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New Product Introductions, Trademarked Advertising and Market Value:

An Econometric Analysis of Coca-Cola vs Pepsi Competition

Andrea Fosfuri♣

and Marco S. Giarratana♦♦♦♦

First draft: March 2005; this version: November 2006

Abstract

This article analyzes how the financial-market value of a firm is influenced by the actions of its rivals.

We investigate the competition in the carbonated soft drink market between 1999 and 2003, a period

characterized by price stability and an almost duopoly of Coca-Cola and Pespi. We focus on new

product announcements as a proxy of new product introductions and on filed trademarks as a measure

of advertising efforts. Our empirical study yields three important results. First, we find that rival

efforts in product introduction produce a negative impact on a classical measure of firm financial-

market value, the Tobin's q. More interestingly, our evidence highlights that the effect of rival

advertising efforts on a firm’s financial-market value is positive. Finally, we find that the channels

through which these two strategic tools affect a firm’s Tobin’s q are different. Trademarked

advertising influences mainly total market demand, while new product introductions affect directly the

distribution of market shares.

Keywords: Production Introduction, Advertising, Firm Value, Rivalry

♣ Department of Business Administration, Universidad Carlos III de Madrid, fosfuri@emp.uc3m.com

♦ Department of Business Administration, Universidad Carlos III de Madrid, marco.giarratana@uc3m.es

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1. Introduction

Both the financial and the management literature show that a firm’s actions and investments influence

its market value (Wernerfelt and Montgomery 1988; Bayus et al. 2003; Hall et al. 2005). This body of

research highlights extensively a wide spectrum of competitive moves that range from price changes

to new product releases, from alliance formation to patented technological innovations.

Firms’ actions are the result of a strategic decision process that often time is ignited by the

information available outside firm boundaries (Mintzberg 1978). Therefore, firms not only are

constantly engaged in detecting new competitive actions, but also in responding to rival moves with

strategic reactions. How firms react to competitive stimuli depends indeed on the nature of the

stimulus itself. Porter (1980) argues that when firm moves threaten the profitability of other

competitors, countermoves must be expected. By contrast, cooperative or non-threatening actions

trigger different competitive responses.

However, the empirical evidence that analyzes how a firm’s market value responds to rival

moves is rather underdeveloped. A recent paper by Silverman and McGahan (2006) focuses on the

effect of patents granted to competitors. Other scholars have looked at alliances (Das, Sen and

Sengupta 1998) and price changes (Chen and MacMillan 1992). To the best of our knowledge, no

research has been conducted on the effect on a firm’s market value of rival moves along non-price

dimensions of the marketing mix. We tackle this issue by seeking to understand how rival moves in

new product introductions and advertising affect a firm’s market value. This is an important research

question because the expected effect is not clear-cut. Brandenburger and Nabeluff (1997) argue that

strategic postures of rival firms go behind the mere market-stealing actions. For example, competitors

might be key to sustain market demand, making a product more attractive to customers, thereby

benefiting all the players in an industry. Thus, while some rival actions may increase a firm’s market

value, others may dampen it. That granted, the perception of different levels of aggressiveness in the

rival moves appear an almost necessary requirement for strategy makers.

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Empirically, we investigate our surmises by analyzing the Carbonated Soft Drink (CSD)

industry from January 1999 to December 2003, an USD60 billions industry in the US alone. The

choice of the market makes a natural experiment possible: During the sample period, the CSD market

is virtually a duopoly (Coca-Cola and Pepsi controlled more than 75% of total sales) characterized by

relatively high price stability (Dubè 2004). The duopoly condition allows us to investigate with greater

precision the effects of strategic actions and reactions on the two competitors' market value. Moreover,

since in the CSD market prices are not major tools of competition, we are able to isolate more

effectively the impact of new product introductions and advertising efforts. It is worthwhile noting that

Coca-Cola and Pepsi invest together more than USD3 billions in advertising each year.

Our empirical results show that the impact of such rival actions on a firm’s financial-market

value is dichotomous. We find that a rival effort in product innovation has a clear negative impact on a

firm's financial-market value, while promotion efforts have a positive effect. A second rather

interesting empirical finding also emerges: The two strategic tools impact on a firm’s financial-market

value through different channels. While rival promotion efforts affect the firm’s financial-market

value through total demand, rival new product introductions act directly on the firm’s market share.

The article provides the following contributions to the literature. First, we enrich the evidence

on the impacts of marketing investments of a firm’s financial-market value, that is somewhat less

extensive compared to that focused on other competitive moves, for instance, R&D efforts. For

notable exceptions see Pauwels et al. (2004) and Srinivasan et al. (2006). Moreover, this constitutes

one of the few studies, which highlights the impact of rival strategic moves on a firm’s financial-

market value, especially within the marketing literature. In this respect, product releases and

advertising can theoretically take the form of defensive or offensive strategic tools (Bayus and Putsis

1999; Kadiyali et al. 1999; Shaffer and Zhang 2002). Our evidence suggests that in the case of Coca-

Cola and Pepsi the non-threatening effect prevails for advertising, the aggressive for new product

introductions. Although this finding might be specific to the CSD market, the fact that advertising

efforts are generally less aggressive than product innovation could be a more general insight.

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Additionally, it is worth noting that, although competition between Coca-Cola and Pepsi has

been analyzed elsewhere (Chintagunta and Vilcassim 1992; Golan et al. 2000), we offer two

improvements with respect to the extant literature. First, we utilize both product and advertising efforts

of the firm and its direct rival. Second, we can count on higher frequency data during a period of non-

price competition in a specific niche where the two major firms constitute almost a duopoly. This

increased empirical flexibility is due to the use of trademarks filed at the US Patent and Trademark

Office (USPTO) as a proxy for advertising efforts. This is a novel measure that has been only recently

employed in empirical studies (Seethamraju 2003; Fosfuri and Giarratana 2007). Trademarks are

available at high frequency with a precise product niche classification. Higher frequency data allow a

better match with the promotion process, which is characterized by pulsing, i.e. firms systematically

switch promotion on and off at a high frequency (Ofek and Sarvary 2003).

Interesting enough, the article shades also new light on the channels through which new

product introductions and advertising efforts influence a firm’s market value. We show that the two

strategic actions act through separated channels, advertising mostly affects total demand, while

product innovation acts directly on the distribution of market shares.

The remainder of the paper is organized as follows. The next section lays down the theoretical

model and defines our hypotheses. Section 3 offers a brief description of the CSD market. Section 4

presents the data, and our empirical methodology. Section 5 shows the results and discusses several

robustness checks. Section 6 concludes.

2. Theory and hypotheses

Marketing actions can occur along different dimensions of the marketing mix: price, product,

promotion and position (Levitt 1980). Such actions might affect the firm’s and its rivals’ market

values in different ways. Due to the ease in data collection, pricing strategies have been largely

analysed in the literature (i.e. Merrilees 1983) even if they do not constitute the prime variable of

competition in many markets in which demand is more brand-sensible and/or in which firms try to

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avoid price wars (Ilinitch et al. 1996). The easy-to-eat breakfast cereal market (Corts 1996) and the

beauty and health products are classical examples (Koehn 2001).

The analysis of the other dimensions of the marketing mix is rising only recently, especially

for new product releases and advertising efforts (see, for instance, Azoulay 2002; Bayus et al. 2003;

Hui 2004).1 New product introductions modify the space of observable characteristics, such as size,

shape, colour, weight, design material, reliability, and taste that are relevant to customers' preferences

and condition their choice processes. Advertising is linked to the image and positioning perceived by

the consumers beyond the physical features (Miniard et al. 1993). Advertising is build upon a complex

constellation of psychological attributes that the consumer assigns from purchasing, owing, and

consuming a particular product. It involves social, emotional, psychological, and esthetical

considerations. To a great extent, advertising creates brand power that influences and reinforces

customer perceptual connections among products, identity and lifestyle (Upshaw 1995).

The effects on a firm’s market value of its own product and advertising efforts are quite

straightforward. Several works propose sound empirical evidence on the positive returns of these two

manoeuvres (Bayus et al. 2003; Pawels et al. 2004; Srinivasan et al. 2006). However, the way firms

structure their product and advertising strategies depends largely on the information that comes from

their environment, being rival actions a salient part of this information (Minztberg 1978; Porter 1980).

Coherent empirical evidence on this latter point is missing. Rival actions could produce diverse

impacts on a firm’s market value because they could considerably exhibit distinct levels of

aggressiveness and cooperation (Brandenburger and Nabeluff 1997). Therefore, our theoretical

argument will leave in the background the effects of a firm’s product and advertising efforts on its

market value, while it will focus on the effect of rivals’ actions.

To structure the discussion, let Vit be the market value of firm i at time t, that is the net present

value of its future stream of profits. Other things equal, Vit depends positively on firm i’s market share

and on total demand. Precisely, market value increases if the firm attains a larger market share and/or

if total demand for the product expands. This could be easily modelled (more details in the Appendix)

1 Position is normally seen as a combination of the other three variables.

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by assuming, for instance, that the number of goods sold by firm i follows a Poisson process where the

Poisson’s λ and T parameter (the rate of number of occurrences per unit of time and the time duration)

are interpreted as firm i‘s market share and total demand, respectively (see also Russell and Kamakura

1994). We keep this in mind, since in the following arguments, the effects on a firm’s market value of

rival product introductions and advertising will be channelled through these two different avenues.

New Product Introduction. We analyze the impact of rival efforts to expand, modify and deepen their

product offering on firm i’s market value. First consider firm i’s market share. A larger number of

varieties by a rival produces a reduction in competitors’ market shares in almost any model of product

differentiation (Champsaur and Rochet 1989; Kekre and Srinivasan 1990; Baye et al. 1996).

Consumers might switch to the new varieties offered by the rival because they satisfy better their

tastes or because of their increased quality. In fact, firms, through continuous refinements of their

products, can develop versions that adapt and match more closely the needs of their customers. This is

especially important in environments where product introduction usually has a strong customer-driven

component (Schmalensee 2000). As long as some customers are substituting firm i's products for rival

new offerings, then the impact on market share is negative.

Concerning the effect on total demand, by adding new product varieties a firm can attract

customers who try the product for the first time. This is equivalent to build additional location sites in

areas of the Hotelling line where there are un-served consumers (Schmanlensee and Thisse 1987). In

other words, the arrival of products with novel characteristics could induce individuals that have never

bought a product to initiate a process of purchase. However, new costumers usually reveal scarce

brand loyalty and could later spread across different brands, the so-called churn demand (Neslin et al.

2006). This implies that a firm’s product introduction could increase industry demand without

affecting the distribution of market shares or affecting market shares without cannibalizing rival

profits. For example, Bayus and Putsis (1999) find that broad product lines in the personal computer

industry increase the overall demand. In a duopoly market, Kadiyali et al. (1999) highlight that a

firm’s new product introduction increases the revenues of the competitor as well. Indeed, if there is

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enough complementarity among competing branded products it is possible that new product

introductions by rivals might actually increase both a firm’s profits and value.

In sum, product introductions by a rival are likely to decrease a firm’s market share thus

reducing its market value. However, this negative effect might be counterbalanced by a positive effect

due to an increment in total demand. Granted these arguments, our first hypothesis reads:

Hypothesis 1: The net effect of rival product introductions of firm i’s market value is ambiguous.

However, if rival product introductions do not increase total demand, then the expected effect must be

negative.

Advertising efforts. Rivals’ advertising efforts might as well be channelled through an effect on firm

i’s market share and an effect on total demand. The literature classifies advertising in two categories:

generic and brand advertising (Bass et al. 2005). Generic advertising aims to increase primary demand

sales. Brand advertising affects market shares. Therefore, we can identify several potential effects.

Rival generic advertising has a positive effect on a firm’ market value because it increases

total demand. Generic advertising boosts primary demand by attracting new consumers (Berndt et al.

1997), increasing per capita consumption of the product and lengthening the product life cycle

(Friedman and Friedman 1976; Krishnamurthy 2000). Lancaster (1984) claims that it is the most

common effect of advertising.

Rival brand advertising influences directly firm i’s market share. First, brand advertising could

increase the probability that a costumer changes its purchase decision. It provides consumers with

information about the brand’s value proposition that differentiates it from its competitors, thereby

encouraging consumers of other brands to move to the advertised product (Krishnamurthy 2001).

Second, it can induce actual consumers of rival brands to increase the frequency of consumption

(Chaudhuri and Holbrook 2001). Indeed, brand advertising fosters brand loyalty and the consumption

intensity of a particular brand. Therefore, if rival brand advertising increases the consumption among

its costumers, the market shares and the revenues of the rivals will increase accordingly. If this

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happens, the most expected sign on a firm’s market value is negative. This analysis leads to the

following hypothesis:

Hypothesis 2: The net effect of rival advertising efforts of firm i’s market value is ambiguous.

However, if rival advertising efforts do not increase total demand, then the expected effect must be

negative.

3. Market Background

A carbonated soft drink consists of a flavor taste, a sweetener and carbonated water. The production

and distribution of CSDs involve four major participants: 1) suppliers of inputs, 2) concentrate

producers, 3) bottlers, and 4) retail channels. A concentrate producer blends a few raw materials that

for most regular colas consist of caramel coloring, phosphoric and/or citric acid, natural flavors, and

caffeine. Such inputs are produced at competitive prices. Bottlers purchase concentrate, add

carbonated water and high fructose corn syrup, bottle or can the CSD, and deliver it to customer

accounts.

The majority of US CSDs are packaged in metal cans (around 60%), then plastic bottles (about

38%) and glass bottles (2%). Metal cans and bottles are commodities with several manufacturers

competing for a single contract. Almost 60% of the distribution of CSDs in the US takes place either

through food stores or through fountain outlets. CSDs are among the five largest selling product lines

sold by supermarkets, accounting for 3%-4% of food store revenues. Although the industry involves

many different players at different stages of the value chain, two firms stand on top of all others:

Coca-Cola and Pepsi. At this respect, it is interesting to notice that both firms raised concentrate prices

throughout the 1980s and early 1990s, even if the real (inflation-adjusted) retail prices for CSD went

down.

The CSD industry grew in the US at approximately 3% per year over the period 1970-2000. It

is now a USD 60 billion industry (only considering the United States), with the cola segment

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accounting for about 60%-70% of the market value. Among concentrate producers, Coca-Cola and

Pepsi, controlled 76% of the US CSD market in sales volume in 2000. Coca-Cola was formulated in

1886, and Pepsi-Cola in 1893. Coca-Cola was the uncontested leader in the early history of the CSD

market, with 50% of market share in 1950. During the 1960s Coca-Cola and Pepsi began to

experiment with new cola and non-cola flavors and a variety of packaging options, abandoning their

traditional single product strategy.

Although Coca-Cola and Pepsi have aggressively competed for market shares, they have often

avoided direct price competition. It is illuminating the fact that the announcements of increases in

concentrate price by one company are followed immediately by upwards adjustments by the rival.

According to Roger Enrico, former CEO of Pepsi-Cola, "the warfare must be perceived as a

continuing battle without blood…If the Coca-Cola company didn't exist, we'd pray for someone to

invent them" (Yoffie 2004).

The two firms are instead aggressively competing on non-price dimensions, i.e. product

introduction and advertising. Both product introductions and advertising are important in the CSD

industry. In this industry, new product introduction refers to a change in the physical characteristics of

the product like flavors, ingredients, colors, tastes, packaging, etc. This is a long-played variable in the

Coca-Cola and Pepsi competition: Diet Coke for example was introduced the 8th of July, 1982. The

role of promotion is documented by the heavy investment in advertising and trademarks over time,

usually with innovative and sophisticated marketing campaigns. In the year 2000, Coca-Cola has spent

more than USD 200 million in its flagship brand "Coke Classic" (Yoffie 2004). Snapshot data tell that

Coca-cola filed for the first trademark in 1927 and at the end 2004 has been granted 1,105 trademarks.

Pepsi registered the first trademark in 1907 and at 2004 has filed 1,402 trademarks. As a matter of

fact, from the annual reports, Coca-Cola advertising expenditures top USD 1.9 billion dollars in 2003

and USD 1.8 in 2002. Pepsi shows similar numbers (1.6 and 1.5).

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4. Data and Methodology

4.1. Sample and estimation technique

Data on Coca-Cola and Pepsi were collected on a monthly basis during the period January 1999 -

December 2003. We have therefore 60 observations per firm, and 120 observations in total. Since the

market for soft drinks can be divided in two main niches, namely the Carbonated Soft Drink (Coke,

Pepsi, Sprite, Fanta, Mountain Dew, etc.) and the Non-Carbonated Soft Drink (juices, milky and

energy drinks, tea, coffee, water, etc.), our analysis will be mainly focused on the Carbonated niche

where Coca-Cola and Pepsi detain a stable duopoly position. This allows us to level off the noise of a

too scattered competition.

We chose a monthly base because we think that it matches better the fact that firms tend to

systematically switch promotion on and off at a high frequency (Ofek and Sarvary 2003) and

nonetheless it is still reliable for a robust econometric analysis.2

For clarity of exposition, we build our empirical evidence in three stages. First we estimate the

following regression:

titjtitjtiti AAPPv ,,4,3,2,10, )ln()ln()ln()ln()ln( εγβββββ +∆+++++= (1)

where vi,t is the market value of firm i at time t, subscript i≠j stands for Coca-Cola or Pepsi, P is

product introduction, A is advertising efforts and ∆∆∆∆ is a vector of control variables. This regression

allows us to investigate the direct effect of firm j’s marketing actions (product and advertising) on firm

i‘s market value.

However, the resulting estimations of our core parameters could be the sum of different forces

that claim for a more in-depth exploration.

2 This is not the classical event study that analyses daily market reactions to firms’ announcements and news.

Moreover, as we shall explain below, we use the trademarks filed at USPTO to proxy for advertising efforts.

Typically, the day when a firm files for a trademark does not coincide with the day in which a new advertising

campaign starts. From our interviews with managers of the two companies, it emerges that the delay period

between the filing and the campaign is approximately one week. Thus a monthly scale is likely to overcome this

potential problem.

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We therefore make a step further and estimate the following equation:

tititti SDv ,,210, )ln()ln()ln( κθφφφ +∆+++= (2)

where Dt is total demand of carbonated soft drinks at period t and Si,t is firm i’s market share in period

t. As one could easily note, equation [2] is inefficient since Dt and Si,t are endogenous variables that

produce heteroscedastic residuals. We show the results only to provide qualitative information on the

effect of total demand and firm i’s market share on its market value. Moreover, this estimation cannot

directly test our Hypotheses on rival product introductions and advertising efforts.

Thus, as a final step we propose a Generalized Method of Moments (GMM) estimation of [2]

in which Dt and Si,t are instrumented with new product introductions (Pit, Pjt) and advertising efforts

(Ait, Ajt) of firm i and its rival.

This estimation not only allows us to test if the effects of our core variables are similar to [1],

but also how these covariates affect separately total demand Dt and firm i’s market share Si,t. This is

important in order to understand which of the different effects discussed in the hypotheses is more

likely to prevail. A GMM estimation is well suited because it controls for potential correlation across

residuals and for possible heteroscedasticity. Thus, the estimation model will be:

+∆+++++=

+∆+++++=

+∆+++=

titjtitjtiti

titjtitjtit

tititti

AAPPS

AAPPD

SDv

,,4,3,2,10,

,,4,3,2,10

,,210,

)ln()ln()ln()ln(

)ln()ln()ln()ln(

)ln()ln(

ζηϑϑϑϑϑ

θιψψψψψ

κθβββ

(3)

4.2. Dependent Variable

As dependent variable, we employ a classical measure of firm financial-market value: the Tobin’s q.

Tobin’s q is a widely used indicator of firm value in the economic and management research,

especially to measure intangible assets (Dowell et al. 2000; Hall et al. 2005). Tobin’s q has been

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widely used in the literature that relates a firm’s innovation activity to its financial performance.

Tobin’s q is defined as the firm’s financial-market value per dollar of replacement costs of tangible

assets. Firm financial-market value equals to outstanding shares times share price plus book value of

long-term debt and net current liabilities. In a monthly scale, we take both the monthly averages of the

daily closing share prices and that of the outstanding shares. Replacement cost of tangible assets is the

sum of book value of inventory and net value of physical plant and equipment. Data are from SEC

filings. Since accounting data change every four months only, we use linear interpolation to assign the

change to each month. Precisely, if we observe that the replacement cost increases by USD 20

between two quarters, we count for each month an increase of USD 5. Although this might sound like

a limitation, one needs to keep in mind that more than 90% of the variability of Tobin’s q is due to the

variability of the numerator, not the denominator.

4.3. Variables of theoretical interest

The two variables of theoretical interest are new product introductions and advertising efforts by firm

i’s rival. We proxy these two variables with the announcements of new product introductions and the

trademarks filed at the US Patent and Trademarks Office, respectively.

Product introduction data come from Infotrac’s Promt database that, from a large set of trade

journals, magazines and other specialized press, reports several categories of events like product

introductions classified by SIC codes. This database is the new version of the old Predicast database

that has been used extensively in the literature (e.g. Pennings and Harianto 1992). We searched for all

press articles that are classified as a ’Product announcement’, a ’New product release’ and a ’Product

introduction’ for Coca-Cola and Pepsi between January 1999 and December 2003. Then, from each of

these articles, we extracted the date of product introduction (month and year) and we used the text and

the precise SIC code of the article to select only those products introduced in the CSD niche.3 We then

create the variable PRODUCTj,t that represents the number of product introductions announced by firm

3 Precisely, we excluded tropical/milky and other types of juices, tea/coffee/infusions, water,

energetic/vitamin/sport drinks and yoghurts.

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i’s rival in a given month. By and large, product announcements can be attributed to two categories: i)

new products (i.e. Cherry Mountain Dew, Lime Diet Coke, etc.), and ii) new packages, cans or bottles

of existing products (i.e. limited edition, silver-colored plastic 2 litre bottle, etc.).

Trademarks are combinations of ‘words, phrases, symbols or designs that identify and

distinguish the source of the goods or services of one party from those of others’ (USPTO

Documentation, http://tess.uspto.gov). Firms can register as a trademark a new name, a jingle or a

slogan, a new image or a logo (i.e. ‘can’t leave without it’).4

An interesting example that better explains the role of trademarks as a measure of advertising

efforts in CSD is the following. In 2004, the 25th of October, Coca-Cola filed for the trademark make

it real, that the US Patent and Trademark Office granted with the number 78,505,276. Randy Ransom,

senior vice-president for Coca-Cola trademark marketing at Coca-Cola North America described the

new campaign in this terms: “The new campaign, called make it real, attempts to take the message of

the Atlanta beverage company’s two-year-old ’Coca-Cola Real’ campaign to a new level [. . . ] Many

industry observers applauded the Real campaign for making strides in connecting with younger

consumers” (Wall Street Journal 2005).

We downloaded all the trademarks whose owner is Coca-Cola or Pepsi and with a filing date

between January 1999 and December 2003. Looking at the good and service description, we selected

only the CSD-related trademarks. Similarly to product introductions, we created the variable

TRADEMARK j,t that is the monthly number of trademarks filed by the rival firm.5

It is worth noting that the two firms use to concentrate advertising in different periods of the year:

Coca-Cola during Christmas holidays and Pepsi during summer holidays. Indeed, the correlation

between the time series of Coca-Cola and Pepsi trademarks equals to 0.126, not significant at 10%

level. Note also the variation especially of the trademarks variables in the Table 1. This rich variation

4 The US trademark owners pay different types of fees for each class of goods/services for which a trademark is

registered, and they have to prove periodically that they are using the trademark in the US market. The front

page of the trademark provides useful information - e.g. the owner’s name and address, the date when a complete

application was received by the USPTO (filing date), and the good and services description. 5 The good and service description contains a written explanation that clearly identifies the nature of the goods

and/or services as set forth in the application or registration.

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is difficult to extract from accounting data that in the period 1999-2003 show for the two firms an

average growth rate of advertising expenses of only 1.1%, with a standard deviation of 0.8%.

4.4. Controls

As exogenous control variables, we employ firm i’s product introductions and trademarks in the

carbonated niche, PRODUCTi,t and TRADEMARKi,t. As we also mentioned in the theory section there is a

direct effect of a firm’s product and advertising efforts on its financial-market value.

We construct also a firm dummy variable that takes the value of 1 for Pepsi and 0 for Coca-

Cola. The firm dummy aims to proxy for ’the fixed effect of attraction of the brand’ (Russell and

Kamakura 1994).

Month dummies are inserted to control for possible seasonal variations in the potential

demand.

From the Consumer Expenditure Survey of the Bureau of Labour Statistics, we obtained the

US annual consumer expenditure for food and beverages products (in USD millions). This variable

proxies for household income effects that might affect total potential demand for soft drinks

(FOODEXPENDITUREt).

Finally, we have two endogenous controls that are total demand Dt and firm i’s market share

Si,t. From Beverage Digest (www.beverage-digest.com) we obtain the annual number of 192-oz cases

sold (one case equals to 1.5 gallons). We divide this number by 12 to obtain our proxy for Dt

(DEMANDt). With the same data, we construct the proxy for Si,t. dividing the number of cases sold by

each firm by the total number of cases sold in a month (MARKETSHAREi,t). Table 1 shows the basic

descriptive statistics.

[Insert Table 1 about here]

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5. Results

5.1. Robust OLS

The results of robust OLS estimations are reported in Table 2. This regression estimates Equation [1].

The scale is logarithmic. Model 1 omits the core covariates, showing only the baseline model with all

control variables. Model 2, 3, and 4 progressively add our covariates of interest.

The first result is that PRODUCTj,t has a negative impact on firm i’ market value, that is, the

higher the rate of product introduction in a given month for Coca-Cola (Pepsi), the less will be Pepsi

(Coca-Cola) Tobin’s q. This suggests that product introduction has a strong ‘business stealing’ effect

or that the impact on total demand is rather modest.

On the other hand, TRADEMARK j,t positively impacts firm i’s market value, that is, the higher

the number of trademarks registered by Coca-Cola (Pepsi) in a given month, the larger will be Pepsi

(Coca-Cola) Tobin’s q. This finding suggests that the type of advertising protected through trademarks

is more likely to be generic rather than brand advertising. In fact, according to Hypothesis 2, firm i’s

market value can only increase with rival advertising efforts if the effect is mostly channelled through

an increment in total demand. Put differently, the business stealing effect of trademarked advertising

seems to be modest.

[Insert Table 2 about here]

It is possible to gain additional insights from considering the following experiment: using Model 4 and

all other variables held at their mean values, if firm j increases the monthly average number of product

introductions from 1 to 11 (the observed maximum), firm i’s Tobin’s q decreases by more than 60%.

Instead, a change in firm j’s average monthly trademarks from 1 to 29 enhances firm i’s Tobin’s q by

more than 30%.

As expected, PRODUCTi,t and TRADEMARK i,t show a positive and significant coefficient,

confirming that a firm’s efforts in product innovation and advertising increase its market value.

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It is also worth comparing the magnitude of the own and rival’s effects. If a firm introduces

new products, it increases its Tobin’s q with the same intensity than it reduces the rival’s market value

(0.824 vs 0.887 from Model 4). If the firm files new trademarks, it increases its and the rival’s Tobin’s

q by about the same amount (1.113 vs 1.145). Thus, the market interprets that product introductions

give raise to quite a perfect shift of market shares from one firm to the other, while advertising efforts

generate an overall boost in profitability that is evenly shared.

Finally, FOODEXPENDITUREt has a positive and significant impact on firm i’s market value,

while Pepsi has, other things equal, a lower Tobin’s q.

5.2. GMM estimation

Table 3 shows the results of the simple OLS estimation of Equation [2].

[Insert Table 3 about here]

As expected, both DEMANDt and MARKETSHAREi,t produce a positive impact on firm i’s market value.

However, this estimation has only a qualitative interpretation, due to its inner inefficient feature.

The next step is to estimate Equation [3], using firm i and firm j’s efforts in product

introductions and advertising as exogenous instruments influencing DEMANDt and MARKETSHAREi,t

(all other control variables are also included as instruments). Results are reported in Table 4.

[Insert Table 4 about here]

First, Table 4 highlights that the main effects of DEMANDt and MARKETSHAREi,t are confirmed.

Second and far more interesting, we can sort out how PRODUCTj,t and TRADEMARKj,t impact firm i’s

financial-market value. MARKETSHAREi,t does not seem to depend on the advertising efforts of neither

firm i nor firm j. Instead, it is directly influenced by the product releases of the two firms, so that

PRODUCTj,t has a negative and PRODUCTi,t a positive effect. By contrast, total demand only depends on

17

advertising efforts. Both TRADEMARKj,t and TRADEMARKi,t show a positive effect on DEMANDt,

whereas product introductions do not have a significant effect.

Therefore, the GMM estimation not only does it confirm our main finding that rival advertising

increases a firm’s financial-market value, while rival product introduction decreases it, but it also

suggests that these two dimensions of the marketing mix act through different channels. Advertising

boosts total industry demand, so that rival efforts have a positive spillover on a firm’s market value.

Product introduction is an aggressive tool that directly affects the distribution of market shares,

implying a negative impact.

5.3. Robustness checks

To validate our analysis, we perform some robustness checks. We address the presence of potential

endogeneity due to autoregressive trends that could bias our findings. Endogeneity could arise from

different sources. The efforts the firms exert in product innovation or advertising are likely to be the

result of a chosen strategy. So, it is possible that both product and trademark introductions follow a

common firm’s strategy that also encompasses a response to the moves of its direct rival. Moreover,

given the lag at which firms observe success or failure of their strategies, the actual decisions could be

influenced by past actions and manoeuvres. Additionally, measurement errors in our dependent

variables might produce endogeneity, and thus bias our estimates. To tackle this problem, we resort to

instrumental variable techniques.

Specifically, we use as instruments for the number of product introductions and trademarks the

lagged variables (one month lag) of firm i’s and firm j’s product introductions and trademarks. For

example, PRODUCTj,t is explained with PRODUCTj,t-1, PRODUCTi,t-1, TRADEMARKj,t-1, TRADEMARKi,t-1,

plus all other exogenous variables.

In Table 5, Model 1 presents the results of the GMM estimation. As a matter of facts, results

are similar. Overall, GMM estimation with autoregressive instruments does not substantially alter the

main results.

18

[Insert Table 5 about here]

We also replicate the regression with a different measure of firm financial-market value: the

unexpected cumulate average excess of stock market returns (CAR). CAR is proposed in this study as

a robustness check, since it is usually employed in event studies on a daily base. In fact, even if the

interpretation of a monthly CAR is far from its pure financial meaning, we think that our hypotheses

could gain additional support from this test. To obtain a monthly CAR we follow a consolidated

procedure (Chaney et al. 1991). For each firm we calculate the realized returns as RRi,t = (Pi,t+di,t−Pi,t-

1)/Pi,t-1 where Pi,t is the closing daily price and di,t is the eventual dividend paid. Then, we predict RRi,t

using the estimated parameters of the following equation: ERRi,t = αi + βiSPi,t, where SPi,t is the

composed Standard and Poor’s 500 index. For each ERRi,t, the sample period employed for the

estimation of αi and βi is equivalent to 200 days before the day t. We then calculate the rough

unexpected daily return as RCARi,t = RRi,t − ERRi,t and finally we obtain the unexpected cumulate

average excess of stock market returns as a moving average in a 3 day time window of RCARi,t. Since

we have monthly data, we take the monthly average of RCARi,t for each firm. Stock market prices and

indexes are from Yahoo!Finance and corporate websites. Model 2 of Table 5 shows the results that

hold un-changed in terms of sign and significance for our core covariates.

We have performed some other regressions that we do not show here but are available upon

request. For example, results hold unchanged when we use the quarter rather than the month as a time

unit of observation, and when we introduce multiplicative effects among our core variables, which are

not statistically significant.

6. Conclusions

Our primary aim in this article was to provide empirical evidence on how the financial-market value of

a firm reacts to rival moves along two dimensions of the marketing mix: product introductions and

advertising efforts.

19

In so doing, we contributed to two rising research trajectories that investigate i) how non-price

moves impact on a firm market value (Bayus et al. 2003; Azoulay 2002; Hui 2004; Pauwels et al.

2004; Srinivasan et al. 2006) and ii) how other firms’ actions influence this value (Das, Sen and

Sengupta 1998; Silverman and McGahan 2006).

We tackle this issue by focusing on the efforts in product introduction and advertising of

Coca-Cola and Pepsi in the Carbonated Soft Drink market in a period characterized by price stability

(1999-2003). Our study generates two main findings. First, we show that Coca-Cola (Pepsi) new

product introductions are associated with a decrease of Pepsi (Coca-Cola)’s Tobin’s q, while Coca-

Cola (Pepsi) advertising efforts have a positive significant effect on Pepsi (Coca-Cola)’s financial-

market value. Overall, the evidence seems to suggest that while product innovation tends to be an

aggressive competitive manouvre, advertising is a more accommodating tool.

Second, our findings highlight that product introduction and advertising affect a firm’s

financial-market value through different channels: Advertising acts mostly on total demand, while

product releases affect mostly the distribution of market shares.

Our conclusions include several implications for investors, managers and policy makers. For

investors, our results make clear that, especially in oligopolistic markets, the financial value of a firm

is sensibly affected by the actions of its competitors. Financial models that understimate the impacts of

rival actions, could indeed produce bias forecasts of firm potentialities. We think that this represents

an important lens to measure a firm financial value, especially for shareholders who want to assess the

performance of the strategies proposed by the board of managers.

The previous argument implies that a necessary condition for managers to elaborate correct

strategies is to single out the different impulses that come from the surrounding enviroment, especially

in terms of competitors’ moves. Advertising and new product introductions are two major sources of

firm investments and, thus, key areas of managerial decisions. In our view, managers not only have to

understand the impact of a particular move, but also the channels through which this move acts. We

show that in the CSD market advertising increases a firm’s and a rival’s financial-market value by

increasing total demand, while new product releases steal market shares. Therefore, the correct timing

20

and alternation of these two moves, adapted also to the observed rival moves, could increase the value

of a firm in an important way. The very fact that at the end of the year 2005 Pepsi overtakes Coca-

Cola in terms of market capitalization for the first time in more than a century of history has been

interpreted as a “humiliating reversal” by the Atlanta-based company shareholders (Economist 2005).

Policy makers and antitrust analysts could benefit from this study by noting that some of the

firms’ investments could have a more collusive effect. We claim that more detailed investigations

particularly on the role of advertising in oligopolistic industries could shed new light of the extent to

which promotion is used to increase entry barriers and to consolidate dominant position.

These final conjectures open up several extensions that we could not address in this work but

are avenues for future research. In this respect, we think the most intriguing are: i) the relationship

between efforts in advertising, in product innovation and the size of churn demand; ii) the patterns of a

firm response functions – both for advertising and product releases.

Acknowledgements

We would like to thank Kai-Lung Hui, Nora Lado, Marcello Mariani, Marco Seregni, Anna Torres and

seminar participants at the 2nd EIASM Co-opetition Conference (Bocconi University) and Universidad Carlos

III de Madrid for comments and suggestions on an earlier draft. The usual disclaimer applies.

7. References

Azoulay P. 2002. Do pharmaceutical sales respond to scientific evidence? Journal of Economics &

Management Strategy 11(4): 551-594.

Bass F.M., Krishnamoorthy A., Prasad A., Sethi S.P. 2005. Generic and brand advertising strategies in

a dynamic duopoly. Marketing Science 24(4): 556-568

Baye M.R., Crocker K.J., Ju J.D. 1996. Divisionalization, franchising, and divestiture incentives in

oligopoly. American Economic Review 86(1): 223-236

Bayus B.L., Erickson G., Jacobson R. 2003. The financial rewards of new product introductions in the

personal computer industry. Management Science 49(2): 197-210

21

Bayus B.L., Putsis W.P. 1999. Product proliferation: An empirical analysis of product line

determinants and market outcomes. Marketing Science 18(2): 137-153

Berndt, E.R, Bui T.M., Lucking–Reiley D.H.L. and Urban G.L. 1997. The roles of marketing, product

quality, and price competition in the growth and composition of the US antiulcer drug

industry, in Bresnahan, T.F., Gordon R.J. (Eds.) The Economics of New Goods UCP: Chicago:

277–322

Brandenburger A.M., Nabeluff B.J. 1997. Co-opetition. Doubleday: New York

Champsaur P., Rochet J.C. 1989. Multiproduct duopolist. Econometrica 57(3): 533-557

Chaney P.K., Devinney T.M., Winer R.S. 1991. The impact of new product introductions on the

market value of firms. Journal of Business 64(4): 573-610.

Chaudhuri A., Holbrook M.B. 2001.The chain of effects from brand trust and brand affect to brand

performance: The role of brand loyalty. Journal of Marketing 65(2): 81-93

Chen M.J., MacMillan I.C. 1992. Non-response and delayed-response to competitive move. The role

of competitor dependence and action irreversibility. Academy of Management Journal 35(3):

539-570

Chintagunta P.K., Vilcassim N.J. 1992. An empirical investigation of advertising strategies in a

dynamic duopoly. Management Science 38(9): 1230-1244

Corts K. 1996. The Ready-to-Eat Breakfast Cereal Industry in 1994. Harvard Business School. Case 9-

795-191

Das S., Sen P.K., Sengupta S. 1998. Impact of strategic alliances on firm valuation. Academy of

Management Journal 41(1): 27-41

Dowell G., Hart S., Yeung B. 2000. Do corporate global environmental standards create or destroy

market value? Management Science 46(8): 1059-1074

Dube J.P. 2004. Multiple discreteness and product differentiation: Demand for carbonated soft drinks.

Marketing Science 23(1): 66-81

Economist (the). 1998. Indiscriminate pricing. July 30: 35.

Economist (the). 2005. Coke v Pepsi. Things go worse with Coke. December 14: 44.

22

Fosfuri A., Giarratana M.S. 2007. Product strategies and start-up survival in Schumpeterian

environments: evidence from the Security Software Industry. Organization Studies

forthcoming.

Friedman, H.H., Friedman L. 1976. Advertising myopia. Journal of Advertising 5(2): 29-31

Ilinitch A., R. D’Aveni, A. Lewin 1996. New organizational forms and strategies for managing in

hypercompetitive environments. Organization Science 7(3): 211-220

Golan A., Karp L.S., Perloff J.M. 2000. Estimating Coke's and Pepsi's price and advertising strategies.

Journal of Business & Economic Statistics 18(4): 398-409

Hall B.H., Jaffe A., Trajtenberg M. 2005. Market value and patent citations. Rand Journal of

Economics 36(1): 16-38

Hui, K. 2004. Product variety under brand influence: an empirical investigation of personal computer

demand. Management Science 50(5): 686-700

Kadiyali V., Vilcassim N., Chintagunta P. 1999. Product line extensions and competitive market

interactions: An empirical analysis. Journal of Econometrics 89(1-2): 339-363

Kekre S., Srinivasan K. 1990. Broader product line – a necessity to achieve success. Management

Science 36(10): 1216-1231

Koen N.F. 2001. Estee Lauder and the market for prestige cosmetics. Harvard Business School. Case

Study #9-801-362

Krishnamurthy S. 2001. The effects of provision points on generic advertising funding. Marketing

Letters 12(3): 315-325

Krishnamurthy, S. 2000. Enlarging the pie vs. increasing one’s slice: an analysis of the relationship

between generic and brand advertising. Marketing Letters 11(4): 37-48

Lancaster K.M. 1984. Brand advertising competition and industry demand. Journal of Advertising

13(4): 19-30

Levitt T. 1980. Marketing success through differentiation of anything. Harvard Business Review

58(1): 83-91

23

Merrilees WJ. 1983 Anatomy of a price leadership challenge: an evaluation of pricing in the

Australian newspaper industry. Journal of Industrial Economics 31(3) 291-311.

McGahan, A.M., B.S. Silverman. 2006. Profiting from technological innovation: The effect of

competitor patenting on firm value. Research Policy forthcoming

Miniard P.W., Rose R.L., Barone M.J., Manning K.C. 1993. On the need for relative measures when

assessing comparative advertising effects. Journal of Advertising 22(3): 41-57

Mintzberg H. 1978. Patterns in strategy formation. Management Science 24(9): 934-948

Neslin S.A., Gupta S., Kamakura W., Lu J.X., Mason C.H. 2006. Defection detection: measuring and

understanding the predictive accuracy of customer churn models. Journal of Marketing

Research 43 (2): 204-211

Ofek E, Sarvary M. 2003. R&D, marketing, and the success of next-generation products. Marketing

Science 22(3): 355-370

Ofek E., Sarvary M. 2003. R&D, marketing, and the success of next-generation products, Marketing

Science 22(3): 355-37

Pauwels K., Silva-Risso J., Srinivasan S., Hanssens D.M. 2004. New Products, Sales Promotions and

Firm Value, With Application to the Automobile Industry. Journal of Marketing 68: 142-156.

Pennings J.M., Harianto F. 1992. The diffusion of technological innovation in the commercial banking

industry. Strategic Management Journal 13(1): 29-46

Porter M.J. 1980. Competitive Strategy. New York: Free Press.

Russell G.J., Kamakura W.A. 1994. Understanding brand competition using micro and macro scanner

data. Journal of Marketing Research 31(2): 289-303

Schmalensee R. 2000. Antitrust issues in Schumpeterian industries. American Economic Review 90(2):

192-196

Seethamraju, C. 2003. The value relevance of trademarks. in Hand, J.R.M. and Lev, B. (eds)

Intangible Assets. Values, Measures and Risks Oxford University Press: Oxford.

Shaffer G., Zhang Z.J. 2002. Competitive one-to-one promotions. Management Science 48(9): 1143-

1160

24

Singh A. 2003. Competition, corporate governance and selection in emerging markets. Economic

Journal 113(491): 443-464

Srinivasan S., Pauwels K., Silva-Risso J., Hanssens D.M. 2006. Product Innovation, Advertising

Spending and Stock Market Returns. Marketing Science. Forthcoming.

Upshaw E. 1995. An Economist view of research on term limits. Social Science Quarterly 76(4): 730-

733.

Wall Street Journal (the). 2005. Coke to debut 'Real' Ad on 'Idol', January 17: 14

Wernerfelt B., Montgomery C.A. 1988. Tobin’s Q and the importance of focus in firm performance.

American Economic Review 78(1): 246-250

Yoffie D. 2004. Cola wars continue: Coke and Pepsi in the twenty-first century. Harvard Business

School. Case Study #9-702-442

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Table 1: Descriptive Statistics

Variable Obs Mean Std.Dev. Min Max

TOBIN’S Qi,t 120 3.88 2.72 1.68 6.31

PRODOCTj,t 120 1.325 1.572 0 11

TRADEMARKj,t 120 2.675 4.203 0 29

FOODEXPENDITUREt 120 437.08 10.915 419.25 447.916

MONTHDUMMY 120 0.83 0.277 0 1

FIRMDUMMY 120 0.5 0.52 0 1

DEMANDt 120 631.20 6.302 624.750 640.625

MARKETSHAREi,t 120 0.5 0.083 0.415 0.584

26

Table 2: OLS Robust Regression, estimation of Equation [1], Y=Tobin’s qi,t

Model1 Model2 Model3 Model4

PRODUCTj,t -0.851**

(0.384)

-0.887**

(0.341)

TRADEMARKj,t 1.103**

(0.339)

1.113**

(0.331)

Controls

PRODUCTi,t 0.608*

(0.331)

0.908*

(0.480)

0.524*

(0.333)

0.824*

(0.456)

TRADEMARKi,t 1.102**

(0.432)

1.068**

(0.333)

1.140**

(0.335)

1.145**

(0.343)

FOODEXPENDITUREt 0.048**

(0.016)

0.050**

(0.018)

0.043**

(0.012)

0.044**

(0.012)

FIRMDUMMY -0.263**

(0.046)

-2.823**

(0.456)

-1.762**

(0.531)

-2.101**

(0.519)

MONTHDUMMY Yes Yes Yes Yes

CONSTANT 2.243**

(0.378)

2.246**

(0.462)

2.294**

(0.685)

2.834**

(0.687)

R2 0.231 0.299 0.293 0.322

OBS. 120 120 120 120

Notes: i) Heteroskedastic consistent standard errors in parenthesis. ii) **significance 0.05; * 0.10

27

Table 3: OLS Robust Regression, estimation of Equation [2], Y=Tobin’s qi,t

Model 1 Model 2 Model 3

DEMANDt 3.915**

(0.745)

3.867**

(0.821)

MARKETSHAREi,t 11.103**

(5.339)

13.252**

(4.331)

Controls

FOODEXPENDITUREt 0.750**

(0.051)

0.643**

(0.061)

0.751**

(0.055)

FIRMDUMMY -3.263**

(1.046)

-2.823**

(1.024)

-4.232**

(1.387)

MONTHDUMMY Yes Yes Yes

CONSTANT 2.849**

(0.845)

3.051**

(0.775)

3.111**

(0.949)

R2 0.493 0.502 0.653

OBS. 120 120 120

Notes: i) Heteroskedastic consistent standard errors in parenthesis. ii) **significance 0.05; * 0.10

28

Table 4: GMM Regression, estimation of Equation [3]

Model 1 Model 2 Model 3

Y=Tobin’s qi,t

DEMANDt 16.738**

(1.273)

15.795**

(6.608)

15.795**

(7.608)

MARKETSHAREi,t 7.892**

(3.971)

4.495**

(1.808)

8.248**

(3.688)

CONSTANT 10.508**

(3.740)

10.204**

(3.335)

10.249**

(4.749)

Y=MarketShare

PRODUCTj,t -0.042**

(0.009)

-0.040**

(0.009)

TRADEMARKj,t 0.504

(0.954)

0.344

(0.651)

PRODUCTi,t 0.051**

(0.010)

0.050**

(0.010)

TRADEMARKi,t 0.302

(0.938)

0.443

(0.657)

CONSTANT 0.487**

(0.018)

0.489**

(0.017)

Y=Demand

PRODUCTj,t 1.993

(1.549)

0.993

(0.909)

TRADEMARKj,t 0.600**

(0.002)

0.053**

(0.008)

PRODUCTi,t 2.700

(1.855)

0.600

(0.862)

TRADEMARKi,t 1.233**

(0.573)

0.032**

(0.008)

CONSTANT 6.263**

(1.242)

6.283**

(1.248)

MONTHDUMMY

FIRMDUMMY

FOODEXPENDITUREt

Always inserted in all equations

R2 0.893 0.835 0.895

OBS. 120 120 120

Notes: i) Heteroskedastic consistent standard errors in parenthesis. ii) **significance 0.05; * 0.10

29

Table 5: Robustness Checks

Y=Tobin’s qi,t Y=RCARi,t

PRODUCTj,t -0.010**

(0.003)

-0.0039**

(0.0017)

TRADEMARKRIVALj,t 0.011**

(0.002)

0.0065**

(0.0024)

Controls

PRODUCTi,t 0.007*

(0.004)

0.0035**

(0.0017)

TRADEMARKi,t 0.010**

(0.003)

0.0012*

(0.0007)

FOODEXPENDITUREt 0.011

(0.012)

0.004

(0.005)

FIRMDUMMY -0.019**

(0.004)

0.004

(0.006)

MONTHDUMMY Yes

CONSTANT 2.243**

(0.378)

-0.006

(0.005)

R2 0.308 0.06

OBS. 118 120

Notes: i) Model 1 GMM estimation; ii) Model 2 uses CAR as dependent variable. iii) Standard error in

parentheses. Standard Errors computed from heteroscedastic-consistent matrix (Robust–

White). iv) ** significance 0.05; * 0.10.

30

APPENDIX

Let vit be the market value of firm i at time t, that is the net present value of its future stream of profits

∑∞

= +

−=

0 )1(tt

ititititit

r

xcxpv where p is the product price, x the number of goods sold, c constant marginal

cost and r the discount rate. Let us assume that p, c and r are constant over time, a fair assumption for

the Carbonated Soft Drink market. Indeed, in the period 1999-2003, the cost of good sold for the two

firms increased only by 4.5%. Therefore, the market value solves as )()( itit xEr

p

r

cv +

−= . Assume

also that the number of goods sold by the firm i follows a Poisson process !

)(~

x

Tex

xT

it

λλ−

. The λ

parameter, that in a Poisson process is the probability that an event occur in a unit of time, could be

interpreted as firm i‘s market share (Si,t), that is the probability that a consumer will buy the brand i.

The parameter T, the total period of time elapsed, could be interpreted as total demand (Dt) (see also

Russell and Kamakura 1994). Therefore, the expected value of x equals to titit DSxE =)( . This means

that firm i’s market value reads ittit SDr

p

r

cv )( +

−= that in logarithm scale is

)ln()ln()ln()ln( ittit SDr

pcv ++

+−= , very similar to our equation [2].