Promotion

6.1

Workload

8.1

Baseline Sales

6.1

Sales Potential Forecast

8.1

6.2

Sales Total

Incremental
Sales/Promotion Lift

6.3

Sales Force Effectiveness

8.2

Redemption Rates

6.4

Compensation

8.2

6.4

Break-Even Number of
Employees

Costs for Coupons andRebates

8.2

6.5

Sales Funnel, Sales Pipeline

Percentage Sales with
Coupon6.6

Numeric Distribution %8.2

Percent Sales on Deal

6.6

All Commodity Volume
(ACV)

8.2

Percent Time on Deal

8.2

Average Deal Depth

6.6

Product Category Volume
(PCV)

8.3

Pass-Through8.4

Price Waterfall

6.6

Total Distribution %6.6

Facings

6.7

Out of Stock %6.7

Inventories

6.8

Markdowns

6.8

Direct Product Profitability
(DPP)

6.8

Gross Margin Return on
Inventory Investment(GMROII)

Pricing Strategy
7.1

Price Premium

7.2

Reservation Price

7.2

Percent Good Value

7.3

Price Elasticity of Demand

Advertising Media and Web Metrics
9.1

Impressions

9.1

Gross Rating Points (GRPs)

9.2

Cost per Thousand
Impressions (CPM)

9.3

Net Reach

9.3

Average Frequency

9.4

Frequency Response

9.5

Effective Reach

9.5

Effective Frequency

9.6

Share of Voice

9.7

Pageviews

9.8

Clickthrough Rate

9.9

Cost per Click
Continues

Chapter 1 Introduction

9

Table 1.1 Continued
SectionMetricSectionMetric9.9

Cost per Order

10.2

Return on Investment—ROI

9.9

Cost per Customer Acquired

10.3

Economic Profit—EVA

9.10

Visits10.4

Payback

9.10

Visitors

10.4

Net Present Value (NPV)

9.10

Abandonment Rate

10.4

Internal Rate of Return
(IRR)

10.5

Return on Marketing
Investment—ROMI;
Revenue

Marketing and Finance
10.1

Net Profit

10.1

Return on Sales—ROS

10

MARKETING METRICS

2
SHARE OF HEARTS, MINDS,
AND MARKETSIntroduction
Metrics covered in this chapter:
Market Share

Share of Requirements

Relative Market Share

Heavy Usage Index

Market Concentration

Awareness, Attitudes, and Usage (AAU)

Brand Development Index (BDI)

Customer Satisfaction

Category Development Index (CDI)

Willingness to Recommend

Penetration

Willingness to Search“As Wal-Mart aggressively rolls out more stores, it continues to capture an increasing
share of wallet. Three out of five consumers shopped for gifts at Wal-Mart this pastholiday season. U.S. households now buy, on average, 22% of their groceries at
Wal-Mart. A quarter of all shoppers indicate that they are spending more of theirclothing budget at Wal-Mart now compared with a year ago. These ShopperScapefindings lend credence to Retail Forward’s premise that Wal-Mart will continue topush the boundaries of what consumers will allow it to be.”1

11

At first glance, market share appears to involve a relatively simple calculation: “us/(us  them).” But this raises a host of questions. Who, for example, are “they?” That is,how broadly do we define our competitive universe? Which units are used? Where in thevalue chain do we capture our information? What time frame will maximize our signalto-noise ratio? In a metric as important as market share, and in one as closely monitored for changes and trends, the answers to such questions are crucial. In this chapter,
we will address them and also introduce key components of market share, including
penetration share, heavy usage index, and share of requirements.
Probing the dynamics behind market share, we’ll explore measures of awareness, attitude, and usage––major factors in the decision-making process by which customers
select one brand over another. We’ll discuss customer satisfaction with products anddealers, the quantification of which is growing in importance among marketing professionals. Finally, we’ll consider metrics measuring the depth of consumer preference andsatisfaction, including customers’ willingness to search if a brand is unavailable andtheir disposition to recommend that brand to others. Increasingly, marketers rely onthese as leading indicators of future changes in share.

MetricConstruction

Considerations

Purpose

2.1

Revenue Market
Share

Sales revenue as apercentage ofmarket sales
revenue.

Scope of market
definition.
Channel level
analyzed. Before/
after discounts.
Time period
covered.

Measure ofcompetitiveness.

2.1

Unit Market Share

Unit sales as apercentage ofmarket unit sales.

Scope of market
definition.
Channel level
analyzed. Time
period covered.

Measure ofcompetitiveness.

2.2

Relative Market
Share

Brand market
share divided bylargest competitor’s market
share.

Can use either
unit or revenue
shares.

Assesses
comparative
market strength.

12

MARKETING METRICS

Metric

Construction

Considerations

Purpose

2.3

Brand
Development
Index

Brand sales in a
specified segment,
compared withsales of thatbrand in themarket as awhole.

Can use either
unit or revenue
sales.

Regional orsegment differences in brandpurchases andconsumption.

2.3

Category
Development
Index

Category sales ina specified segment, comparedwith sales of thatcategory in themarket as awhole.

Can use either
unit or revenue
sales.

Regional orsegment differences in categorypurchases andconsumption.

2.4

Decomposition ofMarket Share

Penetration Share* Share of
Requirements *Heavy Usage
Index.

Can be based onunit or revenue
shares. Time
period covered.

Calculation ofmarket share.
Competitive
analysis.
Historical
trends analysis.
Formulationof marketing
objectives.

2.4

MarketPenetration

Purchasers of a
product category
as a percentage of
total population.

Based on population. Therefore,
unit/revenue
consideration not
relevant.

Measures category acceptance by adefined population. Useful intracking acceptance of new product categories.

2.4

Brand
Penetration

Purchasers of abrand as a percentage of total
population.

Based on population. Therefore,
unit/revenue
consideration not
relevant.

Measures brandacceptance bya defined
population.

2.5
2.6

Continues

Chapter 2 Share of Hearts, Minds, and Markets

13

Metric

Construction

Considerations

Purpose2.4

Penetration Share

Brand penetration
as a percentage
of market
penetration.

A component of
the market shareformula.

Comparative
acceptance ofbrand within
category.

2.5

Share of
Requirements

Brand purchases
as a percentage of
total categorypurchases by buyers of that brand.

Can use either
unit or revenue
shares. May riseeven as sales
decline, leaving
only most loyal
customers.

Level of commitment to a brandby its existingcustomers.

2.6

Heavy Usage
Index

Category purchases by customers of abrand, comparedwith purchasesin that category
by average customers in the
category.

Can use either
unit or revenue
sales.

Measures relative
usage of acategory bycustomers for a
specific brand.

2.7

Hierarchy of
Effects

Awareness;
attitudes, beliefs;
importance;
intentions to try;
buy; trial, repeat.

Strict sequence isoften violated andcan be reversed.

Set marketing
and advertisingobjectives.
Understandprogress in stagesof customer
decision process.

2.7

Awareness

Percentage of
total populationthat is aware of abrand.

Is this prompted
or unpromptedawareness?

Consideration of
who has heard of
the brand.

2.7

Top of Mind

First brand to
consider.

May be subject
to most recent
advertising orexperience.

Saliency of brand.

14

MARKETING METRICS

Metric

Construction

Considerations

Purpose

2.7

Ad Awareness

Percentage of
total populationthat is awareof a brand’s
advertising.

May vary byschedule, reach,and frequency ofadvertising.

One measureof advertising
effects. May
indicate “stopping
power” of ads.

2.7

Knowledge

Percentage of
population withknowledge of
product, recollection of its advertising.

Not a formal
metric. Is thisprompted or
unprompted
knowledge?

Extent offamiliarity withproduct beyond
name recognition.

2.7

Beliefs

Customers/
consumers viewof product, generally captured via
survey responses,
often throughratings on ascale.

Customers/
consumers may
hold beliefs withvarying degrees ofconviction.

Perception ofbrand byattribute.

2.7

Purchase
Intentions

Probability ofintention topurchase.

To estimate probability of purchase, aggregate
and analyze ratings of statedintentions (forexample, top twoboxes).

Measures preshopping disposition to purchase.

2.7

Purchase Habits

Frequency of purchase. Quantity
typically purchased.

May vary widely
among shopping
trips.

Helps identify
heavy users.

2.7

Loyalty

Measures include
share of requirements, willingness
to pay premium,willingness tosearch.

“Loyalty” itself is
not a formal metric, but specificmetrics measureaspects of thisdynamic. New
product entries
may alter loyalty
levels.

Indication of basefuture revenue
stream.

Continues
Chapter 2 Share of Hearts, Minds, and Markets

15

Metric

Construction

Considerations

Purpose2.7

Likeability

Generally measured via ratings
across a numberof scales.

Often believed tocorrelate withpersuasion.

Shows overall
preference prior
to shopping.

2.8

Willingness to
Recommend

Generally measured via ratings
across a 1–5 scale.

Nonlinear in
impact.

Shows strength ofloyalty, potential
impact on others.

2.8

Customer
Satisfaction

Generally measured on a 1–5
scale, in whichcustomers declaretheir satisfaction
with brand ingeneral or specificattributes.

Subject toresponse bias.
Captures viewsof current
customers, notlost customers.
Satisfaction is afunction of
expectations.

Indicates
likelihood of
repurchase.
Reports ofdissatisfaction
show aspects that
require improvement to enhanceloyalty.

2.9

Willingness to
SearchPercentage of customers willing todelay purchases,change stores, or
reduce quantities
to avoid switching
brands.

Hard to capture.

Indicates
importance ofdistribution
coverage.

2.1

Market Share

Market share is the percentage of a market (defined in terms of either units or revenue) accounted for by a specific entity.
Unit Sales (#)
Unit Market Share (%) 
Total Market Unit Sales (#)
Sales Revenue ($)
Revenue Market Share (%) 
Total Market Revenue ($)
Marketers need to be able to translate sales targets into market share because this will
demonstrate whether forecasts are to be attained by growing with the market or bycapturing share from competitors. The latter will almost always be more difficult toachieve. Market share is closely monitored for signs of change in the competitive
landscape, and it frequently drives strategic or tactical action.

16

MARKETING METRICS

Purpose: Key indicator of market competitiveness.
Market share is an indicator of how well a firm is doing against its competitors. This
metric, supplemented by changes in sales revenue, helps managers evaluate both primary and selective demand in their market. That is, it enables them to judge not only
total market growth or decline but also trends in customers’ selections among competitors. Generally, sales growth resulting from primary demand (total market growth) is
less costly and more profitable than that achieved by capturing share from competitors.
Conversely, losses in market share can signal serious long-term problems that require
strategic adjustments. Firms with market shares below a certain level may not be viable.
Similarly, within a firm’s product line, market share trends for individual products are
considered early indicators of future opportunities or problems.

Construction
Market Share: The percentage of a market accounted for by a specific entity.
Unit Market Share: The units sold by a particular company as a percentage of
total market sales, measured in the same units.
Unit Market Share (%) 

Unit Sales (#)
Total Market Unit Sales (#)

This formula, of course, can be rearranged to derive either unit sales or total market unit
sales from the other two variables, as illustrated in the following:
Unit Sales (#)  Unit Market Share (%) * Total Market Unit Sales (#)
Total Market Unit Sales (#) 

Unit Sales (#)
Unit Market Share (%)

Revenue Market Share: Revenue market share differs from unit market share inthat it reflects the prices at which goods are sold. In fact, a relatively simple way to
calculate relative price is to divide revenue market share by unit market share (seeSection 7.1).
Revenue Market Share (%) 

Sales Revenue ($)
Total Market Sales Revenue ($)

As with the unit market share, this equation for revenue market share can be rearranged
to calculate either sales revenue or total market sales revenue from the other two variables.

Data Sources, Complications, and Cautions
Market definition is never a trivial exercise: If a firm defines its market too broadly, it
may dilute its focus. If it does so too narrowly, it will miss opportunities and allow
Chapter 2 Share of Hearts, Minds, and Markets

17threats to emerge unseen. To avoid these pitfalls, as a first step in calculating market
share, managers are advised to define the served market in terms of unit sales orrevenues for a specific list of competitors, products, sales channels, geographic areas,customers, and time periods. They might posit, for example, that “Among grocery
stores, we are the revenue market share leader in sales of frozen Italian food entrées in
the Northeastern U.S.”
Data parameters must be carefully defined: Although market share is likely the single
most important marketing metric, there is no generally acknowledged best method for
calculating it. This is unfortunate, as different methods may yield not only different
computations of market share at a given moment, but also widely divergent trends overtime. The reasons for these disparities include variations in the lenses through whichshare is viewed (units versus dollars), where in the channel the measurements are taken(shipments from manufacturers versus consumer purchases), market definition (scopeof the competitive universe), and measurement error. In the situation analysis that
underlies strategic decisions, managers must be able to understand and explain thesevariations.
Competitive dynamics in the automobile industry, and at General Motors in particular,
illustrate the complexities involved in quantifying market share:“With market share sliding in the first two months of the year, from 27.2% to24.9%––the lowest level since a two-month strike shut the company down in 1998––GM
as a whole expects a net loss of $846 million the first quarter.”2Reviewing this statement, drawn from Business Week in 2005, a marketing manager
might immediately pose a number of questions:■

Do these figures represent unit (auto) or revenue (dollar) market shares?

■

Does this trend hold for both unit and revenue market shares at GM?

■

Was revenue market share calculated before or after rebates and discounts?

■

Do the underlying sales data reflect factory shipments, which relate directly tothe manufacturer’s current income statement, or sales to consumers, which arebuffered by dealer inventories?

■

Does the decline in market share translate to an equivalent percentage decreasein sales, or has the total market size changed?

Managers must determine whether a stated market share is based on shipment data,channel shipments, retail sales, customer surveys, or some other source. On occasion,
share figures may represent combinations of data (a firm’s actual shipments, for example, set against survey estimates of competitors’ sales). If necessary, managers must also
adjust for differences in channels.

18

MARKETING METRICS

The time period measured will affect the signal-to-noise ratio: In analyzing shortterm market dynamics, such as the effects of a promotion or a recent price change, managers may find it useful to measure market share over a brief period of time. Short-term
data, however, generally carry a low signal-to-noise ratio. By contrast, data covering alonger time span will be more stable but may obscure important, recent changes in themarket. Applied more broadly, this principle also holds in aggregating geographic areas,channel types, or customers. When choosing markets and time periods for analysis,
managers must optimize for the type of signal that is most important.
Potential bias in reported shares: One way to find data for market sizing is throughsurveys of customer usage (see Section 2.7). In interpreting these data, however, managers must bear in mind that shares based on reported (versus recorded) sales tend to bebiased toward well-known brands.

Related Metrics and Concepts
Served Market: That portion of the total market for which the firm competes. This
may exclude geographic regions or product types. In the airline industry, for example, as of mid 2005, Ryan Air did not fly to the United States. Consequently, theU.S. would not be considered part of its served market.

2.2 Relative Market Share and Market Concentration
Relative market share indexes a firm’s or a brand’s market share against that of its
leading competitor.
Brand’s Market Share ($,#)
Relative Market Share (I) (%) 
Largest Competitor’s Market Share ($,#)
Market concentration, a related metric, measures the degree to which a comparatively
small number of firms accounts for a large proportion of the market.
These metrics are useful in comparing a firm’s or a brand’s relative position acrossdifferent markets and in evaluating the type and degree of competition in thosemarkets.

Purpose: To assess a firm’s or a brand’s success and its positionin the market.
A firm with a market share of 25% would be a powerful leader in many markets but a
distant “number two” in others. Relative market share offers a way to benchmark a firm’s
or a brand’s share against that of its largest competitor, enabling managers to compare
relative market positions across different product markets. Relative market share gains

Chapter 2 Share of Hearts, Minds, and Markets

19

 



 
            

 

 

Figure 2.1 The BCG Matrix

some of its significance from studies––albeit controversial ones––suggesting that major
players in a market tend to be more profitable than their competitors. This metric wasfurther popularized by the Boston Consulting Group in its famous matrix of relative
share and market growth (see Figure 2.1).
In the BCG matrix, one axis represents relative market share––a surrogate for competitive strength. The other represents market growth––a surrogate for potential.
Along each dimension, products are classified as high or low, placing them in one offour quadrants. In the traditional interpretation of this matrix, products with highrelative market shares in growing markets are deemed stars, suggesting that theyshould be supported with vigorous investment. The cash for that investment may begenerated by cash cows, products with high relative shares in low-growth markets.
Problem child products may have potential for future growth but hold weak competitive positions. Finally, dogs have neither strong competitive position nor growthpotential.

Construction
Relative Market Share (I) 

Brand’s Market Share ($,#)
Largest Competitor’s Market Share ($,#)

Relative market share can also be calculated by dividing brand sales (#,$) by largest
competitor’s sales (#,$) because the common factor of total market sales (or revenue)
cancels out.

20

MARKETING METRICS

EXAMPLE:

The market for small urban cars consists of five players (see Table 2.1).
Table 2.1 Market for Small Urban Cars
Units Sold (Thousands)

Revenue (Thousands)

Zipper25€375,000

Twister

10.0€200,000

A-One

7.5

€187,500

Bowlz

5€125,000

Chien

2.5

€50,000

Market Total

50.0€937,500

In the market for small urban cars, managers at A-One want to know their firm’s market
share relative to its largest competitor. They can calculate this on the basis of revenues orunit sales.
In unit terms, A-One sells 7,500 cars per year. Zipper, the market leader, sells 25,000.
A-One’s relative market share in unit terms is thus 7,500/25,000 or 0.30. We arrive at thesame number if we first calculate A-One’s share (7,500/50,000 = .15) and Zipper’s share(25,000/50,000  .50) and then divide A-One’s share by Zipper’s share (.15/.50 = .30).
In revenue terms, A-One generates €187.5 million in car sales each year. Zipper, the market leader, generates €375 million. A-One’s relative market share in revenue terms is thus
€187.5m/€375m, or 0.5. Due to its comparatively high average price per car, A-One’s relative market share is greater in revenue than in unit terms.

Related Metrics and Concepts
Market Concentration: The degree to which a relatively small number of firms
accounts for a large proportion of the market. This is also known as the concentration ratio. It is usually calculated for the largest three or four firms in a market.3
Three (Four) Firm Concentration Ratio: The total (sum) of the market shares
held by the leading three (four) competitors in a market.

EXAMPLE: In the small urban car market, the three firm concentration ratio iscomprised of the market shares of the top three competitors—Zipper, Twister, and
A-One (see Table 2.2).

Chapter 2 Share of Hearts, Minds, and Markets

21

Table 2.2 Market Share––Small Urban Cars
Units Sold(Thousands)

Unit Share

Revenue(Thousands)

Revenue Share

Zipper25

50%€375,000

40.0%

Twister

10.0

20%€200,000

21.3%

A-One

7.5

15%

€187,500

20.0%

Bowlz

5

10%

€125,000

13.3%

Chien

2.5

5%

€50,000

5.3%

Market Total

50.0

100%

€937,500

100%

In unit terms, the three firm concentration ratio is 50%  20%  15%  85%.
In revenue terms, it is 40%  21.3%  20%  81.3%.

Herfindahl Index: A market concentration metric derived by adding the squaresof the individual market shares of all the players in a market. As a sum of squares,
this index tends to rise in markets dominated by large players.

EXAMPLE: The Herfindahl Index dramatically highlights market concentration in
the small urban car market (see Table 2.3).
Table 2.3 Calculation of the Herfindahl Index for Small Urban Cars
Units Sold
Herfindahl Revenue
Revenue Herfindahl(Thousands) Unit Share Index(Thousands) Share
Index
Zipper

25

50%

0.25

€375,000

40%

0.16

Twister

10.0

20%

0.04

€200,000

21%0.0455

A-One

7.5

15%

0.0225€187,500

20%

0.04

Bowlz

5

10%

0.01

€125,000

13%0.0178

Chien

2.5

5%

0.0025€50,000

5%0.0028Market Total 50.0

100%

0.325

€937,500100%

0.266122

MARKETING METRICS

On a unit basis, the Herfindahl Index is equal to the square of the unit market share of
Zipper (50% ^ 2 = 0.25), plus that of Twister (20% ^ 2 = 0.04), plus those of A-One,
Bowlz, and Chien = 0.325.
On a revenue basis, the Herfindahl Index comprises the square of the revenue market
share of Zipper (40% ^ 2  0.16), plus those of all its competitors  0.2661.
As demonstrated by the Herfindahl Index, the market for small urban cars is slightly
more concentrated in unit terms than in revenue terms. The reason for this is straightforward: Higher-priced cars in this market sell fewer units.
Note: For a given number of competitors, the Herfindahl Index would be lowest if shareswere equally distributed. In a five-firm industry, for example, equally distributed shareswould yield a Herfindahl Index of 5 * (20% ^ 2)  0.2.

Data Sources, Complications, and Cautions
As ever, appropriate market definition and the use of comparable figures are vital prerequisites to developing meaningful results.

Related Metrics and Concepts
Market Share Rank: The ordinal position of a brand in its market, when competitors are arranged by size, with 1 being the largest.
Share of Category: This metric is derived in the same manner as market share,
but is used to denote a share of market within a certain retailer or class of retailers(for example, mass merchandisers).

2.3 Brand Development Index and Category
Development Index
The brand development index (BDI) quantifies how well a brand is performing within a specific group of customers, compared with its average performance among all
consumers.

Brand Development Index (I) [Brand Sales to Group (#)/Households (#)
in the Group][Total Brand Sales (#)/Total Household (#)]

The category development index (CDI) measures the sales performance of a categoryof goods or services within a specific group, compared with its average performance
among all consumers.

Chapter 2 Share of Hearts, Minds, and Markets

23

Category Development Index (I) [Category Sales to Group (#)/Households in Group (#)]
[Total Category Sales (#)/Total Household (#)]

The brand and category development indexes are useful for understanding specificcustomer segments relative to the market as a whole. Although defined here withrespect to households, these indexes could also be calculated for customers, accounts,
businesses, or other entities.

Purpose: To understand the relative performance of a brandor category within specified customer groups.
The brand and category development indexes help identify strong and weak segments(usually, demographic or geographic) for particular brands or categories of goods andservices. For example, by monitoring the CDI (category development index), marketers
might determine that Midwesterners buy twice as many country-western music CDs
per capita as Americans in general, while consumers living on the East Coast buy lessthan the national average. This would be useful information for targeting the launch
campaign for a new country-western performer. Conversely, if managers found that a
particular product had a low brand development index in a segment that carried a high
CDI for its category, they might ask why that brand suffered relatively poor performance in such a promising segment.

Construction
Brand Development Index—BDI (I): An index of how well a brand performs
within a given market group, relative to its performance in the market asa whole.
Brand Development Index––BDI (I) [Brand Sales to Group (#)/Households in Group (#)]
[Total Brand Sales (#)/Total Household (#)]

The BDI (brand development index) is a measure of brand sales per person or per
household within a specified demographic group or geography, compared with its
average sales per person or household in the market as a whole. To illustrate its use:
One might hypothesize that sales per capita of Ben & Jerry’s brand ice cream would begreater in the brand’s home state, Vermont, than in the rest of the country. By
calculating Ben & Jerry’s BDI for Vermont, marketers could test this hypothesis
quantitatively.

24

MARKETING METRICS

EXAMPLE: Oaties is a minor brand of breakfast cereal. Among households withoutchildren, its sales run one packet per week per 100 households. In the general population,
Oaties’ sales run one packet per week per 80 households. This translates to 1/100 of apacket per household in the childless segment, versus 1/80 of a packet in the general
populace.
BDI ==(Brand Sales/Household)(Total Brand Sales/Household)
1/100

= 0.8

1/80

Oaties performs slightly less well in the childless segment than in the market as a whole.

Category Development Index—CDI: An index of how well a category performs
within a given market segment, relative to its performance in the market as a whole.
Category Development Index (I) [Category Sales to Group (#)/Households in Group (#)]
[Total Category Sales (#)/Total Household (#)]

Similar in concept to the BDI, the category development index demonstrates where acategory shows strength or weakness relative to its overall performance. By way of
example, Boston enjoys high per-capita consumption of ice cream. Bavaria and Irelandboth show higher per-capita consumption of beer than Iran.

Data Sources and Complications
In calculating BDI or CDI, a precise definition of the segment under study is vital.
Segments are often bounded geographically, but they can be defined in any way forwhich data can be obtained.

Related Metrics and Concepts
The term category development index has also been applied to retail organizations. In
this application, it measures the extent to which a retailer emphasizes one category
versus others.
Category Development Index (I) 

Retailer’s Share of Category Sales (%)
Retailer’s Total Share of Market (%)

This use of the term is similar to the category performance ratio (see Section 6.6).

Chapter 2 Share of Hearts, Minds, and Markets

25

2.4 Penetration
Penetration is a measure of brand or category popularity. It is defined as the numberof people who buy a specific brand or a category of goods at least once in a given
period, divided by the size of the relevant market population.
Market Penetration (%) 

Customers Who Have Purchased a Product in the Category (#)
Total Population (#)

Brand Penetration (%) 

Customers Who Have Purchased the Brand (#)
Total Population (#)

Penetration Share (%) 

Penetration Share (%) 

Brand Penetration (%)
Market Penetration (%)

Customers Who Have Purchased the Brand (#)
Customers Who Have Purchased a Product in the Category (#)

Often, managers must decide whether to seek sales growth by acquiring existing category users from their competitors or by expanding the total population of categoryusers, attracting new customers to the market. Penetration metrics help indicate
which of these strategies would be most appropriate and help managers to monitor
their success. These equations might also be calculated for usage instead of purchase.

Construction
Penetration: The proportion of people in the target who bought (at least once inthe period) a specific brand or a category of goods.
Market Penetration (%) 

Customers Who Have Purchased a Product in the Category (#)

Brand Penetration (%) 

Total Population (#)
Customers Who Have Purchased the Brand (#)
Total Population (#)

Two key measures of a product’s “popularity” are penetration rate and penetration
share. The penetration rate (also called penetration, brand penetration, or market penetration as appropriate), is the percentage of the relevant population that has purchased
a given brand or category at least once in the time period under study.

26

MARKETING METRICS

EXAMPLE: Over a period of a month, in a market of 10,000 households, 500 households purchased Big Bomb brand flea foggers.
Brand Penetration, Big Bomb ==Big Bomb Customers
Total Population500
10,000 5%

A brand’s penetration share, in contrast to penetration rate, is determined by comparing that brand’s customer population to the number of customers for its category in therelevant market as a whole. Here again, to be considered a customer, one must havepurchased the brand or category at least once during the period.
Penetration Share (%) 

Brand Penetration (%)
Market Penetration (%)

EXAMPLE: Returning to the flea fogger market, during the month in which 500
households purchased Big Bomb, 2,000 households bought at least one product of any
brand in this category. This enables us to calculate Big Bomb’s penetration share.
Penetration Share, Big Bomb ==Big Bomb Customers
Category Customers
500
20,000 25%

DECOMPOSING MARKET SHARE
Relationship of Penetration Share to Market Share: Market share can be calculated as the product of three components: penetration share, share of requirements,and heavy usage index.
Market Share (%)  Penetration Share (%) * Share of Requirements (%)* Heavy Usage Index (I)Share of Requirements: The percentage of customers’ needs in a category that areserved by a given brand or product (see Section 2.5).

Chapter 2 Share of Hearts, Minds, and Markets

27

Heavy Usage Index: A measure of how heavily the people who use a specific product use the entire category of such products (see Section 2.6).
In light of these relationships, managers can use this decomposition of market share toreveal penetration share, given the other inputs.
Penetration Share (%) Market Share (%)[Heavy Usage Index (I) * Share of Requirements (%)]

EXAMPLE: Eat Wheats brand cereal has a market share in Urbanopolis of 6%. The
heavy usage index for Eat Wheats cereal is 0.75 in Urbanopolis. Its share of requirements
is 40%. From these data, we can calculate the penetration share for Eat Wheats brandcereal in Urbanopolis:
Penetration Share ==Market Share(Heavy Usage Index * Share of Requirements)
6%(0.75 * 40%)

=

6%
.30

 20%

Data Sources, Complications, and Cautions
The time period over which a firm measures penetration can have a significant impact
on the penetration rate. For example, even among the most popular detergent brands,
many are not purchased weekly. As the time period used to define penetration becomesshorter, managers can expect penetration rates to decline. By contrast, penetration share
may be less subject to this dynamic because it represents a comparison between brands,
among which the effects of shorter periods may fall approximately evenly.

RELATED METRICS AND CONCEPTS
Total Number of Active Customers: The customers (accounts) who purchased atleast once in a given time period. When assessed at a brand level, this is equivalent
to brand penetration. This term is often used in shorthand form––total number ofcustomers––though this would not be appropriate when a distinction must be madefor ex-customers. This is discussed in more detail in Section 5.1 (customers of a specified recency).
Acceptors: Customers who are disposed to accept a given product and its benefits:
the opposite of rejecters.
Ever-tried: The percentage of a population that has tried a given brand at any
time. (See Section 4.1 for more on trial.)

28

MARKETING METRICS

2.5 Share of Requirements
Share of requirements, also known as share of wallet, is calculated solely among buyers of a specific brand. Within this group, it represents the percentage of purchases
within the relevant category, accounted for by the brand in question.
Unit Share of Requirements (%) 

Brand Purchases (#)
Total Category Purchases by Brand Buyers (#)

Revenue Share of Requirements (%) 

Brand Purchases ($)
Total Category Purchases by Brand Buyers ($)

Many marketers view share of requirements as a key measure of loyalty. This metriccan guide a firm’s decisions on whether to allocate resources toward efforts to expand
a category, to take customers from competitors, or to increase share of requirements
among its established customers. Share of requirements is, in essence, the market
share for a brand within a market narrowly defined as the people who have alreadypurchased that brand.

Purpose: To understand the source of market share in terms of breadthand depth of consumer franchise, as well as the extent of relative category
usage (heavy users/larger customers versus light users/smaller customers).
Construction
Share of Requirements: A given brand’s share of purchases in its category, measured solely among customers who have already purchased that brand. Also known
as share of wallet.
When calculating share of requirements, marketers may consider either dollars orunits. They must ensure, however, that their heavy usage index is consistent withthis choice.
Unit Share of Requirements (%) 

Brand Purchases (#)
Total Category Purchases by Brand Buyers (#)

Revenue Share of Requirements (%) 

Brand Purchases ($)
Total Category Purchases by Brand Buyers ($)

The best way to think about share of requirements is as the average market shareenjoyed by a product among the customers who buy it.
Chapter 2 Share of Hearts, Minds, and Markets

29

EXAMPLE: In a given month, the unit purchases of AloeHa brand sunscreen ran
1,000,000 bottles. Among the households that bought AloeHa, total purchases of sunscreen came to 2,000,000 bottles.
Share of Requirements =AloeHa Purchases
Category Purchases by AloeHa Customers
1,000,000=

2,000,000

 50%

Share of requirements is also useful in analyzing overall market share. As previously
noted, it is part of an important formulation of market share.
Market Share  Penetration Share * Share of Requirements * Heavy Usage Index

Share of requirements can thus be calculated indirectly by decomposing market share.
Share of Requirements (%) Market Share (%)[Penetration Share (%) * Heavy Usage Index (I)]EXAMPLE: Eat Wheats brand cereal has a market share in Urbanopolis of 8%. The
heavy usage index for Eat Wheats in Urbanopolis is 1. The brand’s penetration share in
Urbanopolis is 20%. On this basis, we can calculate Eat Wheats’ share of requirements in
Urbanopolis:
Share of Requirements ==Market Share(Heavy Usage Index * Penetration Share)
8%(1 * 20%)

=

8%
20%

 40%

Note that in this example, market share and heavy usage index must both be defined in
the same terms (units or revenue). Depending on the definition of these two metrics, thecalculated share of requirements will be either unit share of requirements (%) or revenue
share of requirements (%).

30

MARKETING METRICS

Data Sources, Complications, and Cautions
Double Jeopardy: Some marketers strive for a “niche” positioning that yields high market share through a combination of low penetration and high share of requirements.
That is, they seek relatively few customers but very loyal ones. Before embarking on thisstrategy, however, a phenomenon known as “double jeopardy” should be considered.
Generally, the evidence suggests that it’s difficult to achieve a high share of requirements
without also attaining a high penetration share. One reason is that products with highmarket share generally have high availability, whereas those with low market share may
not. Therefore, it can be difficult for customers to maintain loyalty to brands with low
market share.

Related Metrics and Concepts
Sole Usage: The fraction of a brand’s customers who use only the brand in question.
Sole Usage Percentage: The proportion of a brand’s customers who use only thatbrand’s products and do not buy from competitors. Sole users may be die-hard,loyal customers. Alternatively, they may not have access to other options, perhaps
because they live in remote areas. Where sole use is 100%, the share of walletis 100%.
Sole Usage (%) 

Customers Who Buy Only the Brand in Question (#)
Total Brand Customers (#)

Number of Brands Purchased: During a given period, some customers may buy onlya single brand within a category, whereas others buy two or more. In evaluating loyalty
to a given brand, marketers can consider the average number of brands purchasedby consumers of that brand versus the average number purchased by all customers inthat category.

EXAMPLE: Among 10 customers for cat food, 7 bought the Arda brand, 5 bought
Bella, and 3 bought Constanza. Thus, the 10 customers made a total of 15 brand purchases (7  5  3), yielding an average of 1.5 brands per customer.
Seeking to evaluate customer loyalty, a Bella brand manager notes that of his firm’s fivecustomers, 3 bought only Bella, whereas two bought both Arda and Bella. None of
Bella’s customers bought Constanza. Thus, the five Bella customers made seven brandpurchases (1  1  1  2  2), yielding an average of 1.4 (that is, 7/5) brands per Bellacustomer. Compared to the average category purchaser, who buys 1.5 brands, Bellabuyers are slightly more loyal.

Chapter 2 Share of Hearts, Minds, and Markets

31

Repeat Rate: The percentage of brand customers in a given period who are also
brand customers in the subsequent period.
Repurchase Rate: The percentage of customers for a brand who repurchase thatbrand on their next purchase occasion.
Confusion abounds in this area. In these definitions, we have tried to distinguish a metric based on calendar time (repeat rate) from one based on “customer time” (repurchaserate). In Chapter 5, “Customer Profitability,” we will describe a related metric, retention,
which is used in contractual situations in which the first non-renewal (non-purchase)signals the end of a customer relationship. Although we suggest that the term retention
be applied only in contractual situations, you will often see repeat rates and repurchaserates referred to as “retention rates.” Due to a lack consensus on the use of these terms,
marketers are advised not to rely on the names of these metrics as perfect indicators of
how they are calculated.
The importance of repeat rate depends on the time period covered. Looking at one
week’s worth of purchases is unlikely to be very illuminating. In a given category, mostconsumers only buy one brand in a week. By contrast, over a period of years, consumers
may buy several brands that they do not prefer, on occasions when they can’t find thebrand to which they seek to be loyal. Consequently, the right period to consider depends
on the product under study and the frequency with which it is bought. Marketers areadvised to take care to choose a meaningful period.

2.6 Heavy Usage Index
The heavy usage index is a measure of the relative intensity of consumption. It
indicates how heavily the customers for a given brand use the product category towhich that brand belongs, compared with the average customer for that category.
Heavy Usage Index (I) 

Average Total Purchases in Category by Brand Customers (#,$)
Average Total Purchases in Category by All Customers forThat Category (#,$)

or
Heavy Usage Index (I) 

Market Share (%)[Penetration Share (%) * Share of Requirements (%)]

The heavy usage index, also called the weight index, yields insight into the source ofvolume and the nature of a brand’s customer base.

32

MARKETING METRICS

Purpose: To define and measure whether a firm’s consumers
are “heavy users.”
The heavy usage index answers the question, “How heavily do our customers use thecategory of our product?” When a brand’s heavy usage index is greater than 1.0, this signifies that its customers use the category to which it belongs more heavily than the average customer for that category.

Construction
Heavy Usage Index: The ratio that compares the average consumption of products
in a category by customers of a given brand with the average consumption of products in that category by all customers for the category.
The heavy usage index can be calculated on the basis of unit or dollar inputs. For a given
brand, if the heavy usage index is greater than 1.0, that brand’s customers consume an
above-average quantity or value of products in the category.
Heavy Usage Index (I) 

Average Total Purchases in Category by Brand Customers (#,$)
Average Total Purchases in Category by All Customers
for That Category (#,$)

EXAMPLE: Over a period of one year, the average shampoo purchases by households
using Shower Fun brand shampoo totaled six 15-oz bottles. During the same period,
average shampoo consumption by households using any brand of shampoo was four 15oz bottles.
The heavy usage index for households buying Shower Fun is therefore 6/4, or 1.5.
Customers of Shower Fun brand shampoo are disproportionately heavy users. They buy
50% more shampoo than the average shampoo consumer. Of course, because Shower
Fun buyers are part of the overall market average, when compared with non-users ofShower Fun, their relative usage is even higher.

As previously noted, market share can be calculated as the product of three components: penetration share, share of requirements, and heavy usage index (see Section 2.4).
Consequently, we can calculate a brand’s heavy usage index if we know its market share,
penetration share, and share of requirements, as follows:
Heavy Usage Index (I) 

Market Share (%)[Penetration Share (%) * Share of Requirements (%)]

Chapter 2 Share of Hearts, Minds, and Markets

33This equation works for market shares defined in either unit or dollar terms. As noted
earlier, the heavy usage index can measure either unit or dollar usage. Comparing abrand’s unit heavy usage index to its dollar heavy usage index, marketers can determine
whether category purchases by that brand’s customers run above or below the averagecategory price.

Data Sources, Complications, and Cautions
The heavy usage index does not indicate how heavily customers use a specific brand,
only how heavily they use the category. A brand can have a high heavy usage index, forexample, meaning that its customers are heavy category users, even if those customers
use the brand in question to meet only a small share of their needs.

Related Metrics and Concepts
See also the discussion of brand development index (BDI) and category development
index (CDI) in Section 2.3.

2.7 Awareness, Attitudes, and Usage (AAU): Metricsof the Hierarchy of Effects
Studies of awareness, attitudes, and usage (AAU) enable marketers to quantify levels
and trends in customer knowledge, perceptions, beliefs, intentions, and behaviors. In
some companies, the results of these studies are called “tracking” data because they
are used to track long-term changes in customer awareness, attitudes, and behaviors.
AAU studies are most useful when their results are set against a clear comparator.
This benchmark may comprise the data from prior periods, different markets,or competitors.

Purpose: To track trends in customer attitudes and behaviors.
Awareness, attitudes, and usage (AAU) metrics relate closely to what has been called the
Hierarchy of Effects, an assumption that customers progress through sequential stages
from lack of awareness, through initial purchase of a product, to brand loyalty (seeFigure 2.2). AAU metrics are generally designed to track these stages of knowledge,
beliefs, and behaviors. AAU studies also may track “who” uses a brand or product––in
which customers are defined by category usage (heavy/light), geography, demographics,
psychographics, media usage, and whether they purchase other products.

34

MARKETING METRICS

Awareness

Customers must first become aware of a product, then . . .

Attitudes

They develop attitudes and beliefs about that product, and finally . . .

Usage

Customers purchase and experience the product.

Figure 2.2 Awareness, Attitudes, and Usage: Hierarchy of Effects

Information about attitudes and beliefs offers insight into the question of why specificusers do, or do not, favor certain brands. Typically, marketers conduct surveys of large
samples of households or business customers to gather these data.

Construction
Awareness, attitudes, and usage studies feature a range of questions that aim to shedlight on customers’ relationships with a product or brand (see Table 2.4). For example,
who are the acceptors and rejecters of the product? How do customers respond to a
replay of advertising content?
Marketers use answers to these questions to construct a number of metrics. Amongthese, certain “summary metrics” are considered important indicators of performance.
In many studies, for example, customers’ “willingness to recommend” and “intention topurchase” a brand are assigned high priority. Underlying these data, various diagnostic
metrics help marketers understand why consumers may be willing––or unwilling––torecommend or purchase that brand. Consumers may not have been aware of the brand,
for example. Alternatively, they may have been aware of it but did not subscribe to one
of its key benefit claims.

AWARENESS AND KNOWLEDGE
Marketers evaluate various levels of awareness, depending on whether the consumer
in a given study is prompted by a product’s category, brand, advertising, or usage
situation.
Awareness: The percentage of potential customers or consumers who recognize––orname––a given brand. Marketers may research brand recognition on an “aided” or“prompted” level, posing such questions as, “Have you heard of Mercedes?”Alternatively, they may measure “unaided” or “unprompted” awareness, posing suchquestions as, “Which makes of automobiles come to mind?”

Chapter 2 Share of Hearts, Minds, and Markets

35

Table 2.4 Awareness, Attitudes, and Usage: Typical Questions
Type

Measures

Typical Questions

Awareness

Awareness and Knowledge

Have you heard of Brand X?
What brand comes tomind when you think“luxury car?”

Attitudes

Beliefs and Intentions

Is Brand X for me?
On a scale of 1 to 5, is Brand
X for young people?
What are the strengths andweaknesses of each brand?

Usage

Purchase Habits and Loyalty

Did you use Brand X thisweek?
What brand did you
last buy?

Top of Mind: The first brand that comes to mind when a customer is asked an
unprompted question about a category. The percentage of customers for whom agiven brand is top of mind can be measured.
Ad Awareness: The percentage of target consumers or accounts who demonstrate awareness (aided or unaided) of a brand’s advertising. This metric can be
campaign- or media-specific, or it can cover all advertising.
Brand/Product Knowledge: The percentage of surveyed customers who demonstrate specific knowledge or beliefs about a brand or product.

ATTITUDES
Measures of attitude concern consumer response to a brand or product. Attitude is acombination of what consumers believe and how strongly they feel about it. Although adetailed exploration of attitudinal research is beyond the scope of this book, the following summarizes certain key metrics in this field.
Attitudes/Liking/Image: A rating assigned by consumers––often on a scale of 1–5 or
1–7––when survey respondents are asked their level of agreement with such propositions as, “This is a brand for people like me,” or “This is a brand for young people.” A
metric based on such survey data can also be called relevance to customer.
Perceived Value for Money: A rating assigned by consumers––often on a scale of
1–5 or 1–7––when survey respondents are asked their level of agreement with suchpropositions as, “This brand usually represents a good value for the money.”

36

MARKETING METRICS

Perceived Quality/Esteem: A consumer rating––often on a scale of 1–5 or 1–7––of
a given brand’s product when compared with others in its category or market.
Relative Perceived Quality: A consumer rating (often from 1–5 or 1–7) of brandproduct compared to others in the category/market.
Intentions: A measure of customers’ stated willingness to behave in a certain way.
Information on this subject is gathered through such survey questions as, “Would
you be willing to switch brands if your favorite was not available?”
Intention to Purchase: A specific measure or rating of consumers’ stated purchaseintentions. Information on this subject is gathered through survey respondents’reactions to such propositions as, “It is very likely that I will purchase thisproduct.”

USAGE
Measures of usage concern such market dynamics as purchase frequency and units
per purchase. They highlight not only what was purchased, but also when and where
it was purchased. In studying usage, marketers also seek to determine how many
people have tried a brand. Of those, they further seek to determine how many
have “rejected” the brand, and how many have “adopted” it into their regular portfolio of brands.
Usage: A measure of customers’ self-reported behavior.
In measuring usage, marketers pose such questions as the following: What brand of
toothpaste did you last purchase? How many times in the past year have you purchased
toothpaste? How many tubes of toothpaste do you currently have in your home? Do you
have any Crest toothpaste in your home at the current time?
In the aggregate, AAU metrics concern a vast range of information that can be tailoredto specific companies and markets. They provide managers with insight into customers’
overall relationships with a given brand or product.

Data Sources, Complications, and Cautions
Sources of AAU data include■

Warranty cards and registrations, often using prizes and random drawings toencourage participation.

■

Regularly administered surveys, conducted by organizations that interview
consumers via telephone, mail, Web, or other technologies, such as hand-heldscanners.

Chapter 2 Share of Hearts, Minds, and Markets

37

Even with the best methodologies, however, variations observed in tracking data fromone period to the next are not always reliable. Managers must rely on their experience todistinguish seasonality effects and “noise” (random movement) from “signal” (actual
trends and patterns). Certain techniques in data collection and review can also help
managers make this distinction.
1. Adjust for periodic changes in how questions are framed or administered.
Surveys can be conducted via mail or telephone, for example, among paid or
unpaid respondents. Different data-gathering techniques may require adjustment in the norms used to evaluate a “good” or “bad” response. If suddenchanges appear in the data from one period to the next, marketers are advised
to determine whether methodological shifts might play a role in this result.
2. Try to separate customer from non-customer responses; they may be very different. Causal links among awareness, attitudes, and usage are rarely clear-cut.
Though the hierarchy of effects is often viewed as a one-way street, on whichawareness leads to attitudes, which in turn determine usage, the true causal flow
might also be reversed. When people own a brand, for example, they may be
predisposed to like it.
3. Triangulate customer survey data with sales revenue, shipments, or other data
related to business performance. Consumer attitudes, distributor and retailsales, and company shipments may move in different directions. Analyzingthese patterns can be a challenge but can reveal much about category dynamics.
For example, toy shipments to retailers often occur well in advance of theadvertising that drives consumer awareness and purchase intentions. These, inturn, must be established before retail sales. Adding further complexity, in the
toy industry, the purchaser of a product might not be its ultimate consumer. In
evaluating AAU data, marketers must understand not only the drivers ofdemand but also the logistics of purchase.
4. Separate leading from lagging indicators whenever possible. In the auto industry, for example, individuals who have just purchased a new car show a heightened sensitivity to advertisements for its make and model. Conventional
wisdom suggests that they’re looking for confirmation that they made a good
choice in a risky decision. By helping consumers justify their purchase at thistime, auto manufacturers can strengthen long-term satisfaction and willingness
to recommend.

Related Metrics and Concepts
Likeability: Because AAU considerations are so important to marketers, andbecause there is no single “right” way to approach them, specialized and38

MARKETING METRICS

proprietary systems have been developed. Of these, one of the best known is the Q
scores rating of “likeability.” A Q Score is derived from a general survey of selectedhouseholds, in which a large panel of consumers share their feelings about brands,celebrities, and television shows.4
Q Scores rely upon responses reported by consumers. Consequently, although thesystem used is sophisticated, it is dependent on consumers understanding and being
willing to reveal their preferences.
Segmentation by Geography, or Geo-clustering: Marketers can achieve insight
into consumer attitudes by separating their data into smaller, more homogeneousgroups of customers. One well-known example of this is Prizm. Prizm assigns U.S.
households to clusters based on ZIP Code,5 with the goal of creating small groups ofsimilar households. The typical characteristics of each Prizm cluster are known,
and these are used to assign a name to each group. “Golden Ponds” consumers, forexample, comprise elderly singles and couples leading modest lifestyles in small
towns. Rather than monitoring AAU statistics for the population as a whole, firms
often find it useful to track these data by cluster.

2.8 Customer Satisfaction and Willingness
to Recommend
Customer satisfaction is generally based on survey data and expressed as a rating. For
example, see Figure 2.3.
Very
Dissatisfied

Somewhat
Dissatisfied

Neither Satisfied
nor Dissatisfied

Somewhat
Satisfied

Very
Satisfied!

"#Figure 2.3 Ratings

Within organizations, customer satisfaction ratings can have powerful effects.
They focus employees on the importance of fulfilling customers’ expectations.
Furthermore, when these ratings dip, they warn of problems that can affect sales andprofitability.
A second important metric related to satisfaction is willingness to recommend. Whena customer is satisfied with a product, he or she might recommend it to friends, relatives, and colleagues. This can be a powerful marketing advantage.

Chapter 2 Share of Hearts, Minds, and Markets

39

Purpose: Customer satisfaction provides a leading indicator of consumerpurchase intentions and loyalty.
Customer satisfaction data are among the most frequently collected indicators of market perceptions. Their principal use is twofold.
1. Within organizations, the collection, analysis, and dissemination of these data
send a message about the importance of tending to customers and ensuringthat they have a positive experience with the company’s goods and services.
2. Although sales or market share can indicate how well a firm is performing
currently, satisfaction is perhaps the best indicator of how likely it is that thefirm’s customers will make further purchases in the future. Much research has
focused on the relationship between customer satisfaction and retention.
Studies indicate that the ramifications of satisfaction are most strongly realized
at the extremes. On the scale in Figure 2.3, individuals who rate their satisfaction level as “5” are likely to become return customers and might even evangelize for the firm. Individuals who rate their satisfaction level as “1,” by contrast,
are unlikely to return. Further, they can hurt the firm by making negative
comments about it to prospective customers. Willingness to recommend is a
key metric relating to customer satisfaction.

Construction
Customer Satisfaction: The number of customers, or percentage of total customers,
whose reported experience with a firm, its products, or its services (ratings) exceeds
specified satisfaction goals.
Willingness to Recommend: The percentage of surveyed customers who indicatethat they would recommend a brand to friends.
These metrics quantify an important dynamic. When a brand has loyal customers, it
gains positive word-of-mouth marketing, which is both free and highly effective.
Customer satisfaction is measured at the individual level, but it is almost always
reported at an aggregate level. It can be, and often is, measured along various dimensions. A hotel, for example, might ask customers to rate their experience with its front
desk and check-in service, with the room, with the amenities in the room, with therestaurants, and so on. Additionally, in a holistic sense, the hotel might ask about overall satisfaction “with your stay.”
Customer satisfaction is generally measured on a five-point scale (see Figure 2.4).
Satisfaction levels are usually reported as either “top box” or, more likely, “toptwo boxes.” Marketers convert these expressions into single numbers that show the40

MARKETING METRICS

Very
Dissatisfied

Somewhat
DissatisfiedNeither Satisfied
nor Dissatisfied!

Somewhat
Satisfied"

Very
Satisfied#Figure 2.4 A Typical Five-Point Scale

percentage of respondents who checked either a “4” or a “5.” (This term is the same asthat commonly used in projections of trial volumes; see Section 4.1.)

EXAMPLE: The general manager of a hotel in Quebec institutes a new system of customer satisfaction monitoring (see Figure 2.5). She leaves satisfaction surveys at checkout. As an incentive to respond, all respondents are entered into a drawing for a pair offree airline tickets.
Very
Dissatisfied  
!


Somewhat
Dissatisfied

Neither Satisfied Somewhat
nor Dissatisfied Satisfied

Very
Satisfied

%
"

!
"


#
#
#

"

#

Figure 2.5 Hotel Customer Survey Response

The manager collects 220 responses, of which 20 are unclear or otherwise unusable.
Among the remaining 200, 3 people rate their overall experience at the hotel as very
unsatisfactory, 7 deem it somewhat unsatisfactory, and 40 respond that they are neither
satisfied nor dissatisfied. Of the remainder, 50 customers say they are very satisfied, while
the rest are somewhat satisfied.
The top box, comprising customers who rate their experience a “5,” includes 50 people
or, as a percentage, 50/200  25%. The top two boxes comprise customers who are“somewhat” or “very” satisfied, rating their experience a “4” or “5.” In this example, the“somewhat satisfied” population must be calculated as the total usable response pool, lesscustomers accounted for elsewhere, that is, 200  3  7  40  50 = 100. The sum of
the top two boxes is thus 50  100  150 customers, or 75% of the total.

Customer satisfaction data can also be collected on a 10-point scale. Regardless of thescale used, the objective is to measure customers’ perceived satisfaction with their experience of a firm’s offerings. Marketers then aggregate these data into a percentage of topbox responses.
Chapter 2 Share of Hearts, Minds, and Markets

41

In researching satisfaction, firms generally ask customers whether their product orservice has met or exceeded expectations. Thus, expectations are a key factor behindsatisfaction. When customers have high expectations and the reality falls short, they
will be disappointed and will likely rate their experience as less than satisfying. For thisreason, a luxury resort, for example, might receive a lower satisfaction rating than abudget motel––even though its facilities and service would be deemed superior in“absolute” terms.

Data Sources, Complications, and Cautions
Surveys constitute the most frequently used means of collecting satisfaction data. As aresult, a key risk of distortion in measures of satisfaction can be summarized in a single
question: Who responds to surveys?
“Response bias” is endemic in satisfaction data. Disappointed or angry customers often
welcome a means to vent their opinions. Contented customers often do not.
Consequently, although many customers might be happy with a product and feel noneed to complete a survey, the few who had a bad experience might be disproportionately represented among respondents. Most hotels, for example, place response cards intheir rooms, asking guests, “How was your stay?’ Only a small percentage of guests everbother to complete those cards. Not surprisingly, those who do respond probably had abad experience. For this reason, marketers can find it difficult to judge the true level ofcustomer satisfaction. By reviewing survey data over time, however, they may discoverimportant trends or changes. If complaints suddenly rise, for example, that may constitute early warning of a decline in quality or service. (See number of complaints in thefollowing section.)
Sample selection may distort satisfaction ratings in other ways as well. Because only customers are surveyed for customer satisfaction, a firm’s ratings may rise artificially as
deeply dissatisfied customers take their business elsewhere. Also, some populations may
be more frank than others, or more prone to complain. These normative differences canaffect perceived satisfaction levels. In analyzing satisfaction data, a firm might interpret
rating differences as a sign that one market is receiving better service than another,when the true difference lies only in the standards that customers apply. To correct forthis issue, marketers are advised to review satisfaction measures over time within thesame market.
A final caution: Because many firms define customer satisfaction as “meeting or exceeding expectations,” this metric may fall simply because expectations have risen. Thus, ininterpreting ratings data, managers may come to believe that the quality of their offering has declined when that is not the case. Of course, the reverse is also true. A firmmight boost satisfaction by lowering expectations. In so doing, however, it might suffer
a decline in sales as its product or service comes to appear unattractive.

42

MARKETING METRICS

Related Metrics and Concepts
Trade Satisfaction: Founded upon the same principles as consumer satisfaction,
trade satisfaction measures the attitudes of trade customers.
Number of Complaints: The number of complaints lodged by customers in agiven time period.

2.9 Willingness to Search
Although many metrics explore brand loyalty, one has been called the “acid test.”That is,Willingness to Search (%)  Percentage of Customers Willing to Delay Purchases,
Change Stores, or Reduce Purchase Quantities to
Avoid Switching Brands

This metric can tell a company much about the attitudes of its customers andwhether its position in the market is likely to be defensible against sustained pressure
from a competitor.

Purpose: To assess the commitment of a firm’s or a brand’s
customer base.
Brand or company loyalty is a key marketing asset. Marketers evaluate aspects of it
through a number of metrics, including repurchase rate, share of requirements, willingness to pay a price premium, and other AAU measures. Perhaps the most fundamental
test of loyalty, however, can be captured in a simple question: When faced with a situation in which a brand is not available, will its customers search further or substitute thebest available option?
When a brand enjoys loyalty at this level, its provider can generate powerful leverage intrade negotiations. Often, such loyalty will also give providers time to respond to a competitive threat. Customers will stay with them while they address the threat.
Loyalty is grounded in a number of factors, including■

Satisfied and influential customers who are willing to recommend the brand.

■

Hidden values or emotional benefits, which are effectively communicated.

■

A strong image for the product, the user, or the usage experience.

Purchase-based loyalty metrics are also affected by whether a product is broadly
and conveniently available for purchase, and whether customers enjoy other options in
its category.
Chapter 2 Share of Hearts, Minds, and Markets

43Construction
Willingness to Search: The likelihood that customers will settle for a secondchoice product if their first choice is not available. Also called “accept no
substitutes.”
Willingness to search represents the percentage of customers who are willing to leave astore without a product if their favorite brand is unavailable. Those willing to substituteconstitute the balance of the population.

Data Sources, Complications, and Cautions
Loyalty has multiple dimensions. Consumers who are loyal to a brand in the sense of
rarely switching may or may not be willing to pay a price premium for that brand or
recommend it to their friends. Behavioral loyalty may also be difficult to distinguishfrom inertia or habit. When asked about loyalty, consumers often don’t know what they
will do in new circumstances. They may not have accurate recall about past behavior,especially in regard to items with which they feel relatively low involvement.
Furthermore, different products generate different levels of loyalty. Few customers will
be as loyal to a brand of matches, for example, as to a brand of baby formula.
Consequently, marketers should exercise caution in comparing loyalty rates acrossproducts. Rather, they should look for category-specific norms.
Degrees of loyalty also differ between demographic groups. Older consumers have beenshown to demonstrate the highest loyalty rates.
Even with these complexities, however, customer loyalty remains one of the mostimportant metrics to monitor. Marketers should understand the worth of their brands
in the eyes of the customer––and of the retailer.

44

MARKETING METRICSINDEXSYMBOLS# (Count), 7$ (Dollar Terms), 6% (Percentage), 6

A
A.C. Nielsen, 183
Aaker, David, 116
brand equity ten, 117
AAU (Awareness, Attitudes, and Usage), 34
attitude, 36-37
awareness and knowledge, 35
calculating, 35
cautions, 38
data sources, 37
purpose, 34-35
usage, 37
abandoned purchases, 302
abandonment, 299
abandonment rate, 302
acceptors, 28
accountability, 2acquisition versus retention, 152-154
ACV (all commodity volume), 160, 178
calculating, 180-181
ad awareness, 36
adjusting for periodic changes, 38
advertising. See also impressions
as a percentage of sales, 81
price versus cost, 289
advertising effectiveness, 282, 284
advertising exposures, 282
all commodity volume. See ACV

allowances, slotting, 81
apparel retailers, customers, 137
assumptions
infinite horizon assumption, customer
lifetime value, 148
test markets, 100-101
attitude, AAU, 36-37
attitudes/liking/image, 36
attrition, 135
availability of data, 3
AVC on display, 185
AVC on promotion, 185
average acquisition cost, 152-153
average deal depth, 240
average frequency, 270, 273, 277
average margin, 62-64
average price, 65
average price charged, 200
average price displayed, 200
average price paid, 199
average price per unit, 66-67
calculating, 67-70
complications, 70
purpose, 66-67
average retention cost, 152-153
awareness, 35
customer awareness, 327
trial rate, 94
Awareness, Attitudes, and Usage (AAU), 34

Bbalancing sales force territories, 163-164
banks, counting customers, 136baseline sales, 241, 243345

calculating, 244-249
complications, 249
profitability, 249
purpose, 243
BAV (brand asset valuator), 116
BCG (Boston Consulting Group)
matrix, 20
BDI (brand development index), 23-24
calculating, 24-25
purpose, 24
segments, 25
Big Tobacco Company, 330-331
bonuses. See sales force compensation
Boomcustomer awareness, 327
customer profit, 326
financial statements, 323-324
marketing metrics, 326
Boston Consulting Group matrix, 20
brand asset valuator (BAV), 116
brand development index. See BDI
brand equity methodology (Moran), 117
brand equity metrics, 91, 115, 117-118
brand equity methodology(Moran), 117
brand equity ten (David Aaker), 117
brand valuation model, 118
purpose, 116
Y&R brand asset valuator, 116
brand equity ten, 117
brand penetration, 26
brand valuation model, 118
brand/product knowledge, 36
brands, number of brands purchased, 31
breadth of distribution, 184
break-even analysis, 81-85
break-even on incremental
investment, 85
classifying costs, 85
purpose, 82
break-even number of employees, 171, 173

346

MARKETING METRICS

break-even on incremental investment, 85
break-even point, 82-84
break-even sales level, 48
breakage, 253
Brita water filters, 66
budgeting risk, assessing, 77-78
budgets, 2
buying power, 164C
CAGR (compound annual growth rates),
89, 91
calculating, 109
cannibalization, 110-112, 114
weighted contribution margin, 112
cannibalization rates, 91, 110-111
complications, 114-115
cash flows, internal rate of return, 317
category development index (CDI), 23, 25
calculating, 25
purpose, 24
category performance ratio, 178, 183
cautions, AAU, 38
CDI (category development index), 23, 25
calculating, 25
purpose, 24
segments, 25
chaining margins, 55
channel margins, 55
complications, 61
hybrid channel margins, 61
channel metrics, Prestige Luggage, 329
choosing metrics, 3
churn, 135
classification of variable costs, 76
clickstream, 301-302
clickthrough rates, 291-293
calculating, 292-293
purpose, 292
cluster analysis, 125
CLV. See customer lifetime valuecohort and incubate, customer lifetime
value, 144-145
cold leads, 176
commission. See sales force compensationcommissioned sales costs, 79
company profit from new products, 105
comparing sales force territories, 164
compensation, 171, 173. See also sales
force compensationcompensatory decisions versus noncompensatory consumer decisions,121-123competitor price elasticity, 220, 230
competitor reaction elasticity, 220, 228
complications
average price per unit, 70
channel margins, 61
compound annual growth rates. See CAGR
compounding growth, 106, 108-109
concentration ratio, 21
conjoint analysis, 118-119, 204
calculating, 120-121
purpose, 119conjoint utilities, 92, 119, 123
complications, 123-124
segmentation, 124
complications, 126construction, 124-126
purpose, 124
volume projection and, 126-127
constant elasticity, 212-214
constructing frequency response
functions, 282-283
consumer off-take, 190
consumer preference, 119
compensatory versus noncompensatory decisions, 121-123
consumer ratings, 36-37
contractual situations, 132
counting customers, 133
contribution analysis, 81
contribution margins, 46, 48, 84

contribution per unit, 48, 81-83
converting markups to margins, 60
cookies, 302
cost, total cost per unit, 74
cost effectiveness, Internet marketing, 295
cost of incremental sales, 243
cost per click, 295, 297-298
calculating, 296
cost per customer acquired, 299
cost per impression, 294-295, 297
calculating, 296
cost per order, 295-297
cost per point (CPP), 276
cost per thousand impressions.
See CPM
cost-plus pricing, 220, 224
costs
assigning to customers, 141
average acquisition cost, 152-153
average retention cost, 152-153
classifying for break-even analysis, 85
commissioned sales costs, 79
fixed costs, 71, 80
calculating, 71-72, 74-75
classification of, 76
purpose, 71
overhead costs, 309
total cost, 72, 75
total selling costs, 78
total variable selling costs, 78
variable costs, 71
calculating, 71-72, 74-75
classification of, 76
purpose, 71
Count (#), 7
counting customers, 132, 135-137
contractual situations, 133
non-contractual situations, 133-134
recency. See recency, 134retention, 134

Index

347

coupons, 251
evaulating, 254
percentage sales with coupons, 251
profitability, 252redemption rate, 251-252
calculating, 253
CP. See customer profit
CPM (cost per thousand impressions),
265, 274-275
CPP (cost per point), 276
cross elasticity, 220, 227
cross price elasticity, 220, 228, 230
Cruisecustomer awareness, 327
customer profit, 326
financial statements, 323-324
marketing metrics, 326
customer awareness, 327
customer counts, 132
customer lifetime value, 129, 142-143, 150
calculating, 145-146
cohort and incubate, 144-145
discount rate, 147
finite-horizon, 148
infinite horizon assumption, 148versus prospect lifetime value, 150-152
purpose, 143-144
retention rate, 146
customer lifetime value with initial
margin, 147
customer profit (CP), 129, 137-138, 141
Boom, 326
calculating, 138-140
Cruise, 326
purpose, 137-138
quantifying, 143
whale curve, 143
customer responses, separating customer
from non-customer responses, 38
customer satisfaction, 39-40
measuring, 40-42
purpose, 40

348

MARKETING METRICS

sample selection, 42
surveys, 42
customer selling price, 55, 57-58
customer service, 170
customer survey data, triangulating, 38
customer time, 135
customers, 132, 135
abandoning, 142
acceptors, 28
acquisition versus retention, 152-154
assessing value of, 143-144
assigning cost to, 141
brand penetration, 26
counting, 132-133, 135-137
non-contractual situations, 133-134
recency. See recency
retention, 134
deciding who to serve, 142
defining, 135-136
ever-tried customers, 28
impressions. See impressions
market penetration, 26
purpose, 132
Second Tier customers, 138
surveys. See surveys
Third Tier customers, 138
Top Tier customers, 138
total number of active customers, 28
unprofitable customers, 142D
dashboards, 331
data, availability of, 3data parameters, market share, 18
data sources
AAU, 37
heavy usage index, 34
decline, life cycle, 109
decomposing market share, 27
deductions, 190, 260

demandlinear demand
optimal price, 216, 218-222
price elasticity, 209-212
reservation prices, 204, 206-207
price tailoring, 261
demand curves, constant elasticity,
212-214demand functions, 195
direct product costs, 192
direct product profitability. See DPP
discount rate, 317
customer lifetime value, 147
net present value, 317
discounted trial, 104discounts, 259
distribution, trial rates, 95
distribution chains, 55
distribution channels, calculating selling
prices at each level in the distribution
channel, 56
distribution metrics, 178
ACV, 180-181
data sources, 183-184
numeric distribution, 179-180
PCV, 182-183
purpose, 179
districts, 166
diverted goods, 190
diverted merchandise, 190
Dollar Terms ($), 6
double jeopardy, 31
DPP (direct product profitability), 158,
162, 191, 194
calculating, 192-193
Drucker, Peter, 45
durability/loyalty, 117E
earning before interest taxes, depreciation,
and amortization (EBITDA), 309
eBay, active users, 134

EBITDA (earning before interest taxes,
depreciation, and amortization), 309
economic profit, 306, 310, 312
calculating, 312
purpose, 311
economic value added (EVA), 305,
310-311EDLP (everyday low prices), 260
effective frequency, 266, 285-287
effective market share, 117
effective reach, 285-286
calculating, 286-287
Internet, 287
purpose, 285-286
effectiveness. See sales force effectivenesselasticity. See also price elasticity
cross elasticity, 220, 227
EVA (economic value added), 305, 310-311
evaluating
coupon programs, 254
inventories, 189
multi-period investments, 313
sales goals, 167
temporary price promotions, 240
workload distribution, 174
ever-tried customers, 28
ever-tried, 104everyday low prices (EDLP), 260
evoked set, 105
examplesBig Tobacco Company, 330-331
Boom. See Boom
Cruise. See Cruise
Prestige Luggage. See Prestige Luggage
expenses, sales force effectiveness, 170
exposures, 269

Ffacings, 184
fair share draw, 91, 110, 112-114
features in store, 184
Federal Trade Commission, 261

Index

349

FIFO (First In, First Out), 189
financial statements, Boom and Cruise,323-324
finite-horizon, customer lifetime
value, 148
first channel member’s selling price, 58-59
First In, First Out (FIFO), 189
first-time triers in period, 93
fixed costs, 71, 80
calculating, 71-72, 74-75
classification of, 76
purpose, 71
forced trial, 104
forecasting
marketing spending, 77-78
trial volume, 96
upcoming sales, 174
Fortune, 135
frequency, 276
average frequency, 277
effective frequency. See effective frequency
frequency response functions, 265, 280,
284-285
construction, 282-283
learning curve response model, 280-281
linear response model, 280-281
purpose, 281-282
threshold response model, 281

G
geo-clustering, 39
globalization, 3
GM (General Motors), retail sales, 45
GMROII (gross margin return on
inventory investment), 158, 162, 191-193
goals, sales goals
calculating, 166-167
purpose, 165-166
goodwill, 116
gross margin, 55, 215

350

MARKETING METRICS

gross margin return on inventory investment. See GMROII
gross rating points. See GRPs
growth, 106
CAGR, 109
compounding, 106, 108-109
life cycle, 109
percentage growth, 106
adjusting, 109
same stores growth, 106-108
value of future period, 108-109
year-on-year growth, 105
GRPs (gross rating points), 264, 269-271
calculating, 271-273, 277

Hheavy usage index, 28, 32-33
calculating, 33-34
data sources, 34
purpose, 33
Herfindahl Index, 22-23
HI-LO (high-low), 260
hierarchy of effects, 38hits, 289-290
hybrid channel margins, 61

I-J
I (Index), 7
identifying profitability of individual
customers, 137-138
impressions, 269
calculating, 270-271
clickthrough rates, 291-293
complications, 274
cost per click, 295-298
cost per impression, 294-297
cost per order, 295-297
CPM. See CPM
data sources, 273-274
frequency response functions. See
frequency response functions

GRPs, 269-271
calculating, 271-273
net reach. See net reach
pageviews, 289-291
purpose, 269-270
share of voice, 287-288
incentive plans, 173-174
timing, 174
income statement, Prestige Luggage, 328
incremental sales, 243-244
cost of, 243
Index (I), 7
indexesbrand development index, 23-25
CDI (category development index),
23-25
heavy usage index, 28
Herfindahl Index, 22-23
indicators, separating leading from lagging
indicators, 38
infinite horizon assumption, customer
lifetime value, 148
inflation, estimating, 70
intention to purchase, 37
intentions, 37
Interbrand, 116
brand valuation model, 118interest creation, 176
internal rate of return (IRR), 307,
313, 316
calculating, 315-316
cash flows, 317
Internet, 264. See also web pages
assessing cost effectiveness, 295
effective reach, 287
search engine marketers, 299
search engines, 297-298
introductory life cycle, 109
inventories, evaluating, 189
inventory, 184inventory days, 187-188
inventory tracking, 187
inventory turns, 185, 187

investments, evaluating multi-period
investments, 313
invoice price, 257-258
IRR. See internal rate of return

K
Kaplan, Robert, 139
Keller, Kevin, 116
Kelvin, Lord, 2
key assumptions, test markets, 100-101
knowledge, brand/product knowledge, 36

L
Last In, First Out (LIFO), 189
leading national advertisers (LNA), 288
learning curve, 266
learning curve response model, frequency
response functions, 280-281
life cycle, 109
LIFO (Last In, First Out), 189
likeability, 38
linear cost model, 76
linear demand
optimal price, 216, 218-222
price elasticity, 209-212
reservation prices, 204, 206-207
linear response model, frequency response
functions, 280-281
list price, 257
LNA (leading national advertisers), 288
loyalty, 102, 325
double jeopardy, 31
number of brands purchased, 31
willingness to search, 43-44

M
mail-in rebates, 253
make-goods on promotions, 190
margin on new products, 105

Index

351

margins, 45, 49
average margin, 62-64
chaining, 55
channel margins. See channel margins
contribution margins, 46, 48
converting from markups, 60
costs, including or excluding, 55
customer lifetime value with initial
margin, 147
gross margin, 55, 215
versus markup, 53-55
as percentage of costs, 52
percentage margins, 49
calculating, 50-51, 62
purpose, 49
reported margins, 52
reporting, 54
selling prices, defining, 52
unit margins, 49-51
weighted contribution margins, cannibalization, 112
markdowns, 190-192
market concentration, 19, 21
market penetration, 26
market share, 12, 16
bias in reported shares, 19data parameters, 18
decomposing, 27
effective market share, 117
measuring over time, 19
purpose of, 17
quantifying, 18
relative market share, 19-21
revenue market share, 17
served market, 18
unit market share, 17market share rank, 23
marketing as a percentage of sales, 81
marketing budgets, developing, 80
marketing dashboards, 331, 333
marketing metrics, 333-334
Prestige Luggage, 329
marketing spending, 77

352

MARKETING METRICS

calculating, 79-80
fixed costs, 80
purpose, 77-78
slotting allowances, 81
markupconverting to margins, 60
versus margins, 53-55
Marlboro Friday, 331
mastering metrics, 4mature, life cycle, 109
maximum reservation price (MRP),
205, 216
maximum willing to buy (MWB), 205-206,
220, 222
measuringcustomer satisfaction, 40-42
market share, over time, 19
media exposure return on marketinginvestment, 321-322
media plans, net reach, 278
metrics
defined, 1reasons for having, 2
middlemen, 254
misshipments, 190
Moran, Bill, 116
MRP (maximum reservation price), 205,
216, 220, 222
multi-period investments, evaluating, 313
MWB (maximum willing to buy), 205-206,
220, 222

N
net operating profit after tax (NOPAT),309
net present value (NPV), 307, 313, 316
calculating, 314-315
discount rate, 317
net price, 257-258
net profit, 306, 308
calculating, 308-309
overhead costs, 309

net reach, 273, 276, 278
complications, 280
overlap, 279-280
purpose, 276-279
noise, 38
non-compensatory consumer decisions
versus compensatory decisions, 121-123
non-contractual situations, 132
counting customers, 133-134
NOPAT (net operating profit aftertax), 309
NPV. See net present value
number of complaints, 43
number of new products, 105
numeric distribution, 160, 178
calculating, 179-180

Oobsolescence, 190
opportunities-to-see (OTS), 269
optimal price, 215
calculating, 220, 222-224
complications, 220, 224
purpose, 216
linear demand, 216, 218-222
slope, 220
optimal price, relative to gross margin,
220, 223
optimality condition, 220, 223
OTS (opportunities-to-see), 269
out-of-stock, 161, 185-186
PCV net out-of-stocks, 186
over-servicing, 163
overhead costs, 309
overlap, assessing, 280
overlap effects, 279-280
own price elasticity, 220, 228, 230

P
pageviews, 289-291, 300
pass-through, 242, 254
calculating, 255
complications, 255-256
payback, 313-314

payback period, 86
PCV (product category volume), 160, 178
calculating, 182-183
PCV net out-of-stocks, 186
penetration, 26, 92
brand penetration, 26
calculating, 26, 93-94
cautions, 28
market penetration, 26
share, 26
penetration rate, 26
penetration share, 26-28
Peppers, Don, 143
perceived quality/esteem, 37
perceived value for money, 36
percent good value, 202
Percentage (%), 6
percentage growth, 106
adjusting, 109
percentage margins, 49
calculating, 50-51, 62
percentage of unit sales, 62
percentage sales on deal, 254
calculating, 255
percentage sales with coupons, 251
performance, 2monitoring firm performance inattracting and retaining customers, 132
performance reviews. See sales force
effectivenessperiodic changes, adjusting for, 38
Philips Consumer Electronics, 116
pipeline analysis, 174construction, 175-177
purpose, 174-175
sales funnel, 177-178
pipeline sales, 190
PLV. See prospect lifetime value
post-purchases, 176
pre-purchase, 176
Prestige Luggage, 329
income statement, 328
marketing and channel metrics, 329
retail margins, 328
retail profit, 329

Index

353

price discrimination, 220, 224,
226-227, 260
regulation, 220, 227, 261
price elasticity, 196, 208-209, 215
calculating, 209-212
constant elasticity, 212-214
linear demand, 209-212
purpose, 209
price increases, evaluating, 70
price of a specified competitor, 198
price per statistical unit, 47, 66, 68
calculating, 68-69
price premiums, 198
calculating, 198-201
complications, 201
purpose, 198
price promotions. See promotions
price tailoring, 220, 224, 226-227, 260
demand, 261
regulation, 220, 227
regulations, 261
price waterfalls, 240, 242, 256-257
calculating, 258
complications, 259
purpose, 257
pricesaverage price charged, 200
average price displayed, 200
average price paid, 199
average price per unit, 66-67
calculating, 67-70
complications, 70
purpose, 66-67
average price, 65
competitor price elasticity, 220, 230
cost-plus pricing, 220, 224
cross elasticity, 220, 227
cross price elasticity, 220, 230
customer selling price, 55, 57
calculating, 57-58
first channel member’s selling price,
58-59

354

MARKETING METRICS

invoice price, 257-258
list price, 257
net price, 257-258
optimal price, 215
calculating, 220, 222-224
complications, 220, 224
linear demand, 216, 218-222
purpose, 216
relative to gross margins, 220, 223
slope, 220
own price elasticity, 220, 230
percent good value, 202
price discrimination, 260
price elasticity, 208-209, 215
calculating, 209-212
constant elasticity, 212-214
linear demand, 209-212
purpose, 209
price of a specified competitor, 198
price per statistical unit, 66, 68
calculating, 68-69
price premiums, 198
calculating, 198-201
complications, 201
purpose, 198
price tailoring, 220, 224, 226-227, 260
demand, 261
regulation, 220, 227
regulations, 261
price waterfalls. See price waterfalls
prisoner’s dilemma pricing, 220,232-234, 236-237
deciding if you face this situation, 220,237-238
relative price, 117
reservation price, 202
calculating, 202, 204finding, 204
linear demand, 204, 206-207
residual price elasticity, 220, 227
calculating, 230-231

complications, 231-232
purpose, 228-230
selling price, 56
defining, 52
calculating prices at each level in the
distribution channel, 56
supplier selling price, 55
calculating, 57
calculating average, 65
theoretical price premiums, 202
primary line competitive injury, 220, 227
prisoner’s dilemma pricing, 220, 232-234,
236-237
deciding if you face this situation, 220,237-238
Prizm, geo-clustering, 39
product category volume. See PCVProfessional Pricing Society, 259
profit-based sales targets, 86-87
profitability
baseline sales, 249
coupons, 252
price tailoring, 260
redemption rates, 252
profitability metrics, 190
complications, 193-194
DPP, 191
calculating, 192-193
GMROII, 191-192
markdowns, 191-192
purpose, 191
profitability or promotions, 247
projected volume, repeat volume, 97-98
promotional discount, 255
promotions, 239
baseline sales. See baseline sales
complications, 255-256
coupons. See coupons
evaluating temporary pricepromotions, 240
long-term effects of, 250-251
profitability, 247
rebates, 251

mail-in rebates, 253redemption rates. See redemption rates
short-term promotional objectives, 239
prospect lifetime value (PLV), 149
calculating, 149-150
complications, 150-152
versus customer lifetime value, 150-152
purpose, 149
prospects, 176
pull marketing, 179
purchases, 176
push marketing, 179

Q
quantifying
customer profit, 143
market share, 18

R
R (Rating), 7
rain checks, 190
Ramsellar, Leon, 116
Rating (R), 7
rating point, 269
reach, 276-278. See also net reach
rebates, 251
mail-in rebates, 253
recency, 132, 134
redemption rates, 251
calculating, 253
coupon redemption rate, 252
profitability, 252
purpose, 251
regulations, price discrimination, 220,
227, 261
relationships, 136
relative market share, 19
calculating, 20-21
purpose, 19-20
relative perceived quality, 37
relative prices. See price premiums

Index

355

repeat, 104
repeat rate, 32, 101
repeat volume, 97-98
reported margins, 52
reporting margins, 54
repurchase rate, 32
resellers, 255
reservation prices, 202
calculating, 202, 204finding, 204
linear demand, 204, 206-207
purpose, 202
residual price elasticity, 220, 227
calculating, 220, 230-231
complications, 220, 231-232
purpose, 220, 228-230
response bias, 42
responses, customer survey responses, 96
retail margins, 328
retail profit, 329
retailers, apparel retailers (customers), 137
retention, 32, 134-135
versus acquisition, 152-154
retention rate, 132, 135
customer lifetime value, 146
return, 305
return on assets (ROA), 309
return on capital (ROC), 309
return on incremental marketinginvestment (ROIMI), 319
return on invested capital (ROIC), 309
return on investment (ROI), 306,
309-310, 323
return on marketing investment, 307
return on marketing investment (ROMI),
306, 318
budgeting, 321
calculating, 318-320
complications, 321
media exposure return on marketinginvestment, 321-322
purpose, 318
return on net assets (RONA), 309
return on sales (ROS), 306, 308-309, 323

356

MARKETING METRICS

returns and target, 88
revenue attributable to marketing, 319
revenue from new products, 105revenue market share, calculating, 17revenue return to incremental
marketing, 319
revenue return to total marketing, 319
revenue share of requirements, 29
reward structures, supply chain
metrics, 189
ROA (return on assets), 309
Robinson-Patman Act, 220, 227, 261
ROC (return on capital), 309
Rogers, Martha, 143
ROI (return on investment), 306,
309-310, 323
ROIC (return on invested capital), 309
ROIMI (return on incremental marketinginvestment), 319
ROMI. See return on marketinginvestment
RONA (return on net assets), 309
ROS (return on sales), 323

Ssalaries. See sales force compensation
sales force compensation, 171
calculating, 172-173
incentive plans, 173-174
purpose, 172
sales force effectiveness, 168
calculating, 168-171
customer service, 170
expenses, 170
purpose, 168
sales force funnel, 175
sales force objectives, 165, 167
calculating, 166-167
purpose, 165-166
sales force territories, 162
balancing, 163-164
comparing, 164
estimating size of, 165

purpose, 163
redefining, 165
sales force tracking. See pipeline analysis
sales funnel, 160, 177-178
sales goal, 166
evaluating, 167
sales pipeline, 160
sales potential, 158, 162-165
goals, 165, 167
calculating, 166-167
purpose, 165
same stores growth, 106-108
sample selection, customer satisfaction, 42
search engine marketers, 299
search engines, 297-299
seasonal variations, return on
investment, 310
second-price auctions, 204
secondary line competitive injury,
220, 227
segment utilities, 92
segmentation by geography, 39segments
BDI, 24
CDI, 25
conjoint utilities, 124-126
selling price, 56
calculating at each level in the distribution channel, 56
defining, 52
separating customer responses from noncustomer response, 38
served market, 18-19
service levels, 185-186
Shames, Erv, 330
share of category, 23
share of requirements, 27, 29
calculating, 29-30
double jeopardy, 31
purpose, 29
share of shelf, 184
share of voice, 288
share of wallet. See share of requirements

shopping basket margin, 194
shrinkage, 190
signals, 38
SKU (stock keeping unit), 66, 191
slope, optimal price, 220
slotting allowances, 81
sole usage, 31
sole usage percentage, 31
spreadsheets, calculating NPV, 317
State Farm, 133
statistical units, 68, 70
stepped payments, 80
stock keeping unit (SKU), 66, 191
store versus brand measures, 184
supplier selling price, 55
calculating, 57
calculating average, 65
supply chain metrics, 185complications, 188-189
inventories, evaluating, 189
inventory days, 187-188
inventory tracking, 187
inventory turns, 187
out-of-stocks, 186
purpose, 185
reward structures, 189
service levels, 186
surveys, 94
customer satisfaction, 42
customer survey responses, 96

Ttarget market fit, 105target rating points. See TRPs
target revenue, 86-87
target volume, 48, 86
target volumes not based on target
profit, 88
targets, profit-based sales targets, 86-87
terminal values, 317

Index

357

territories, sales force territories. See sales
force territories
test markets. See also trials
assumptions, 100-101
awareness, 94
distribution, 95
simulated results and volume
projections, trial volume, 94“the trade,” 254
theoretical price premiums, 202
three (four) firm concentration ratio,
21-22
threshold, 265
threshold response model, frequency
response functions, 281
time, measuring market share over, 19
tolerable discrimination, 261
top of mind, 36
total cost, 72, 75
total cost per unit, 74
versus variable cost per unit, 76
total coupon cost, 252
total distribution, 160, 183
total number of active customers, 28
total outlet sales, 184
total selling costs, 78
total variable selling costs, 78
total volume, 98-99
trade satisfaction, 43
trial rates, 93-94
distribution, 95
trial volume, 96-97
trial-repeat model, 104
trials, 92, 101, 104discounted trials, 104
forced trials, 104
purpose, 93
repeat volume, 97
total volume, 98-99
TRPs (target rating points), 264, 271-273

358

MARKETING METRICS

U
under-servicing, 163
unit margin, 49
calculating, 50-51
unit market share, 17unit share of requirements, 29-30
units, 49
USAA, 133
usage, AAU, 37
user behavior, web sites, 299-302

Vvalue of future period, 108-109
variable cost per unit versus total cost perunit, 76
variable costs, 71
calculating, 71-72, 74-75
classification of, 76
purpose, 71
Venn Diagram, 279
visitors, 299, 302-303
visits, 268, 299-300, 303
volume projection, 92-93
conjoint utilities, 126-127
volume projection spreadsheet, 99

W
Wal-Mart, 11
economic profit, 313
warm leads, 176
wear-in, 285
wear-out, 285
web pages. See also Internet
hits, 289-290
pageviews, 289-291, 300
visitors, 299, 303
visits, 299, 303
web sites, user behavior, 299-302

website traffic, assessing, 289-290
weighted contribution margin,
cannibalization, 112
weighted share of sales allotment, 166
whale curve, customer profit, 143
willingness to recommend, 39-40
willingness to search, 43-44
workload, 162-163
evaluating workload distribution, 174

X-Y-Z
Y&R (Young and Rubicam), 116
year-on-year growth, 91, 105

Index359