ESSAYS ON MARKETING ANALYTICS

By

YUYING SHI

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2015

© 2015 Yuying Shi

To my parents and my husband,

for their tremendous love, trust and unconditional support

4

ACKNOWLEDGMENTS

I would like to express my gratitude to my committee members: Dr. Barton Weitz,

Dr. Steven M. Shugan, Dr. Deb Mitra and Dr. Walter Leite for their generous help and support in

the completion of this dissertation. Most gratefully, I thank Dr. Barton Weitz and Dr. Steven M.

Shugan for their continuous encouragement and guidance. Both of them are among the best

marketing researchers in their respective area. I am impressed by their knowledge, deep insight

and the determination to produce high quality research. It is great pleasure to work with them.

They have been among the best mentors in the department.

I also would like to thank Dr. Lyle Brenner and Dr. Alan Cook for their generous help in

this degree study. I also like to thank my fellow PhD friends, Dr. Jeremy Lim, Dr. Jack Xu, and

Dr. XiaoQing Jing for sharing with me their ideas and thoughts and making my life in this PhD

study more enjoyable.

5

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF TABLES ...........................................................................................................................7

LIST OF FIGURES .........................................................................................................................8

LIST OF ABBREVIATIONS ..........................................................................................................9

ABSTRACT ...................................................................................................................................10

CHAPTER

1 INTRODUCTION ..................................................................................................................12

Marketing Analytics ...............................................................................................................12

Problems in Marketing Measures ....................................................................................12

Consequence of Multiple Measures ................................................................................13

Developing New Measures and Evaluating Current Measures ..............................................14

2 TWO NEW INTENT METRICS FOR IMPROVED PREDICTIVE VALIDITY ................16

Intent Metrics ..........................................................................................................................16

Extant Research on Intent Metrics ..........................................................................................20

Literature Review on Intent-Purchase Relationship ...............................................................21

Derivation of New Intent Measures ........................................................................................22

Derivation of ORIM ........................................................................................................22

New Intent Metric for Niche Products ............................................................................26

Empirical Illustration ..............................................................................................................28

Context ............................................................................................................................28

Data Collection ................................................................................................................29

Intent Measures ...............................................................................................................29

Outcome Measures ..........................................................................................................30

Mass Market and Niche Market ......................................................................................30

Analysis ..................................................................................................................................31

Results for Mass Market ..................................................................................................31

Results for Niche Market .................................................................................................33

Robustness Checks .................................................................................................................34

Vary the Number of Points ..............................................................................................34

Change the Estimation Method .......................................................................................35

Discussions .............................................................................................................................35

3 VALIDATION OF BRAND EQUITY MEASURES ............................................................45

Brand Equity Validation .........................................................................................................45

6

Brand Equity and Market Performance ..................................................................................47

The Measurement of Brand Equity .................................................................................47

Current Brand Equity Measure ........................................................................................48

Impact of Marketing Action on Brand Equity .................................................................50

Impact of Brand Equity on Marketing Action .................................................................52

Brand Equity Measures ...........................................................................................................52

Intercept Brand Approach ...............................................................................................54

Premium Approach ..........................................................................................................56

Assessing Brand Equity Measures ..........................................................................................57

Relationship with Market Performance ...........................................................................57

Within and Between Relationship ...................................................................................58

Empirical Analysis ..................................................................................................................58

Data ..................................................................................................................................58

Descriptive Statistics .......................................................................................................59

Endogeneity Issue ............................................................................................................59

Estimation ........................................................................................................................60

Results and Discussion ...........................................................................................................61

Relationship between Intercept Measure and Premium Measure ...................................63

The Stability of the Measure ...........................................................................................63

The Difference of Magnitude among Brands ..................................................................64

Findings and Discussions .......................................................................................................64

4 CONCLUSIONS ....................................................................................................................75

APPENDIX: MOVIE GOING SURVEY ......................................................................................77

LIST OF REFERENCES ...............................................................................................................79

BIOGRAPHICAL SKETCH .........................................................................................................85

7

LIST OF TABLES

Table page

2-1 Traditional and new intent metric ......................................................................................38

2-2 Results for mass-market product for first week revenue (11-point scale) .........................39

2-3 J test results for mass-market movies for 1st week revenue (11-point scale survey) ........40

2-4 Results for niche-market product for 1st week revenue (11-point scale survey) ..............41

2-5 J test results for niche-market product for 1st week revenue (11- point scale scale) ........42

2-6 J test results for mass-market product for first-week revenue (9-point scale survey) ......43

2-7 J test results for niche-market product for first-week revenue (5-point scale survey) .......44

3-1 Selected research on company based brand equity measure .............................................66

3-2 Descriptive Statistics of Data .............................................................................................67

3-3 Rankings of brand equity measures ...................................................................................68

3-4 Brand equity measure ........................................................................................................69

3-5 The rank order the intercept measures ...............................................................................70

3-6 The correlation for intercept measure ................................................................................71

3-7 The correlation between intercept measure and premium measure ...................................72

3-8 Magnitude difference .........................................................................................................73

8

LIST OF FIGURES

Figure page

3-1 Relationship between brand equity measures. ..................................................................74

9

LIST OF ABBREVIATIONS

AORIM Adjusted Ratio Intent Metric

ORIM Odds Ratio Intent Metric

10

Abstract of Dissertation Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Doctor of Philosophy

ESSAYS ON MARKETING ANALYTICS

By

Yuying Shi

May 2015

Chair: Steven M. Shugan

Major: Business Administration

How to measure marketing effectiveness has always been a bottleneck for firms. This

quantification of marketing activity is the center theme of marketing analytics. This dissertation

is aim to better quantify the impact of marketing actions on firms’ performance by developing

new marketing measures and evaluate current marketing measures. In the first essay, I use

customer behavior data to evaluate current intent-to- buy metrics and to develop better metrics to

understand and forecast customer purchasing activities. The collection of customer purchase

intention data has been widely used in marketing practice. How to effectively utilize these data

for a better prediction of customers’ shopping/purchasing activities has always been a perennial

issue in marketing literature. I develop two new metrics that effectively capture customers’

future purchasing pattern. Different from traditional metrics (e.g., top box or mean) lacking

sound theory, the two new metrics are based on economics theory and statistical properties. The

empirical analysis provides evidence that the two new metrics better reflect future outcomes than

the most popular extant intent-to-buy metrics.

The second essay focuses on validating current brand equity measures. The measurement

of brand value has been a popular topic in marketing research. Considerable academic and

practitioner attention has been directed towards developing a better understanding of the

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factors that build brand equity. Accurately measuring brand equity is a critical issue in assessing

approaches for building brand equity. However, there is a lack of research directed towards

understanding, both empirically and theoretically, the conditions under which these different

measures are appropriate. My study is the first comprehensive study in validating the existing

firm based brand equity measures. I validated the representative brand equity measures in two

major categories: the premium approach and the intercept approach. Results demonstrate that

product market based measures are not necessarily correlated with market performance. Product

feature, marketing action and consumer heterogeneity each played a different role in different

equity measures. We found that revenue premium and logit estimates reflect more of the market

share, while price premium and structural estimates concern more on consumer preference.

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CHAPTER 1

INTRODUCTION

Marketing Analytics

It has been realized that marketing is losing its influence within organizations (e.g.,

Webster, Malter, and Ganesan 2003; Nath and Mahajan 2008; Verhoef and Leeflang 2009). As

one CMO put it, “Marketing has lost its seat at the table.” (Webster, Malter, and Ganesan 2003).

Researchers argue that to regain its central role in corporate strategy, marketing has to

demonstrate its contribution to firm value. In this regard, marketers must develop ways to

quantify the contribution of marketing activity to firm value. This quantification of marketing

activity is called marketing analytics. Marketing analytics comprises the processes and

technologies that enable marketers to evaluate the success of their marketing initiatives by

measuring performance (e.g., blogging versus social media versus channel communications)

using important business metrics, such as ROI, marketing attribution and overall marketing

effectiveness. In other words, it tells you how your marketing programs are really performing. In

response to calls for quantification of marketing activity, researchers have conducted various

studies, such as developing new marketing metrics or establishing the link between marketing

activity and firm value. However, the appropriate measure of marketing effectiveness has never

been an easy problem. The academia already knows that many marketing activity is still not well

understood and well measured. MSI has listed the performance measure problem as priority

topics for several years (1998, 2000, 2002, 2004, and 2006).

Problems in Marketing Measures

Despite academia’s efforts, a sad fact is that marketing analytics are not well recognized

by the firm. Take the marketing metrics as an example, Ambler (2003) found that there is no

relationship between incidence of marketing measure used and measures going to the board. The

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common metrics he studied include market share, awareness, loyalty, and customer satisfaction,

all of which are commonly used and investigated metrics in marketing literature. Many problems

exist in quantifying the marketing activity. In this dissertation, we focus on two specific

problems: the credibility problem and the multiple measure problems. The first essay addresses

the credibility problem of intention metric. The second essay addresses the multiple measure

problems. The current commonly used intent metric is at most a rule of thumb. Therefore, in the

first essay, we provide theoretical foundation of the current used intent metric and also

developed new metrics based on item response theory.

As mentioned before, the multiple measures for the same construct is another problem in

marketing analytics. A typical example is the brand equity. Researchers have developed no less

than 30 measures of brand equity. These measures could be classified into three general

categories: customer oriented approach, company based approach and financial approach (e.g.,

Keller and Lehmann 2001, 2006; Sriram, Balachander and Kalwani 2007). Under each category,

there are a variety of measures. Take the company based approach as an example, the most

commonly mentioned measure is price premium (e.g., Aaker 1996; Agarwal and Rao 1996;

Sethuraman 2000; Sethuraman and Cole 1997). Many other measures were also developed, such

as profit premium (Dubin, 1998), quantity premium (e.g., Kamakura and Russell 1993), revenue

premium (Ailawadi, Neslin and Lehmann 2003) and etc. There are also measures developed by

industry, such as Young & Rubicam, Interbrand and Brand Finance. A search in the literature

clearly indicates that researchers use whatever brand equity that suits their needs in their studies.

Consequence of Multiple Measures

Because of the multiple measures for a certain construct, it is not difficult to find conflict

findings in literature. For example, if we investigate the relationship between customer

satisfaction and firms’ business performance (a representative example is firms’ net operating

14

cash-flow), according to Morgan and Reno (2006), average satisfaction scores and Top 2 Box

satisfaction scores have good predictive value. Proportion of customers complaining did not

predict the performance. As we mentioned before, the three are all measures of customer

satisfaction. Clearly we cannot make a general conclusion regarding whether customer

satisfaction is a good predictor of firms’ net operating cash-flow. Researchers did not follow a

standard way of calculating metric also caused confusion. An example is the net promoter

metric. This metric is said to well predict company growth rate (Reichheld 2003). However,

researchers have different opinions on it. Morgan and Reno (2006) found that net promoter has

no predictive value in any of the six firms performance variables. As criticized by Keiningham

et al (2007), due to their incorrect calculation of this metric, their result is not convincing.

Developing New Measures and Evaluating Current Measures

As discussed before, marketing metrics/measures help quantify a firm’s marketing

performance. While developing these metrics/measures has always been an imperative for

academics, many are not valued by the industry. Among many reasons behind this disconnection

between academics and managers, this dissertation study focuses on two issues: the effectiveness

issue and the lack of credibility issue. My two essays addressed the two concerns respectively.

The first essay involves the use of metrics to summarize behavioral intention data and to

predict sales. Although using consumer data to predict sales is well recognized by marketers, and

many researchers have made contributions in improving the prediction accuracy, current

intention metrics are not well understood and are used loosely in application. My study develops

two new metrics that effectively capture customers’ future purchasing pattern. Unlike traditional

metrics (e.g., top box or mean) lacking sound theory, the two new metrics are based on

economics theory and statistical properties. The empirical analysis provides evidence that the

two new metrics better reflect future outcomes than all of the most popular intent-to-buy metrics.

15

The second essay in my dissertation examines the effectiveness of current marketing

measures. Specifically, I used multiple measures of the “brand equity” to address this objective. I

develop criteria in evaluating several product-based brand equity measures using both structure-

model based approach and reduced form approach. This is the first comprehensive study in

validating the existing firm based brand equity measures. This study validated the representative

brand equity measures in two major categories: the premium approach and the intercept

approach. Results demonstrate that product market based measures are not necessarily correlated

with market performance. Product feature, marketing action and consumer heterogeneity each

played a different role in different equity measures. We found that revenue premium and logit

estimates reflect more of the market share, while price premium and structural estimates concern

more on consumer preference.

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CHAPTER 2

TWO NEW INTENT METRICS FOR IMPROVED PREDICTIVE VALIDITY

Intent Metrics

Consumer intention and purchase relationship has a long history in both business theory

and practice (Moll 1943). Some renowned researchers have made significant contribution to the

early intention-behavior literature by demonstrating that consumer intention has predictive value

on their subsequent behavior (e.g., Tobin 1959, Juster 1964). Fishbein and Ajzen (1975, p. 368–

369) concluded that, “If one wants to know whether or not an individual will perform a given

behavior, the simplest and probably the most efficient thing one can do is to ask the individual

whether he intends to perform that behavior.”

Consequently, the collection of intent data becomes a routine practice in marketing

(Jamieson and Bass 1989) and other fields (Salisbury et al. 2001). Even though it has been well

recognized that there is a systematic relationship between purchase intention and purchase

behavior (Juster 1964), researchers also realized the existence of a discrepancy between intention

and behavior. Many factors contribute to this discrepancy. For example, the design of the

intention survey itself may not invoke respondents’ true disclosure of their intention (Hsiao and

Sun 1999; Ding, Grewal and Liechty 2005, Ding 2007, Sun and Morwitz 2010). Or even when

the true intention is invoked, respondents can change their intention overtime due to various

reasons such as the change of financial status or interests (e.g., Morrison’s 1979; Bemmaor 1995;

Howard and Sheth 1969). For a detailed study on the factors and possible gaps, readers can refer

to Morwitz et al. (2007) and Young et al (1998) for reference. Even though researchers have

made many applauding improvements capturing intent purchase relationship, they also realized

that to better improve the predictability using intention measure is never an easy job. As Sun and

Morwitz (2010) pointed out, obstacles remain because it is impossible to account for all the

17

potential discrepancy factors in one study. In summary, using intention measure to predict

consumer purchase behavior has been a perennial issue in marketing research and yet a topic still

awaits further exploration.

This study focused on a rarely investigated area: how to summarize intention data. In a

typical intention survey, researchers ask respondents to indicate their purchase intent using a

discrete odds numbered scale. For example, respondents might be given the option of choosing

among eleven categories (referred to as an 11-point scale) where the first category (1) represents

an extremely low likelihood of purchasing and the eleventh category (11) represents an almost

certain likelihood of purchasing. Researchers have used a number of different metrics to

summarize the distribution of individual intent measures. Researchers have used the mean of the

weighted intent responses (e.g., Warshaw 1980, Miniard, Obermiller and Page 1983), the top box

or top 2 boxes (e.g., Wells 1961; Newberry, Kleinz and Boshoff 2003), and the median of the

responses (e.g., Sewall 1981). These summary intent metrics have gained wide application in

both industry and academia. They can be used for benchmarking, predicting, monitoring and so

on, or be used in various quantitative models as explanatory variables to improve prediction

(e.g., Chintagunta and Lee 2012). However, given the wide application of these summary

metrics, surprisingly little studies have been conducted on metrics themselves. And the

application of the metrics can best be considered loosely. Researchers barely state why they use

one metric over another. At one time, many researchers considered the mean to be the

appropriate metric for summarizing a distribution of individual intent measures (e.g., Warshaw

1980, Sewall 1981). However, considerable industry experience and subsequent empirical

research (e.g., Kalwani and Silk 1982) have found that the top box metric had better predictive

validity than the mean. Top 2 boxes is also a widely used metric. Even though the top box and

18

the top 2 boxes metric are currently the most commonly used metrics in industry, mean metric is

still widely used. Furthermore, it seems that people have no intention to devote their effort in

exploring the possibilities of new metric. Are the current metrics good enough in summarizing

data? Are there better metrics? Under what conditions each metric is most appropriate? Curiosity

towards these questions inspired our interests in exploring further on the metrics themselves. Not

only are intent metrics, as one subcategory of metrics, barely investigated, but metric as a whole

is not an area that is explored enough. As Shugan and Mitra (2010) pointed out: “no prior

research has developed the necessary theory for constructing, selecting and employing metrics”.

We intend to bridge this gap.

Therefore in this study, our purpose is to (1) develop new metrics with sound theory

foundation; (2) illustrates their superior predictive validity over traditional metrics; (3) identify

the conditions for selecting metrics. And we set up the following three criteria for our new

metrics.

The theory foundation should be convincing. The mean metric expresses an overall score

of consumers’ intention. The extreme metric (top box and top 2 boxes) captures the percent of

people who have the highest intention. The variance metric describes the distribution of people’s

intention. No matter whether they are good metric for prediction purpose, at least all these

metrics have certain statistics property that justifies the respective use. Therefore a good metric

is expected to have a sound theory foundation. There are attempts in developing new metrics,

although not in purchase intention area. The famous one is the “Net Promoter” score (NPS),

which we will introduce in detail later. Even though it gains wide application and many

proponents, the most serious attack to it is lack of theory foundation. Therefore provide a

convincing theory is the first criteria we aim to meet especially as academia researchers.

19

A good metric, when theoretically justified, is expected to perform better than metrics

that are not theoretically justified, or justified but only under specific condition. For example,

mean metric is generally considered an adequate metric in many conditions, such as average

customer satisfaction score as a superior metric in predicting future business performance

(Morgan and Rego 2006), but is inferior to extreme metrics under certain situations, which is

referred as the impact of Muth effects (Shugan and Mitra 2009). Therefore when a metric is

theoretically justified to fit in a certain domain (purchase intention, for example), a superior

performance such as prediction should be demonstrated over traditional metrics since these

traditional metrics may not be the best metrics in this specific domain.

A good metric is expected to summarize the information as much as possible but not at

the expense of complexity. Considering nowadays performance measures require constant

monitoring, managers value metrics that are both easy to comprehend and communicate, and to

have a simple and directive predictive relationship with future business outcomes (Morgan and

Rego 2006). We strive to derive a simple metric that is theoretically sound but computationally

easy.

Our new developed metrics meet all the above criteria we set. We analytically derive two

new simple intent metrics (ORIM (odds-ratio intent metric) and AORIM (adjusted odds-ratio

intent metric)), illustrate the superior predictive validity of these new metrics over traditional

metrics. Furthermore, we demonstrate that the two new metrics are appropriate in different

product-markets. ORIM has the best predictive validity for mass-market products and AORIM is

best for niche products.

In the next section, we describe the commonly used traditional intent metrics and review

the prior research. Then we analytically derive a new metric (Ratio metric). Then we show how

20

our ORIM and AORIM derived from this ratio metric. Later, we empirically test the predicative

validity of our new metrics by comparing traditional metrics with our new metrics. We also

present our validation studies. The paper concludes with the implications of our results and

directions for future research.

Extant Research on Intent Metrics

This section describes the most commonly used intent metrics and the corresponding

research on the predictive ability of these metrics.

Table 2-1 describes commonly used intent-to-buy metrics for forecasting new product

sales from typical consumer intent data. The mean and variance metrics are typical well-defined

statistics for summarizing any distribution including the distributions of responses choices found

in intent data. As noted earlier, the top box and top 2 boxes metrics are very popular in industrial

applications.

Recently, some researchers began advocating a relatively new metric as a superior

alternative to the “top box” metrics. This metric, called “Net Promoter Score (NPS),” was

developed in industry and was first introduced to a broader audience by Frederick Reichheld

(Reichheld 2003). This metric uses consumer responses on an 11-point intent scale. The NPS

refers to the intent-to-recommend a product rather than intent-to-buy the product. Respondents

who select the highest two categories are called “promoters” and those who select the lowest 7

categories are called “detractors”. The metric is the difference between the percentage of

promoters and the percentage of detractors in the consumer sample. The range of this metric is -

100 to 100. Proponents of the NPS metric claim it is “the single most reliable indicator of a

company’s ability to grow” (Reichheld 2003), which is considered an important strategic goal.

However, some empirical findings (e.g., Keiningham et al. 2007, Morgan and Rego 2006)

showed that the NPS metric was inferior to some other metrics in terms of predictive validity.

21

Although the NPS is designed for existing products, we adapt its computation on our purchase

intention questions as a possible benchmark.

Literature Review on Intent-Purchase Relationship

Many researchers in both academics and industry use individual stated purchase intention

as a surrogate measure of actual individual purchasing behavior. However, it is well known that

there are often discrepancies between individual stated purchase intentions and actual purchase

behavior (e.g., Juster 1966; Manski 1990; Bemmaor 1995). Researchers have identified many

factors that contribute to the discrepancies: The intention survey measure itself may not invoke

respondents’ true disclosure of their intention (Hsiao and Sun 1999; Sun and Morwitz 2010).

Even if the true intention is invoked, respondents can change their intention overtime due to

various reasons such as the change of financial status or interests (e.g., Morrison’s 1979;

Bemmaor 1995; Howard and Sheth 1969). Considerable research has been directed toward

modeling the difference between the purchase intent and actual behavior. These efforts include

adjusting the change of intention from survey time to actual behavior (e.g., Morrison’s 1979 and

Bemmaor 1995), incorporating individual level consumer heterogeneity (e.g., Sun and Morwitz

2010), incorporating the history of intention data rather than a one-time survey (Chintagunta and

Lee 2012), among others. Shugan and Swait (2000) provide a detailed review from modeling

perspective. Morwitz, Steckel and Gupta (2007) conducted a comprehensive study investigating

various factors that affect the purchase and behavior relationship. Although different studies

have made significant contributions in improving predictive validity, obstacles remain because it

is impossible to account for all the potential discrepancy factors in one study (Sun and Morwitz

2010). Besides the commonly seen modeling approach, another stream of approach focuses on

developing intention response scales (Van Ittersum and Feinberg 2010). Juster (1966) developed

a probability based scale by using subject purchase probabilities to replace the commonly used

22

Likert scale. His study indicates that the probability scale explained about twice the cross

sectional variance as buying intentions. Van Ittersum and Feinberg (2010) proposed a

cumulative timed intent through the integration of time into intention measurement.

In summary, to capture purchase intention and actual behavior is never an easy job and

studies on intention –purchase behavior relationship never stops.

Derivation of New Intent Measures

In the section, we derive two new purchase intent metrics -- the odds-ratio intent metric

(ORIM) for mass-market products and adjusted odds-ratio intent metric (AORIM) for niche

products.

Derivation of ORIM

Intent data are collected by asking consumers to choose an intent category ranging from

very likely to buy to very unlikely to buy. Each category or box receives some percentage of the

responses. Let pi denotes that a randomly chosen individual chooses the ith

box i=1, 2, where i=1

denotes the “most likely to buy” category or top box (i.e., definitely intend to buy). Now,

consider a consumer who is determining the intent to purchase a target product over using the

money for an outside option (i.e., an alternative product or other opportunity). We would expect

that the probability of choosing the top box (i.e.,i=1) increases as the utility of the product

increases. We would also expect that the probability of choosing the top box decreases as the

utility of the outside option increases. This relationship is captured by the well-known Logit

model, given in Equation 2-1. It provides a formal relationship between these two utilities

(which vary randomly across the population) and the probability of a random respondent

choosing the top box.

w

ep

e e

, (2-1)

23

where:

p = the probability of choosing the top box;

ν = the utility of the target product;

w = the utility of the outside option;

e = the natural logarithm base.

The probabilities for the other boxes have somewhat more complicated relationships with

the latent consumer utilities. However, consistent with past empirical findings, we initially

assume that the top box contains all the relevant information because, given that probabilities

must add to one, the choice probabilities for the other boxes are necessarily correlated with the

top box. Later, we relax that assumption.

As noted earlier, Equation 2-1 represents a well-known Logit model for the probability of

choosing an option with utility v given outside option w. As Daniel L. McFadden notes in his

December 8th 2000 Nobel Prize Lecture in Economics, if the utility of different options w and v

from a choice set following an extreme value (Gumbel) distribution, Equation 2-1 represents the

probability of choosing the option with utility . In our case, the boxes in the intent scale are the

options and the top box is a surrogate measure for choosing the target product.

Note that the utilities in the Logit model are random utilities. Economists believe the

utilities are random at the aggregate level because individual consumer utilities differ (i.e., are

heterogeneous), but they could also be random because of either measure error or state

dependency. Like McFadden (2001), we do not directly measure consumer utilities (i.e., they

are latent) but infer those utilities from observed responses on the questionnaire.

Given that we are interested in predicting aggregate sales rather than individual choices,

we need to model aggregate sales or demand. We model aggregate demand as a function of a

24

latent variable that we interpret as the product’s latent quality. To be precise, we adopt the linear

demand in Equation 2-2 where aggregate demand or sales S is a function of latent quality Q. We

will find the reduced form of our system of Equations that will only contain observed variables.

0 1 * ,S Q (2-2)

where:

S=revenue of product;

Q=latent quality of product;

λ0, λ1= unknown parameters.

Equation 2-2 models the sales of a product as a linear function its quality. We expect a

positive correlation between sales and quality so we hypothesize that λ1 > 0 . Of course, our

empirical analysis will reveal whether the linear shape approximates the data and whether the

coefficients take the hypothesized signs. Note that, quality is usually defined as some composite

measure of the offering’s desirable characteristics. It is conceptually possible to measure a

product’s quality (e.g., the durability of an automobile or the number of funny jokes on a DVD)

as some function of observable characteristics. However, it is unnecessary to measure directly

the offering’s quality because the latent consumer utility in Equation 2-1 and the latent product

quality in Equation 2-2 should be functions of many of the same characteristics. Hence, we

follow the ubiquitous multi-attribute approach in marketing as well as Lancaster’s seminal article

(1966), to link the individual choices made in the Logit model (i.e., Equation 2-1) with our

aggregate demand model (i.e., Equation 2- 2). We assume that that the consumer’s incremental

utility from choosing an option is a function of the offering’s quality. Hence, we assume that the

utility function is increasing in quality but that the utility function also obeys the law of

diminishing marginal utility. For example, if the quality is the expected durability of a product,

25

the consumer’s utility has the property that the consumer’s utility increases with increases in

durability, but those increases become smaller as the durability increases. We quantify this

property with the logarithmic function which is a flexible function displaying the appropriate

shape (i.e., derivatives).

w log Q (2-3)

Equation 2-3 allows us to solve Equations 2-1 and Equation 2-2 to find the reduced form

of the structural Equation system. Solving Equation 2-1 for ν-w yields Equation 2-4.

log1

p

p

(2-4)

Note that the ratio 1

p

p in Equation 2-4 is known as the odds-ratio. The odds ratio is a

commonly used metric that measures the effect size or strength of association between two

binary data values. In this case, the two binary choices are choosing the top box and not

choosing the top box. Next, we substitute Equation 2-3 and Equation 2-4 into Equation 2-1 to

obtain Equation 2-5.

0 1 *1

p

pS

. (2-5)

We see that the sales of a product, in theory, should be a linear function of 1

p

p which

we call the odds ratio intent metric (ORIM). Empirical analysis will reveal whether the

assumptions underlying this derivation are adequate. However, unlike many other metrics (such

as top box, and top 2 boxes, and Net Promoter), the assumptions underlying our derivation are

transparent. Moreover, our new odds-ratio metric has the following desirable properties:

26

1. The odds-ratio metric is positive. This is desirable because sales, profits and other

accounting outcomes are constrained to be positive. Note that some alternative metrics,

e.g., Net Promoter, can be negative.

2. The odds-ratio metric is unbounded. This is desirable because sales, profits and other

accounting outcomes are unbounded and when estimating Equation 2-5, only an

unbounded metric is consistent with the usual assumptions about the error term. Note

that, many competing metrics (such as top box, top 2 boxes and Net Promoter) are

bounded above and below.

3. The odds-ratio metric increases exponentially as the probability of choice increases.

Sales, profits and other accounting outcomes must also increase exponentially as the

probability of choice increases because purchase probabilities are asymptotic to 1 while

accounting outcomes, as previously noted, are unbounded. The odds ratio intent metric (

ORIM ) reflects a non-linear relationship. That non-linear relationship is embedded in

many previously successful empirical studies involving prediction.

New Intent Metric for Niche Products

The derivation in the preceding section provides a metric for predicting outcomes.

However, the previous section assumed that the percent of respondents that chose the top box

category is sufficiently correlated with actual outcomes so that the other categories on the scale

provide relatively little information. That assumption might be more reasonable for mass-market

products than niche products. The reason is that it is easy to obtain a random sample of

respondents when the target market for a product is the entire market. In contrast, the target

market for niche products is often relatively small and, despite selective sampling efforts, the

sample may still contain many respondents who are not in the target market and who would

never buy the product. For example, Kosher Jews would never buy pork products and Mormons

might never buy products containing alcohol. Hence, despite careful sampling, latent respondent

characteristics make it very difficult to sample only respondents in the target market.

Consequently, these respondents, who are not in the target market, will have no intent to

purchase the product regardless of the product’s latent quality (Q) violating the assumptions

underlying Equation 2-3.

27

In these cases, Equation 2-5 might not be accurate for those respondents. These

respondents, who do not belong to the target market, are likely to have very low (or bottom box)

purchase intentions which to do not accurately reflect Q. Hence, we develop a different metric

for niche products. We refer to this new metric as the adjusted odds ratio intent metric

(AORIM). Recall that the formula for the ORIM metric is ORIM =1

p

p. Analogously, we

compute the AORIM metric with the formula AORIM= 1

p

p where p is the adjusted top box.

We define the adjusted top box p by making the assumption that, for niche products, respondents

choosing the bottom two boxes are not in the target market. We could only exclude the bottom

box. However, it is well known that respondents may not respond truthfully. This phenomenon

is called Socially Desirable Tendencies (SDR) in survey research (e.g., Steenkamp et al.2009;

Tellis and Chandrasekaran 2010). Therefore, we know some respondents might avoid the bottom

box when the respondent would never buy the product (for personal reasons) but the respondent

might find that the product is completely acceptable for others.

Hence, we define the adjusted top box p as the number of people who choose the highest

scale category divided by the number of people who choose all but the lowest two scale

categories. The adjusted top box p excludes people from the lowest-two categories. Hence, the

AORIM uses a similar formula to the ORIM metric, but the AORIM metric uses the adjusted top

box when calculating the metric. The AORIM metric shares the same desirable properties as

ORIM but has an important additional property. The AORIM metric helps remove sample

selection bias for niche products.

Finally, note that by excluding the bottom boxes, we could also eliminate respondents

who are in the target market and who merely dislike the product being evaluated and have no

28

intent of purchasing the product. However, we expect that the top box responses already capture

that information given our analysis of ORIM for mass-market products. For example, it is likely

that the percent of top box responses is negatively correlated with the bottom box.

Consequently, we assume that there is little loss in this type of information in the AORIM

metric. Of course, only an empirical analysis can reveal the predictive validity of the AORIM

metric. The new intent measures are summarized in Table 2-1.

Empirical Illustration

Having provided the theoretical support for ORIM and AORIM, in this section, we

illustrate the superior predictive validity of these new metrics compared to traditional metrics.

The context for this empirical illustration is predicting the sales of movies based on intent data

collected from a sample of consumers prior to the launch of the movies.

Context

The movie industry offers a useful context for demonstrating the predictive validity of

intent metrics for predicting aggregate behavior. First, the industry launches a large number of

new products (movies) each month providing the statistical power to detect differences in the

predictive validity of the various intent metrics. Second, information about the new products

(movie trailers) is available prior to the product launch. This information can be presented to

consumers enabling them to form purchase intentions for the new products before they are

launched. Third, the product life cycles of movies are short and sales data are publically

available facilitating the collection of reliable aggregate sales data for many new products in a

relatively short time period. Fourth, college students, who represent an easily available

population of subjects for collecting intent measures prior to the new product launch, are a large

market for the product. Fifth, each moviegoer typically sees a movie only once. This property

is consistent with our derivation that gives equal weight to each respondent. Of course, it is

29

possible that intent metrics can still predict well when respondents buy different quantities of the

product or service.

Data Collection

The data for this illustration of predictive validly were all PG, PG13 or R rated movies

scheduled for wide release in the U.S between June 1, 2012 and June 31, 2013 during each week

that the University was in session. We excluded X and NR movies, foreign movies, and limited

distribution movies because these movies are typically not available for our sample of movies

goers.

Approximately 1,500 college students in a large Southeast University received extra

credit for participating in our study. At the beginning of each month, we collected 10 to 16

trailers for the upcoming new movie. Then we show these trailers to college students in a

behavioral lab before the movies were launched. Each month, we collected students’ purchase

intentions on upcoming movies. This procedure resulted in a data set of between 30 and 191

observations for 132 movies.

Intent Measures

After viewing each trailer, participants answered two questions: (1) How likely are you

going to see the movie? (2) How likely are you going to recommend this movie to your friends?

These intent measures used an eleven-point scale with the anchor points zero indicating very

unlikely and 11 very likely. Except for Net Promoter, we computed all the metrics using

responses to the question: “How likely are you going to see the movie when it is released”.

Consistent with the existing literature, we calculated the Net Promoter metric from the

responses to the question: “How likely are you going to recommend this movie to your friends?”

To minimize order effects, we randomized the order of the trailers and the order of the questions.

We also collected other information at the end of the session to check for other biases. For

30

example, we asked respondents whether they had already seen the trailer. Subsequent analysis

revealed that the responses were similar for subjects who had previously seen the trailer and

those who had not.

Outcome Measures

To assess the predictive validity of the traditional and new intent metrics, we used the

first week revenue as our outcome measure. First week revenue is the sales from the first Friday

through the following Thursday. It is one of the mostly frequently used motion picture industry

standards for measuring the initial success of a movie and an important signal of performance

(boxofficemojo.com). The measure reflects the fact that distributors release most movies on a

Friday. The first week revenue reflects outcomes related to the intent-to-buy metrics. That

revenue is not contaminated by adjustments to marketing efforts taken after the movie’s launch

(e.g., changes in advertising budgets or the number of screens).

Mass Market and Niche Market

As previously indicated, we propose different metrics for mass-market products and

niche market products. For this analysis, we define mass-market products as films, with an

Motion Picture Association of America (MPAA) rating of PG or PG-13 (abbreviated as PG

movies from below), and niche products as films with an MPAA rating of R. The classification

of PG and R movie into mass market and niche market is consistent with the way MPAA rates

movies. R-rated movies usually target at a smaller well-defined market, which is the key

characteristic of a niche product. PG movies target at a larger mass market that is the

characteristics of a mass product. Furthermore, it is well known that most PG- movies and R-

movies have the outcome characteristics of niche and mass market products. For example,

according to Medved (1992), the typical “PG” film generates nearly three times the revenue of

the typical “R” blood- bath or shocker. A data analysis by De Vany and Walls (2002) that also

31

controlled for the movie budget, confirmed Medved’s claim. Moreover, many published studies

differentiate between these two types of movies when investigating their reactions to different

marketing activities (e.g., Joshi and Hassens 2010; Moon, Bergey, and Iacobucci 2010).

Analysis

We estimated Equation 2-6 for each of the seven metrics (two new metrics plus five

traditional metrics) to determine each metric’s ability to predict the 1st week movie (new

product) revenues. For each estimation, we applied both the Smirnov-Kolmogorov test for non-

normally distributed residuals and the Breusch-Pagan / Cook-Weisberg test for

heteroscedasticity. When the estimation failed either test, we applied regression analysis with

robust error to account for non-normal residuals and heteroscedasticity. Equation 2-6

summarizes.

1 *j ok k jk jkS , (2-6)

where

Sj = 1st week revenue for movie j;

φjk= the value of intent metric k for movie j;

βok, β1k = unknown parameters for metric k;

ɛjk = error term for movie j metric k regression.

Results for Mass Market

Table 2-2 provides the regression results for the mass-market product (P and PG rated

movies). Except for the variance metric, all the metrics are significantly correlated to the first

week revenue. When predicting first week revenue, ORIM, AORIM, top box and top 2 boxes

alone each can explain 42% to 50% of variance in sales. Net Promoter and mean each explains

32.5% and 26.2% of data variance respectively. The variance is the worst predictor. It only

32

explains less than 3% of total variance. ORIM has the best (largest) R-square and F-statistic.

The next best metrics, in order of explained variance, are AORIM, top box, top 2 boxes, Net

Promoter and mean.

Since the predictive models for each metric are non-nested, we use a j-test (Davidson and

MacKinnon1981) to compare the predictive ability of our metrics and traditional metrics. The J-

test (for details please refer to Wooldridge, 2003) allows pairwise model comparisons. It

produces either conclusive results (i.e., one metric predicts better than the other does) or

inconclusive results (i.e., neither metric predicts better). In our analysis, all the inconclusive

results occurred when neither metric could significantly improve the predictions of the other

metric.

Table 2-3 provides the results of the J test for the 1st week revenue for mass-market

movies. Table 2-3 provides three possible J-test outcomes for each pairwise metric comparison:

(1) the row metric is superior to the column metric, (2) the row metric is inferior to the column

metric, or (3) the two metrics are tied. For example, the ORIM metric (row O) predicts better

than the Net Promoter metric (column N) because the ORIM metric’s incremental improvement

is significant with a t-stat of 4.16 (p < .001). The top box metric (row T1) predicts worse than

the ORIM metric (column O) because the T1 metric does not predict significantly better (t = -

0.53, p > .05) than the ORIM metric. The latter predicts significantly better (t = 2.22, p < .05,

see column T1). The top box metric (row T1) ties with the AORIM metric (column A) because

the T1 metric is not significantly better (t=.78, p > .05) and the AORIM metric is not

significantly better (t =1.14, p > 0.05, see column T1). Ties imply an inconclusive J-test when

neither metric is superior.

33

In sum, Table 2-3 reveals that ORIM is a significantly better predictor than every other

metric in the table. The AORIM metric is better than every metric in the table except two. It is

worse than the ORIM and ties with the top box metric. Hence, for the mass market, the ORIM

metric is best for predicting 1st week revenue.

The results in Table 2-3 are important for at least three reasons. First, many consider top

box the best predictor of future and distant future sales (e.g., Kalwani and Silk 1982; Jamieson

and Bass 1989). Table 2-3 reveals that, for our data, top box is the best traditional metric but our

new ORIM metric is better. Second, although Net Promoter is also popular, Table 2-3 reveals

that the top box metric, as well as our new metrics, beat Net Promoter. Third, Table 2-3 indicates

that mean is not a good predictor, even at the aggregate level. This finding is consistent with

literature (e.g., Morrison 1979; Shugan and Swait 1997). As Bemmaor (1995) argued,

aggregation often does not resolve individual level discrepancies.

Results for Niche Market

Table 2-4 provides results for the niche market movies. Each metric provides statistically

significant predictions of the 1st week revenue. ORIM, AORIM, top box and top 2 boxes metrics

can each alone explain more than 50% of total variance in revenue, while Net Promoter explains

around 30%. The mean and variance each explain around 20% of data variance. AORIM has

the largest R square and F statistics, followed by ORIM, top 2 boxes and top box. The mean

metric, Net Promoter and the variance metric have the lowest R square and F statistics.

The J-test comparisons in Table 2-5 indicate that AORIM is the best metric for the niche

market. Although top box is the best traditional metric, our new AORIM metric is superior to

top box. Similar to mass-market movies, Net Promoter is inferior to top box, top 2 boxes, ORIM

and the AORIM metrics. The mean continues to yield poor predictions.

34

In sum, our analysis provides evidence that the ORIM metric is a good (best) predictor

for mass-market products and the AORIM metric is a good (best) predictor for niche-market

products. Top box is the next best. Net Promoter and mean are not good predictive metrics.

Robustness Checks

Vary the Number of Points

We explored the robustness of our conclusions by varying many different factors. For

example, instead of using an 11-point scale, we collected data with both 5-point and 9-point

scales by using the same survey questions and the same survey procedure used in this research

study. The 5-point scale is the minimum number scale recommended by Lehmann (1972). The 9-

point scale analysis employed PG movies released between 1996 and 1998. The 5-point scale

data includes 57 movies launched from November of 2012 to the end of March in 2013. The

nine-point scale survey used 28 movies released from 1996 to 1998. These three different data

sets allow us to check the robustness of our findings across different movies and different point

scales. Results indicated that our two new metrics are still among the best metrics for the two

questions regardless of whether we use a 9-point scale survey or 5- point scale survey. For

example, Table 2-6 reveals mass-market results using the 9-point scale and Table 2-7 reveals

niche market results using a 5-point scale.

Tables 2-6 and Table 2-7 reveal that in the mass market, ORIM is always among the best

predictive metrics (never worse). In the niche market, AORIM is always the best predictive

metrics (never worse).

Excluding our new metrics, either top box or top 2 boxes are the best metrics in both

markets. Net Promoter is inferior to both our new metrics and the traditional metrics (i.e., top

box and top 2 boxes). The mean metric is generally inferior to other metrics including Net

Promoter. Our robustness findings are consistent with our results for the first week revenue

35

using 11 point-scale. Therefore, we can safely conclude that our results are robust to different

survey scale points. ORIM is the best metric for mass product and AORIM is the best metric for

niche product.

Change the Estimation Method

To check the robustness of our results to the estimation method, we used robust

regression that controls for outliers that might influence our results. We employ the widely used

M-estimators in our regression (Huber 1973). This method is commonly employed in the

marketing literature (e.g., Morgan and Rego 2006). Robust regression usually produces more

conservative test results due to larger standard errors relative to those obtained by OLS (Kennedy

2003).To compare models with different metrics, we employ several fit statistics: adjusted R

square, AICR, BICR, (Ronchetti 1985), and deviance as reference. The analysis indicates that

the ORIM or the AORIM metrics are usually superior or tied with other metrics when

considering all fit statistics. Therefore, our results seem robust to the estimation method.

Discussions

Intent data are extremely useful for many purposes including comparing advertising

copies, different new product designs and the effectiveness of different marketing treatments in

an experiment. Intent metrics summarize the information in intent data.

Traditional intent metrics are objective and computation is straightforward. Some

traditional metrics (e.g. top box, top 2 boxes, Net Promoter) appear correlated with future

outcomes but the magnitude of that correlation seems controversial. Moreover, few traditional

metrics have a rigorous theoretical foundation. They also often exhibit inconsistent scaling.

A good intent metric should have at least three properties. First, like any good metric

(e.g., financial metrics), computation should be objective, replicable and employ a

straightforward formula, making computations unambiguous and not easy to manipulate.

36

Second, the metric should be forward looking (highly correlated with future outcomes). Third,

the metric should have some theoretical underpinning or conceptual foundation. A theoretical

underpinning is useful for judging when different approaches are applicable.

We develop and propose new intent metrics that excel on all three properties. First,

computation of our new metrics is unambiguous and straightforward. Second, we show

empirically that our new metrics are as correlated or exhibit higher correlations with future

outcomes than all traditional intent metrics. Finally, we base our new metrics on explicit and

popular assumptions from economic theory and choice models. The theory underlying our

metrics suggests that our metrics should out-perform traditional metrics.

Top box and Net Promoter have proved valuable in both academic and industrial

applications. We expect our new metric will add still more value. Moreover, unlike traditional

metrics that attempt to apply to all products, we designed our two new metrics for two different

types of products (i.e., the mass product and the niche product).

The contribution of this study goes beyond the development of two new metrics. It has

been realized that marketing is losing its influence within organizations (e.g., Webster, Malter,

and Ganesan 2003; Nath and Mahajan 2008; Verhoef and Leeflang 2009). Researchers argue that

to regain its central role in corporate strategy, marketing has to demonstrate its contribution to

firm value. In this regard, marketers must develop ways to quantify the contribution of marketing

activity to firm value. In response to these calls, researchers have conducted various studies,

including the use and development of new marketing metrics, for establishing the link between

marketing activities and firm value. However, despite these efforts, marketing metrics are not

prominent within organizations (e.g., Ambler 2003). Many reasons contribute to this lack of

recognition, such as issues relating to domain, lack of credibility, and problems involving

37

multiple measures. Marketing Science Institute (MSI) has listed these and related issues as

priority topics for several years (1998, 2000, 2002, 2004, 2006). Some academics and industry

professionals have established Marketing Accountability Standards Board (MASB) to

specifically focus on increasing the contribution of the marketing function through the

development of metrics and other standards for measurement. This paper hopes to contribute to

these initiatives.

Our study tested one specific customer attitudinal feedback - the purchase intention for

new product. Whether this result can be generalized to existing products awaits future research.

Our robustness checks found that our new metric is better (or no worse) than all traditional

metrics. However, our analysis only considered new products. It is possible that there are still

better metrics for existing products. For example, Morgan and Rego (2006) found that the best

metrics to predict a firms’ future performance is mean score of customer satisfaction for existing

products. Furthermore, since our study is in the marketing domain, we focus on consumers’

purchase intention. It might be interesting to consider other domains such as litigation (e.g.,

intention to purchase given different market condition) and public policy (intention to use public

services).

38

Table 2-1. Traditional and new intent metric

Traditional Metric New Metric

Description Description

Mean The average score across

respondents. For example,

after assigning points to each

intent-to-buy category (e.g.,

“very likely to buy”), the

means is the average of those

points across respondents.

ORIM

(Odds Ratio Intent

Metric)

The top box metric

divided by one minus

the top box metric.

Top box

The percentage of respondents

who select the highest

category. For example, for an

11- point scale survey, it is the

number of respondents who

select category 11 divided by

the total sample size

AORIM

(Adjusted Odds

Ratio Intent Metric)

The adjusted odds

ratio is the odds ratio

excluding respondents

choosing the bottom

two intent categories

(i.e., no intent to buy).

Top 2 boxes

Similar to top box, it refers to

the percentage of people who

select the two highest intent-

to-buy categories. For an 11

point intent-to-buy scale, it is

the number of people who

select category 11 and 10

divided by the total sample

size. The range of top box and

top two boxes if 0 to 100

percent.

Net Promoter Net Promoter is calculated

using an 11-point scale and

subtracting the percent of

respondents who select the

highest two categories (11and

10) from the percent selecting

the lowest 7 categories (1 thru

7). The range of this metric is

-100 to 100.

Variance

This metric is the sample

variance. It captures the

distribution of the response

data.

39

Table 2-2. Results for mass-market product for first week revenue (11-point scale)

Metric Coefficient SE t- statistic R2

F-statistic N

ORIM 1.00E+08 1.33E+07 7.68*** .49 59.06 68

AORIM 8.60E+07 1.14E+07 7.55*** .46 57.01 68

Top box 1.60E+06 275312.70 5.77*** .46 33.34 68

Top 2 boxes 1.20E+06 244999.80 4.70*** .42 22.09 68

Mean 7.70E+06 2227871.00 3.45*** .26 11.91 68

Net Promoter 4.60E+05 118525.60 3.89*** .33 15.10 68

Variance 1.80E+06 1208846.00 1.48 .03 2.19 68

*p<0.05; ** p<0.01; *** p<0.001

40

Table 2-3. J test results for mass-market movies for 1st week revenue (11-point scale survey)

O A T1 T2 N M V

ORIM - O wins O wins O wins O wins O wins O wins

(O) 2.05* 2.22* 3.00** 4.16*** 5.46*** 7.15***

AORIM A loses - Tie A wins A wins A wins A wins

(A) -.56 1.14 2.32* 3.76*** 5.01*** 6.62***

Top box T1 loses Tie - T1 wins T1 wins T1 wins T1 wins

(T1) -.53 .78 2.01* 3.45** 5.20*** 5.07***

Top 2 T2 loses T2 loses T2 loses - T2 wins T2 wins T2 wins

(T2) -.32 .81 -.24 2.73** 4.73*** 4.14***

Net

Promoter N loses N loses N loses N loses - N wins N wins

(N) -.15 1.13 .18 .16 2.13* 3.37**

Mean M loses M loses M loses M loses M loses - M wins

(M) -.35 -.75 -.72 -1.53 -.14 3.15**

Variance V loses V loses V loses V loses V loses V loses -

(V) -.64 -.19 -.06 .90 .150 1.01

*p<0.05; **p<0.01; ***P<0.001

Notes: Table cells tests the null hypothesis that the coefficient of the predicted value of row

metric is zero in the model with the column metric (t-statistic in parentheses for that test).

Significant t test indicates that the null is rejected. Wins denotes significantly better row metric,

Loses denotes other metric wins, and Tie denotes inconclusive test. The conclusion is based on J

test results of two t statistics that are presented in symmetric cells.

41

Table 2-4. Results for niche-market product for 1st week revenue (11-point scale survey)

*p<0.05; **p<0.01; ***P<0.001

Metric Coefficient SE t- statistic R2 F-statistic N

ORIM 6.60E+07 7.63E+06 8.71*** .57 75.80 60

AORIM 5.60E+07 6.01E+06 9.30*** .60 86.52 60

Top box 9.10E+05 137171.80 6.61*** .56 43.66 60

Top 2 boxes 6.50E+05 91242.88 7.14*** .56 51.01 60

Mean 3.20E+06 923222.10 3.50** .23 12.25 60

Net rom. 2.04E+05*** 46285.45 4.41*** .33 19.45 60

Variance 1.7e+06*** 436864.60 3.90*** .19 15.21 60

42

Table 2-5. J test results for niche-market product for 1st week revenue (11- point scale scale)

*p<0.05; **p<0.01; ***P<0.001

Notes: Table cells tests the null hypothesis that the coefficient of the predicted value of row

metric is zero in the model with the column metric (t-statistic in parentheses for that test).

Significant t test indicates that the null is rejected. Wins denotes significantly better row metric,

Loses denotes other metric wins, and Tie denotes inconclusive test. The conclusion is based on J

test results of two t statistics that are presented in symmetric cells.

O A T1 T2 N M V

ORIM - O loses Tie Tie O wins O wins O wins

(O)

.40 1.13 1.71 5.55*** 6.66*** 7.17***

AORIM A wins - A wins A wins A wins A wins A wins

(A) 2.18*

2.44* 2.89** 6.18*** 7.36*** 7.65***

Top box Tie T1 loses

Tie T1 wins T1 wins T1 wins

(T1) -.04 -.10 - 1.31 5.39*** 6.51*** 6.87***

Top 2 boxes Tie T2 loses Tie

T2 wins T2 wins T2 wins

(T2) 1.27 1.47 1.31 - 5.77*** 6.82*** 6.92***

Net Promoter N loses N loses N loses N loses

N wins N wins

(N) -.41 .62 -.56 -1.76 - 3.04** 4.11***

Mean M loses M loses M loses M loses M loses

Tie

(M) -.45 1.03 -.57 -1.65 -.61 - 3.50**

Variance V loses V loses V loses V loses V loses Tie

(V) 1.07 -.60 .43 .74 1.97* 2.98** -

43

Table 2-6. J test results for mass-market product for first-week revenue (9-point scale survey)

O A T1 T2 M V

ORIM

Tie Tie Tie O wins O wins

(O) - 0.87 1.55 3.11** 3.3** 2.76*

AORIM Tie

Tie Tie A wins A wins

(A) -0.19 - -0.34 -0.92 3.61** 2.83**

Top box Tie Tie

Tie T1 wins T1 wins

(T1) -1.35 -0.31 - 2.89* 3.34** 2.61*

Top 2

boxes Tie Tie Tie

Tie T2 wins

(T2) -2.20* -1.22 -1.62 - 2.72** 2.01*

Mean M loses M loses M loses Tie

Tie

(M) -1.96 -0.81 -1.74 -2.2 - 1.58

Variance V loses V loses V loses V loses Tie

(V) 0.58 1.1 0.69 0.26 0.1 -

*p<0.05; ** p<0.01; ***P<0.001

Notes: Table cells tests the null hypothesis that the coefficient of the predicted value of row

metric is zero in the model with the column metric (t-statistic in parentheses for that test).

Significant t test indicates that the null is rejected. Wins denotes significantly better row metric,

Loses denotes other metric wins, and Tie denotes inconclusive test. The conclusion is based on J

test results of two t statistics that is presents in symmetric cells.

44

Table 2-7. J test results for niche-market product for first-week revenue (5-point scale survey)

O A T1 T2 M V

ORIM

Tie Tie O wins O wins O wins

(O) - 1.06 1.83 6.79*** 7.36*** 12.18***

AORIM Tie

Tie A wins A wins A wins

(A) 0.87 - 1.32 5.20*** 6.88*** 11.94***

Top box Tie Tie

T1 wins Tie Tie

(T1) 0.76 1.02 - 4.64*** 7.04*** 7.72***

Top 2

boxes T2 loses T2 loses T2 loses

Tie T2 wins

(T2) 0.65 1.47 -0.41 - 3.95*** 3.31**

Mean M loses M loses M loses Tie

Tie

(M) -0.44 0.89 -0.53 -3.17** - 2.74***

Variance V loses V loses V loses V loses Tie (V) 1.04 -0.74 0.27 1.88 2.45*** -

*p<0.05; **p<0.01; ***P<0.001

Notes: Table cells tests the null hypothesis that the coefficient of the predicted value of row

metric is zero in the model with the column metric (t-statistic in parentheses for that test).

Significant t test indicates that the null is rejected. Wins denotes significantly better row metric,

Loses denotes other metric wins, and Tie denotes inconclusive test. The conclusion is based on J

test results of two t statistics that is presents in symmetric cells.

45

CHAPTER 3

VALIDATION OF BRAND EQUITY MEASURES

Brand Equity Validation

Brand equity is a widely discussed concept in marketing literature. Although the term

was prevalent only in the early 1980s by US advertising practitioners, the importance of brand

has been emphasized both by academia and by industry. In academia, extensive studies have

been conducted on the formation, measurement, management and extension of the brand equity

(e.g., Aaker 1991; Ailawadi, Neslin and Lehmann 2003; Keller 1993; Keller and Lehmann 2006;

van Osselaer and Alba 2003). In industry more and more companies realized that brand equity is

one of the most intangible assets and perhaps the only most important asset that the market

contributes to a company (Goldfarb, Lu, Moorthy 2009). Its importance is especially reflected in

many company merger and acquisitions.

In recognizing the importance of brand equity, developing the appropriate brand equity

measure has been regarded as a priority Marketing Science Institute (MSI) topic for a long time.

Correspondingly, researchers have devoted many efforts in developing new brand equity

measures. MSI has recommended interested researchers to allocate efforts to build a dataset in

validating existing and new measures of brand equity. Despite this suggestion, surprisingly very

few empirical studies have been conducted at this direction. Several difficulties contribute to

this type of validation study: 1. Current brand equity measures differ in many perspectives.

Some measures are constructed from consumer perspective, some are based on products’ market

performance, and still others consider brand equity as financial assets (Keller and Lehmann

2006). Therefore different types of data sets are required to compute these measures. For

example, equity measures based on consumer perspective often require consumer level survey

data while financial perspective based measures requires the financial measures such as

46

discounted cash flow valuation of brand equity components. 2. The comparison criteria are not

easy to be defined. Even though MSI has listed 10 desiderata for the ideal measure (details see

following section), it is generally accepted that no single measure can satisfy all criteria. 3.

Given existence of many different brand equity measures, it is impossible to include all brand

equity measures.

In this study, we overcome the above difficulties by the following strategies. First, our

study focused on product based brand equity measures. Utilizing a national grocery data with all

brand market performance, we are able to compute several brand equity measures using the same

dataset. Second, we validate these measures based on their correlation with market performance,

their intra and inter correlations, its stability over time and its sensitivity to marketing mix and

product components. Third, regarding whether marketing mix should be included in the equity

measure, and whether consumer subjective valuation of brand equity should be included in the

estimates, researchers have mixed arguments (Ailawadi et al. 2003). Therefore we progressively

estimate brand equity measures that differ in their inclusion of marketing mix, brand component,

product characteristics and consumer heterogeneity. We made the selection based on equity

measures proposed and validated in top marketing journals. Our study provides readers with a

better understanding of the impact of these components on brand equity measure.

We found that even though these equity measures are product performance based, they

are not necessarily correlated with the product performance. The marketing activity, consumer

heterogeneity, and consumer perceived attribute each plays an important role in the equity

estimates. Each of them will make the calculated equity measure different from product

performance.

47

The structure of this article is as follows. We first give a review of the existing brand

equity measure and the motivation of managers using brand equity measure. Then we introduce

the selected brand equity measures. Subsequently, we present the empirical results and validate

these measures. We conclude with managerial implications.

Brand Equity and Market Performance

The Measurement of Brand Equity

Regarding the role and measurement of brand equity, despite the varied definition of

brand equity (find literature) and the measurement of the brand equity, there is a general

agreement on the importance of measuring brand equity. According to MSI (1999) workshop,

the purpose of measuring brand equity is: (1) to guide marketing strategy and tactical decisions,

(2) to assess the extendibility of a brand, (3) to evaluate the effectiveness of marketing decisions,

(4) to track the brand’s health compared with that of competitors and over time, and (5) to assign

a financial value to the brand in balance sheets and financial transactions. And the workshop also

listed 10 desiderata for the ideal measure,

1. grounded in theory;

2. complete, i.e., encompassing all the facets of brand equity, yet distinct from other

concepts;

3. diagnostic, i.e., able to flag downturns or improvements in the brand’s value and provide

insights into the reasons for the change;

4. able to capture future potential in terms of future revenue stream and brand extendibility;

5. objective, so that different people computing the measure would obtain the same value;

6. based on readily available data, so that it can be monitored on a regular basis for multiple

brands in multiple product categories;

7. a single number to enable easy tracking and communication;

8. intuitive and credible to senior management;

48

9. robust, reliable, and stable over time, yet able to reflect real changes in brand health.

Validated against other equity measures and constructs that are theoretically associated

with brand equity.

However, attendants generally agreed that it is unlikely to have one single measure to

meet all these criteria. But these measures should be used as an evaluation guideline to develop

new measures.

Current Brand Equity Measure

Based on the categorization of Keller and Lehmann (2001), current brand equity

measures could be classified into three categories: the consumer based measure, the company

based measure (also called the product market based measure) and the financial measure. For

detailed review including the advantage and disadvantage of each approach, please refer to

Keller and Lehmann (200) and Ailawadi, Lehmann and Neslin (2003). Here we only review the

product market based brand equity measure. Table 3-1 presented a list of these brand equity

measures.

Among the product market based measures, many measures fall into two categories based

on their theory foundation. The first one is the incremental value approach. This approach

reflects the definition that is widely accepted by market researchers. Brand equity, among many

definitions, is defined as the incremental value that is owned by a product with its brand

compared with the same product if it did not have the brand name (Aaker 1991; Dubin 1998;

Keller 2003; Shocker and Weitz 1998). Many brand equity measure reflects this idea, e.g., the

price premium (e.g., Aaker 1991, 1996; Agarwal and Rao 1996), volume premium and revenue

premium (Ailawadi, Lehmann and Neslin 2003). Among these measures, price premium is the

most direct product of the above definition. The intuition behind this measure is that the price

difference between two products reflects a value attributable to the branded good. This premium

maybe the most direct indicator of the what the consumers are willing to pay for the brand

49

(Aaker 1996, P.121), which has been considered as a single most important metric for brand

equity. This method has been criticized because of its failure to include other factors that

contribute to the difference (e.g., quality and production cost). However, it is still often

implemented in practice due to its simple computation, but with some adjustment to remove

other components. For example, Holbrook (1992) defined the price premium as the increment

value that a brand name contributes to the price after controlling for its quality. Contending that

neither price volume nor sales volume can capture the accrued brand value well, Ailawadi et al.

(2003) proposed a revenue premium measure. Their calculation was based on two key

assumptions. The first one assumes that firms pursue rational equilibrium so that brand revenue

approximates the actual revenue. The second assumption assumes that the private label products

behave the same as unbranded goods. The revenue premium measure is defined as the difference

in revenue (i.e. net price x volume) between a branded good and a corresponding private label

good. Shankar, Azar and Fuller (2008) argued that the previous brand equity models are for one

single brand and therefore implicitly assumes that brand equity is the same in each category.

They developed a multicategory brand equity model based on consumer survey and financial

measures and applied this model to an insurance company. This model estimated the incremental

cash flow attributable to brand in each category in which the brand competes. For a

multicategory brand, brand equity was defined as the sum of the products of relative brand

importance and offering value in each category.

Except the above incremental value approach, in the product based measure, an intercept

approach is widely employed by researchers. The idea is that brand equity is what remains of

consumer preferences after subtracting out objective characteristics of the product. Kamakura

and Russell (1993) is among the first who measure brand equity as the component of utility that

50

is not explained by tangible attributes and other marketing variables. The brand equity

calculation is based on individual choice data. With the new development of structural model,

researchers later are able to estimate brand equity based on aggregate sales data (e.g., Nevo

2001; Goldfarb et al. 2009; Sriram, Balachander and Kalwani 2007). Although the estimation

technique is complex, the brand equity is still the utility not explained by product search

attributes and other marketing variables. This structural form demand model, explicitly includes

the individual characteristics and corrects the endogeneity problem caused by the correlation

between the price and the error term. Due to its realistic estimate of demand elasticity and

incorporation of price endoegeniety concerns, this structural form has gained wide recognition

and application in marketing research. Goldfarb et al. (2009) employed this structural approach

to measure brand in an equilibrium framework, taking into account both the demand and supply

side. In their model, brand value is defined as the difference in equilibrium profit between the

brand and the unbranded equivalent on search attributes. They argued that their profit measure is

more in accordance with the accounting and finance practice in classifying brand equity as

intangible assets than other measures such as price premium, quantity premiums or revenue

premium, all of which are not profits but components of profits. Sriram, Balachander and

Kalwani (2007) also used the same random coefficient logit demand model with the store-level

data to obtain brand equity estimates. The above mentioned intercept measure is also called

“residual approach” in that brand equity is what remains of consumer preferences after

subtracting out objective characteristics of the product.

Impact of Marketing Action on Brand Equity

All the marketing mix variables and product characteristics contribute to the formation of

brand equity measure. Therefore, they are expected to have certain relationship with brand

equity. In these marketing variables, the impact of advertising effect has been found to have

51

positive effect on building the brand equity. Keller (1998) suggested that advertising affected

brand equity through favorable associations, perceived quality, and use experience. Sriram et al.

(2007), using store-level data in two consumer packaged goods categories, found that the

advertising has a positive effect on brand equity in both the product categories. Other researchers

also find a positive relationship between the advertising and brand equity (e.g. Simon and

Sullivan, 1993; Jedidi, Mela and Gupta 1999).

In contrast to the effect of advertising, the effect of sales promotion on brand equity is

ambiguous. The positive and negative effect has both been proved by theoretical and empirical

evidence. Some researchers argued that price promotions can support the brand (e.g. Aaker

1996) and according to learning theory, promotion is a help to shape the new consumer behavior

and reinforce the existing consumer behavior. Therefore, when promotions appropriately used, it

leads to attitudinal loyalty and thus enhances brand equity (Rothschild and Gaidis 1981). It is

shown by some research that the frequent use of sales promotions would introduce a negative

impression on the brand quality, thereby diminishing the brand equity (Keller 1998). Jedidi et al.

(1999) found that in the long run the promotions had a negative effect on brand equity.

Furthermore, consumers would wait to buy the product only when they are on sale, which

lowering the reference price and reducing the price premium (Blattberg, Briesch and Fox 1995).

Sriram et al. (2007) did not find any significant effect of sales promotions on brand equity in

either of the two consumer packaged goods category that they investigated. One possible

explanation to the above inconsistent findings can be explained by the total marketing activity. In

the immediate period, price promotion usually results in increases sales. But with time pass by,

the post promotion period, due to many reasons such as the erosion of brand image or consumers

52

already stored enough, the promotion effect might be negative. Reflected in the total sales, the

impact is difficult to be judged good or bad.

Impact of Brand Equity on Marketing Action

All the above studies investigate how marketing mix variables affect brand equity. Since

there is a feedback effect between marketing mix variable and brand equity, it is expected that if

marketing mix variables affect brand equity, brand equity reversely affects the effect of

marketing action. Slotegraaf and Pauwels (2008) found that brand equity played an important

role in the impact of marketing promotions on sales. Brand equity influenced both permanent

and cumulative sales effects from marketing promotions. These effects were greater for brands

with higher equity and more product introductions, whereas brands with low equity gained

greater benefits from product introductions. There is also a reverse impact of brand on

promotion. Prentice (1975) argued that as promotion is a short time activity, to make it work, the

brand had to provide its value.

Brand Equity Measures

As mentioned in the literature review section, there are two big categories of firm based

brand equity measures: the intercept based approach measure and the incremental value based

approach (premium approach). The intercept approaches can be classified into structural

approach and reduced form approach. The structural approach brand equity estimates employs

the BLP method, and is also referred to as random coefficient model (Nevo 2001; Goldfarb et al

2009, Sriram et al. (2007). The reduced form is a simplification of the structural form by

assuming homogenous consumer. Both measures consider price as endogenous and were built on

aggregate sales. They differ in whether consumer heterogeneity is considered. We refer the first

approach as BLP measure (heterogeneous consumers) and the second one as Logit measure

(homogenous consumers). The detailed introduction of each method will be illustrated later.

53

In terms of construction theory, these measures include different components. For

example, the price premium directly reflects consumer’s perception of the brand quality. It

reflects less of the marketing action such as advertising. Or we can argue that the impact of

marketing action is implicitly reflected in the brand equity. In contrast, the revenue premium

considers the both price and volume effect. When (Ailawadi et al. 2003) proposes this measure,

they argue that the volume sale reflects the impact of a variety of factors, including marketing

mix, price, equity, competitor’s impact, pre-existing firm strength, category characteristics and

the interaction of these factors. So the revenue is the equilibrium outcome of the equilibrium set

of all these factors. To explicitly model the impact of all these factors are not feasible. But the

form of revenue premium implicitly includes all these factors. In contrast to the implicit

approach, the intercept approach explicitly includes these factors. However, the intercept

approach includes the product characteristics, which is not considered in the equilibrium set of

revenue premium. The BLP approach further includes the consumer heterogeneity, which

interact with the product characteristics. The relationship between these measures is illustrated in

Figure 3-1.

From Figure 3-1, the simplest brand equity measure is price premium. Then logit model

adds other marketing mix, as well as the product characteristic. The BLP further incorporates

consumer heterogeneity. In contrast, the revenue premium does not explicitly include any of

these factors, but it implicitly includes other factors such competitor’s equity and firms existing

strength etc.

In selecting brand equity measure, we include equity measures that are representative of

the two categories (i.e., intercept approach and premium approach) and are different in

constructing theory. We end up selecting the following five brand equity measures: price

54

premium, revenue premium, profit premium, logit model and BLP model. The details of these

measures will be introduced below.

Intercept Brand Approach

Structural approach. In the structural form, following Nevo (2001), the model specifies

the individual i ’s utility of choosing product j on choice occasion t (a city- period combination)

as:

ijt i jt jt i jt j jt ijtu x adv p

where jtx is a 1m vector of observed variables, jtadv is the jtp is price of alternative j

at time t , jtadv is the advertising of product j at time t , jt is the unobserved product

characteristics, j is the mean valuation of unobserved cereal characteristics, jt is equal to

j jt ,, which varies by city-quarter (market-specific) and ijt is the stochastic error term with

mean of zero.

The parameter i and i are two random coefficients, which vary across different

consumers. These two parameters are a function of individual characteristics:

i

i i

i

D v

,

Where

are ( 1) 1m vector of coefficients, iD is a d-vector of individual

demographic characteristics, which is preferably drawn from real data set but can also use

parametric distribution, iv captures additional demographic characteristics and 1~ (0, )i mv N I ,

is a ( 1)m d matrix of coefficients that measures how the product characteristics and price

vary with demographics and is a ( 1) ( 1)m m matrix of parameters. iD and iv are assumed

55

to be independent for simplification. In this model, the and the are linear parameters and

the and the are nonlinear parameters.

The utility function can be written as a sum of mean utility jtu , an individual

heteroskedastic term ijt and an error term ijt :

,

, [ , ]( ),

( , , ) ( , , , )

ijt jt ijt

jt i i ijt i i

i ijt jt jt jt jt ijt

jt jt jt jt jtp D v

u D vu x p x p

u x p x

The mean utility is a function of observed characteristics, unobserved (by the

econometrician) product characteristics and consumers’ choice. The difference between the

perceived mean utility and perceived individual utility is captured by individual heterogeneity.

Following Nevo (2000a, b), we converted volume sales into number of servings sold. To

define the total market size, each person in a market is assumed to consumer one “cereal unit”

per day and therefore the total market size is the total population in a market. So the market share

is defined as the number of servings sold divided by the total potential number of servings in a

city in a quarter. The outside market otS is defined as the total market size minus the consumed

(purchased) cereal amount. The brand equity j is defined as the fixed effect j estimated

minus the product attributes effect ( jx ), which can be specified as follows:

j j jx

Logit equity measure. In the reduced form of intercept measure, we are estimating a

homogenous logit model with aggregate data:

1

1

1 exp( )exp( )

log log log *1

1 exp( )

J

jtjt

jt ot jt jt jt jtJ

jt

uu

s s u x p

u

,

56

where

1

exp( )

1 exp( )

jt

ijt J

jt

us

u

and 0

1 1

exp( ) 1

1 exp( ) 1 exp( )

tot J J

jt jt

us

u u

.

The brand equity is the residual jt .

Premium Approach

Revenue premium. The revenue premium was defined as the revenue difference

between brand i and a corresponding private label good (Ailawadi et al. 2003). It is defined as

follows:

Revenue premium volume price volume pricei i i p p ,

where the volumei and pricei is the brand i ’s volume and price respectively, and

volume p and price p is the private label good 'p s volume and price respectively. One problem

of the data is that the private goods vary by city and store. Therefore we use the category level

private label goods in each city and each quarter as an approximate.

According to Ailawadi et al. (2003), the unit sales of brand j is a function of a variety of

factors but not including actual product characteristics:

( , , , , , , ,P , )

( , , , , )

j j j j k k j j j j k j k j j

j j j j j j k k

S f M P M P M E P E M E E E

E g M P F C M P

,

where

S:unit sales,

M: Marketing mix

P: price,

E: equity,

F: preexisiting firm strength,

57

C: category characteristics and

J and K: indexes of brand j and k.

Durbin’s measure. Durbin’s (1998) measure is the incremental profits of branded goods

over an unbranded version. Durbin (1998) derived this measure from oligopoly economic

theory. Through a series simplifying assumptions, Durbin derives the following formula for the

equity of brand i :

i i

(1 )( 1)Durbin's equity (volume )(price ) 1

(1 Share )( )

i i ii

i pr i

VS VS

VS

,

where iVS is the volume share of brand i on the sum of volumes of brand i and private

label products, Sharei is the volume share of brand i in the whole market, and i and pr are the

price elasticity of brand i and private label product respectively. To get the price elasticity of

brand I and private label good, we employ the BLP method. As the elasticity calculated based on

consumer heterogeneity in taste is considered a more realistic estimate. The calculated price

elasticity is:

* *

* *

ˆ(1 ) ( ) ( ) if ,

ˆ ( ) ( ) otherwise,

jt

i ijt ijt D v

jtjt kt

ijt

kt jt kti ijt ikt D v

jt

ps s dP D dP v j k

ss p

p s ps s dP D dP v

s

.

Price premium. The price premium is defined as the incremental price of brand

compared to private label good price.

Assessing Brand Equity Measures

Relationship with Market Performance

According to Keller et.al. (2006), the logic underlying product-market measures is that

the positive impact of brand equity would be ultimately reflected in their marketing performance

58

The above mentioned equity measures are all based on market performance of a brand, no matter

they are the incremental value or the residual left after deducing the impact of marketing activity

from sales. Therefore we cannot help wonder whether these calculated equity measures are

positively correlated with the performance of a brand. The performance is reflected in the market

share, price, volume and total revenue, all of which are valid performance measures. We decide

to use market share (measured both in volume and revenue) as a benchmark.

Within and Between Relationship

As mentioned before, these measures differ in whether they include product feature,

marketing action and consumer heterogeneity. Therefore, for intercept measure, by progressing

incorporating these elements, we can identify how each measure itself was affected by these

factors. In the meantime, different measure may capture different underlying dimensions of

brand equity.

Empirical Analysis

Data

The data used in this study is provided by Information Resource Inc. (IRI). This is a

weekly data set coving the period from 2001 to 2006 and contains 31 product categories. The

data covers U.S. fifty markets, which is defined by IRI. The data is quite comprehensive. It

includes the product level sales, feature advertising, store display and etc.

We supplement the IRI data with other data. The city population data is the year 2000

U.S. census data. The city-level demographic data are obtained from the March Current

Population Survey from each year. We obtain data on income, age, whether there is child in the

family (1=if the child is under 16 years old). For each of the 50 city, 20 real individuals were

drawn from the survey. All these demographics data are mean centered. The advertising data is

59

obtained from TNS media intelligence. However, only yearly data from year 2001 and 2002 is

available.

The product category investigated in this study is breakfast cereal. We select this

category is because this category was also examined in Nevo (2000, 2001) and Goldfarb et al.

(2009). Therefore, their study provides a benchmark for comparison. There are four big national

parent brand which covers more than 40 percent of the cereal market. The estimates were based

on the top-selling 24 cereal brands selected in Nevo (2001). They are General MillsTM

(GM),

KelloggsTM

, PostTM

and QuakerTM

. Each of them have several subbrands. Nevo and Goldfarb

investigated all 25 brands. The estimates were based on the top-selling 24 cereal brands selected

in Nevo (2001). As Kellogg’s mini wheatsTM

and Quaker 100% naturalTM

were not shown in

each quarter in the six years, they were deleted from the study, which makes the final number of

selected brand equal to22.

Data are taste (e.g., sugar, protein) were obtained online and in local supermarkets from

cereal boxes and pictures. The search attribute (e.g., whether the cereal is considered soggy in

milk, i.e., mushy), and whether the subbrand is family or kids oriented follow Nevo (20010).

Descriptive Statistics

The descriptive statistics of market share, price, advertising, product characteristics are

presented in Table 3-2. The descriptive statistics is similar to those in Nevo (2001).

Endogeneity Issue

There are two sources of endogeneity. First, the price is correlated with j .Following

Nevo (2000, 2001), a dummy variable for each of the 22 cereals were included to capture the

mean differences between cereals that are constant across city or quarter. The inclusion of

dummy variables, which represents the brand-specific fixed effects, were strongly advocated by

60

Nevo (2000, 2001) for two reasons. First, the inclusion of this fixed effect captures the

unobserved product characteristics that are not modeled, which helps to improve the fit of the

model. jt The second reason, also the major motivation, is that the brand dummy variable

captures the characteristics that are constant across market and also the mean of the brand

unobserved components, and therefore fully accounts for the correlation between the price and

j . The second endogeneity comes from the correlation possible correlation between jt and

the price. As the endogeneity problem caused by the possible correlation between j and jtp is

controlled, what left to us is to deal with the possible correlation between jt and the price. We

use the price for the same brand in the same period but in other cities as instruments. Following

Hausman (1996), Nevo (2000, 2001) proposed the identifying assumption that after controlling

for brand specific effect and demographics, jt was city-specific valuation of the products and

therefore was independent across cities but can be correlated in a city over time. Under this

independent assumption, the price of the same brand was correlated across cities due to the same

marginal cost but are uncorrelated with the city-specific effect jt . So the price for a certain

brand in other cities is a valid IV.

For the logit model, the endogeneity problem is addressed similarly as we did in the

structural model. That is, we include two sets of IVs. One set is the price IV and another is the

set of 22 brand dummies.

Estimation

The structural BLP approach followed the procedure described in Nevo (2000, 2001).

The estimation procedure combined generalized method of moments (GMM) with an

“inversion” technique, which is explained in detail in Berry (1994) and Berry, Levinsohn and

61

Pakes (1995). For logit model, we use 2 stage instrumental variable approach. For Durbin

approach, to get the cross elasticity estimate between private label and brand j, we have to

include the private label band in addition to the 22 brands in the structural approach.

Results and Discussion

To better understand how different consideration characteristics affect the brand equity

measure, we did the following things. Each step, we add only one set of variables for the

intercept approach. In the first step, we only include price in all measures. Then we add product

characteristics. Then we add marketing action (e.g., advertising). Finally we add experience

attribute. Goldfarb et al (2010) argued that experience attribute should not be included in the

brand equity estimates because adding them means that consumers already know this attribute

without the aid of the brand. Brand does not provide signaling value for this attribute.

Since the brand value calculated based on different methods are not directly comparable,

we compare the rank order of brands from different methods. As no one knows the true story of

the competitive brand position, the rank order of the market share was introduced as references

across our comparisons. We start with the revenue premium and price premium as their

calculation is not directly related to marketing action and product characteristics. We then

introduce other measures.

Results indicated that both the ranking given by revenue premium (Table 3-3) and the

revenue premium estimates (Table 3-3) are highly correlated with market share (p <.000, either

in volume or in revenue), while ranking (Table 3-3) and estimates (Table 3-4)given by price

premium are negatively correlated with market share.

Table 3-5 and Table 3-6 indicated that when only price is included, no matter it is the

ranking or the actual estimates, BLP approach is highly correlated with market share but logit

62

measure is not. However, when product characteristics are included, it is now the logit measure,

not the BLP measure, that is highly correlated with market share ( 0.5 ).

An interesting finding is that brand that has high market share was ranked much lower

using the structural approach, and vice versa. For example, the top 3 brands in terms of market

share in revenue is Kellogg’s Frosted FlakesTM

(ranked no.1), GM CheeriosTM

(ranked no. 2) and

Kellogg’s Raisin BranTM

(ranked no.3). They are ranked as 16, 13 and 18 respectively by BLP3.

The three lowest brand in terms of marketing revenue, Kellogg’s CrispixTM

(No.20), GM TrixTM

(No. 21), GM Raisin Nut BranTM

(No.22) were ranked as 4, 8 and 7 respectively. For the middle

ranked brand equity, their new ranking by BLP also vary from market share ranking but not so

drastically different as the top and low brands.

In terms of the relationship between these two measures, the two are positively correlated

only under the simplest case, i.e., only when neither marketing action nor product characteristics

is included. As we know that the BLP method explicitly models the interaction between product

characteristics and consumer heterogeneity while logit model assumes homogenous consumers,

the consumer heterogeneity clearly affects the equity estimates through product characteristics

and therefore change the equity estimate as such that the two changes their relationship.

Also whether product characteristics or marketing action is included does not affect the

logit measure, but does affect the BLP measure. All logit measures are highly correlated among

themselves while none of the three BLP measures are inter correlated. The BLP measure changes

not only its relationship with market share when more factors are considered, but also changes

the equity measure by itself.

63

Relationship between Intercept Measure and Premium Measure

The relationship between revenue premium and the two intercept measures are similar to

what we found for market share, which is expected as revenue premium based on our calculation

is highly correlated with either type of market share.

One interesting finding is that price premium now has a significant correlation with logit1

and BLP3 measure. The correlation with logit1 indicates that if price is the only element

included and consumer heterogeneity is not considered, both the premium approach and the

intercept approach reach similar conclusions. But the positive and significant correlation with

BLP3 is unexpected, given the two are constructed on complete different theories and the

estimation technique is drastically different. As we mentioned before, price premium is

considered a single most important metric for brand equity, but was criticized as too simple as to

include necessary other factors. However, our results indicated that the price itself might already

capture many underlying dimensions of brands.

The Stability of the Measure

According to the MSI list desiderata (number 9), an ideal measure should be robust,

reliable, and stable over time, yet able to reflect real changes in brand health. We measure the

stability based on (1) the rank order and/or (2) the correlation of each measure with its lagged

value. The rank order based on revenue premium, price premium and logit measure is consistent

across years, with only a little variation for a few brands. The correlation between each brand

equity measure in each quarter with its preceding quarter is all above .9, indicating high stability

over time. For the BLP measure, we calculated the correlation when advertising and product

characteristics both are included. The correlation is .88 ( .001p ) between 2001 and 2002,

indicating good stability. Therefore we can conclude that all equity measures are stable.

64

The Difference of Magnitude among Brands

The brand value based on each measure is not directly comparable. Therefore we

use the relative magnitude to see the difference. We use the adjacent brand equity as a reference

in each measure and then see the percentage increase of each measure for each brand compared

with the one that is one position lower than itself. For example, in the revenue premium ranking,

GM CheeriosTM

is ranked number 1, followed by GM Cinnamon Toasted CrunchTM

. Then we

calculate the increased percentage of GM cheeriosTM

brand equity compared to GM Cinnamon

Toasted CrunchTM

. For the two intercept measures, we report the brand equity calculated based

on all product characteristics and marketing action included.

Results (Table 3-8) indicated that the difference between adjacent equity measures is

smallest for revenue premium, followed by the two market share measures. Brand equity values

based on the two intercept measures vary more widely than the market share measures, but vary

less than price premium measures.

Findings and Discussions

Brand equity, as a multidimensional construct, is difficult to be defined. Given its

importance, correspondingly, many brand equity measures are proposed. And each measure may

capture different dimensions. Given the existence of multiple brand equity measures, it is

imperative to understand their constructing underlying theories for better understanding and

application. Our study made such an attempt. To be specifically, our study aims to investigate the

relationship between these measures and market performance, as well as their inter and intra

relationships. By comparing several representative brand equity measures developed by

researchers, we found that these measures, although all are market performance based measures,

they are not necessarily correlated with their respective market share. On the contrary, some are

just the opposite of the market share. In terms of the application, we found that if marketers are

65

interested in equity measure that is consistent with the market share, then revenue premium and

logit model is recommended. If marketers’ focus is on the impact of consumer heterogeneity,

then the price premium and structural approach is recommend. The price premium, due to its

straightforward calculation, can be regarded as a rule of thumb measure of structural approach.

In terms of stability, all measures are valid. However, in terms of the extent they differ,

they differ in the relative magnitude. The difference magnitude is the highest for price premium,

followed by the intercept approach (BLP and logit model). The revenue premium varies the least.

66

Table 3-1. Selected research on company based brand equity measure

Author Metric Data Used Method

Ailawadi et al.(2003) Revenue premium Dominick scan

data

Incremental

revenue

Dubin (1998) Profit premium Nielsen and Econometric

method

SAMI data Ferjani et al. (2009) Incremental WTP Experiments Conjoint study

Goldfarb et Al. (2009) Intercept brand

measure IRI Infoscan

Random

coefficient logit

Holbrook, M.B (1992) Price premium Consumer reports Hedonic

regression

Mail-order-retailer Kamakura and Russel

(1993)

Intercept brand

measure Scan panel data Logit model

Park and Srinivasan (1994) Intercept brand

measure Survey

Regression,

Logit model

Randall et al.(1998) Price premium Magazine Hedonic model

Shankar et al. (2008) Incremental cash flow Customer survey Logit model

Srinivasan, et al. (2005) Price/volume

premium Survey, firm data

Logit and

conjoint

Sririam et at. (2007) Intercept brand

measure

Dominick scan

data

Random

coefficient logit

67

Table 3-2. Descriptive Statistics of Data

Mean Median Min Max Std.

Price (cents per serving) 18.965 18.967 12.291 28.389 0.044

Advertising((M$ per year) 10.855 9.355 0.001 27.092 7.838

Revenue Share within Cereal Market (%) 1.934 1.701 0.534 5.990 0.013

Volume Share within Cereal Market (%) 1.918 1.402 0.474 7.610 0.016

Calories/100 1.13 1.1 1 1.6 0.142

Fat/100 0.02 0.01 0 0.07 0.016

Sugar/100 0.08 0.08 0.01 0.14 0.043

Mushy 0.35 - - 0 1

Fiber/100 0.02 0.015 0 0.04 0.013

Protein/100 0.02 0.02 0 0.04 0.010

68

Table 3-3. Rankings of brand equity measures

MS MS Revenue Price

Name Revenue Volume Premium Premium

GM CheeriosTM

2 1 1 7

GM Cinnamon Toast CrunchTM

7 5 4 9

GM Honey Nut CheerTM

4 3 3 12

GM KixTM

18 15 14 1

GM Lucky CharmsTM

9 6 6 11

GM Raisin Nut BranTM

22 22 22 8

GM TotalTM

17 14 15 2

GM TrixTM

21 21 20 5

GM WheatiesTM

16 17 17 10

Kellogg’s Corn FlakesTM

5 9 8 18

Kellogg’s Corn PopsTM

13 13 13 13

Kellogg’s CrispixTM

20 18 18 4

Kellogg’s Froot LoopsTM

6 7 7 15

Kellogg’s Frosted FlakesTM

1 2 2 19

Kellogg’s Raisin BranTM

3 4 5 21

Kellogg’s Rice KrispiesTM

11 8 9 6

Kellogg’s Special KTM

15 12 12 3

Post Grape NutsTM

12 16 16 20

Post Honey Bunches of OatsTM

10 11 10 14

Post Raisin BranTM

14 19 19 22

Quaker Cap’n CrunchTM

19 20 21 16

Quaker LifeTM

8 10 11 17

Corr. with MS Rev. ranking 1 0.93 0.93 -0.50

69

Table 3-4. Brand equity measure

MS MS Revenue Price

Volume Revenue Premium Premium

MS(Volume) 1

MS (Revenue) 0.90 1

Revenue Premium 0.92 0.91 1

Price Premium -0.46 -0.19 -0.15 1

Note: absolute correlation that is less than .3 is insignificant at 5% level.

70

Table 3-5. The rank order the intercept measures

MS

Name Revenue BLP1 BLP2 BLP3 Logit1 Logit2 Logit3

GM CheeriosTM 2 1 22 13 5 3 6

GM Cinnamon Toast CrunchTM 7 4 16 17 7 4 4

GM Honey Nut CheerTM 4 2 9 22 10 6 15

GM KixTM 18 10 20 1 3 13 9

GM Lucky CharmsTM 9 9 2 9 6 10 5

GM Raisin Nut BranTM 22 21 8 7 15 21 20

GM TotalTM 17 12 18 21 1 5 7

GM TrixTM 21 20 19 8 9 17 16

GM WheatiesTM 16 15 1 5 12 19 18

Kellogg’s Corn FlakesTM 5 11 3 10 14 15 14

Kellogg’s Corn PopsTM 13 17 17 20 17 18 19

Kellogg’s CrispixTM 20 14 6 4 4 12 11

Kellogg’s Froot LoopsTM 6 13 14 12 11 14 10

Kellogg’s Frosted FlakesTM 1 5 21 16 18 2 2

Kellogg’s Raisin BranTM 3 18 15 18 20 1 1

Kellogg’s Rice KrispiesTM 11 7 7 3 8 9 8

Kellogg’s Special KTM 15 8 4 2 2 8 3

Post Grape NutsTM 12 16 5 19 22 20 22

Post Honey Bunches of OatsTM 10 3 10 11 13 7 13

Post Raisin BranTM 14 22 12 15 21 16 17

Quaker Cap’n CrunchTM 19 19 11 6 19 22 21

Quaker LifeTM 8 6 13 14 16 11 12

Corr. with MS Rev. ranking - 0.61 -0.18 -0.51 -0.19 0.65 0.50

Note: 1-only price included; 2-add product characteristics; 3-add advertising.

71

Table 3-6. The correlation for intercept measure

MS (Rev.) BLP1 BLP2 BLP3 Logit1 Logit2 Logit3

MS (Rev.) 1

BLP1 0.69 1

BLP2 -0.43 -0.22 1

BLP3 -0.36 -0.09 0.17 1

Logit1 0.28 0.63 -0.26 0.22 1

Logit2 0.71 0.66 -0.41 -0.23 0.53 1

Logit3 0.58 0.49 -0.33 0.08 0.49 0.9 1

Note: 1-only price included; 2-add product characteristics; 3-add advertising. Absolute

correlation that is less than .42 is insignificant at 5% level.

72

Table 3-7. The correlation between intercept measure and premium measure

Rev. Premium Price Premium

Ranking Equity Ranking Equity

Rev. Premium 1 1 1 -0.145

BLP1 0.773 0.831 0.191 0.198

BLP2 -0.2264 -0.449 -0.089 -0.079

BLP3 -0.3755 -0.342 0.482 0.475

Logit1 0.1474 0.371 0.897 0.845

Logit2 0.7911 0.744 0.094 0.121

Logit3 0.6849 0.548 0.216 0.186

Note: 1-only price included; 2-add product characteristics; 3-add advertising.

Absolute correlation that is less than .40 is insignificant at 5% level.

73

Table 3-8. Magnitude difference

MS MS Rev. Price

Name Brand Volume Rev. Prem. Prem. BLP3 Logit3

GM CheeriosTM 1 23.36 0.49 23.85 2.52 298.69 5.92

GM Cinnamon Toast

CrunchTM 2 11.96 13.84 0.69 2.71 8.6 5.25

GM Honey Nut CheerTM 3 20.02 41.45 9.89 2.62 0 13

GM KixTM 4 5.91 5.75 0.19 1.4 0.16 60.68

GM Lucky CharmsTM 5 2.69 0.68 2.12 0.42 21.78 5.16

GM Raisin Nut BranTM 6 0 14.02 0 12.73 0.24 16.84

GM TotalTM 7 5.81 30.6 3.28 0.12 42.94 128.55

GM TrixTM 8 41.46 5.6 0.28 29.18 2.03 7.64

GM WheatiesTM 9 7.27 0 0.97 37.61 33.21 3.78

Kellogg’s Corn FlakesTM 10 2.3 3.9 2.5 110.39 10.02 39.95

Kellogg’s Corn PopsTM 11 5.18 11.46 1 1.09 58.04 46.77

Kellogg’s CrispixTM 12 9.99 12.94 1.31 17.21 36.2 67.1

Kellogg’s Froot LoopsTM 13 10.97 6.08 0.2 7.45 103.88 477.56

Kellogg’s Frosted FlakesTM 14 28.54 31.54 16.7 93.69 11.9 20.88

Kellogg’s Raisin BranTM 15 0.19 11.48 4.62 0 41.6 15.1

Kellogg’s Rice KrispiesTM 16 0.89 11.43 0.61 0.26 4.32 5.82

Kellogg’s Special KTM 17 9.92 17.35 3.28 11.5 78.7 62.35

Post Grape NutsTM 18 13.16 19.8 2.04 1411.79 22.65 0

Post Honey Bunches of

OatsTM 19 10.76 0.77 0.47 6.09 50.38 57.81

Post Raisin BranTM 20 10.94 9.69 0.47 105.51 11.14 42.85

Quaker Cap’n CrunchTM 21 1.82 1.11 4.38 12 1.05 2.3

Quaker LifeTM 22 4.25 5.83 2.57 77.89 43.92 118.97

Average 10.34 12.18 3.70 88.37 41.97 54.74

74

Figure 3-1. Relationship between brand equity measures

75

CHAPTER 4

CONCLUSIONS

In this chapter, I summarize the major findings in this dissertation and the managerial

implications for firms.

Considering the popular practice of collecting intention data in marketing research, we

feel it is important to develop a good metric to summarize the information in intent data. In essay

1, we developed two new intent metrics. Although traditional metrics have been extensively

used, we found two new metrics that perform better than traditional metrics. Our metric meets

the three criteria we set before. First, the derivation of our new metric follows naturally from

well accepted distributions. Second, our metric is better correlated with future outcomes than

traditional metrics. Finally, our metric is easy to communicate and calculate. Furthermore, the

two metrics are designed for different products. We differentiate products into mass products and

niche products and our two new metrics are most appropriate for each of the two different types

of products.

The benefit of our findings is also especially important for early prediction. How early

could it be? In our movie data, consumers only see a trailer of an average 2.5 minutes, which is

only 1/48 of a typical two- hour length movie. The intention based on the 1/48 products alone

can capture more than 40% of the sales variance. For other products, we can definitely provide

more than 1/48 of the products to consumers. Consumer intention data are obtained before a

product is actually launched, which enables early prediction and corresponding strategies. These

early information are especially useful for managers to take actions in advance. For example, in

the movie industry, before a movie is released, the theater managers have to decide the

respective marketing efforts and the time table towards each individual movie. The intention data

collected before movie launching enables the theaters to strike a first blow.

76

Marketing metrics help quantify a firm or its brand’s marketing performance. While

developing these metrics has always been an imperative for academics, the validation of current

metrics is considered also important by academia. In essay 2, we validated the current brand

equity measures. We classify product based brand equity measures into two big categories: the

premium approach and the intercept based approach, and selected the representative product

based brand equity measures. We found that these measures, although all are market

performance based measures, they are not necessarily correlated with their respective market

share. On the contrary, some are just the opposite of the market share. In terms of the

application, we found that if marketers are interested in equity measure that is consistent with the

market share, then revenue premium and logit model is recommended. If marketers’ focus is on

the impact of consumer heterogeneity, then the price premium and structural approach is

recommend. The price premium, due to its straightforward calculation, can be regarded as a rule

of thumb measure of structural approach.

77

APPENDIX

MOVIE GOING SURVEY

You will see some upcoming movie trailers. Later we will ask you some questions about your

opinion of each movie. We will also ask you some questions to help us understand why different

people like different movies.

Thanks for your cooperation!

Movie Title: _________________

Please circle your answer.

How likely are you to see the movie at a theater when it is released?

Very Unlikely Very Likely

0 1 2 3 4 5 6 7 8 9 10

How likely are you to recommend this movie to a friend?

Very Unlikely Very Likely

0 1 2 3 4 5 6 7 8 9 10

How much do you think you will like the movie?

Not at all Very much

0 1 2 3 4 5 6 7 8 9 10

Please do not proceed until you have

answered the above questions.

Movie Title: _________________

Please circle your answer.

How likely are you to see the movie at a theater when it is released?

Very Unlikely Unlikely Undecided Likely Very Likely

1 2 3 4 5

78

How likely is it that you would recommend this movie to a friend?

Very Unlikely Unlikely Undecided Likely Very Likely

1 2 3 4 5

How much do you think you will like the movie?

Dislike Very Much Dislike Neutral Like Like Very Much

1 2 3 4 5

Please do not proceed until you have

answered the above questions.

1. Please circle your gender.

Female Male

2. Please indicate your age. _____________

3. How many movies have you seen at the theater in a typical month? ________________

4. When deciding to see a movie at the theatre, how important are the following factors?

(Check the best box)

Not at all

Important

Very

Unimportant

Neither

Important nor

Unimportant

Very

Important

Extremely

Important

Interesting

storyline/script

Favorite leading star

Favorite director

Movie will be popular

among my friends.

Great special effects

Movie widely

advertised

79

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85

BIOGRAPHICAL SKETCH

Yuying Shi earned her Bachelor of Economics from Shanghai University of Finance and

Economics. She later obtained her Ph.D. in Research Methodology at the University of Florida in

2009. She is expected to receive her Master of Statistics in Dec. 2014 and Ph.D. in Business

Administration at the University of Florida in May 2015.

Yuying’s research interests focus on marketing analytics. Her research centers on

developing new measurement techniques to quantify firms’ marketing activities. She uses

analytics, econometrics and psychometrics to analyze data and to identify important marketing

problems. Her current research is the analysis of customer behavior data to understand and

forecast customer purchasing activities, and the analysis of firm aggregate sales data to identify

long-term marketing effectiveness and to capture the dynamic nature of brand equity.