effect of marketing efficiency, brand equity by OpeBello




                               Luis Fernando Angulo
        Autonomous University of Barcelona, Business Economics Department
             08193 Bellaterra (Cerdanyola del Vallès), Barcelona, Spain
                   Tel. +34 93 581 1209, Fax +34 93 581 2555
                       Email: LuisFernando.Angulo@uab.es


This research focuses its attention to support empirically and not separately the impact
of marketing activities, brand equity and customer satisfaction on firm performance. In
addition, this study intends to fill the gap of marketing efficiency effect on long-term
profits. Through methodology of three stages, two by econometric models and one
using data envelopment analysis, the authors provide empirical evidence for the
marketing link to firm value. Firstly, the results in the first stage confirm the impact of
marketing assets on short-term performance. Secondly, the marketing efficiency shows
that there are firms that have better abilities to maximize results in terms of marketing
activities. Finally, some future research lines were considered.

Keywords: Advertising, Brand Equity, Customer Satisfaction, Data Envelopment
Analysis, Marketing Efficiency, Marketing Impact, Firm Value.


Nowadays, Marketing has to face some situations that the new business environment
brings with it. One of them is related to the evolution of business atmosphere from
Marshall Economy labelled as bulk-processing (Arthur 1996) to the Positive Feedback
Economy known as the increasing returns (Arthur 1989, 1990, 1999) as well as the
knowledge-processing. In addition, the second situation is linked to the operational and
business unit level that Marketing take up in the organization (Ambler 2000). The last
situation is connected not only with the increasing expectative of the board to get short-
term profits but also with the rising relevance of the financial perspective on the top
management (Webster et al. 2003).

In the called bulk-processing perspective, firms wondered how much money is spent
(Ambler 2000), focused in the denominator of the business ratio (Hamel and Prahalad
1994), and oriented to reduce inputs. In the new environment, firms have to wonder
how much money is generated (Ambler 2000) expand the outputs (Hamel and Prahalad
1994) and look for the generators of cash flow (Srivastava et al. 1998). Through
Marketing firms can answer. The response has been the market, the customer. This may
seem nothing new but the great majority of firms do not follow the logic through
(Ambler 2000).

To make worse the board only focuses 10% of its time on figuring out about the
originators of money (Ambler 2000). As Marketing is the link between firms and
markets, its organizational process, position and path give the firms the possibility to be
aware of where the profits come by. Nevertheless, Marketing has been considered at
business unit level (Ambler 2000). In addition during 90s and 2000s the financial
function in the firms grew in importance (Webster et al. 2003) giving relevance to
short-term results.

Consequently, as the short time dedicated to discuss marketing metrics as well as the
interest of corporate board in short-term returns, marketing managers have to develop
tools to quantify its contribution to firm growth and profitability in terms that are
meaningful to CEOs, CFOs, and investors (Webster et al. 2003).

Focusing on Marketing, Customer Satisfaction and Brand Equity are relevant aspects to
the firm’s profitability. Theoretical aspects have been considered in the research of

customer satisfaction construct (e.g. Anderson and Sullivan 1993; Anderson et al. 1994,
Fornell et al. 1996) as well as in the research of brand equity (e.g. Aaker 1991; Keller
1993, 1999). Some empirical aspects of the link of marketing positions on firm
performance have been developed (e.g. Anderson et al. 2004, Gruca and Rego 2005,
Madden et al. 2006).

However studies which focus on researching the impact of customer satisfaction and
brand equity at the same time is left unexamined. In addition empirical investigations
that focus on considering marketing efficiency as an additional influencer of firms’
profits is a research gap. We expect to fill this gap by shedding light on the following
objectives. The first is related to find out more empirical support to the effect of
marketing activities and positions on short and long term profits. The other objective
intend to demonstrate if the marketing efficiency increase the long term profits of firms
more than the augment generated by the marketing positions.

The remaining part of this work in progress is organized as following. Firstly, a
theoretical and empirical review of marketing impact and efficiency is presented.
Secondly, the theoretical model adopted and the hypotheses are stated. Thirdly, the
research methodology stage by stage is developed. Fourthly, the discussion and
conclusion are presented. Finally, the academic and managerial relevance is suggested.


The Resource Based View (RBV) recognizes the importance of a firm’s internal
organizational resources as determinants of the firm’s strategy and performance (Barney
1991; Grant 1991; Wernerfelt 1984). Barney (1991) defines the term internal
organizational resources as all assets, capabilities, organizational processes, firm
attributes, information, knowledge, that are controlled by a firm and that enable it to
envision and implement strategies to improve its efficiency and effectiveness.

Although the RBV recognizes that a firm’s physical resources are important
determinants of performance, it places primary emphasis on the intangible skills and
organizational resources of the firm (Barney 1991; Collis 1991). Some intangibles
resources of the firm are the market-assets (Srivastava et al. 1998) such as customer
satisfaction and brand equity.

In addition, the Dynamic Capabilities, strengthening RBV, put emphasis in how
combinations of resources and competences (Teece et al. 1997) can be developed,
deployed and protected. The factors that determine the essence of a firm’s dynamic
capabilities are the organizational processes where capabilities are embedded, the
positions the firms have gained (e.g. assets endowment) and the evolutionary paths
adopted and inherited (Teece et al. 1997). Based on this perspective, the marketing
factors that determine the competitive advantage are marketing efficiency resulted of
marketing organizational process and the endowments of market assets that has
generated such as customer satisfaction and brand equity, i.e. marketing positions.

In the context of global competition, RBV and Dynamic capabilities theory suggest that
historical evolution of a firm (accumulation of different physical assets and acquisition
of different intangible organizational assets through tacit learning) constrains its
strategic choice and so will affect market outcomes (Collis 1991). According to Douglas
and Craig (1989), the development of a Marketing Strategy is carried out during the
stage of global rationalization. It means that the firm has had to take the step of initial
foreign market entry and expansion of national markets during its process of
internationalization. Consequently, in the two previous stages, the firm learned and
accumulated not only different physical assets but also different intangible
organizational assets; likewise, it faced and took risks in different and complex market
contexts. This process of learning affected its performance (Collis 1991).

Marketing Impact

The need for measuring marketing impact is intensified as firms feel increasing pressure
to justify their marketing expenditures (Gupta and Zeithaml 2005; Rust et al. 2004;
Srivastava et al. 1998). According to this, marketing practitioners and scholars are
under increased pressure to be more accountable for showing how marketing activities
link to shareholder value.

It is important to know that marketing actions, such as packaging, brand name, density
of the distribution channel, advertising, permanent exhibitions, sponsoring, press
bulletins, among others (Van Waterschoot and Van den Bulte 1992) can help build
long-term assets or positions (Teece et al. 1997) as brand equity and customer
satisfaction (Srivastava et al. 1998). These assets can be leveraged to deliver short-term
profitability (Rust et al. 2004) and shareholder value (Srivastava et al. 1998).

Marketing impact studies have evolved in three stages of development. The first one is
related to research that link marketing activities to accounting-based measures. The
most studied activity in this stage has been advertising, and it has been connected to
sales (Assmus et al. 1984; Clarke 1976; Dekimpe and Hanssens 1995), to profits (Jedidi
et al. 1999), and return on investment (Danaher and Rust 1996; Fitzgerald 2004).

The second stage is associated with research that connects marketing activities to
marketing assets. Cornwell et al. (2001) explores how the use of advertising and
promotion to support the sponsorship, and active management involvement are
significant predictors of both the perceived differentiation of the brand from its
competitors and adding financial value to the brand. Other studies are from Bolton and
Drew (1991) and Dodds et al. (1991).

The last stage is related to studies that link marketing activities and marketing assets to
shareholder and firm value, measured by data from capital markets such as Tobin’s Q,
Operating Cash Flow, Market Value, Earnings per Share, among others. Srivastava et
al. (1998) develop a conceptual framework of the marketing-finance interface which
concerns with the task of developing and managing market-based assets such as
customer satisfaction and brand equity with the objective of increasing shareholder
value by accelerating and enhancing cash flows, lowering the volatility and
vulnerability of cash flows, and increasing the residual value of them. The research of
Maden et al. (2006) intends to cover the propositions of Srivastava et al. They find
empirical evidence to the impact of brand equity on the generation of long-term profits.

In addition, Anderson et al. (1994) investigate the nature and strength of the link
between customer satisfaction and economics returns. They discuss how expectations,
quality and price should affect customer satisfaction and why customer satisfaction, in
turn, should affect profitability. The findings support a positive impact of quality on
customer satisfaction, and in turn, profitability measured as return on investment. In the
same vein, Gruca and Rego (2005) find empirical support to the link between customer
satisfaction, cash flow and shareholder value.

The research which associates the three stages mentioned above is the one of Rust et al.
(2004). They propose a conceptual framework that can be used to evaluate marketing as
a whole. It is a chain-of-effects model that relates the specific actions taken by the firm
(marketing actions) to the overall condition and standing of the firm. The chain model

includes besides the marketing actions, the impact on customer, on the market, and on
financial and firm value.

Marketing Efficiency

The other way that research in Marketing has faced Marketing performance is related to
efficiency. Charnes, Cooper and Rhodes (1978) define the efficiency as the comparison
among firms of the ratio of outcomes over the inputs required to achieve them. On the
other hand, Sheth et al. (2002) define marketing efficiency as the ratio of marketing
output over input. Both of them are the definitions that will be used.

Sheth et al. (2000) and Sheth and Sisodia (1995), in referring to their definition of
marketing productivity, include two the dimensions, efficiency as well as effectiveness,
i.e. getting loyal customers at low marketing costs. On the other hand, Rust et al. (2004)
use the term marketing productivity to refer how marketing activities are linked to
short-term and long-term profits.

In reference to literature review, Charnes et al. (1985) first suggested applying DEA to
gain insights into efficiency of marketing efforts. Since then, there have been some
marketing studies that used the DEA as a methodology. Kamakura et al. (1988) used
DEA to measure welfare loss and market efficiency. Mahajan (1991) studied a DEA
model for assessing the relative efficiency of sales units that simultaneously
incorporates multiple sales outcomes, controllable and uncontrollable resources, and
environmental factors.

Boles et al. (1995) propose a DEA based approach that provides a measure of relative
performance efficiency to evaluate the salesperson. Kamakura et al. (1996) evaluated
multiple retail stores (branches from a commercial bank) for their efficiency using DEA
and multiple translog cost function estimation, while Donthu and Yoo (1998) compared
the results obtained in the evaluation of multiple retail stores (restaurant chain) using
DEA and regression.

Färe et al. (2004) use techniques from the efficient measurement literature to evaluate
the performance of six United States beer firms in terms of their ability to translate
advertising messages into sales. Luo and Donthu (2001) demonstrate the application of
DEA to benchmark advertising efficiency and to estimate the relative efficiency of
advertising campaigns characterized by multiple inputs and multiple outputs.

Heskett et al. (1994) propose the framework for the Service-Profit Chain for linking
service operations, employee assessments, and customer assessments to firm’s
profitability, and Kamakura et al. (2002) develop an approach to assess it. The approach
combines data such as measures of operational inputs, customers’ perceptions and
behaviours, and financial outcomes from multiple sources. They use de DEA for the
operational analysis.

Donthu et al. (2005) try to fill the gap in research of the lack of appropriate
methodological tools for analysing the benchmarking process in marketing. DEA is
suggested to aid traditional benchmarking activities and is useful in identifying the best
performing units to be benchmarked against, as well as in providing actionable
measures for improvement of a company’s marketing performance. On the other hand,
Keh et al. (2006) intend to answer how a service firm (49-unit Asia-Pacific Hotel) can
right-size marketing expenses and yet strive to maximize revenue. They employ a
triangular DEA model with total expenses (controlling for number of rooms) as the raw
input, marketing expenses as intermediate output/input and revenues from room rentals,
and food and beverages as final outputs.


According to Rust et al. (2004) the firm’s marketing strategies, which might include
product strategy, price strategy, promotion strategy, lead to tactical marketing actions
such as packaging, brand name, density of the distribution channel, advertising,
permanent exhibitions, sponsoring, press bulletins (Van Waterschoot and Van den Bulte
1992), or any other initiative designed to have marketing impact. Under RBV, these
initiatives are organizational resources of the firm (Barney 1991). In addition, under
Dynamic Capabilities, these initiatives are the decisions of marketing organizational
processes (Teece et al. 1997). Then, the tactical actions and intangible resources of
marketing management (Srivastava et al. 2001) influence and generate firm’s marketing
positions such as customer satisfaction and brand equity (Srivastava et al. 1998).
Besides, the current marketing positions are often shaped by the path of marketing
activities; it means that firms’ previous routines constrain their future behaviour (Teece
et al. 1997).

Hence (See figure 1) we expect that:

       H1a: The actual marketing activities influence positively the generation of
             customer satisfaction and brand equity.

       H1b: The path of marketing activities influence positively the generation of
             customer satisfaction and brand equity.

At any point in time, tactical actions will have made more marketing positions (e.g.
brand equity and customer satisfaction), but they may not yet have influenced firm’s
profit and loss account (Rust et al. 2004). Under RBV, the drivers that determine the
better performance of the company are intangible resources (Barney 1991). Likewise,
based on Dynamic Capabilities perspective, one of the drivers of firm performance is
the marketing positions or the strategic posture of the firm (Teece et al. 1997). As a
consequence, the path and current positions can be leveraged to deliver short-term
profitability (Rust et al. 2004) and long-term profitability (Srivastava et al. 1998).
Therefore we look forward to:

       H2a: The actual Customer Satisfaction and Brand Equity have a positive
             impact on long-term profitability.

       H2b: The path of Customer Satisfaction and Brand Equity has a positive
             impact on long-term profitability.

       H2c: The path of Customer Satisfaction and Brand Equity has a positive
             impact on short-term profitability.

Sheth et al. (2000) suggest that the objective of being efficient and effective is to get
loyal customers at low marketing costs, and consequently increasing profits (Rust et al.
2004; Srivastava et al. 1998). Nowadays, one of the top priorities of marketing is
concerned about measure the effect of its assets or positions on firm value; it is being
tested as has been shown by the works of Anderson et al. 2004, Gruca and Rego 2005,
and Madden et al. 2006. The two formers found evidence about the effect of customer
satisfaction on shareholder value. The latter supported the effect brand equity over firm
value. Nevertheless, marketing research has not considered the differences of efficiency
among firms to demonstrate with strong support the relationship between marketing and
firm performance. Under Dynamic Capabilities perspective, marketing efficiency is
associated with the combination of marketing abilities and resources to generate rents;
i.e. the ability of marketing to maximize firms’ short-term financial results better than

competitors with the restriction of the same level of marketing expenditures. In
addition, according to Williamson (1991) the best strategy to increase long term profits
is to organize and operate efficiently.

Hence we expect that:

       H3: Marketing efficiency will increase the impact on long-term profits.

                                       Satisfaction        H2a, b        LONG-TERM
                                       Brand Equity        H2a

                                       H1b            H3

         MARKETING               H2c                                      SHORT-
         ACTIVITIES                                                        TERM

                      [Inputs]         MARKETING             [Outputs]

                             Figure 1 Conceptual Model Adopted
                                  It has not been tested


First Stage: Econometric Approach

The objective of this stage is to examine the chain of effects of marketing activities and
marketing positions on short-term performance. The construct of marketing activities
(See Table 1) is composed by the variable advertising spending (Rust et al. 2004; Van
Waterschoot and Van den Bulte 1992). The construct of marketing assets is shaped by
the variables brand equity and customer satisfaction (Srivastava et al. 1998). The
variables used to measure short-term profits are revenues and operating profits
(Anderson et al. 1994). The long-term profits will be measured by the Tobin’s Q
(Anderson et al. 2004). Control variables that will be used are R&D intensity, number
of brands, segments (Gruca and Rego 2005), market share and concentration level of
firm in the industry (Anderson et al. 2004; Gruca and Rego 2005).

        TABLE 1 Model Specifications

                                                                            Years of                                  Kind of
   Constructs                Variables                  Indicators                     Sources of Information
                                                                            Analysis                                  Source

   Marketing                                        Annual spending in                  Advertising Age Digital
                            Advertising                                  2002 - 2004                                 Secondary
    Activities                                         million US$                         and Print Edition

                                                    Annual Brand Value
                           Brand Equity                                  2002 - 2004        Interbrand Data          Secondary
                                                      in million US$
Marketing Assets                                                                       National Quality Research
                       Customer Satisfaction           Annual Index      2002 - 2004   Center at the University of   Secondary

                                                    Annual revenues in
                             Revenues                                    2002 - 2004        Fortune Review           Secondary
Short-term Profits                                     million US$
  (Accounting                                        Annual profits in
                         Operating Profits                               2002 - 2004        Fortune Review           Secondary
      Data)                                            million US$
                     Return on Investor (ROI) a      Annual ROI in %     2002 - 2004        Fortune Review           Secondary

                                                     Annual in million
                          Market Valuea                                  2002 - 2004        Fortune Review           Secondary
Long-term Profits
                                                     Annual in million
 (Capital Market           Book Valuea                                   2002 - 2004        Fortune Review           Secondary
                         Earnings p/ sharea           Annual in US$      2002 - 2004        Fortune Review           Secondary
                            Tobin’s Q                Annual Quotient     2002 - 2004           Compustat             Secondary

                                                    Annual % of R&D
                          R&D Intensitya                                 2002 - 2004        Compustat Data           Secondary
                                                     spending / Sales
                                                      Herfindahl and
                       Concentration Levela                              2002 - 2004        Compustat Data           Secondary
                                                     Hirshman Index
                        Number of Brandsa                Number          2002 - 2004           Web Page              Secondary
                           Market Share                 Annual %         2002 - 2004        Compustat Data           Secondary
    Control                                         Number of distinct
                             Segmentsa                                   2002 - 2004        Compustat Data           Secondary
                                                     Business Segment
                           International                Number of
                                                                             2006              Web page              Secondary
                            Expansion                   Countries
                          Stock Exchange            Number of Abroad
                                                                             2006             Web pages              Secondary
                        Internationalizationa        Stock Exchange
                     International Experiencea            Years          2002 - 2004           Web page              Secondary

        Source: Self-devised                     a. It is being gathered.

        The research has been applied to the top United States firms reported in Advertising
        Age, Business Week, American Customer Satisfaction Index (ACSI) and Fortune 500

Review. The data of marketing expenditures in advertising was obtained from the report
of Global Marketers published by Advertising Age. This report contains information
about the rank in terms of the amount of advertising spent, the worldwide advertising
spending, the U.S. measured media spending, and the spending by regions (Asia,
Europe, and Latin America). All data are from secondary sources, hence objective; none
of them are the result of applying a survey, through susceptible of subjective response.

The data of brand value was found in the Business Week report about the ranking of the
world’s most valuable brands. This report has information about the rank, the brand
value in millions of dollars, the percentage of change of brand value, the country of
ownership, and the main news that caused such value. The customer satisfaction index
was obtained from the annual ACSI from 2002 to 2004 that were made available to us
by the National Quality Research Centre at the University of Michigan. The ACSI
methodology provides a uniform, independent, customer-based, cumulative, firm level
satisfaction measure for 200 companies in 40 industries and in 7 sectors of the U.S.
economy (Anderson et al. 2004). In reference to financial information, Fortune 500
Review was the source. The report about 500 largest U.S. corporations permitted us to
get data of revenues, profits, assets, stockholders’ equity, market value, earnings per
share and total return to investor.

The sample of analysis considered is composed by U.S. largest companies published in
Fortune (2005); that meanwhile is equal to 15. The methodology employed is
structured, quantitative, and explanatory. Multiple Regression Analysis (Anderson et al.
2004; Gruca and Rego 2005) was used as the technique for measuring the effects of the
constructs of the model. The definition of the regression models1 are the following:

             Ln (Revt / Revt-1) = f [Ln (ADt / ADt-1), Ln (CSt / CSt-1), Ln (BVt / BVt-1)]

         Ln (Profitst / Profitst-1) = f [Ln (ADt / ADt-1), Ln (CSt / CSt-1), Ln (BVt / BVt-1)]

where, Ln (Revt / Revt-1) is the annual variation of firm revenues, Ln (ADt / ADt-1)
represents the annual variation of advertising spending, Ln (CSt / CSt-1) is the annual
variation of customer satisfaction index of each firm, Ln (BVt / BVt-1) represents the
annual variation of financial brand value, and Ln (Profitst / Profitst-1) is the annual
variation of operational profits.

    This is an approach of the final model. Some variables information is still in gathering process.

In this stage, we examined the relationship between model variables. Significant
relationship was found among advertising, customer satisfaction, brand value and
revenues (See Table 2). Nevertheless, when we examine the second model, we observe
that the impact of customer satisfaction and brand value on operational profits is
significant, but advertising does not have impact on them.

Table 2 Initial marketing effects on firm performance

  Model      Intercept       AD         CS          BV        R2        F       Sig.    n

 Revenues      .030*       0.322***   0.992**     0.513***   0.759    11.534    0.001   15
  Profits      0.038        0.223     3.154*       1.15**    0.45      3.005    0.077   15

               Source: Self-devised
               (*) 90% (**) 95%       (***) 99%
According to Williamson (1991), the best strategy to increase profits is to organize and
operate efficiently. Departing from this suggestion, we measure whether the inclusion of
the efficiency in the model increases the explanatory effect of marketing activities on
long term value. Based on what Charnes et al. (1985) suggested about applying DEA to
gain insights into efficiency of marketing efforts, the next stage is to calculate the
efficiency score through a DEA.

Second Stage: Data Envelopment Analysis

The objective of this stage is to measure the efficiency of marketing. Marketing strategy
plays a central role in winning and retaining customers, ensuring business growth and
renewal, developing sustainable competitive advantages, and driving financial
performance through business processes (Srivastava et al., 1999). Consequently, the
marketing actions (advertising) taken by the U.S. companies permit them to have
business growth and profits. Therefore, the efficiency measure is related to the ability of
the firm to maximize the level of short-term profits with the restriction of the same level
of marketing expenditure.

The methodology employed in this stage is quantitative and evaluative. DEA (Charnes
et al. 1985) will be used as the technique for measuring the efficiency of marketing.
Charnes et al. (1978) first proposed DEA as an evaluation tool to measure and compare
decision-making unit’s (DMU) productivity. Data Envelopment Analysis is a method
for mathematically comparing different DMUs productivity based on multiple inputs

and multiple outputs. The ratio of weighted inputs and outputs produces a single
measure of productivity called relative efficiency. DMUs that have a ratio of 1 are
referred to as efficient, given the required inputs and produced outputs. See Seiford
(1996) for a more technical description of DEA. The model of marketing efficiency has
two outputs and one input, the formers are Revenues and Operating profits, and the
latter is Advertising.

Technical Efficiency: Characterization of Marketing Efficiency

The output-oriented DEA model (Charnes et al., 1978)2 is employed to measure
Marketing Efficiency. According to Hamel y Prahalad (1994) to be a revolutionary in
the market and compete for the future, firms have to change the perspective of
management, it means forget to concentrate efforts in the denominator (inputs) and to
concentrate on the numerator (the outputs). This orientation is similar to the applied by
Seiford and Zhu (1999). The output-oriented DEA model is presented next:
maxβ0 ;t=1,2,3,4

∑ λ .Y
                     ≥ β.Y;i=1,2,...,I


∑ λ .X
        k       k
                j    ≤ X j ;j=1,2,...,J
β0 ,λk ≥ 0

where                 Yik and Xk
                               j          are the amount of the ith output produced and the amount of the jth

input consumed by the kth DMU, respectively. We have n = 16 DMUs, i = 2 outputs –
revenues and operating profits, j = 1 input – advertising.

Let β be the optimal value for Marketing Efficiency. If
            0                                                                  β1 =
                                                                                0     1, then a firm is said to be
CCR-efficient in marketing. Tables 3 reports CCR efficiency scores. Only one firm,
namely General Electric is CCR-efficient in Marketing.

    The model is known as CCR in reference to Charnes, Cooper and Rhodes (Charnes et al. 1978).

Table 3 Efficiency Results and Return-to-Scale

                                                      2003                2004
                         DMU                  CCR     BCC      CCR        BCC

Altria Group                                   2.63     1.93     3.47    2.20
Anheuser-Busch Cos.                            4.25     2.59     5.23    3.24
Coca-Cola Co.                                  6.33     5.87     8.48    5.87
Colgate-Palmolive Co.                          7.60     5.97     7.83    5.31
Dell Computer                                  1.75     1.35     2.74    2.61
Ford Motor Co.                                 1.87     1.00     2.37    1.00
General Electric Co.                           1.00     1.00     1.00    1.00
Hewlett-Packard Co.                            1.35     1.22     1.48    1.32
Kellogg Co.                                    8.56     6.76    11.81    10.31
Yum Brands                                    13.18    12.42    15.74    15.31
McDonald’s Corp.                               8.58     7.96    11.05    8.19
Microsoft Corp.                                1.43     1.15     2.33    1.76
Nike                                           3.44     1.00     4.09    1.00
Pepsi Co.                                      5.64     5.09     6.42    5.30
Time Warner                                    6.57     3.45     8.50    3.88

                                       Source: Self-devised
Scale efficiency has been recognized in the literature as an important issue (Seiford and
Zhu 1999). However, the CCR model (2) assumes constant return to scale (CRS). In
order to determine the scale efficiency of these firms, we employ the output-oriented
BCC3 DEA model which allows variable return to scale (VRS) (Banker et al. 1984).

maxγ 0 ;t=1,2,3,4


∑ λ .Y
                         ≥ γ .Y;i=1,2,...,I


∑ λ .X
          k          k
                     j   ≤ X j ;j=1,2,...,J

∑ λ =1    k

γ 0 ,λk ≥ 0

Let            γ0
                          be the optimal value for (2) in Marketing Efficiency. Define a scale efficiency
measure by:

              β 0t
π0 =


for : t = 1,..., 4                                                                                  (4)

    The model is known as BCC in reference to Banker, Charnes, and Cooper (Banker et al. 1984).

Obviously,     π0
                     ≥ 1. If    π0
                                     = 1, a firm is called scale efficient; otherwise, if                       π0
                                                                                                                     > 1, a firm is
called scale-inefficient. Table 3 displays                   γ0
                                                                   for each firm when (3) is employed to
measure Marketing Efficiency. Note that                     π0 =
                                                                   1 if and only if          β 0t   =   γ0
                                                                                                             . In our case, only
firms that were CCR-efficient are scale-efficient. A paired-difference t-test was applied
to CCR and BCC scores in each stage. The results of the t-test were significant,
indicating that serious scale inefficiency was present for the firms analyzed (See Table

Table 4 Paired-Difference t-test between CCR and BCC

      Stage                    Paired          Mean Difference            t-test                        Sig.

      2003                CCR-BCC                   -1.03                  4.40                         0.001
      2004                CCR-BCC                   -1.62                 -4.72                         0.00

        Source: Self-devised

We next determine whether increasing or decreasing returns to scale (IRS or DRS) is
the primary cause of scale inefficiency. As shown in Banker (1984), the optimal
solution for    λk   (k = 1,…, n) in (4), i.e., the magnitude of ∑ λ , contains the information


for return to scale (RTS) classification. To determine the RTS classification we run the
data on Efficiency Measurement System (EMS) software. Using this method, possible
misclassification errors from multiple optimal solutions for                                 λk     are avoided. The RTS
classifications are readily obtained from optimal solutions to (2) and (3) (Seiford and
Zhu 1999). Table 3 shows that CCR-inefficient and scale-inefficient firms were
operating on IRS frontier. As economists have long recognized, an IRS frontier firm
would generally be in a more favourable position for expansion, compared to a firm
operating in CRS or DRS region (Arthur 1996; Seiford and Zhu 1999). The next step is
to introduce the marketing efficiency score as explaining variable through an
econometric approach, but the last stage is in process of development and has been
introduced in future research lines.


The objective of the research was to find out more empirical evidence that supports the
impact of marketing activities and positions on firm’s performance, as well as, to

demonstrate if marketing efficiency increase the influence of marketing on financial
value of the firm more than the generated by marketing assets. After running the
econometric models, we observe, in the first stage, significant effects of advertising
spending, customer satisfaction and brand value over short-term performance. These are
consistent with Ittner and Larcker’s (1996). They examine the correlation between
customer satisfaction and a firm’s raw market value and also find mixed and
inconclusive results. On the other hand, in the second stage, we expect significant
relationship among marketing activities, positions and Tobin’s q after the inclusion of
the efficiency scores. According to these results, the application of DEA permits us to
discriminate between efficient and inefficient firms, and to incorporate the efficiency
score in a-posteriori regression analysis. In the same vein, these results would be
consistent with other research that link positively marketing actions and positions to
firm’s value (Anderson et al. 2004, Gruca and Rego 2005, Maden et al. 2006).

In addition, we expect that level of firms’ marketing efficiency has been obstructing the
effectiveness of marketing activities over the firms’ value improvement. These results
would be consistent with Villalonga (2004), who affirms that intangibles play an
effective role in sustaining a firm’s competitive advantage. These intangibles are
responsible for the difference between market value and book value. Marketing, through
Brand Equity and Customer Satisfaction, is the main origin of intangibles, and
consequently contributes to firm’s position (Teece et al. 1997) and value. Nevertheless,
it is also clear that marketing activities cannot be responsible for the whole of these

The main contributions of this research are related to methodological and empirical
aspects. The first one allows research in marketing to observe some significant effects
of marketing using regression and DEA as complementary techniques. On the other
hand, the empirical data of top U.S. firms according to Fortune 500, Advertising Age
Review, and Business Week permit us to provide evidence that Marketing actions and
positions contribute to increase the value of the firm. The main conclusion of this
research is that marketing actions can prove its effectiveness in efficient firms. This is
not new, but this affirmation needed of empirical confirmation.


The measurement of the additional effect on long-term profits generated by the
inclusion of the efficiency score of the U.S. Largest firms as an additional explanatory
variable is still in process of development. The methodology that will be employed is
structured, quantitative, and explanatory. Econometric model approach (Anderson et al.
2004; Gruca and Rego 2005) is the technique for measuring the effects of the constructs
of the model. The definition of the model is the following:

 Ln (Tobin’s Qt / Tobin’s Qt-1) = f [Ln (ADt / ADt-1), Ln (CSt / CSt-1), Ln (BVt / BVt-1),
                                   Ln (MktEfft / MktEfft-1)]

where, Ln (Tobin’s Qt / Tobin’s Qt-1) is the annual variation of Tobin’s Q. To calculate
the Q coefficient will be employed the proxy proposed by Chung and Pruitt (1994). In
addition, Ln (ADt / ADt-1) represents the annual variation of advertising spending, Ln
(CSt / CSt-1) is the annual variation of customer satisfaction index of each firm, Ln (BVt
/ BVt-1) represents the annual variation of financial brand value, and Ln (MktEfft /
MktEfft-1) is the annual change in marketing efficient score. This is the future part of the
working paper.


In reference to academic relevance, this research pretended to support empirical
evidence to the field of marketing metrics in a global context. The suggestion of Rust et
al. (2004) about search for empirical support to the chain of marketing activities has
been taken into account. Another relevant aspect is the conjunction between
effectiveness and efficiency of marketing (Sheth et al. 2000). Studies of marketing
efficiency evaluate the maximization of sales in terms of advertising. Studies of
efficiency focus on measuring the efficiency score and then treat this score as a
dependent variable. This study measures the efficiency score and then uses it as an
explaining variable. Consequently it works based on the rationale of being effective but
with an efficient use of inputs.

In reference to managerial relevance, this research pretended not only to tell the board
that marketing contributes to the company, but also to give them the effectiveness of
marketing in money terms. On the other hand the use of Data Envelopment Analysis
(DEA) as an evaluative technique is practical because managers can measure their
efficiency based on multiple inputs and outputs.


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