corporate brand impact on sales / revenue per share

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Corporate Brand Impact on Sales / Revenue Per Share Corporate Brand Impact on Sales / Revenue Per Share

Brad A. Puckey University of South Florida

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Corporate Brand Impact on Sales / Revenue Per Share

by

Brad A. Puckey

A dissertation submitted in partial fulfillment

of the requirements for the degree of

Doctor of Business Administration

Muma College of Business

University of South Florida

Co-Major Professor: Loran Jarrett, D.B.A.

Co Major Professor: Paul Solomon, Ph.D.

Jung Chul Park, Ph.D.

Joann Quinn, Ph.D.

Date of Approval:

October 30, 2020

Keywords: Branding, Quantitative, CHAID, Branding Value

Copyright © 2020, Brad A. Puckey

DEDICATION

To John B. “Jack” Frey; he was a brilliant colleague, a wonderful mentor, and an even

better friend. You taught me how to put education to practice with knowledge and wisdom as

well as how to turn numbers into intelligence. You are dearly missed.

ACKNOWLEDGMENTS

The completion of this dissertation would not have been possible without the contribution

of many wonderful people. I’d like to start by thanking my committee members: Defense Chair,

Dr. Jung Chul Park, co-chairs Dr. Joran Jarrett and Dr. Paul Soloman for all their wisdom and

advice throughout the process and committee member Dr. Joann Quinn for her wisdom and

guidance.

I’d also like to acknowledge and thank the entire 2020 cohort of fellow doctoral students

for sharing their experiences, knowledge, and general good wit during the entire course of

instruction. I’d especially like to thank the cohort members on my committee, Mark Mattia,

Michael Summers, and Toufic “Tom” Chebib. Your constant encouragement and assistance

helped put this work over the line.

Thank you to Hampton Bridwell at Tenet Partners for sharing the CoreBrand Index®

database used in this research. A sincere thank you to Kellan Williams, my analytical partner in

this analysis; his skill and wisdom helped turn our pile of data into a story.

A special thank you to my Uncle and Aunt, Dr. James and Evelyn Gregory. Your help in

every aspect of this process made it possible. I simply could not have done this without your

support. Your encouragement, advice and counsel, friendly ear when I was about to come off the

rails, and material support made this happen. Thank you!

Countless others deserve my thanks, but I have run out of room. I am truly blessed and

say thanks to all of you!

i

TABLE OF CONTENTS

List of Tables ............................................................................................................................ iii

List of Figures ............................................................................................................................ iv

Abstract ...................................................................................................................................... v

Chapter One: Introduction and Background ................................................................................. 1

Introduction ..................................................................................................................... 1

Stating the Problem ......................................................................................................... 2

Research Question and Units of Analysis ......................................................................... 3

Substantive Focus ............................................................................................................ 3

Specific Theoretical Discussions Addressed .................................................................... 5

Concepts and Definitions ................................................................................................. 7

Summary and Organization ............................................................................................. 8

Chapter Two: Literature Review ................................................................................................. 9

The Corporate Brand Impact ............................................................................................ 9

Literature Search Strategy.............................................................................................. 10

Analysis Process ............................................................................................................ 10

Types of Brand Measurement Methodologies ................................................................ 11

Theory of Intangible Capital .......................................................................................... 13

Corporate Brand Construct ............................................................................................ 14

Marketing Theory .......................................................................................................... 15

Brand Theory ................................................................................................................ 16

Product Market Model ................................................................................................... 16

Gaps in the Literature .................................................................................................... 17

Hypotheses .................................................................................................................... 17

Chapter Three: Methodology ..................................................................................................... 19

Purpose.......................................................................................................................... 19

Sales/Revenue-per-Share Explained............................................................................... 19

Practical Business Application of the Model .................................................................. 20

Research Design and Data Collection ............................................................................ 20

Key Descriptive Statistics .............................................................................................. 25

Key .................................................................................................................... 26

CHAID Model Overview ............................................................................................... 27

Chapter Four: Results of the Study ............................................................................................ 29

Analysis ........................................................................................................................ 29

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Data Preparation ............................................................................................................ 29

Initial Data Exploration – Univariate Test ...................................................................... 30

Correlation Analysis ...................................................................................................... 32

Hypothesis Testing ........................................................................................................ 34

CHAID Analysis – Limited Micro Model ...................................................................... 35

CHAID Analysis – Full Macro Model ........................................................................... 39

Marco Model Results ..................................................................................................... 42

Additional Models ......................................................................................................... 43

Consumer Staples Sector ............................................................................................... 43

Industrials Sector ........................................................................................................... 44

Technology Sector ......................................................................................................... 46

Comparing the Sector Models ........................................................................................ 48

Industry Analysis ........................................................................................................... 49

Chapter Five: Summary and Discussion .................................................................................... 50

Summary of the Problem ............................................................................................... 51

Research Question ......................................................................................................... 51

Hypotheses .................................................................................................................... 51

Summary of the Methodology ....................................................................................... 52

Summary of the Results ................................................................................................. 52

Contributions to Knowledge .......................................................................................... 53

Practitioner Contribution ............................................................................................... 54

Study Limitations and Future Research .......................................................................... 56

Conclusions ................................................................................................................... 57

References ................................................................................................................................ 60

Appendix A: Descriptive Statistics ............................................................................................ 64

Appendix B: Correlation Analysis ............................................................................................. 70

Appendix C: Sector CHAID Decision Trees .............................................................................. 75

Appendix D: Permission Letter ................................................................................................. 77

About the Author ........................................................................................................... End Page

iii

LIST OF TABLES

Table 1: Quintile Analysis of Key Data for Companies in the Database ................................. 31

Table A1: List of Variables and Descriptive Statistics .............................................................. 64

Table A2: 1-Year Sales / Revenue Per Share, % Growth Correlations ...................................... 70



iv

LIST OF FIGURES

Figure 1: Impact of Industry and Corporate Brand Factors on 5-yr Sales/ Revenue Per

Share % Growth Rate .............................................................................................. 35

Figure 2: CHAID Decision Tree Macro Analysis, Industry and Corporate Brand Model ......... 37

Figure 3: Industries Included in Nodes 1-4 of the CHAID Analysis ........................................ 38

Figure 4: Predictor Importance in Full Macro Model .............................................................. 40

Figure 5: CHAID Full Macro Model Decision Tree ................................................................ 40

Figure 6: Industries Included in Nodes 6, 7, and 8 ................................................................... 41

Figure 7: Industries Included in Nodes 9, 10, 11, 12, and 13 ................................................... 41

Figure 8: Industries Included in Nodes 14, 15, and 16 ............................................................. 42

Figure 9: Results of the Macro Model ..................................................................................... 42

Figure 10: Predictor Importance Consumer Staples Sector ........................................................ 44

Figure 11: Results of Consumer Staples Sector Model .............................................................. 44

Figure 12: Predictor Importance Industrials Sector .................................................................... 45

Figure 13: Results of Industrial Sector Model ........................................................................... 46

Figure 14: Predictor Importance Technology Sector ................................................................. 47

Figure 15: Results of Technology Sector Model........................................................................ 48

Figure A1: CHAID Decision Tree: Consumer Staples Sector ..................................................... 75

Figure A2: CHAID Decision Tree: Industrials Sector ................................................................ 75

Figure A3: CHAID Decision Tree: Technology Sector .............................................................. 76

v

ABSTRACT

The purpose of this dissertation is to identify the linkage between the corporate brand and

sales / revenue per share growth. Utilizing a unique database, the CoreBrand® Index, this study is

a quantitative analysis of the interaction between the corporate brand and financial data. A

relationship between corporate brand and financial data has been suspected by practitioners;

however, it has never been proven. For the first time, quantitative analysis is conducted in this

dissertation using this unique dataset.

This study is intended to help those responsible for managing corporate brands to better

understand their impacts and benefits. The more that value creation can be understood and

explained, the more likely that corporate leadership will view expenditures as investments rather

than simply expenses. This study is intended to help demonstrate how corporate brands can be

used to create value.

A chi-squared automatic interaction detector (CHAID) analysis was performed to predict

whether a company would have high or low sales / revenue per share growth. This analysis was

done on the macro and sector level with results exceeding 80% accuracy. This analysis can be

used to help practitioners understand and predict the corporate brand’s impact on business results

and help them justify and allocate resources to manage this asset.

This work can help corporations to justify and manage their corporate brand budgets and

provide a basis for accountability. The corporate brand’s impact on sales / revenue per share is

but one small piece of intangible capital that can be understood and managed. The more elements

vi

that can be uncovered and managed, the better companies can predict and plan for their financial

growth and reduce the uncertainty surrounding their businesses.

1

CHAPTER ONE:

INTRODUCTION AND BACKGROUND

Introduction

Branding has been a topic of study by marketers since the late 19th century. The branding

concept is that by enriching the awareness, reputation, and perception of a product, higher

customer loyalty and increased sales can be obtained (Peng & Li, 2019).

The corporate brand, while having been studied for decades, is a newer concept. Around

the 1970s, advertisers began creating a personality for their companies to create traction for their

product brands and to make product launches more successful (Holland, 2017). Traditional

beliefs are that product branding can increase revenue and corporate brand can increase

shareholder value and goodwill (Gregory & Wiechmann, 1991; Puckey, 2012). There has always

been a general belief that corporate branding can improve sales, but no macro-level empirical

proof has been obtained. This dissertation uses existing corporate brand data provided by Tenet

Partners, in conjunction with financial statistics and company-paid media investment data, to

seek empirical evidence of corporate brand impact on sales revenue growth. Tenet Partners is a

leading corporate brand consulting agency headquartered in New York, NY, formed by the

merger of Brandlogic and CoreBrand in 2014.

Gregory’s 2018 paper, “Intangible Capital: Culture of Innovation and its Impact on the

Cash Flow Multiple,” examined the corporate brand and its impact on the cash flow multiple as a

component of intangible capital. Further, his work asserted that intangible capital is composed of

2

many layers, and the more fully we can understand those layers, the more complete our

understanding of intangible capital would be (Gregory, 2018). The present study extends

Gregory’s work by attempting to understand another component of the corporate brand’s

contribution to intangible capital with the goal of expanding our understanding of it.

Given the longitudinal nature of the data available, this dissertation examined how the

corporate brand, in its totality and components, changes over time and impacts changes in a

company’s revenue per share. Rather than serving as a precise predictive model, the underlying

objective was to understand the impact and what magnitude it achieves. Additionally, this

research evaluated how the different components of corporate brand have varying impact on

sales/revenue per share. Future research on product branding can use this research to determine

how corporate and product brands may work together to create value and to predict and model

outcomes.

The outcome of this dissertation will help corporate communications executives better

understand how the corporate brand contributes to value creation. This increased understanding

can offer additional evidence to prove the value to senior company leaders and to make better

decisions when employing the corporate brand for use. Better decisions will help businesses

secure resources and make strategic and tactical communications decisions.

Stating the Problem

Intuitively, marketers and senior executives believe that the corporate brand contributes

to a company’s business performance. However, the questions raised since the 1970s are: How

much? How long does it take? How much does it cost? Answers to these questions have been

elusive because there has been no quantitative tangible measure of corporate brand performance

on a large enough scale. Without answers to these questions, senior executives can be reluctant

3

to adequately fund an effort that they have difficulty measuring its performance and tracking

accountability. The goal of this research is to answer those questions in an effort to make the

corporate brand more tangible to those outside of corporate marketing and help senior executives

better understand the value of investing in the corporate brand.

Research Question and Units of Analysis

The research question was: How does the performance of the corporate brand impact

sales / revenue per share?

This question is raised and measured at the corporate level. By measuring the strength

and growth in the corporate brand, in conjunction with strength and growth in corporate

sales/revenue per share, quantitative analysis of the impact can be performed. This study

examines archival data collected on the corporate brand of 621 corporate brands from 2011 to

2016. Also, it includes total paid media spending annually for each of those companies as well as

corporate level financial data, using sales revenue per share as the dependent variable.

This dissertation focuses on the corporate brand as a component of intangible capital.

Intangible capital is composed of multiple layers of value; this research attempts to identify one

of those layers. It looks for how the corporate brand, in conjunction with other factors, can

impact a company’s sales / revenue per share. Differences in industry and sectors as well as

brand architecture are examined to help identify how the impact of corporate brand varies in

different conditions. The learning generated is intended to help corporate brand managers make

better informed decisions and manage expectations for their efforts.

Substantive Focus

The data used is archival research collected by Tenet Partners for clients’ use. The

companies studied are among the largest global companies in the world, including Apple,

4

Microsoft, GE, and many others. The data is collected in the United States and based on the

company’s corporate brand, which is measured by evaluating respondents’ familiarity with a

brand and, if familiar, evaluating their perceptions across several key favorability attributes. As

the centerpiece of the research, this data is used as a quantitative representation of the corporate

brand to be used in conjunction with fundamental financial variables and paid media spending.

This quantitative research study is designed to isolate and identify the impact of a company’s

corporate brand on sales/revenue per share growth.

The corporate brand data consists of measures of familiarity and three favorability

attributes gathered in Tenet Partners’ CoreBrand® Index (CBI). Tenet Partners is a corporate

brand consulting firm based in New York, NY. The favorability attributes consist of overall

reputation, perception of management, and investment potential. These attributes are averaged to

create a Favorability score. All corporate brand measures are expressed as scores from 0–100. To

create a single measure of the corporate brand (known as BrandPower), Familiarity and

Favorability are multiplied together and then multiplied by 0.01 to reduce the result to a 100-

point scale. This data was continuously collected throughout the year via telephone interviews

averaging 10 – 12 minutes each.

Ultimately, sales / revenue per share was used as the dependent variable with the goal of

identifying if stronger corporate brands result in higher sales revenue. Because of the time-series

nature of this data from all the sources, the research was focused on change over time. This focus

allowed the evaluation of growth rates, the impact of the level of brand and its changes, and how

things like paid media investment may influence results.

Ultimately, it is hoped that this study can change the way the corporate brand is

perceived. The sought-after outcome is for senior corporate leadership to evolve its thinking

5

relative to the value of the corporate brand. Over time and with the right evidence, it is desirable

for senior leadership to view the corporate brand as an asset that has value and should be

invested in, rather than an expensive nuisance that consumes resources with dubious return.

Specific Theoretical Discussions Addressed

Primary to this research is the theory of intangible capital, as discussed by Gregory

(2018). In that work, the author theorizes that intangible capital is comprised of many

components, including product brand, corporate brand, customer loyalty, and other factors. He

also shows that, while it is not Generally Accepted Accounting Principles (GAAP) compliant nor

on the balance sheet, the corporate brand contributes value to an organization, i.e., value that can

be measured and managed for company growth. Gregory’s work concentrates on the corporate

brand’s contribution to market cap as measured through the cash flow multiple (stock price/cash

flow per share). As an extension of his analysis, the present study should be viewed as

complementary to Gregory’s work. By understanding how the corporate brand impacts

sales/revenue per share as well as cash flow multiple--as demonstrated in Gregory’s work--a

more complete picture emerges of how the corporate brand contributes to total value creation.

The present study reveals the sales/ revenue component of intangible capital.

In the marketing context, institutional theory is another useful theoretical base for the

present study. Marketing helps communications executives direct their messages towards

markets and consumers, rather than satisfy institutional pressures and inter-organizational

relations (Slimane et al., 2019). This clarity allows marketing executives to convey their

messages to clients to sell product, rather than simply please internal audiences but forfeit any

inroads on sales. The present study extends this theory by emphasizing that the corporate brand

6

can be communicated much like a product brand to increase its value (Gregory & Weichmann,

1991).

One issue of concern is that a specific theory for the construct of a brand name element is

nonexistent (Round & Roper, 2012). Round and Roper identified that when brand equity is

measured in the literature, it typically focuses on measures of brand awareness and brand

association. These measures assess the respondent’s knowledge of a brand and his/her

perceptions of it on various traits of that brand. This approach is similar to the measures used in

the present study to gage equity in the corporate brand. These measures are Tenet Partners’ data

on corporate brand Familiarity and Favorability. By comparison, both constructs are measures of

the size of the brand, brand awareness and Familiarity, and brand perception, brand association

and Favorability. In sum, I examined the brand’s mass and quality because these measures offer

a quantitative representation of the brand, which is an emotional concept that can be difficult to

infer.

Traditional approaches to corporate brand assert that the corporate brand is an entity that

is created and carefully managed by corporate leadership (Balmer, 2001). Meanwhile, Social

constructionists’ approaches assert that the corporate brand is a function of the social interaction

between the organization and the environment in which it operates (Leitch & Richardson, 2003).

Often, these two concepts are presented as differing points-of-view regarding corporate branding

(Melewar et al., 2012). It is likely that both approaches are concepts that are correct and the

weight of influence of each concept on a corporate brand would depend on the brand in question

and the way in which the company operates.

7

Concepts and Definitions

The construct used to measure the strength of the corporate brand in this research is

BrandPower, which is the result of survey data collected by Tenet Partners. Quantitative

measures of a corporate brand’s Familiarity and Favorability (as measured by overall reputation,

perception of management, and investment potential) were collected to be used as input variables

in any empirical research where the corporate brand could be considered an independent or

dependent variable. BrandPower represents the interaction between Familiarity and Favorability

as represented by a single number for each company at a point in time. This data was collected

continuously each year via telephone interviews.

The data is intended to be a quantitative representation of the corporate brand that can

interact with other quantitative performance measures of company performance, such as stock

price, sales, product volume, and other measures. While BrandPower is not actually the

corporate brand, which is an emotional concept defined by the individual, it is a viable

quantitative representation of the corporate brand that can be used to measure what impacts

corporate brand performance and how it can impact business results.

Conducting the BrandPower research is critical to building accountability for corporate

brand performance within organizations. By creating a quantitative measure of the brand,

researchers can evaluate the corporate brand’s performance and impact among other forms of

corporate data, such as financial data and marketing spend data. Most managers intuitively

understand the impact that corporate brand can have and the results that can be produced

(Puckey, 2012). However, the magnitude of the impact needs to be defined and measured before

true accountability can be achieved.

8

The concept of intangible capital also is key to this research. There are many components

of intangible capital; these components need to be measured and valued to understand their

contribution. These measurements and valuations are not GAAP compliant and do not end up on

the balance sheet; however, this does not diminish their importance (Gregory, 2018). They are

operational factors that reflect the health of the business and help executives diagnose procedures

that may be necessary if the company is functioning poorly.

Summary and Organization

This introduction provides a high-level overview of the problem that faces

communications executives and the difficulty they have in demonstrating the value of their work.

That difficulty can impede their ability to secure the resources needed to adequately perform

their job. This dissertation introduces a way to measure the corporate brand, identify its impact,

and provide a conduit through which marketing executives can be recognized for contributing to

their companies’ success. This research is not an attempt to change GAAP principles, but rather

a practical way to measure the corporate brand’s contribution to a company’s performance.

Specifically, this dissertation utilizes the CoreBrand Index historical data to identify the

corporate brand’s contribution to a company’s sales/revenue per share growth. The measures of

familiarity, overall reputation, perception of management, investment potential, and culture of

innovation combine to give a measure of the corporate brand known as “BrandPower.” These

measures provide a quantitative measure of the corporate brand that can be evaluated in the

context of quantitative financial measures and communications efforts.

9

CHAPTER TWO:

LITERATURE REVIEW

The Corporate Brand Impact

According to Clancy and Krieg’s (2007) book, Your Gut Is Still Not Smarter Than Your

Head: How Disciplined, Fact-based Marketing Can Drive Extraordinary Growth and Profits,

the marketing industry has been driven by gut instinct for decades. Nobody understood what

worked or why, and executives rarely had to defend their budgets. Things have changed, and

marketers are now accountable to the bottom line like everyone else. Research and scientific

modeling are now required to justify expenditures and identify expected returns. This approach

allows marketers to change from thinking with their gut to using state-of-the-art analytics to help

them think with their heads (Clancy & Krieg, 2007).

The corporate brand is a unique asset for each company in operation. It is different than

the product brand in that it is the brand of the company, not the brand of an individual product or

line of products. The question posed in this research is: “Does the corporate brand contribute to

sales per share growth, and if so, how does it contribute?”. To better understand this relationship,

Tenet Partners CoreBrand Index® was utilized. The primary measure of the corporate brand is

BrandPower, which is a measure of Familiarity and Favorability. Familiarity relates to public

recognition of the brand. Favorability can be further broken into Overall Reputation, Perception

of Management, and Investment Potential. The goal of this research was to understand how the

corporate brand, represented by BrandPower, impacts sales- per-share growth and what role is

10

played by these components of BrandPower. This analysis used time-series data across hundreds

of companies, representing 10 sectors and 52 industries to explore this relationship.

Literature Search Strategy

For the literature review, I utilized the University of South Florida (USF) library and my

literature collection on the topic of branding. I examined the topic of branding from a consumer-

and corporate perspective. Theory of brand and brand architecture were also examined. Also,

Google Scholar was utilized; search terms included brand architecture, brand theory, consumer

branding, corporate brands, corporate vs. product brands, marketing theory, and brand familiarity

and favorability.

Analysis Process

Before discussion of the corporate brand and its role, the analytical framework should be

briefly discussed. This study used a data driven quantitative analysis. These types of analyses

must be methodical in their process. The researcher cannot “make it up as s/he goes.” It requires

forethought and process application. In his textbook, Basic Econometrics, Gujarati (1995)

outlines the Methodology of Econometrics, which follows the steps listed below for conducting

this type of quantitative research:

1. Statement of theory or hypothesis

2. Specification of the mathematical model of the theory

3. Specification of the econometric model of the theory

4. Obtaining the data

5. Estimation of the parameters of the economic model

6. Hypothesis testing

7. Forecasting or prediction

11

8. Using the model for control or policy purposes.”

(Gujarati, 1995, p. 3).

While the modeling in this dissertation is a statistical model rather than an econometric

model, these steps are a good basic procedural overview. The research in this dissertation did not

follow this process to the letter, but the process was loosely adhered to for this study.

Types of Brand Measurement Methodologies

Keller’s (2001) model for building a strong brand includes four steps:

• Establishing the proper brand identity

• Creating appropriate brand-meaning through strong and positive associations

• Eliciting positive responses

• Establishing loyal customer relationships

When a marketer can do these steps, positive benefits are gained. As a result of

accomplishing the above four steps, six brand-building blocks are achieved:

• Brand salience

• Brand Performance

• Brand imagery

• Brand judgments

• Brand feelings

• Brand resonance

Establishing these principles helps a marketer establish market-research priorities, set

strategic direction, and inform brand-related decisions.

Fischer (2007) asserts that measures of financial brand valuation must adhere to modern

accounting standards. As such, they should be:

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• Future oriented

• Objective

• Complete

• Comparable

• Simple

• Cost effective

Additionally, they should be adaptable to specific conditions of the organization, such as

reporting and planning.

Mintz and Currim (2003) acknowledge that marketers and senior management do not

speak the same language. In an effort to increase accountability, the authors mention that the

Journal of Marketing, the Marketing Science Institute (MSI), and the Institute for the Study of

Business Markets (ISBM) recommend the use of marketing metrics that link marketing and

marketing-mix inputs with financial business outputs to define success. This approach was

influential for the research in this dissertation as it seeks to measure how the corporate brand can

impact sales/revenue-per-share growth. The corporate brand is not expected to be the primary

driver of sales/revenue growth; however, it must be tied to the success metrics used by senior

management to have value to them.

A small sample of some types of measurement activities and outcomes mentioned by

Mintz and Currim (2003) is outlined below:

• Market share (units or dollars) leading to profit margin

• Awareness (product or brand) leading to return-on-investment

• Satisfaction (product or brand) leading to return-on-sales

• Likeability (product or brand) leading to return-on-marketing-investment

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• Preference (product or brand) leading to net present value

• Willingness to recommend (product or brand) leading to economic value added.

The wide array of measurement and outcome methodologies highlights the importance of

understanding the brand being measured and the outcomes that are important to senior

management. It also highlights key components to measurement that must be understood. There

are two key reasons to do brand measurement: 1) to influence marketing activities to optimize

outcomes and 2) to convince leadership of the need to support communications efforts. The

measurement activities in this research study were intended to satisfy the latter. For that reason,

the measurement must be relevant and matter to senior management.

Theory of Intangible Capital

The Theory of Intangible Capital was advanced by Gregory in his 2018 dissertation that

measured the impact of the Culture of Innovation attribute on the cash-flow multiple. Gregory

identifies the Culture of Innovation as an added contributor to the valuation of a company. The

key idea in the Theory of Intangible Capital is that a company creates value in many forms; the

more value we can quantitatively identify, the better we can measure and manage value creation.

This dissertation identifies yet another layer of value that has not yet been revealed. The

understanding is this additional layer will never be a GAAP-recognized value measure that is on

the balance sheet, but it is a measurement system that can be used to help communications

executives better identify value-creation to help better manage their assets and do their jobs. This

dissertation is intended to continue the understanding of the value of the corporate brand. This

study does not identify all of the value of corporate brand; rather, it highlights one additional

component of value. Future research will identify other elements of value and possibly combine

them to create a more complete picture.

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A further reason to better understand intangible capital was highlighted by Haigh (2017)

in his response to a critique by MARKABLES. He states that, in the United States and American

companies, a tendency exists to understate the value of identifiable, intangible assets. Further,

there is a trend towards over-reliance on goodwill, leading to an overvaluation of the brand

during acquisitions, something that may not be recognizable in the immediate future. This

overvaluation provides further evidence for the need to identify and understand intangible

capital. In sum, there is reason to recognize brand value, not only for the optimization of

marketing activities, but also for recognizing brand value during acquisitions.

Corporate Brand Construct

A specific theory for the construct of a brand-name element has not existed (Round &

Roper, 2012). According to Round and Roper, when brand equity is measured, it typically

focuses on brand awareness and brand association. These measures assess the respondent’s

knowledge of a brand and his or her perceptions of it on various traits of that brand. This

assessment is similar to the measures that I used to measure equity in the corporate brand. These

measures are from Tenet Partners’ data on corporate-brand Familiarity and Favorability. By

comparison, both constructs are measures of the size of the brand, brand awareness and

Familiarity, and brand perception, brand association and Favorability. In other words, they are

the brand’s mass and quality. These measures offer a quantitative construct of the brand (i.e., an

emotional concept that can be difficult to articulate).

Aaker (2004) identifies the corporate brand as the brand that defines the organization,

stands behind the offering, and is defined by organization associations. The corporate brand may

or may not be relevant to product brands. The corporate brand defines the personality and

characteristics of the company as it relates to other organizations. This definition is true of the

15

BrandPower data studied in my research. This data identifies the strength of the organizational

brand and measures it consistently across companies, industries, and sectors, which is why

BrandPower and its components are an appropriate, discreet, and quantitative representation of

the corporate brand to use in statistical modeling.

Marketing Theory

Before an understanding of the corporate brand’s impact on sales-per-share can be

developed, key building blocks must be understood. A logical starting point is marketing theory.

In his work at University St. Gallen, Ludicke (2006) identified marketing as a social system, with

brand being the main pressure point. The social system is based on communications systems that

have brand as the focus of those communications.

Marketing helps communications executives direct their communications towards

markets and consumers, rather than satisfy institutional pressures and inter-organizational

relations (Slimane et al., 2019). This clarity allows marketing executives to convey their

messages to clients to sell product, rather than simply please internal audiences but not make any

inroads on sales.

It is important for marketers to abide by legal and ethical considerations as they pursue

their practice. Regulatory bodies, such as the Federal Communications Commission (FCC),

provide legal oversite to communications activities (Gazley et al., 2016). Many recent scandals

from Enron to B.P.’s Deepwater Horizon have eroded the trust between corporate leadership and

the consuming public. This harm extends beyond trust and bleeds into significant financial

consequences for the stakeholders.

16

Brand Theory

Chandler and Owen (2002) indicate that brands affect customer behaviors by transmitting

signals that provide symbolic meanings that appeal to customers’ wants and needs. Brands like

iPhone, Google, and Uber are a few such brands. When they are mentioned, no one needs to tell

you what services are provided; you simply know.

Product Market Model

According to Ailawadi et al., (2003), brand equity measures exist in three distinct markets

and consist of various outcomes. These are the consumer mind-set, the product market outcome,

and the financial market outcome. The consumer mind-set is where the customer is the source of

brand equity. It is based on measures of awareness, attitudes, associations, attachments, and

loyalties that customers associate with brands. These perceptions and knowledge are built over

time, based on experiences. The product market outcome is based on the logic that the impact of

the brand should be indicated by the company’s performance in the marketplace. This theory

explains the price premium reflected in strong brands. Other measures of this performance

include market share, relative price, and other, more complex measurement methodologies. The

financial market outcome measures the financial value of the brand. Some measurements of this

outcome can include the price of the asset when it is sold or through discounted cash flow

analyses.

The product market outcome is the market modeled in this dissertation research. This

analysis is intended to demonstrate that strong and/or growing brands will increase the

performance of the company in the marketplace. This increase can be demonstrated through

price premium and/or through increased sales volume. The customer mind-set is also represented

in the data collected to measure the strength of the corporate brand.

17

The data collected by Tenet Partners to measure BrandPower is an important component

of this research, not only because it quantifies measures of awareness and perception of

corporate brands, but because it is also available on a large enough scale in terms of the history

and number of companies tracked to provide generalizable results. This Brand Power data

eliminates the subjectivity that can be found in many other models (i.e., Gregory, 2018). The

quantitative measures do not force the modeler or interpreter to make value judgments based on

brands that are derived from their own biases. The BrandPower, Familiarity and Favorability

measures are the collective opinion of the impartial observers living in the marketplace.

Gaps in the Literature

Despite the tremendous volume of literature on the topic of branding, a noticeable gap

exists in the literature. This gap represents the topic of corporate brand impact on sales/revenue

performance. Such a gap is likely due to a lack of information available to measure and quantify

corporate brand strength on a significant scale. The absence of quantifiable data can be addressed

through Tenet Partner’s Corporate Branding Index. This study provides the data with which to

fill this gap in the literature.

Hypotheses

The reviewed literature has influenced the development of hypotheses related to how the

corporate brand can contribute to growth in sales/revenue-per-share. Answering these hypotheses

help to clarify the relationship between the corporate brand and sales/revenue-per-share growth.

Four hypotheses guided this dissertation, along with several tests to prove or disprove

them. The hypotheses are outlined below, with “H” representing “Hypothesis,” in sequential

order.:

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H1: Growth in BrandPower will result in growth in sales-/revenue-per-share change.

H2: Change in Familiarity and Favorability will have different, but positive, impacts on

sales/revenue-per-share growth.

H3: The different attributes that make up Favorability have a different, but positive,

impact on

sales/revenue-per-share.

H4: Various conditions, such as industry affiliation, will positively alter the impact of

brand factors on sales/revenue-per-share growth.

BrandPower, as identified in H1, is the interaction between Familiarity and Favorability.

The other hypotheses test the components of BrandPower or other conditions, such as industry

and sector affiliation. Taken together, these hypotheses should help to understand whether

corporate brand has an impact on sales/revenue-per-share generation, and if so, how. This

understanding will help communications executives across industries offer prescriptive guidance

regarding the allocation of efforts and resources to articulate the corporate brand.

This dissertation offers a quantitative analysis that is supported by the BrandPower data

and other empirical data collected, representing paid media investment, financial performance,

and analyst ratings.

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CHAPTER THREE:

METHODOLOGY

Purpose

The purpose of the research in this dissertation is to identify whether a linkage between

the corporate brand and sales/revenue-per-share growth exists. The data utilized for this

dissertation is the CoreBrand® Index supplied by Tenet Partners, the paid media investment

obtained from Kantar Media Intelligence and the financial data gathered from Yahoo!’s “Finance

and Value Line Investors Survey.” The research question being explored is: “How does the

performance of the corporate brand impact sales/revenue-per-share?” This data has previously

been examined to identify the impact of the corporate brand on stock performance (Gregory,

2018), but this study is the first time it is being used to examine the corporate brand’s impact on

sales/revenue-per-share growth.

Sales/Revenue-per-Share Explained

Sales/revenue-per-share is the total sales of the company divided by the number of total

shares of stock in the company. As a simplistic example, if a company had $100 in total revenue

and issued 10 shares of stock to the public, its sales/revenue-per-share would be $10.

Sales/revenue-per-share is the dependent variable for the analytics in this dissertation.

The goal of this analysis is to predict how fluctuations in a company’s corporate brand impact a

company’s sales/revenue-per-share over time. Specifically, the study explores whether a growing

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corporate brand will result in that company seeing gains in its sales/revenue-per-share over the

same period.

Practical Business Application of the Model

The use of the model for the practitioner is intended to help him/her understand the

corporate brand’s contribution to sales-revenue growth. As executives allocate scarce resources

to communicating their corporate brand and its messages, the executive needs tools in place to

track the performance of his/her corporate brand and its impact on business results. This analysis

is one piece of the brand-intelligence mosaic that should be used to help companies develop their

communications plan. Companies should utilize data gathered at the strategic, operational, and

tactical level of their organizations. Understanding the corporate brand’s contribution is but one

piece at the strategic level.

Ultimately, it is hoped this work can change the way the corporate brand is perceived.

The sought-after outcome is that senior corporate leadership evolves its thinking relative to the

value of the corporate brand. Over time and with the right evidence, it is desirable for senior

leadership to view the corporate brand as an asset that has value and should be invested in, rather

than being perceived as an expensive nuisance that consumes resources with dubious return.

Research Design and Data Collection

The following is the data collection methodology for the CoreBrand® Index (CBI):

BrandPower is a proprietary measure of corporate brand strength developed by Tenet Partners for

its consulting practice. Based on research collected in the CoreBrand® Index Study, BrandPower

is a measure that combines Familiarity and Favorability attributes into a measure of the size of a

company’s audience and its perceptions of that company.

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Respondents are impartial observers, consisting of senior executives within the largest

companies in the United States. They represent senior business leaders and affluent consumers.

The companies have disposable income and investable assets (Gregory, 2018).

The CoreBrand® Index has been in use since 1990 to evaluate corporate-brand strength of

companies, to build brand-valuation models, and to produce predictive return-on-investment

models for corporate brands. No data set like this exists anywhere else. Approximately 1000

brands have been reported annually from 1990–2001; from 2002 to the present, the data has been

reported quarterly.

The following factors are tracked:

• Familiarity – based on respondents who know more than just the company name.

• Favorability – a measure of perception of a company based on average of the below

attributes:

• Overall Reputation – a measure of the general perception of the

company.

• Perception of Management – a measure of the quality of the

management and leadership of a company.

• Investment Potential – a measure of willingness to invest in a

company.

• BrandPower (the interaction of Familiarity and Favorability).

• Culture of Innovation (an attribute that is tracked but is not a part of BrandPower; it

measures the degree to which innovation is in the DNA of a company).

This data was designed to be a quantitative representation of the corporate brand to be

used as a variable in business modeling. BrandPower and its components are the centerpieces of

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this research. The results of this study are intended to help managers understand the impact that

the corporate brand has on business results and how to better allocate resources to direct it.

Tenet Partners secured a license to use this data in the present research (see Appendix A).

The only limitations on the license were that it be used for academic research and not for profit.

The most recent data was not to be used; a verbal agreement was made to use data up to fourth

quarter 2016. Company names were to be sanitized in order to protect the salability of the data

for Tenet Partners.

Respondents were sourced via a purchased telephone list. The specifications for the list

were: V.P., Director, Senior Manager, and higher respondents in the top 20% of U.S. businesses,

based on revenue in the United States. Only one respondent per any company could respond per

quarter. Respondents were random and do not represent a panel. It is possible for a respondent to

respond more than once over history, but repeat participants were not sought, so the incidence

rate of repeat participants is extremely low.

I conducted 8,600 telephone interviews per year. Each company was rated by 100

respondents per quarter, totaling 400 responses each year. The survey contains a list of 40

companies rated for Familiarity and the three Favorability attributes. I tracked 860 companies

and used fourth quarter data (which represents each full year). This research represents the 400

interviews conducted in each year.

The telephone interview was relatively simple. It was designed to result in data that

provided a quantitative variable that represented a company’s corporate brand and key

components. This data is intended to be used in the context of other corporate data, such as

fundamental financial data and marketing spending.

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Telephone interviewers used a system called “ConfirmIt.” This system displays a survey

on the computer screen and randomly selects companies to be queried, ensuring that each

company on the survey had the same number of respondents. The interviewer recorded the

respondents’ results and then ConfirmIt warehoused the data. This system has been in use since

1998; prior to that, interviewers recorded results with pen and paper.

A limitation of the research was the audience was limited to a sub-set of the total

population. While this limitation was intentional, a portion of the total population was excluded.

Also, responses were on a 100-point scale, meaning there is an upper and lower limit on the

BrandPower data while financial data is on a continuous scale and can always grow or decline.

Therefore, with BrandPower, it is important to understand the impact of the absolute level of

BrandPower as well as its rate of growth.

When interviewing, respondents were first read a brief introduction, explaining the

purpose of the study and ensuring confidentiality. Finally, the importance of the respondents’

participation was explained, and they were thanked.

Next, the respondents were asked to rate their Familiarity with a list of 40 randomized

companies. Familiarity was rated using a 5-point scale: 1=Unfamiliar, 2=Know the Name Only,

3=Somewhat Familiar, 4=Familiar, 5=Very Well Known.

Then, if respondents rated a company 3 or higher, indicating they knew more than just the

name of the company, they were asked to rate their Favorability towards the company on three

key attributes. The attributes were: Overall Reputation, Perception of Management, and

Investment Potential. The attributes were each evaluated on a 4-point scale: 1=Poor, 2=Fair,

3=Good, 4=Excellent.

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The data was tabulated based on frequency distribution of the results. To calculate

Familiarity, the percentage of respondents to give each scaled response was calculated. Then,

these results were weighted with a response of 3 being weighted as a 1, a response of 4 being

weighted as a 2, and a response of 5 being weighted as a 3. These results were summed and then

divided by 3, to reduce the score to a 100-point scale. For example, if Company A’s Familiarity

results were as follows: “Unfamiliar” = 10%, “Know the Name Only” = 20%, “Somewhat

Familiar” = 40%, “Familiar” = 20%, and “Very Well Known” = 10%, the result would be:

(10 x 0 + 20 x 0 + 40 x 1 + 20 x 2 + 10 x 3) / 3 = 36.7 Familiarity Score. I refer to these as scores

because they are weighted-average percentages.

Each of the Favorability attributes was calculated in a similar fashion. For example, if

Company A had Overall Reputation ratings of Poor 20%, Fair 10%, Good 60%, Excellent 10%,

the result would be: (20 x 0 + 10 x 1 + 60 x 2 + 10 x 3) / 3 = 50.0 Overall Reputation Score.

The other Favorability attribute scores were tabulated the same way, and then scores for

the three attributes were averaged together to create a Favorability score.

Then, BrandPower was created by multiplying Familiarity x Favorability x 0.01. For

example, if Company A’s Familiarity score was 36.7 and its Favorability score was 50.0, its

BrandPower would be: 36.7 x 50.0 x 0.01 = 18.4 BrandPower score.

In 2016, a new attribute was added to my study: “Culture of Innovation.” It was designed

to be a measure of the innovation in a company’s DNA; therefore, the measure is more than just

product- or service innovation. This attribute was scored and tabulated the same way the other

Favorability attributes were, but it was independent and not incorporated into BrandPower.

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Key Descriptive Statistics

The next step was identifying key descriptive statistics on the variables in the analysis.

Appendix A identifies several of the model variables, to offer a better understanding of the

dimensions of the data. It also contains a key for all variable abbreviations in this dissertation.

The most important number on this table is the mean for the sales per share (SPS) 5-yr

growth%. This is the sales/revenue-per-share, 5-yr % change. This number was used in the target

(dependent variable) in the chi-squared automatic interaction detector (CHAID) model. The

companies studied were broken into high and low sales/revenue-per-share, 5-yr. % change

companies. This model allows predictions of whether a company would be a high- or low-

growth company, based on whether their company falls above or below the mean of 20.6%.

When evaluating the corporate brand, I found that, in Year 0, BrandPower ran from a low

of 0.3 to a high of 82.5, with a mean of 29.3. For Familiarity, in Year 0, there was a low of 1.9, a

high of 98.2, and a mean of 43.3. For Favorability, in Year 0, there was a low of 12.3, a high of

91.4, and a mean of 63.3. The Favorability attributes generally follow the same pattern as

Favorability. All scores are on a 100-point scale. This result means that BrandPower had a lower

ceiling than Familiarity or Favorability. Familiarity ran almost the full range of the scale;

meanwhile, Favorability had a higher floor, but a lower ceiling than Familiarity. This result

indicates that a company can have almost any level of Familiarity, but respondents are not likely

to rate a company zero or 100 for Favorability. Thus, BrandPower tends to be more sensitive to

Familiarity because it has a broader range of scores. For this reason, the BrandPower score--

which is the interaction between Familiarity and Favorability--is important, but understanding all

its components is critical for a deeper understanding.

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One weakness of BrandPower analysis is a crisis situation. Often, in a crisis, a company’s

BrandPower score increases. This increase does not make intuitive sense, but there is a logic to

it. In a crisis, the company tends to be heavily covered by the media, resulting in increasing

Familiarity. However, the nature of that media coverage is often negative, resulting in decreased

Favorability. For that reason, it is important to understand the data to know what it tells us.

Another interesting finding in the data was a precipitous decline in ad spending from Year

-9 to Year -1 (no data was collected in Year 0). While the minimum for each year was 0.0, both

the maximum and the mean over that period declined significantly. More research will need to be

done to determine if it is an actual decline in marketing investments or a reallocation of

marketing resources to other channels that are not considered advertising.

Key

The following abbreviations are used in the charts and tables; names are written out in the

text:

Fam – Familiarity

Rep – Overall Reputation

Mgt – Perception of Management

Inv – Investment Potential

Fav – Favorability

BP – BrandPower

Innov – Culture of Innovation

CF – Cash Flow

EPS – Earnings per Share

SPS – Sales/Revenue per Share

DPS – Dividend per Share

BVPS – Book Value per Share

SO – Shares Outstanding

Sls – Sales / Revenue

Stk – Stock Price per Share

27

CHAID Model Overview

A chi-squared automatic interaction detector (CHAID) analyzes large data sets that are

complex, creating non-binary decision trees. The decision trees can have an unlimited number of

branches and are, therefore, wider than trees using binary methods. Initially. CHAID was

developed for use in marketing models, where finding the best solution often involves more than

one potential answer. CHAID algorithms are designed to identify the best predictive variable at

each branch of the decision tree, which is done using F tests where the CHAID merges

statistically similar (homogeneous), independent values with the dependent variable, and it

maintains all other dissimilar (heterogeneous) independent variables (Gregory, 2018;

International Business Machines Corporation [IBM], 2013).

Exhaustive CHAID identifies the best split for each predictor-independent variable, based

on adjusted p values. Because the dependent variable (sales/revenue per share 5-yr % growth) is

continuous, exhaustive CHAID was used in the present analysis to provide the clearest answers

to the question raised in this dissertation (Gregory, 2018).

Continuous predictive-independent variables were binned into a set of ranking categories.

This binning is the first step in the exhaustive CHAID process, and it is completed for each

scaled predictor in the model. (Gregory, 2018; IBM 2013).

There are compelling reasons for utilizing CHAID instead of multiple regression

analysis. Multiple regression analysis is often flawed due to multicollinearity while CHAID is

not impacted by multicollinearity. CHAID also has greater explanatory power, which results in

greater predictability in the model (Gregory, 2018).

Also, CHAID does not make assumptions of parameters based on frequency distribution.

It provides a high level of confidence that the best predictive variables are identified when

28

utilizing an evidence-based analytical process, which is standard in quantitative market research.

CHAID is the ideal analytics method for identifying and determining the relationship between

independent and dependent variables (Gregory, 2018). For these reasons, CHAID is the best

analytical process for this dissertation.

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CHAPTER FOUR:

RESULTS OF THE STUDY

Analysis

The analysis began with data exploration. The non-modeling phase is designed to

familiarize the researcher with the data and the relationships between the variables. This step

reveals how the data interacts; it is a key step in understanding why model-interactions work,

which is fundamental to building a model that does not only make predictions, but makes sense

and has utility for practitioners and their businesses. This step is the foundation of the process

(Jank, 2011).

The data exploration phase is followed by the modeling phase. During this phase, the

CHAID model was employed, using the software to identify the relationships between the

predictor variables and the target variables. In this case, the objective was to identify whether a

company is expected to rise into the high sales/revenue-per-share category (> 20.6% growth) or

fall into the low sales/revenue-per-share category (< 20.6% growth).

Data Preparation

The first step in the data preparation process was to merge the various data spreadsheets

that composed the dataset for this analysis. The process included BrandPower, paid media (ad-

spend), and fundamental financial data. This process resulted in a total of 845 companies in the

dataset. However, further data prep was required.

30

The next step in the data preparation process was to further narrow the dataset in cases

where BrandPower data was missing, which included companies that were dropped from the

survey or were too recently added to be considered. Companies were included if they had brand

measures available from Year 0 (the target year for the dependent variable) through Year -5, five

years prior to the dependent variable.

Per the license to use the data, companies were assigned a random number and company

names were removed to sanitize the companies (a key has been retained, so that any anomalies

can be examined). Next, the data was sorted based on BrandPower in 2016; any companies that

were not included in the survey in 2016 were removed. Then, the data was sorted based on

BrandPower in 2011; any companies that were not included in the survey in 2011 were removed.

No companies were covered in 2011 and 2016 that were not covered in the intervening years.

Then, the decimal positions were set based on the type of data. For example, BrandPower data

has a single decimal while figures representing dollar values have two decimal places. Changes

were calculated for all the data for 1, 3 and 5-year historical change. These changes were

calculated based on percentage change as well as slope for all variables. Where available,

projections for change in financial data were calculated for 1 and 3-year change. These

projections also were calculated based on percentage change and slope of change.

With these data preparation steps completed, the total universe of companies left for

analysis was 621 companies. At this point, the data was prepped for initial data-exploration, a

step in which four more companies were removed, resulting in 617 companies modeled.

Initial Data Exploration – Univariate Test

The first step in the analytic process is data exploration. As defined in Jank’s (2011)

textbook, data exploration is not a deep statistical analysis or modeling process. Rather, it is a

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process in which the analyst familiarizes himself/herself with the data. This process allows the

analyst to better understand the data so that s/he is better informed when it is time to construct a

model. The first analysis performed in my study was a quintile analysis. The data was first sorted

from highest to lowest, based on BrandPower in Year 0. Interesting findings are revealed in

Table 1 below.

Table 1. Quintile Analysis of Key Data for Companies in the Database.

BP Year 0 BP % 1-yr BP % 3-yr BP % 5-yr BP slope 1-yr BP slope 3-yr BP slope 5-yr

Tier 1 65.1 0.888 3.690 6.600 0.552 0.765 0.712

Tier 2 45.6 2.140 8.990 17.588 0.872 1.091 1.057

Tier 3 22.7 5.011 20.729 45.299 0.882 0.788 0.568

Tier 4 9.8 10.563 27.139 20.453 0.657 0.291 -0.182

Tier 5 3.2 0.322 -1.929 -21.021 -0.099 -0.133 -0.376

Companies with the strongest corporate brands work to maintain their corporate brand

strength because they have already grown them. Their sales/revenue growth is based on the

strong corporate brands they have already built. By contrast, companies with the weakest

corporate brands have yet to build corporate brands with enough leverage to impact

sales/revenue growth. Those companies either continue to struggle, go out of business, are

acquired, or grow their corporate brands and increase success. Companies with mid-level

corporate brands reside in the “sweet spot;” their corporate brands are strong enough to be

leveraged to grow sales/revenue-per-share, and they have room to grow them. These findings are

consistent with findings in CoreBrand research conducted for BusinessWeek and CoreBrand/

Tenet’s Brand Laboratory research.

The shaded cells in Table 1 confirm findings from Koch et al. (2019) paper published by

the American Society for Competitiveness, which asserts that the highest rates of growth for

BrandPower occur in the middle tiers.

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The quintile analysis also showed a directional relationship between corporate brand and

ad spending:

• Tier 1 - $278.1Mil.

• Tier 2 - $91.2Mil.

• Tier 3 - $50.5Mil.

• Tier 4 - $21.8Mil.

• Tier 5 - $11.5Mil.

This finding is expected to be key to uncovering return on investment (ROI) for

communications spending and to help identify which companies should be poised to take the

most advantage of communications spending.

Suttle (2020) may explain what is seen in the quintile analysis in Table 1 (above). Tier 1

brands are more mature and may not have room to grow. Tier 5 brands are small and need to

achieve critical mass before they can grow. Middle tier brands have mass and awareness but have

not totally matured, yet they do have opportunity to grow.

The shape of the relationship (non-linear) provided further justification that CHAID

analysis would be the most appropriate methodology. The fact that growth rates are different at

varying levels of corporate-brand strengths lends itself to the methodology’s binning of the

variables.

Correlation Analysis

The next step in the data exploration stage was to conduct a correlation analysis between

the independent and dependent variables. Because there were so many variables in the dataset, a

traditional correlation matrix would have been unwieldy. Instead, the brand factors, BrandPower,

Familiarity, Favorability, Overall Reputation, Perception of Management, Investment Potential

33

and Culture of Innovation and their associated rates of growth were correlated to the 1- year, the

3- year and 5- year sales/revenue-per-share % growth rates to help identify where the

relationships were strongest (see Appendix B).

The findings of the correlation analysis indicated that a stronger relationship existed

between the brand factors and the sales/revenue-per-share % growth rate over 5- years than in 1

or 3- years. The longer the history of data, the stronger the relationships were. This finding is

likely true for a finite period of time; the suspicion is that at a certain point, this result will

diminish. This result suggests that brand consistency is an important component in driving sales

growth over time.

The other finding of this analysis was that 1 and 3-year BrandPower growth were the

most correlated with 5-year sales/revenue-per-share growth; also important was 1-year growth in

Investment Potential. All these showed a strong relationship. Other influential factors are

Familiarity level in Year -3, Year -2, Year -1, and Year 0; the 5- year Familiarity growth rate; the

1- year growth in Perception of Management and Investment Potential; and the 5-year growth in

BrandPower.

Again, these correlations demonstrate that the corporate brand takes time and consistency

to translate into business growth. Consultants have presented this consistent theme to their clients

for years, and it supports the concept that investment in communications should be consistent

over time, rather than being erratic. Corporate communications should be seen as an investment

rather than simply an expense.

The result of the correlation analysis also supports the hypotheses that suggest that

BrandPower and its components have a separate and varied impact on sales/revenue-per-share

growth over time.

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Hypothesis Testing

The present study performed various tests to confirm or reject the null hypotheses for this

research. The objective of these hypotheses was to determine if various corporate-brand factors

impact sales/revenue-per-share growth. In H1, H2, H3, and H4, the factors tested were BrandPower

and its components, Familiarity and Favorability. Often, Familiarity is expressed as the

awareness of the company and, in this case, it consists of a respondent who knows more than just

the company name. Favorability is an average of the attributes Overall Reputation, Perception of

Management, and Investment Potential. These attributes typically represent a hierarchy of one’s

perception of a company. Typically, Overall Reputation is the highest-rated attribute and

represents an easier attribute to rate highly. It asks if a respondent has an overall positive

perception of the company. Perception of Management is usually the second highest rated

attribute and represents a deeper commitment, as it specifically addresses the leadership and the

people in an organization. Typically, Investment Potential is the lowest rated attribute, for it

represents a more difficult level of commitment; it is an indication of the respondent’s

willingness to invest in the company. A fourth attribute that is not a part of the BrandPower

calculation is Culture of Innovation; though this attribute was not a part of my hypothesis testing,

it was examined. Culture of Innovation is a single attribute that seeks to evaluate the innovation

climate within an organization. It is not a measure specifically of product, service, or process

innovation; rather, it is a measure of the innovation inherent in a company’s DNA.

H4 tests the industry affiliation impact on a company’s sales/revenue-per-share growth. In

the CHAID analysis, this hypothesis identifies how the impact of corporate brand differs for

companies in various lines of business. Sector and industry affiliation were tested as part of the

CHAID analysis. In the CHAID analysis, the best way to test H1, H2, H3, and H4 is

35

simultaneously. This type of testing allows the model to evaluate the importance of each factor

relative to one another. When the CHAID analysis evaluates all factors, it can identify the true

impact of each factor on the dependent variable (IBM, 2013).

CHAID Analysis – Limited Micro Model

The key analytical process employed in this research is the CHAID analysis. This tool

was used to build the analytical model. The model was applied to analyze the impact of corporate

brand on sales/revenue-per-share growth at the macro and micro level. At the macro level, 617

companies were analyzed across sectors and industries. By contrast, at the micro level, sector

and industry level data were analyzed in isolation to determine if effects vary based on business

category. Unfortunately, while sector data could be modeled, the samples became too small to

measure individual industries. Nonetheless, the macro model was able to reveal that industry

affiliation is an important predictor.

The first analysis performed at the macro level was to examine industry affiliation and

the brand factors impact on sales/revenue-per-share 5-yr % growth rate. First, the analysis

identified which factors were the most important (Figure 1).

Figure 1. Impact of Industry and Corporate Brand Factors on 5-yr Sales / Revenue Per Share %

Growth Rate.

36

Figure 1 demonstrates that when industry affiliation and corporate brand factors are

considered in isolation from the financial factors, industry affiliation is the most important

predictor of sales/revenue-per-share 5-yr % growth. This finding is understandable, as companies

in different industries tend to grow at different rates.

After industry affiliation, Familiarity in Year -1 and Perception of Management in Year 0

were the most important predictors of sales/revenue-per- share 5-yr % growth, which is

important because it means the level of performance of these variables matter. To grow sales, a

company must be well- known to the public. If unknown, it is much harder to sell products. The

Perception of Management level is critical because it represents a level of trust between the

company and the public. A high Perception of Management means the leadership is trusted; if

management is not trusted, it creates a headwind that will impede sales.

The next most-important predictor is Investment Potential 3-yr slope, which is an

indicator of absolute growth in Investment Potential, indicating that companies that inspire

investment-confidence also grow their sales.

BrandPower 1-yr slope is the next most important predictor, which indicates that the

absolute change in the summation of the factors that comprise the corporate brand is important

for sales/-revenue-per-share growth. It indicates that companies need growth momentum in the

corporate brand to grow sales.

Investment Potential level in Year -2 was also a predictor. This variable, when considered

with Investment Potential 3-yr slope, indicates that not just Investment Potential growth, but also

level are important factors. Companies need to be strong and regularly improve on this measure.

Again, this predictor is an indicator of investor confidence in the organization.

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Familiarity 1-yr slope and Familiarity Year -5 were predictors of sales/revenue-per-share

5-yr % growth. These factors, in conjunction with Familiarity Year -1, clearly show that a

company must be well-known and growing in that area to drive sales growth.

Figure 2. CHAID Decision Tree Macro Analysis, Industry and Corporate Brand Model.

Figure 2 shows the decision-tree model for the macro analysis that includes industry

affiliation and the corporate-brand components’ impact on sales/revenue-per-share 5-yr growth.

The CHAID analysis examines factors that impact the dependent variable and looks for

differences in impact of the independent variables, if those dependent variables have different

impacts based on how they perform. The model looks for splits in the data and identifies all of

them, as long as the P-values fall below 0.05.

The dependent variable, sales/revenue-per-share 5-yr % growth, was split into two bins:

high growth was above 20.6% and low growth was below 20.6%. In this case, the top predictor

was industry affiliation. Below, Table 2 identifies the industries contained in each of Nodes 1–4

in the decision tree.

Companies in Node 1 were influenced by Familiarity level Year -5, which indicates these

companies were most impacted by Familiarity level at the start of the timeline.

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Node 2 companies did not have other influencing variables, indicating that among the

companies in that particular node, the corporate brand may be of lesser importance than in other

industries. It is important to note that this node had the fewest industries in it.

Figure 3. Industries Included in Nodes 1-4 of the CHAID Analysis.

Node 3 had the most industries in it. These industries were most influenced by

Familiarity in Year -1. Companies with lower Familiarity were most influenced by Investment

Potential in Year -2 because the performance in Nodes 7 and 8 indicate that high Familiarity

companies in Node 8 have a positive impact on performance, and low Familiarity companies in

Node 7 were lower performers. Familiarity Year -1 appears to have been negatively influenced

by Investment Potential for these companies. Perception of Management 2016 influenced the

higher performing companies in Node 8, indicating the importance of trust in corporate

leadership.

The companies in Node 4 were overwhelmingly influenced by 1-yr BrandPower growth.

Node 9 shows that companies with low BrandPower growth also are projected to have low

sales/revenue-per-share 5-yr growth. Companies in Node 10, which is high growth, were

reactive to high BrandPower growth.

Node 1 Node 2 Node 3 Node 4

Aerospace Commercial Banks Diversified Services Home Appliances

Apparel, Shoes Computer Software Electronics, Electrical Equipment Hotel & Entertainment

Beverages Computers & Peripherals Food Restaurants

Building Materials Diversified Financial Paper Products Retailers

Chemicals Furniture Telecommunications

Industrial Equipment Auto Parts

Metal Products Brokerage

Metals Diversified Industrials

Crude Oil Home Builders

Motor Vehicles Insurance

Petroleum Refining Internet

Pharmaceuticals Medical Supplies & Services

Publishing & Printing Office Equipment

Rubber & Plastics Packaging

Scient, Photo, Cntr Eq Educational Services

Toiletries, Household Products Pharmacy Serv.

Textiles Consulting

Tobacco Distilled Spirits

Transportation Athletic Equipment & Apparel

Power & Energy

Electric Utilities

39

This model only accounts for the BrandPower components and industry affiliation. Due

to its extreme importance, industry association was left in as a predictor. The application of this

model is more for the communications team within an organization; its use could help in

prioritizing messaging for any communications program. By comparing an individual company’s

performance on the BrandPower components to predictor importance, communications programs

and messaging could be adjusted.

CHAID Analysis – Full Macro Model

The next step in the research was evaluating all the independent variables and assessing

their impact on sales/revenue-per-share 5-yr % growth rate. The key predictors are shown in in

Figure 3. Once again, the most important predictor was industry affiliation, indicating that within

industries, companies have similar growth characteristics. The next most- important predictor

was dividend per share 3-yr growth rate. This relationship indicates that companies paying a

higher dividend are more likely to be growing companies. The next factor is BrandPower 3-yr

slope, which indicated that BrandPower growth is an important predictor of growth in

sales/revenue-per-share 5-yr % growth rate. Investment potential level in Year -4 was the next

most-important predictor, which indicated that a company perceived as a worthwhile investment

is a leading indicator of future sales/revenue-per-share growth. Earnings predictability, an

analyst ranking, was the next most important predictor. It indicates that companies with more

stable, predictable earnings will grow more than others. The final important predictor of

sales/revenue-per-share 5-yr % growth was stock price 5-yr % growth, which indicated that

sales/revenue-per-share growth and stock price growth are linked.

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The next step in the analysis was to evaluate the CHAID decision tree in figure 4. Once

again, the dependent variable at the top of the tree is sales/revenue-per-share 5-yr % growth. A

total of 617 companies were evaluated in the analysis.

Figure 4. Predictor Importance in Full Macro Model.

Figure 5. CHAID Full Macro Model Decision Tree.

At the top of the decision tree in Node 0, one sees that of the 617 companies evaluated,

379 were predicted to have high 5-yr growth (above 20.6% growth), and 238 were predicted to

have low 5-yr growth (below 20.6% growth).

The next level of the decision tree is Dividend-per-share 3-yr growth percentages. These

were examined in Nodes 1–5. Nodes 1 and 2 show negative- and low-dividend growth rate (up to

21.7% growth), and the companies were predicted to have low sales/revenue-per-share 5-yr %

growth rates. Node 3 shows companies with Dividend-per share growth rates between 21.7% and

65.6% growth. These companies showed a higher predicted sales/revenue-per-share 5-yr growth

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rate. Of 138 companies in Node 3, only 71 companies were predicted to be high sales/revenue-

per-share growth companies, and 67 were predicted to be low-growth companies. Node 4

companies are those above 65.6% Dividend-per-share growth. In this node, 66.6% of the

companies were predicted to have high sales/revenue-per-share growth, and 33.3% of the

companies were predicted to have low sales/revenue-per-share growth. Node 5 includes

companies where no dividend was captured.

Nodes 6 – 16 show how industry affiliation impacts sales/revenue per share. Tables 3, 4,

and 5 show how the industries break out into these nodes.

Figure 6. Industries Included in Nodes 6, 7, and 8.

Figure 7. Industries Included in Nodes 9, 10, 11, 12, and 13.

Node 6 Node 7 Node 8

Aerospace Diversified Services Brokerage

Apparel, Shoes Electronics, Electrical Equipment Diversified Industrials

Beverages Food Home Builders

Building Materials Paper Products Insurance

Chemicals Furniture Internet

Commercial Banks Industrial Equipment Medical Supplies & Services

Computer Software Metal Products Office Equipment

Computers & Peripherals Metals Packaging

Diversified Financial Crude Oil Educational Services

Motor Vehicles Pharmacy Serv.

Petroleum Refining Consulting

Pharmaceuticals Distilled Spirits

Publishing & Printing Athletic Equipment & Apparel

Rubber & Plastics

Scient, Photo, Cntr Eq

Toiletries, Household Products

Textiles

Tobacco

Transportation

Power & Energy

Electric Utilities

Home Appliances

Hotel & Entertainment

Restaurants

Retailers

Semiconductors

Telecommunications

Auto Parts

Node 9 Node 10 Node 11 Node 12 Node 13

Aerospace Computer Software Diversified Services Paper Products Home Appliances

Apparel, Shoes Computers & Peripherals Electronics, Electrical Equipment Furniture Hotel & Entertainment

Beverages Diversified Financial Food Industrial Equipment Restaurants

Building Materials Metal Products Retailers

Chemicals Metals Semiconductors

Commercial Banks Crude Oil Telecommunications

Motor Vehicles Auto Parts

Petroleum Refining Brokerage

Pharmaceuticals Diversified Industrials

Publishing & Printing Home Builders

Rubber & Plastics Insurance

Scient, Photo, Cntr Eq Internet

Toiletries, Household Products Medical Supplies & Services

Textiles Office Equipment

Tobacco Packaging

Transportation Educational Services

Power & Energy Pharmacy Serv.

Electric Utilities Consulting

Distilled Spirits

Athletic Equipment & Apparel

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Figure 8. Industries Included in Nodes 14, 15, and 16.

Macro Model Results

Once the predictors and their interactions had been identified, the results of the model

had to be assessed. Figure 5 identifies the success of the model.

Figure 9. Results of the Macro Model.

This model performed with an 85.9% accuracy rate, which means that, in nearly 86% of

its estimate, the model was correct. Of 617 predictions, 530 were correct while 87 were

incorrect. Of 379 high predictions, the model predicted correctly 337 times, the sales/revenue-

per-share 5-year growth to be above 20.6%. It predicted sales / revenue per share 5-year growth

to be below 20.6% correctly 193 times out of 238.

Node 14 Node 15 Node 16

Aerospace Diversified Services Home Appliances

Apparel, Shoes Electronics, Electrical Equipment Hotel & Entertainment

Beverages Food Restaurants

Building Materials Paper Products Retailers

Chemicals Furniture Semiconductors

Commercial Banks Industrial Equipment Telecommunications

Computer Software Metal Products Auto Parts

Computers & Peripherals Metals Brokerage

Diversified Financial Crude Oil Diversified Industrials

Motor Vehicles Home Builders

Petroleum Refining Insurance

Pharmaceuticals Internet

Publishing & Printing Medical Supplies & Services

Rubber & Plastics Office Equipment

Scient, Photo, Cntr Eq Packaging

Toiletries, Household Products Educational Services

Textiles Pharmacy Serv.

Tobacco Consulting

Transportation Distilled Spirits

Power & Energy Athletic Equipment & Apparel

Electric Utilities

43

This accuracy rate is high and could likely be improved over time as more is learned

about the relationship between the corporate brand and sales/revenue-per-share growth. Also, as

more data becomes available, the model can be improved.

Additional Models

Once the macro model had been constructed, additional work was needed at the sector

level. The sectors under consideration were consumer staples, industrials, and technology. These

sectors were considered because they are vastly different in terms of product, customer, supply

chain, and production. If there were differences in the way the model performed, based on sector

affiliation, it could be proven because these sectors would reveal the diversity.

For each sector, the same data-set was utilized, and the same process was followed. The

CHAID analysis identified the predictors of sales/revenue-per-share 5-year growth, by dividing

them into high- and low 5-year growth bins (+/- 20.6%). Next, a decision tree was developed to

identify the relationships. Finally, I assessed the results.

Consumer Staples Sector

I tracked fifty companies in the consumer staples sector. These companies produce

everyday household items that day-to-day shoppers buy everywhere, from local shopping malls

to grocery stores. These products are purchased by consumers from the top to the bottom of the

economy.

The results of the model in the consumer staples sector were somewhat surprising. Figure

6 indicates that two predictors were of importance in the data set. The most important predictor

was stock price 5-year % growth, which indicated that companies’ sales are growing and

increasing their share price. The next most important predictor was the BrandPower 1-year slope,

which indicated that growth in the corporate brand is critical in this sector.

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Figure 10. Predictor Importance Consumer Staples Sector.

The CHAID decision tree in this study (Figure A1) shows that half of the companies

were predicted to be high and half were predicted to be low sales / revenue per share 5-year %

growth companies. Interestingly, companies without stock values, indicating they are private

companies, were predicted to outperform the public companies.

Overall, this model received an 86% accuracy rate. As indicated in Figure 7 (below), 43

out of 50 predictions were accurate, and 7 were incorrect. High-growth companies were

identified correctly 18 times, and falsely identified 7 times. Low-growth companies were

predicted 25 times, all of which were correct.

Figure 11. Results of Consumer Staples Sector Model.

Industrials Sector

The next model for consideration is the industrials sector model, which represents

everything from hand tools to aerospace. Some products are marketed to individual customers,

but this sector is primarily business-to-business focused and sells large, expensive components or

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machines. In this sector, 121 companies were represented. Once again, the same data set was

considered, and I followed the same process as was developed in the macro model.

This sector has four important predictors of sales / revenue per share 5-year % growth

(Figure 8). The first is stock price 5-year % growth, indicating that companies’ stock growth is a

predictor of sales / revenue per share growth. The next predictor is Familiarity 5-year slope,

indicating that companies that are growing public awareness are more likely to create sales /

revenue growth. The next most important predictor of sales / revenue per share growth is

Perception of Management Year -2, indicating that the level of esteem shown to the leadership of

the company in recent years influences sales / revenue per share growth. Finally, projected 1-

year future dividends paid out is also an important predictor, which is indicative that the

company’s prospects are an important indicator of performance; this indicator is not surprising

given the products sold are often large equipment and machinery.

Figure 12. Predictor Importance Industrials Sector.

The CHAID decision tree (Figure A2) shows that of the 121 companies, 55 are predicted

to be high sales / revenue per share companies while 65 are predicted to be low-growth

companies. The stronger stock-growth companies are predicted to outperform the lower stock-

growth companies; however, the private companies are projected to do better than the publicly

traded companies. High Familiarity companies lead to stronger Perception of Management,

which is important to growth for private companies.

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Companies in the industrial sector are most reliant on the corporate brand factors of

Familiarity growth and strength of Perception of Management, which means they need to be

growing the size of their audience and demonstrating strong leadership. While each company

needs to be evaluated on an individual basis, companies should consider some general advice.

First, a communications audit of the creative messaging and share-of-voice analysis would allow

companies to evaluate their messaging and determine whether their communications spending in

the category is adequate to develop a strong position relative to peers. Findings may indicate a

need to strengthen messages relating to corporate leadership and vision. Also, the company

might increase investment in the corporate brand to boost the Familiarity factor.

Figure 9 (below) demonstrates the accuracy of the industrials sector model. In this case, it

was 81.8% accurate, with 99 correct predictions and 22 incorrect predictions. Meanwhile, of 56

high-growth predictions, 39 were accurate while 17 were incorrect. Of 65 low-growth

predictions, 60 were correct and 5 were incorrect.

Figure 13. Results of Industrial Sector Model.

Technology Sector

The technology sector consists of companies from semiconductors, computers, and

peripherals to cloud-computing companies, software, and internet companies. Fifty companies

were considered in this sector. They were evaluated using the same data set and process used in

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the macro model and the other sectors. The technology sector sells to individual consumers and

business-to-business.

Four important predictors are in the technology sector (Figure 10). The most important

predictor is dividend per share 3-year % growth. These are often cash-wealthy companies and

may be an overall indicator of corporate health. The next most important predictor is industry

affiliation within the sector, which is likely an indicator that the sector is very diverse, with some

companies providing products and others providing services. Perception of Management in Year

0, the target year for sales / revenue 5-year % growth, showed that the absolute strength of

leadership is an important factor. This result is likely due to the importance of securing personal

and financial information for which these companies must be responsible. Finally, the 1-year

slope in Favorability is the last important predictor of sales / revenue per share growth.

Figure 14. Predictor Importance Technology Sector.

In the technology sector, the most important corporate-brand factors are Perception of

Management strength and Favorability growth, which indicates that quality of image is most

important in this sector. A company would want to ensure that its spending level is competitive

with peers; even more important is the company’s vision must be clearly articulated and the

general perception of the company must be on the rise. These issues can be addressed by

communications’ messaging efforts.

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The CHAID decision tree (Figure A3) indicates that companies in Node 3, computers and

peripherals, were impacted by Familiarity growth over the previous year. By contrast, companies

in Node 5, internet companies, were most influenced by Perception of Management in Year 0.

All high sales / revenue per share companies were among companies that were private or paying

a higher dividend.

Figure 11 illustrates the results of the technology-sector model. Of 50 companies, 86% of

the predictions were accurate. Specifically, 47 predictions were accurate and 7 were incorrect.

High sales / revenue growth was accurately predicted 29 times and inaccurately predicted 6

times. Low sales / revenue per share growth was predicted accurately 14 times and inaccurately

predicted only 1 time.

Figure 15. Results of Technology Sector Model.

Comparing the Sector Models

Of the three models in this study, the consumer staples model was the simplest. It appears

that this result reflects the nature of the low-complexity purchase decisions in that particular

market, as compared to the others. Consumers in this market are looking for financial strength in

the company and for growing brands.

A second model, industrial and technology, proved to be more complex, relying on more

factors for sales / revenue per share growth. While the industrial sector was more reliant on long-

and short-term corporate brand factors, the technology sector relied more on short-term brand

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factors. This finding is intuitively understandable as industrial companies are most likely to have

corporate brand reputations built over a long period of time and performance while in the

technology space, new corporate brands emerge all the time.

These three sectors demonstrate the diversity of businesses and how the corporate brand

works to support them and impacts them in different ways. This study also shows that those with

responsibility for a company’s corporate brand must understand the bigger picture of what drives

revenue for their company and industry.

Industry Analysis

While I attempted a more granular analysis of individual industries, using one industry

sub-group from each of the sectors, the industry sub-groups proved to be too small in the number

of companies to build a reliable model.

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CHAPTER FIVE:

SUMMARY AND DISCUSSION

The concepts expressed in this dissertation are grounded in decades of corporate-brand

research. The methods offered are non-traditional, non-GAAP, and may be debated by

accountants and finance executives. However, the results are evidence-based and data-driven;

they are not opinion. In fact, they are borne out in quantitative data.

As it relates to the concept of intangible capital, this research on the corporate brand’s

impact on sales / revenue per share growth uncovers another layer of intangible capital. As stated

in Ocean Tomo LLC’s study “Intangible Asset Market Value Study” (2017), 84% of a market

valuation is based on intangible assets. This percentage is up 17%, compared to the first study of

1975. By 2020, the updated study indicates that the market valuation’s number increased to 90%.

This study was not based on market cap but on sales / revenue per share growth--which is

yet another component of intangible value. The point is the corporate brand contributes to value

creation through multiple avenues. Too often, brand consultants and advocates argue about the

right valuation methodology or the best methodology. The truth is that there are multiple

methodologies, multiple ways in which value is created and, in many cases, they are

complimentary. The method put forth in this dissertation is a unique, new way to capture value

creation. In the study of intangible capital, exploring brand and corporate-brand valuation is like

peeling back the layers of an onion; the present study adds one more layer to our knowledge.

Still, multiple layers of brand-valuation have yet to be identified and explored.

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Summary of the Problem

Consultants often discuss the value created by product brands and corporate brands.

However, historically, there has been little evidence. Subjective models based on experts in the

filed have done little to boost credibility in the category. To tie the claims to the reality, hard data

was needed, which is where Tenet Partners’ CoreBrand Index database is beneficial. This

database creates quantitative variables, representing corporate brands and their various

dimensions over time, in enough volume to be used as a variable in quantitative modeling. With

such data, consultants can offer advice to their clients based on factual data and real analysis.

Rather than offering clients their opinions, consultants can offer factual data.

In my dissertation, the data offers answers to whether the corporate brand contributes to

sales / revenue per share growth. This finding is important because it allows corporate executives

the opportunity to choose between alternatives when allocating scarce budget funds based on

expected returns, rather than relying on intuition and speculations. The executives’ allocation of

resources can be viewed as an investment rather than merely a random distribution of funds.

Research Question

RQ: How does the performance of the corporate brand impact sales / revenue per share?

Hypotheses

H1: Growth in BrandPower will result in growth in sales revenue change.

H2: Change in Familiarity and Favorability will have different, but positive, impacts on

sales/revenue per share growth.

H3: The different attributes that make up Favorability have different, but positive, impact

on sales/revenue per share.

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H4: Various conditions, such as industry affiliation, will positively alter the impact of

brand factors on sales/revenue per share growth.

Summary of the Methodology

The key to removing subjective bias from value-creation research is consistent

quantitative research. The CoreBrand Index is the vehicle for creating this level playing field and

removing all bias. For nearly three decades, the Index has utilized the same collection

methodology and series of question to measure the health of corporate brands. The CoreBrand

Index is at the center of my research and provides the year-over-year consistency to allow

comparisons of value creation across industries, companies, and time.

The importance of having quantitative data like the CoreBrand Index is it provides a

distinct measure of the corporate brand for each company, over time. Many methodologies, such

as discounted cash flow, make assumptions about brand strength based on the performance of the

company. However, the methodology in this study allows the researcher to observe the brand as

an independent variable. For example, it is possible for a company to have a strong brand yet

underperform financially and vice versa. This approach allows for a more accurate, realistic

observation of what is occurring in the marketplace. Undoubtably, more accurate observations

and data lead to more accurate, precise models.

Summary of the Results

The research question in this dissertation was: “How does the performance of the

corporate brand impact sales / revenue per share?”

In response to this question, sales / revenue per share 5-year growth was examined as the

target (dependent) variable. BrandPower and all of its components (Familiarity, Favorability,

Overall Reputation, Perception of Management, Investment Potential, and Culture of Innovation)

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were shown to have various impacts on sales / revenue per share 5-year growth, based on the

results of the macro model and sector sub-models. These models accurately predicted sales /

revenue per share 5-year % growth rates as high (above 25.6%) or low (below 25.6%) with a

high degree of success.

• Macro model – 85.90% accuracy

• Consumer staples sector model – 86.00% accuracy

• Industrials sector model – 81.82% accuracy

• Technology sector model – 86.00% accuracy

The fact that corporate brand and/or its components had a positive influence in each of

these models and improved their predictive powers supports the null hypotheses for H1, H2, and

H3. The fact that industry affiliation was the most important predictor in the macro model and

that the impact of brand factors was different in the sector models supports the null hypotheses in

H4.

The present study supports the idea that the corporate brand can be measured as a

quantitative variable. Thus, the contribution to sales / revenue per share growth can be measured

as well.

Contribution to Knowledge

The present study is the first to measure corporate-brand contribution to sales / revenue

per share growth on this scale. It is a foundational work in the area of brand valuation; it also

contributes to the other measures of brand value, such as product/brand contribution to sales,

corporate brand contribution to stock performance, royalty relief, discounted cash flow analysis,

and many more measures. This study sheds light on what was already assumed-- the corporate

brand could increase product sales--but had never been quantitatively proven.

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Further, this study demonstrates a continued utility of the Corporate Branding Index,

which has been shown widely to be a predictor of future value. The Index dataset has been used

by Tenet Partners/CoreBrand in the development of return-on-communications-investment

models in brand-valuation models (Gregory, 2018). This body of work shows the importance of

building a quantitative database of this order.

It is expected that this work will further the knowledge of the value of intangible assets

and their contribution to corporate performance. Hopefully, this study can serve to educate

business executives and academics, to help implement these findings and expand the base of

knowledge on the subject.

Practitioner Contribution

The intended primary contribution of this research is to help practitioners break the

traditional mindset that product brands contribute to sales / revenue and corporate brands

contribute to stock performance. That belief remains true, but the intention of this research is to

illuminate the fact that corporate brands can also contribute to sales / revenue. Simply stated,

when the company is well-known and respected, it is easier to sell products and services. The

evidence herein should provide a foundation for further work to examine and quantify this

contribution.

More systems like the CoreBrand Index need to be developed to give practitioners

additional options to evaluate and master their brands. The key to the Index is it is consistent,

simple, and makes intuitive sense. Since CEOs do not have the time to digest massive and

complex measurement systems, systems need to be developed to help communications

executives delve into the tactical components of their programs. The CoreBrand Index is

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designed to be a strategic tool to help senior leadership understand the big picture. Such a tool

spurs discussion, identifies strengths and weaknesses, and helps senior leaders make decisions.

This dissertation is intended to help business leaders and communications executives

understand the importance of, and the contribution of, a strong corporate brand. Communications

executives may consider this work as further evidence of the impact of a strong corporate brand

and use it to justify expenditures on communications budgets to grow their brand. Senior

executives may find this work helps them understand the reason to allocate budgets for

supporting the corporate brand. Such budgeting is justified by the evidence herein.

Besides the CoreBrand Index, the present work builds on other research on corporate-

brand value creation. I view my research as filling a gap in the existing literature. My study does

not provide a perfect solution, but it is a first step into this wisdom of value creation. The

literature on corporate-brand impact, as regards revenue generation, is small, largely because the

data has not been made available on a large scale for an in-depth quantitative study.

Intangible capital, which is placing value on elements that have worth but traditionally

are unrecognized, should be the guiding force for brand measurement and valuation systems

moving forward. This concept is likely the most valuable one in this study, and the model herein

is an important first-step to understanding corporate-brand’s contribution to revenue generation.

However, it must be viewed as one element of the corporate brand’s impact on businesses;

further study and development of this concept will require many minds and perspectives.

However, understanding how brands fit within intangible capital is the key to understanding,

unlocking, and unleashing the power of brands.

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Study Limitations and Future Research

While this dissertation is a great first step and foundation into the area of corporate-brand

contribution to sales / revenue per share growth, more work needs to be done.

Some recommendations for further study arose from my research based on the facts that

1) This study used only a single point-in-time observation for each company analyzed. 2) The

data used covers the period 2011–2016, so the dependent variable is only sales / revenue per

share 5-year growth in 2016. Further work should be done to improve the rigor and

generalizability of the study by examining other points in time (for example, 2012–2017, 2013–

2018 and so forth). Doing so will increase the validity of the study and make the results more

reliable for business leaders. Since product/brand data was not available to be examined as part

of this study, I look forward to future research that includes product brands from major lines of

business within these same companies to further specify which result is driven by the product

brands and which is driven by the corporate brand. Future research is also needed to assimilate

and synthesize the various contributions of product- and corporate brand to understand how the

various components of brand work together to create value. This research would help

communications executives better understand how to optimize their efforts to create brand

valuation most efficiently.

The knowledge gained in this study should be used to create a forecasting model to

understand how investments drive the brand and the corresponding value created. This

knowledge will make the model more actionable in practice. The concept of brand-valuation

modeling has likely uncovered only a small portion of the total value created for a company. It

continues to be a fertile landscape for further research and exploration.

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Conclusions

This study demonstrates not only how to measure the contribution of the corporate brand

to sales / revenue per share growth, but it is an empirical approach to brand valuation. The

objective has been to quantitatively measure the corporate brand and seek one of the dimensions

of how it impacts a company, which is the basis of subjective, quantitative brand evaluation.

My analysis demonstrates that a macro view can be generalized by asking, “How does

the corporate brand impact sales / revenue per share.” The key for a communications executive is

to understand how the model behaves for his/her individual company. The model clearly

demonstrates that industry affiliation is a key consideration for every company, which makes

rational sense because brands impact various types of companies differently. For example, for

one type of company, Familiarity may be the most important factor; but in others, Perception of

Management may be the more important factor. The sector models were based on vastly

different types of companies and showed vastly different impact of the various brand

components. These effects should be studied in more detail. In an ideal practice situation, these

models would be customized to the extent possible for individual companies, based on their

competitors and markets. As with most models, the decision on how granular to proceed should

be weighed by the cost of the effort and the additional variance in the dependent variable

explained.

The fact that the strength of the corporate brand can be quantitatively measured is not

new. What is novel is that a brand’s impact on sales / revenue per share also can be

quantitatively measured. More and more companies are embracing the importance of intangible

capital. While this factor is not GAAP compliant, it can be used as a management tool to

effectively manage the corporate brand and the optimization of intangible capital in value

58

creation. The model I offer here, like any other model, can be implemented and refined over

time, as knowledge is gained. The most effective companies will use such tools rather than rely

on experience, instinct, and/or intuition.

The objective in practice is not to secure unlimited funds for communications executives

to flood the various channels of communications, but to understand the impact created from an

informed investment decision to optimize the investment and return of brand valuation. A

methodical approach should be used to measure brand valuation and impact. The data must be

explored and understood before any modeling tools can be used with success. Like many other

disciplines, brand intelligence can best be viewed as a mosaic. No one piece of information tells

the entire story. The most successful companies will start by gathering and examining their data.

The relationships between the corporate brand components and other financial components will

tell a compelling story that must be understood. The relationships between the communications

inputs and the corporate brand components show us what can be done to articulate the corporate

brand. Once these factors are understood, the model can be implemented to the greatest success.

Then, the communications input factors can be manipulated to have the greatest impact on the

corporate brand to maximize its impact on business results, such as sales / revenue per share.

Once that is done, rates of return-on-investment can be considered and used to identify the point

where return-in-business-results exceeds the effort and capital invested.

Without implementing a system like the one defined in this dissertation, companies are

operating without sufficient knowledge, which is unacceptable. Companies must operate in an

environment of data and modeling tools to assist decision making. Note that these tools assist

decision making. Executives are still responsible for making final decisions, yet the tools need to

59

be utilized for the best decisions (and they ensure accountability). Decisions and data are key to

successful business outcomes in today’s business environment.

60

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64

APPENDIX A:

DESCRIPTIVE STATISTICS

Table A1. List of Variables and Descriptive Statistics.*

Field Std. Dev Min Mean Median Max

Fam Y -5 30.3 2.4 41.7 33.7 96.8

Fam Y -4 30.7 2.1 41.6 33.1 96.7

Fam Y -3 31.2 2.6 41.6 32.8 96.9

Fam Y -2 31.6 2.1 41.9 32.0 97.5

Fam Y -1 32.0 2.3 42.5 31.7 98.0

Fam Y 0 32.3 1.9 43.3 32.1 98.2

Fam % 1-yr 11.4 -24.3 2.3 1.4 105.4

Fam % 3-yr 25.9 -51.1 5.4 3.4 205.3

Fam % 5-yr 52.7 -70.5 6.4 3.6 839.5

Fam slope 1-yr 1.8 -6.2 0.8 0.7 9.9

Fam slope 3-yr 1.6 -6.9 0.5 0.5 5.0

Fam slope 5-yr 1.6 -9.0 0.3 0.3 6.1

Rep Y -5 10.9 29.4 66.1 66.6 91.0

Rep Y -4 11.5 28.1 65.4 65.7 89.4

Rep Y -3 12.6 23.2 64.8 64.9 89.7

Rep Y -2 12.9 17.7 63.7 63.9 91.4

Rep Y -1 12.5 14.7 66.5 67.1 91.5

Rep Y 0 12.5 11.4 66.6 67.3 92.7

Rep % 1-yr 5.9 -30.6 0.2 -0.3 50.0

Rep % 3-yr 12.3 -57.8 3.4 1.6 74.8

Rep % 5-yr 15.2 -74.4 1.2 0.5 76.8

Rep slope 1-yr 2.9 -11.8 0.0 -0.2 12.5

Rep slope 3-yr 2.0 -7.3 0.8 0.6 7.3

Rep slope 5-yr 1.8 -7.9 0.1 0.1 6.0

Mgt Y -5 11.0 12.7 60.6 62.0 84.8

Mgt Y -4 11.7 19.6 60.4 62.0 86.2

Mgt Y -3 12.9 18.8 60.9 62.3 88.1

Mgt Y -2 12.6 15.9 62.0 63.0 89.1

Mgt Y -1 12.5 16.4 62.2 63.4 90.1

Mgt Y 0 12.2 12.9 62.6 63.6 91.5

Mgt % 1-yr 7.2 -24.9 1.0 0.2 66.4

Mgt % 3-yr 14.1 -50.7 4.0 1.2 121.7

Mgt % 5-yr 20.6 -70.2 4.5 2.9 282.8

Mgt slope 1-yr 2.9 -12.5 0.4 0.1 13.1

Mgt slope 3-yr 2.0 -7.8 0.5 0.2 8.4

Mgt slope 5-yr 1.7 -6.4 0.5 0.4 7.1

Inv Y -5 12.8 10.9 56.3 57.0 86.4

Inv Y -4 13.6 16.0 55.2 57.0 84.2

Inv Y -3 15.0 12.6 54.4 56.8 85.4

Inv Y -2 15.2 9.8 55.1 57.7 87.8

Inv Y -1 15.2 9.4 55.7 58.3 89.1

Inv Y 0 14.9 10.3 56.7 58.8 90.8

Inv % 1-yr 9.1 -26.4 2.5 0.9 55.2

Inv % 3-yr 20.6 -53.4 6.2 2.5 128.3

Inv % 5-yr 27.2 -68.2 2.4 1.1 323.9

Inv slope 1-yr 3.1 -12.4 1.0 0.5 12.8

Inv slope 3-yr 2.5 -7.5 0.8 0.5 9.8

Inv slope 5-yr 2.1 -8.1 0.1 0.2 6.9

65

Table A1 (Continued)


Fav Y -5 11.2 24.6 61.0 61.6 86.4

Fav Y -4 11.9 23.3 60.3 61.3 84.5

Fav Y -3 13.1 19.7 60.1 61.5 86.9

Fav Y -2 13.1 15.3 60.2 61.4 89.0

Fav Y -1 13.0 15.8 61.5 63.1 90.1

Fav Y 0 12.8 12.3 62.0 63.3 91.4

Fav % 1-yr 6.5 -25.1 1.1 0.3 54.3

Fav % 3-yr 13.8 -51.9 4.2 1.9 96.7

Fav % 5-yr 17.6 -70.9 2.3 1.6 102.2

Fav slope 1-yr 2.7 -11.2 0.5 0.2 11.3

Fav slope 3-yr 2.0 -6.9 0.7 0.4 8.0

Fav slope 5-yr 1.8 -6.9 0.2 0.3 6.0

BP Y -5 22.2 1.0 27.6 20.9 83.4

BP Y -4 22.3 0.8 27.5 20.8 81.7

BP Y -3 22.9 0.6 27.7 20.6 81.0

BP Y -2 23.0 0.4 27.9 21.0 81.2

BP Y -1 23.4 0.4 28.7 21.1 81.8

BP Y 0 23.5 0.3 29.3 21.9 82.5

BP % 1-yr 16.9 -43.3 3.8 2.0 146.8

BP % 3-yr 40.4 -76.5 11.8 5.4 328.3

BP % 5-yr 81.5 -91.4 13.8 4.5 1249.5

BP slope 1-yr 1.4 -5.0 0.6 0.4 6.3

BP slope 3-yr 1.3 -5.2 0.6 0.4 4.8

BP slope 5-yr 1.3 -6.8 0.4 0.2 4.8

Innov Y 0 20.1 0.0 59.1 57.8 97.4

Ad Y -9 DOLS (000) 303993.1 0.0 126351.9 58.4 3171682.8

Ad Y -8 DOLS (000) 269733.9 0.0 105140.9 58.4 2930805.0

Ad Y -7 DOLS (000) 255825.4 0.0 98197.9 59.6 2494849.5

Ad Y -6 DOLS (000) 261338.3 0.0 102114.9 20644.8 2931266.2

Ad Y -5 DOLS (000) 252482.4 0.0 101485.2 13215.6 2799303.0

Ad Y -4 DOLS (000) 248359.9 0.0 101501.2 8565.1 2673944.3

Ad Y -3 DOLS (000) 258437.7 0.0 102916.2 9425.0 2924291.9

Ad Y -2 DOLS (000) 243904.1 0.0 98506.4 9428.4 2313153.6

Ad Y -1 DOLS (000) 230025.2 0.0 92045.8 7924.5 1824096.7

Ad 1-yr % 5888.5 -100.0 319.6 7890.2 139735.6

Ad 3-yr % 2543.7 -100.0 266.4 7489.1 47717.0

Ad 5-yr % 1042.2 -100.0 198.7 7094.7 14780.9

Ad 8-yr % 8909.8 -100.0 668.1 -8.0 166374.1

Ad slope 1-yr 60765.6 -784297.4 -6516.2 -13.7 740319.1

Ad slope 3-yr 25022.2 -316068.1 -3187.4 -11.0 224840.4

Ad slope 5-yr 17676.1 -192684.2 -1500.8 -31.3 99642.4

Ad slope 8-yr 15241.3 -132776.7 -966.4 -62.9 85404.5

Y -5 Cash Flow per share: (Earnings

for Banks and Insurance companies) 3.42 -3.41 4.60 -28.44 26.06






for Banks and Insurance companies) 4.42 -2.33 5.70 1.00 40.89



Y 0 Cash Flow per share: (Earnings


Y +1 Cash Flow per share: (Earnings



for Banks and Insurance companies) 6.79 0.50 8.36 4.67 74.80


for Banks and Insurance companies) 8.05 0.55 8.85 4.80 79.60

CF 1-yr grow % 179.8 -2550.0 6.4 4.9 1836.7

CF 3-yr grow % 108.0 -1533.3 9.8 5.4 1204.3

CF 5-yr grow % 126.7 -648.4 34.1 6.8 1317.3

66



CF 1-yr proj % 55.2 -401.7 9.9 7.2 550.0

CF 3-yr grow proj 120.8 -1200.0 45.5 4.3 1300.0

CF 1-yr grow slope 3.4 -7.5 0.4 12.7 56.6

CF 3-yr grow slope 1.7 -3.2 0.3 26.5 28.4

CF 5-yr grow slope 1.5 -2.1 0.4 8.7 28.4

CF 1-yr proj slope 1.5 -16.1 0.7 40.0 12.4

CF 3-yr proj slope 1.0 -4.5 1.0 0.2 9.6

Y -5 Earnings per share 2.46 -5.55 2.77 0.20 19.47





Y 0 Earnings per share 3.85 -7.60 3.64 2.61 40.70

Y +1 Earnings per share 4.15 -7.10 4.23 2.73 45.45



EPS 1-yr grow % 199.9 -700.0 -0.8 3.0 3900.0

EPS 3-yr grow % 116.2 -600.0 9.2 3.5 1400.0

EPS 5-yr grow % 397.3 -1433.3 48.8 4.6 7650.0

EPS 1-yr proj % 177.9 -1650.0 10.1 4.9 2200.0

EPS 3-yr grow proj 350.5 -3400.0 56.7 2.6 2900.0

EPS 1-yr grow slope 1.6 -7.6 0.1 11.4 20.5

EPS 3-yr grow slope 0.8 -3.9 0.1 27.0 4.3

EPS 5-yr grow slope 0.6 -2.4 0.2 9.3 4.2

EPS 1-yr proj slope 1.0 -8.9 0.5 46.7 10.0

EPS 3-yr proj slope 0.8 -0.8 0.8 0.1 7.4

Y -5 Sales/Revenues per share:

(Earnings for banks and insurance

companies)

59.55 0.43 50.51 0.13 522.27



companies)

61.60 -0.39 52.27 0.16 540.38



companies)

64.33 0.69 53.67 0.35 596.80



companies)

72.40 -0.78 56.78 0.60 773.25



companies)

77.69 0.58 57.51 30.92 867.70

Y 0 Sales/Revenues per share:


companies)

83.25 0.40 58.32 32.73 909.10

Y +1 Sales/Revenues per share:


companies)

91.55 0.60 63.67 33.18 977.25



companies)

102.94 1.05 74.36 35.22 1190.50



companies)

111.03 1.05 76.32 34.10 1190.50

SPS 1-yr grow % 33.8 -100.0 -0.3 33.4 353.3

SPS 3-yr grow % 38.9 -100.0 5.3 36.5 345.6

SPS 5-yr grow % 59.4 -100.0 20.6 45.1 441.0

SPS 1-yr proj % 42.3 -100.0 4.3 44.0 719.2

SPS 3-yr grow proj 51.9 -100.0 28.1 1.8 766.7

SPS 1-yr grow slope 13.9 -146.8 1.5 6.2 84.7

SPS 3-yr grow slope 10.7 -50.3 1.7 13.4 103.1

SPS 5-yr grow slope 9.0 -39.6 1.8 -- 88.4

SPS 1-yr proj slope 13.6 -17.9 4.4 5.3 203.9

SPS 3-yr proj slope 11.2 -38.4 6.2 26.3 105.7

67



Y -5 Dividend per share 0.74 0.02 0.97 0.40 5.50





Y 0 Dividend per share 1.21 0.04 1.49 0.80 11.00

Y +1 Dividend per share 1.19 0.04 1.56 0.87 10.30



DPS 1-yr grow % 50.0 -100.0 6.5 1.2 685.7

DPS 3-yr grow % 111.0 -100.0 46.2 1.3 950.0

DPS 5-yr grow % 213.1 -100.0 104.5 1.4 1800.0

DPS 1-yr proj % 21.1 -100.0 3.2 1.5 60.0

DPS 3-yr grow proj 1081.6 -100.0 112.9 1.7 14566.7

DPS 1-yr grow slope 0.6 -1.9 0.1 6.8 9.6

DPS 3-yr grow slope 0.2 -0.5 0.1 30.6 3.0

DPS 5-yr grow slope 0.2 -0.4 0.1 60.0 1.5

DPS 1-yr proj slope 0.5 -9.3 0.1 5.8 1.1

DPS 3-yr proj slope 1.5 -2.6 0.3 28.8 25.3

Y -5 Book Value per share 17.91 -36.09 19.14 0.08 140.48





Y 0 Book Value per share 27.95 -81.39 24.64 14.63 208.75

Y +1 Book Value per share 28.03 -63.65 25.96 15.82 246.80



BVPS 1-yr grow % 1057.0 -371.6 57.0 17.1 21858.3

BVPS 3-yr grow % 452.6 -324.9 32.3 18.2 9178.2

BVPS 5-yr grow % 329.9 -1190.9 44.7 19.6 4685.7

BVPS 1-yr proj % 129.4 -2500.0 -4.8 23.7 460.0

BVPS 3-yr grow proj 554.3 -10600.0 17.4 25.6 3560.0

BVPS 1-yr grow slope 10.7 -25.9 1.3 3.4 139.0



BVPS 1-yr proj slope 7.8 -135.2 1.6 7.1 38.1

BVPS 3-yr proj slope 4.9 -36.4 3.5 32.9 62.1

Y -5 Shares Outstanding (millions) 1188.2 22.7 732.9 0.6 10573.0





Y 0 Shares Outstanding (millions) 4356.6 21.0 1009.7 335.6 68140.0

Y +1 Shares Outstanding (millions) 4445.9 21.0 1019.1 331.3 68950.0



SO 1-yr grow % 161.2 -100.0 8.8 328.0 2847.2

SO 3-yr grow % 158.7 -100.0 6.4 318.8 2847.7

SO 5-yr grow % 160.5 -100.0 6.5 316.1 2847.6

SO 1-yr proj % 19.5 -100.0 -5.2 310.0 15.6

SO 3-yr grow proj 19.7 -100.0 -7.3 314.0 20.8

SO 1-yr grow slope 4027.5 -497.3 313.3 -1.8 65828.0

SO 3-yr grow slope 1187.8 -414.3 85.2 -4.4 19748.6

SO 5-yr grow slope 566.0 -325.6 37.4 -6.2 9404.1

SO 1-yr proj slope 75.4 -947.0 -8.8 -0.8 810.0

SO 3-yr proj slope 268.0 -5204.5 -25.4 -2.8 122.5

Y -5 Sales / Revenues ($mill): (zero

for banks and insurance companies) 50217.0 0.0 24087.7 -3.0 470171.0





68







Y 0 Sales / Revenues ($mill): (zero

for banks and insurance companies) 42860.4 0.0 23395.1 9134.5 487000.0

Y +1 Sales / Revenues ($mill): (zero






Sls 1-yr grow % 25.6 -100.0 -2.6 10600.0 196.5

Sls 3-yr grow % 35.8 -100.0 1.1 10400.0 254.8

Sls 5-yr grow % 51.4 -100.0 10.6 11122.5 301.7

Sls 1-yr proj % 55.6 -100.0 3.0 13000.0 987.3

Sls 3-yr grow proj 74.3 -100.0 22.1 13400.0 1287.0

Sls 1-yr grow slope 4922.8 -45000.0 175.2 0.2 28250.0

Sls 3-yr grow slope 5845.2 -69850.4 -79.4 1.2 21051.9

Sls 5-yr grow slope 4476.2 -49607.3 98.6 5.6 22350.3

Sls 1-yr proj slope 4878.7 -21000.0 1285.7 3.9 60000.0

Sls 3-yr proj slope 7633.1 -49800.0 1825.8 19.9 81600.0

Y -5 Stock Price: from Yahoo!

Finance 48771.06 0.02 2392.95 0.00 1009018.83


Finance 70386.49 0.02 3435.89 0.10 1459606.61


Finance 63154.61 0.02 3056.67 53.59 1329306.38


Finance 61860.69 0.08 2994.84 327.50 1305014.42


Finance 59593.08 0.90 2880.34 768.00 1260000.00

Y 0 Stock Price: from Yahoo!

Finance 79.40 3.23 72.16 28.36 789.79

Stk % 1-yr 48.9 -100.0 2.4 34.2 319.2

Stk % 3-yr 59.1 -100.0 10.0 43.9 313.0

Stk % 5-yr 123.4 -100.0 94.3 49.5 915.5

Stk slope 1-yr 14.9 -102.5 7.9 45.6 104.8

Stk slope 3-yr 1643.0 -34653.2 -74.1 56.0 141.9

Stk slope 5-yr 1646.4 -119.3 84.9 10.9 34737.0

Timeliness 9.5 1.0 3.6 17.7 188.5

Safety 10.8 1.0 3.1 89.4 224.7

Technical 1.6 1.0 3.1 6.6 29.2

Financial Strength 2.2 1.0 3.8 2.4 28.3

Price Stability 27.0 5.0 63.0 4.8 100.0

*Key: for all variable abbreviations in this dissertation

Fam – Familiarity

Rep – Overall Reputation

Mgt – Perception of Management

Inv – Investment Potential

Fav – Favorability

BP – BrandPower

Innov – Culture of Innovation

CF – Cash Flow

EPS – Earnings per Share

SPS – Sales / Revenue per Share

DPS – Dividend per Share

69

BVPS – Book Value per Share

SO – Shares Outstanding

Sls – Sales / Revenue

Stk – Stock Price per Share

70

APPENDIX B:

CORRELATION ANALYSIS

Table A2. 1-Year Sales / Revenue Per Share, % Growth Correlations.

1yr Growth Variable Correlation Coefficient Strength

Statistics Count 433

Mean -0.256

Min -100.000

Max 353.263

Range 453.263

Variance 1148.609

Standard Deviation 33.891

Standard Error of Mean 1.629

Pearson Correlations Fam 4Q 11 0.058 Weak

Fam 4Q 12 0.057 Weak





Fam % 1-yr -0.066 Weak



Fam slope 1-yr 0.005 Weak



Rep 4Q 11 0.011 Weak



Rep 4Q 14 -0.008 Weak

Rep 4Q 15 -0.011 Weak

Rep 4Q 16 -0.025 Weak

Rep % 1-yr -0.062 Weak



Rep slope 1-yr -0.062 Weak



Mgt 4Q 11 0.020 Weak




Mgt 4Q 15 -0.012 Weak

Mgt 4Q 16 -0.026 Weak

Mgt % 1-yr -0.057 Weak

Mgt % 3-yr -0.089 Medium


Mgt slope 1-yr -0.061 Weak

Mgt slope 3-yr -0.097 Strong


Inv 4Q 11 0.019 Weak




71


1yr Growth Variable Correlation Coefficient Strength

Inv 4Q 15 -0.017 Weak

Inv 4Q 16 -0.027 Weak

Inv % 1-yr -0.048 Weak

Inv % 3-yr -0.094 Medium


Inv slope 1-yr -0.048 Weak

Inv slope 3-yr -0.099 Strong


Fav 4Q 11 0.017 Weak




Fav 4Q 15 -0.014 Weak

Fav 4Q 16 -0.027 Weak

Fav % 1-yr -0.061 Weak

Fav % 3-yr -0.092 Medium


Fav slope 1-yr -0.062 Weak

Fav slope 3-yr -0.095 Strong


BP 4Q 2011 0.050 Weak

BP 4Q 2012 0.047 Weak

BP 4Q 2013 0.045 Weak

BP 4Q 2014 0.043 Weak

BP 4Q 2015 0.042 Weak

BP 4Q 2016 0.041 Weak

BP % 1-yr -0.077 Weak

BP % 3-yr -0.103 Strong

BP % 5-yr -0.052 Weak

BP slope 1-yr -0.013 Weak



Innov 1Q 16 -0.041 Weak





SPS 3yr Growth Variable Correlation Coefficient Strength


Mean 5.923

Min -100.000

Max 345.571

Range 445.571

Variance 1669.280



Pearson Correlations Fam 4Q 11 0.080 Medium

Fam 4Q 12 0.083 Medium





Fam % 1-yr -0.084 Medium

Fam % 3-yr -0.103 Strong


Fam slope 1-yr -0.013 Weak

Fam slope 3-yr -0.007 Weak



72






Rep 4Q 15 -0.002 Weak

Rep 4Q 16 -0.006 Weak











Mgt 4Q 15 -0.009 Weak

Mgt 4Q 16 -0.029 Weak

Mgt % 1-yr -0.084 Medium

Mgt % 3-yr -0.105 Strong


Mgt slope 1-yr -0.087 Medium

Mgt slope 3-yr -0.092 Medium


Inv 4Q 11 -0.020 Weak

Inv 4Q 12 -0.010 Weak

Inv 4Q 13 -0.015 Weak

Inv 4Q 14 -0.023 Weak

Inv 4Q 15 -0.037 Weak

Inv 4Q 16 -0.052 Weak


Inv % 3-yr -0.097 Strong





Fav 4Q 11 -0.001 Weak



Fav 4Q 14 -0.006 Weak

Fav 4Q 15 -0.018 Weak

Fav 4Q 16 -0.031 Weak


Fav % 3-yr -0.095 Strong





BP 4Q 2011 0.064 Weak

BP 4Q 2012 0.065 Weak

BP 4Q 2013 0.066 Weak

BP 4Q 2014 0.065 Weak

BP 4Q 2015 0.064 Weak

BP 4Q 2016 0.061 Weak



BP % 5-yr -0.054 Weak



BP slope 5-yr 0.008 Weak

Innov 1Q 16 0.011 Weak



73







Mean 25.045

Min -10000.000

Max 14700.000

Range 24700.000

Variance 801570.790



Pearson Correlations Fam 4Q 11 0.073 Weak






Fam % 1-yr 0.011 Weak





Fam slope 5-yr 0.081 Medium







Rep % 1-yr 0.010 Weak



Rep slope 1-yr 0.018 Weak









Mgt % 1-yr 0.056 Weak



Mgt slope 1-yr 0.086 Medium

Mgt slope 3-yr 0.040 Weak

Mgt slope 5-yr 0.028 Weak







Inv % 1-yr 0.076 Weak



Inv slope 1-yr 0.106 Strong

Inv slope 3-yr 0.046 Weak

Inv slope 5-yr 0.036 Weak

74









Fav % 1-yr 0.053 Weak



Fav slope 1-yr 0.077 Weak



BP 4Q 2011 0.070 Weak

BP 4Q 2012 0.073 Weak

BP 4Q 2013 0.076 Weak

BP 4Q 2014 0.081 Medium

BP 4Q 2015 0.084 Medium

BP 4Q 2016 0.089 Medium

BP % 1-yr 0.026 Weak



BP slope 1-yr 0.104 Strong

BP slope 3-yr 0.097 Strong

BP slope 5-yr 0.087 Medium





75

APPENDIX C:

SECTOR CHAID DECISION TREES

Figure A1. CHAID Decision Tree: Consumer Staples Sector.

Figure A2. CHAID Decision Tree: Industrials Sector.

76

Figure A3. CHAID Decision Tree: Technology Sector.

77

APPENDIX D:

PERMISSION LETTER

September 27, 2018

Mr. Brad

Puckey 206

Grace Manor

Dr.

Coraopolis , PA 15108

Subject: Permission to use CoreBrand Data

Dear Brad,

This letter grants you a limited, non-exclusive, non-transferable right to utilize the research

database (“Data”) known as the CoreBrand Index® owned by Brandlogic Corp. D/B/A

Tenet Partners (“Tenet Partners”) solely to use the Data in your doctoral dissertation with

the University of South Florida (“Dissertation”).

As consideration, you will give Tenet Partners and CoreBrand Data Science attribution and

recognition of its ownership of the Data in the Dissertation. The use of the Data is limited to

the Dissertation only and no other rights are conveyed. In addition, you agree to not use the

Data or any of Tenet Partner’s trademarks (“Trademarks”) or other use beyond the

publication of your Dissertation nor for any commercial use, and you shall not grant any

rights to use the Data or the Trademarks to any other parties. Tenet Partners reserves all rights

in and to the Data and Trademarks not expressly granted to you, as well as the right to

terminate the rights granted hereunder in the event you breach or abuse your rights under

this agreement or any other agreements between you and Tenet Partners. This agreement

does not replace or augment any other agreements currently active between you and Tenet

Partners.

We are excited to see the culmination of your doctorial work in what will prove to be a

78

fascinating exploration and demonstration of the CoreBrand Index and its potential link to

better understanding intangible assets of corporations.

Sincerely,

Hampton Bridwell

CEO and Managing Partner

Tenet Partners

122 W. 27th Street,

9th Floor New York,

NY 10001

ABOUT THE AUTHOR

Brad A. Puckey has worked for CoreBrand/Tenet Partners for nearly three decades as the

head of the company’s analytics practice. He was there at the beginning of the company’s

analytics efforts; he built the CoreBrand Index® database and created the CoreBrand Analysis,

the company’s flagship analytic tools. The data created was the key to this dissertation. The

CoreBrand Analysis has been employed by many Fortune 500 companies, including General

Electric, Cisco Systems, Dow Chemical, and many, many others. The data has been used to

create brand valuation, communication return-on-investment, and other custom models for

clients.

Brad graduated from Indiana University of Pennsylvania with a Bachelor of Arts in

Economics, where he was involved in student government and a member of the Pi Kappa Phi

fraternity. Prior to attending college, Brad was a member of the United States Army, where he

served as an Electronic Warfare Signals Intelligence (SIGINT) Analyst. He served tours at the

National Security Agency at Ft. Meade, MD; A Company, Military Intelligence Battalion (Low

Intensity) Soto Cano Airbase, Honduras; and 1st Infantry Division (the Big Red One) at Ft. Riley,

KS.

[email protected]

203-722-5910

mailto:[email protected]

corporate brand impact on sales / revenue per share

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