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Using marketing productivity to assess marketing performance

Marketers are understandably preoccupied with measuring marketing performance. For this purpose certain Marketing productivity measures have been proposed by Past researches. However, among the many possible marketing performance measures available, which few should be chosen? On the one hand, the key measures must be simple enough to be usable and on the other hand they must be comprehensive enough to assess the marketing productivity and its impact on marketing success. This paper presents an annotated literature review that provides the foundation for the development of a list of the most valuable marketing performance measures. These performance measures are selected on the basis of a number of criteria, for instance the measures need to occur frequently in literature, they must be valuable to most companies as well as they must have predictive power. Thus, analysis of major measures being considered useful by Marketing and Finance managers has been carried out. Finally, the implications for marketing practice and future research are discussed.

“ Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it” (Miller & Cioffi, 2005).

Introduction and Statement of Problem

Despite its importance, marketing is one of the least understood, least measurable functions at many companies. With sales force costs, it accounts for 10 percent or more of operating budgets at a wide range of public firms. Its effectiveness is fundamental to stock market valuations, which often rest upon aggressive assumptions for customer acquisition and organic growth. Nevertheless, many corporate boards lack the understanding to evaluate marketing strategies and expenditures. Most directors-and a rising percentage of Fortune 500 CEOs-lack deep experience in this field (Farris, Bendle, Pfeifer, & Reibstein, 2010).

Marketing executives, for their part, often fail to develop the quantitative, analytical skills needed to manage productivity. Right-brain thinkers may devise creative campaigns to drive sales but show little interest in the wider financial impact of their work. Frequently, they resist being held answerable even for top-line performance, asserting that factors beyond their control-including competition-make it complicated to examine the results of their programs.

In this environment, marketing decisions are often made without the information, therefore expertise and measurable feedback is needed. As Procter & Gamble’s Chief Marketing Officer has said, “ Marketing is a $450 billion industry, and we are making decisions with less data and discipline than we apply to $100, 000 decisions in other aspects of our business” (Farris et al., 2010).

Marketing measurement and metrics are a current problem of management science. In fact, there is a strong interest of practitioners and academics in order to understand the extent to which marketing is measurable and reportable on the balance sheet, but also the implications that non-financial measures have for the profitability and value of firms. Furthermore, this interest is also demonstrated by such marketing professional bodies as the Marketing Science Institute with their Research Priority guide, and Wharton Business school where marketing metrics are placed at the first place of the research programmes (marketing science, 2002, 2008).

Specialization has been the main trend in the twentieth century, starting from production and extending to all the others areas (Lehmann, 2004). This process was to improve the efficiency in each area of the business, and also to develop expertise in each sector; in this context financial metrics were dominant. Consequently, in the 1990s remarkable changes occurred in the approach to performance measurement. In fact, while traditional performance metrics were still based on financial accounting systems and data, new performance approaches started emphasizing non-financial measures (Yeniyurt, 2003). In this context, marketing, having an external focus (customers), has produced a very solid set of non-financial measures but has done very little to establish a financial assessment of its performance. For this reason, marketing is progressively losing resources and credibility among companies (Woodburn, 2005).

However, the financial evaluation of marketing has seen a considerable improvement in the last 15 years (Greenyer, 2006). In fact Rust, Ambler, Carpenter, Kumar and Srivastava, (2004) sustain that even though methods to estimate financial returns from particular marketing investment have been developed, these approaches do not have practical and usable models that can evaluate and differentiate the effect of integrated marketing strategies. Furthermore, they argue that the complexity and the difficulties in implementing these models have generated, as a collateral effect, that top managements consider marketing as an unaccountable short-term activity. Therefore in this context, marketing measurement has acquired a fundamental role in the development of the current literature of marketing.

Furthermore, there are structural factors that influence and inhibit marketing measurement, McGovern, Court, Quelch and Crawford, (2004) show in a survey on US companies that boards spend less than 10% of their time discussing marketing. They argue that in many companies there are no clear connections between corporate strategy and marketing. These conditions reduce the connection of marketing to corporate goals, hiding the contribution of marketing in the process of accomplishment of those goals. They also point out that marketing managers are usually considered not responsible enough to support directly corporate objectives. While, Shaw (2001) argues that external actors can be an effective means through which to realign top management and marketing, and therefore enhance marketing measurement.

In addition to this, Ambler, Kokkinaki and Puntoni, (2004) and Mouncey (2006) indicate that there are few empirical researches that aim to investigate the connection between marketing and corporate objectives. In this context, this research will explore the relevant literature on marketing measurement and from that develop a thorough model that comprises the major theories so that a complete overview of the phenomenon can be obtained. Through this, major Multinational companies operating in Pakistan are investigated in order to define the current situation concerning marketing measurement tools and techniques being implemented by Marketing and Finance executives.

In this context, Ambler et al. (2004) conducted a research on marketing metrics in order to establish the criteria of metrics selection and to define which metrics are used to assess marketing. This study is the rational continuation of Ambler et al. (2004) work in order to complete the gap left in it. In fact, the research also investigates the importance allocated to marketing metrics and the actors involved in their development and implementation.

Conceptual Background

The measurement of marketing performance has been a central concern in marketing for decades (Morgan, Clark, Gooner, 2002) and Marketing Science Institute and American Marketing Association have repeatedly assigned marketing metrics as a top research priority in recent years. As there are no perfect and absolute measures available for measuring marketing success (Ambler & Kokkinaki, 1997), the question about the most appropriate metrics for marketing performance measurement has been a widely discussed issue among marketing academics and professionals (e. g. Zahay & Griffin, 2010; Lenskold, 2007; Ambler & Roberts, 2006).

Lately, Different metrics like shareholder value and CLV have been emphasized (e. g. Greenyer 2006; Doyle 2000; Lukas et al., 2005; Srivastava et al., 1998). However, considering the importance of literature review, a scanning process of the main literature regarding marketing, performance measurement and general management science was carried out. A bottom-up approach (from latest research paper to older ones) has been adopted. This scanning process has identified three main areas around which the current knowledge on marketing measurement is built. Furthermore, they explore the objectives that this research study aims to investigate. The theme or sequence of literature is illustrated in Figure 1.

Figure 1. Literature Review Sequence/Theme

Marketing Productivity

Charles sevin has been appreciated as being one of the first scholars who incorporated the concept of productivity into Marketing Field. In his work, Marketing Productivity Analysis, he described marketing productivity as the ratio of sales or net profit to marketing expenditure, according to a specific segment of the market (Ambler et al., 2004). Then, marketing productivity was divided into 2 areas by Thomas (1984), one related to the management of the marketing mix/tools and the other one related to bringing efficiency of the marketing expenditure. Therefore, a company needs to create an effective marketing mix in order to target its segments and then perform efficiently the actions needed to obtain the desired output.

A more detailed definition of marketing productivity is given by Rust et al. (2004). They introduce marketing productivity as “ the relationships between all the marketing expenditures and financial variables like sales, profit ratio/Margin and shareholder wealth/equity/value. Same sort of concept has also been discussed by Hall (2010) where he explores that cultivating long-term relationships contribute to productivity, the brand and Revenues.

Rust et al. (2004) proposed a chain of marketing productivity. The chain is a framework used to evaluate marketing productivity that embraces tactical and strategic marketing activities in order to establish their relationship to financial measures. The framework initiates from the marketing strategies that include promotion strategies, product strategies and any other strategy that is related to marketing. These strategies, then, generate tactical marketing activities like advertisement campaigns, branding programs, loyalty enhancement schemes or any other activities that can contribute towards producing a marketing impact. However, the activities taken into account in this framework include only actions that generate expenditures that can be directly connected to marketing. As a result of these activities, customer satisfaction, attitude towards the brand, loyalty and other customer-centered measurements are inclined, generating at the firm level variations in marketing assets (brand and customer equity).

However, it has also been suggested that to make marketing more productive, certain changes need to be implemented at both corporate and strategic levels while goals of marketing activities must be aligned with corporate vision (Seth and Sisodia, 2002; McGrath, 1992). A more broader approach to measuring marketing performance was proposed by Morgan et al., (2002) in order to establish marketing productivity. They concluded that marketing productivity is more related to determining the efficiency of marketing activity while marketing audit is concerned with marketing effectiveness. Thus they proposed a model of Marketing performance assessment (MPA), and this model is being considered one of the very concise and elaborative model for marketing performance assessment.

Figure 2. Marketing performance assessment, Morgan et al., (2002)

Marketing productivity has been used in developing certain marketing metrics by Ambler (1997, 2004, 2006). A metric is a measuring system that quantifies a trend, dynamic, or characteristic (Farris et al., 2010). Ambler et al., (2004) conducted a major empirical study concerning the process of selection of the metrics utilized to assess marketing. They proposed a framework, to assess marketing, based on the connection between input and output in agreement with the marketing productivity theory. This framework is shown in Figure 3. It focuses on the linkages that connect marketing activity and investment (input) to financial outcomes (output).

Figure 3. Metrics Categories (Ambler et al., 2004)

From all the discussion above regarding marketing productivity, it can be deduced that marketing practitioners and academics are successful in developing structured systems to measure the impact of marketing on companies’ revenue, profit and shareholder value. However, although the systems mentioned above have strong academic and practical relevance, there is a gap between theory and practice. In fact, there are few empirical evidences that companies use this system or that these methods are perceived to deliver the results expected.

Brand and Customer Equity

Marketing impact, as discussed above, has a dual effect in both the short-term and the long-term. While marketing productivity theories emphasis on both the effects, brand and customer equity are focused exclusively on the long-term effects that marketing activity generates. Srivastava and Reibstein (2004) emphasize that the use of brand equity and customer equity is an appropriate approach to long-term measurement.

However, these two systems can be considered to be the same measurement from two different perspectives of analysis (Mouncey, 2006). In fact, it can be argued that for some companies, as for example firms in the fast moving consumer goods industry, brand equity is a more appropriate measurement because they may not have a customer database. Whereas, for companies that operate in the business-to-business area, customer equity can provide more accurate and reliable results (Ambler et al., 2004). The examples of such companies can include all the companies related to Telecommunication business etc.

Brand Equity

Branding and Brand Equity have been topics of interest to marketing researchers for many years. A brand can be defined as “ a name, term, sign, symbol, or design, or combination of them which is intended to identify the goods and services of one seller or group of sellers and to differentiate them from those of competitors” (Kotler, 2006, pp. 274). The context and meanings of brand equity has been debated in a number of distinct ways and for entirely different purposes. The concept of brand equity has been discussed in the accounting and marketing literatures, and has the aim to connect marketing impact, especially brand management, to long term focus and results (Wood, 2000).

Farquhar (1989) defines brand equity as the value added by the brand name to a product or service. Aaker (1991, 1996) and Keller (1993) have both provided conceptual schemes that link brand equity with various consumer response variables. Specifically, Aaker (1991) identified five major consumer-related bases of brand equity: Brand loyalty; brand awareness; Perceived quality; Other brand associations; and Proprietary brand assets. Keller (1993) proposed a knowledge-based framework for creating brand equity based on two dimensions: Brand awareness; and Brand Image.

Among the determinants of brand equity, brand loyalty has been found most prominent, consistent and Influential (Atilgan et al., 2005). They verified the findings of Yoo et al., (2000), but this time in a different country and context. Other researchers have found the same results also (e. g. Kepferer, 2004; Jelsema, 2007). However, Jelsema (2007) presented the most comprehensive illustration of the components of Brand management and how the interact with each other.

Srinivasan et al. (2005) define a framework to assess brand equity that involves both customer preferences and financial variables. In fact this method compares the incremental contribution gained by a brand to a base product. This approach considers three main sources that constitute brand equity as shown in figure 4. The three areas that Srinivasan et al. (2005) consider to be the core of brand equity are: Brand Awareness, Brand Preference and Brand Availability. Each of these variables influences the probability that a product is chosen in lieu of another, thus creating value for the firm. These variables are then compared to the probability of a base (generic) product and the differential is aggregated to form brand equity.

Figure 4. Brand Equity Framework, (Srinivasan et al., 2005).

Even though brand equity is considered to be an important method of marketing measurement, it is not free of critics. In fact, Ambler (2004) defines brand equity as an elephant, because it is such a big concept that people have difficulty in describing it. Therefore, because there is a variety of definitions, this may lead different people to have different interpretations of brand equity (Ambler et al., 2004).

Customer Equity

After the founding of brand equity, Blattberg & Deighton (1995) introduced a comparable concept of customer equity. This theory considers the customer base of a firm as a valuable asset that can be measured using the customer lifetime value (CLV). The underlying principle of customer equity is to uncover the exact level of customer retention and customer acquisition that maximizes the value of customer equity.

There are several models that explain customer lifetime value. Berger & Nars (1998) describe different mathematical methods to determine the customer lifetime value according to diverse feasible scenarios, variables such as purchase frequency, overall customer turnover, customer share of wallet, etc. that can or cannot interfere in the estimate. A general definition of CLV is the present value of all the profit generated from a customer (Gupta & Lehmann, 2003).

Rust, Lemon And Zeithamel, (2004) proposed a model to measure marketing performance associated to customer equity analysis. This framework was derived from combining the workings of others researchers and it is central because it is the first endeavour to amalgamate the current literature of customer equity.

The model connects marketing activity to the improvement of one or more drivers of customer satisfaction as shown in figure 5. Consequently once a customer is more satisfied that customer tends to be more loyal or attracted by the company. These improvements lead to a better CLV and therefore to a better customer equity value. The ratio of the CLV to the marketing investment, determines the return on marketing investment.

Figure 5. Customer Equity Model (Rust et al., 2004).

An expansion to this model has been proposed by Venkatesan & Kumar (2004). They put forward a variation of the above model that involves the incorporation of different channels in the system. In addition, they established that CLV is a very good tool to segment customers according to their profitability.

These models based on customer equity have been challenged by recent researches who disagree with some of the assumptions that marketers have held for a long time regarding the financial impact of marketing, as for example regarding the positive correlation between loyalty and profitability (Sheth and Sharma, 2001). It has been empirically proved that for specific products, this supposition is not always valid. In fact, Reinartz & Kumar (2000) have empirically verified that in a non-contractual situation the assumption that, loyal customers are characterized by increased lifetime spending, decreased costs of serving and lower price sensitivity, is not always true.

The discussion on the subject of brand and customer equity has highlighted many current theories and concepts. It is debatable that brand equity is more reliable for companies that do not have identified customer, where customer equity is demonstrated to be more effective.

Marketing and Shareholder Value

From a more financial perspective the return on investment (ROI) has involved the literature of marketing measurement in order to institute the added shareholders’ value of marketing activity. Furthermore, it is considered to be one of the hottest issues that is affecting businesses at present (Munoz, 2005).

Day & Fahey (1988) evaluate an approach of assessing market activities in order to probe into shareholders value. The approach called “ Value-Based Planning” is founded on the concept that “ managers should be evaluated on their ability to make strategic investments that promise returns greater then their cost of capital” (Day & Fahey, 1988). At some stage in analysis, Day & Fahey (1988) identified the method developed by the Alcar Group Inc., a consulting company, as the best documented valuation methodology. The method is essentially based on the following components: Present value (of the present year), Residual value (of the next year) Less the market value of the debt.

Lenskold (2002) proposed a different approach to ROI and marketing activities. In fact, he divides the variables that influence ROI into: Customer lifetime value, Total customers, and marketing expense. Cook and Talluri (2004) advocate a customised system of Return on Marketing investment (ROMI). This framework implies that each company has to put in place an ad hoc set of metrics and procedures with the intention of achieving reliable results. The basic structure of the framework is highlighted in Figure 6, where tools and processes have been recognized. This method does not present any empirical evidence nor is it backed up by relevant literature references. However, this model highlights the value of the people involved in the measurement and their relationship.

Figure 6. Return on Marketing Investment Framework, (Cook & Talluri, 2005)

A different viewpoint of analysis has been followed by White, Miles and Smith (2001). They explored the effects of marketing on shareholders for Small and Medium Enterprises (SMEs). The main difference from the other approaches consists of the fact that this study analyses companies that usually are not quoted on the stock market and therefore, it utilizes owners’ value in the assessment.

In conclusion, Return on Investment theories have demonstrated to be a weak approach to the assessment of marketing value. However, these are the methods that are used at board level to assess investment in relation to shareholder value (Cook and Talluri, 2004) and for this reason, they have to be taken into consideration in the review of the main literature of marketing performance measurement.

Research Questions

The study is intended to identify major marketing metrics being applied to assess relationship of marketing productivity with marketing performance measurement and whether they are perceived to be effective or not. In addition, the study will assess the impact of marketing success on firm’s performance. As literature has been extracted from 3 main areas i. e., marketing productivity, customer and brand equity and shareholder’s value of marketing activity, so following research questions are devised.

What is the current situation of companies regarding Marketing measurement.

Which metrics are perceived to be important within marketing productivity category to assess marketing performance?

How much impact marketing performance has on firm’s overall performance?

Proposed Model

Sales

Product/Brand Awareness

Profitability

Marketing Productivity

Marketing Performance Measurement

Firm’s Market Success

Gross Margin

Total No. Of Customer complaints

Customer Satisfaction

Loyalty

Figure 7. Proposed Model .

Methodology

Instrumentation

The research design is based on Relativism approach because of the implied suppositions made in development of Research questions. The study focuses on correlations instead of focusing on causality or understanding of the phenomenon. The content of questionnaire is based on the conceptual framework proposed in the literature review. The questionnaire is thus divided into 3 categories, general information, Importance of Marketing Measurement, Marketing productivity, and 9th and 10th questions are measuring marketing performance’s impact on overall firm’s performance.

A five-point Likert scale, ranging from very useful to Not applicable, was developed to assess the relationship of each measure to the particular category. To get general information regarding respective business and the manager, introductory questions are modeled . Followings are the main Reference papers from where Variables measurement concepts have been adopted;

Assessing marketing performance: Reasons for metric selection (Ambler et al., 2004).

Benchmarking marketing productivity using data analysis (Donthu et al., 2005).

Measuring marketing productivity: current knowledge and future directions (Rust et al., 2004).

Managing marketing by customer equity criterion (Blattberg et al., 1995).

Customer Lifetime value: marketing models and applications (Berger et al., 1998).

Linking marketing to financial performance and firm value (Lehmann, 2004).

Marketing ROI: Playing to win (Lenskold, 2002).

A survey based method for measuring and understanding brand equity and its extendibility (Park & Srinivasan, 1994).

Customer metrics and their impact on financial performance (Zeithaml & Gupta, 2005).

Two questionnaires are developed, 1 for researcher-administered way and 2nd for collecting data through personalized e-mails. However, to reduce the chances that managers might not be having sound understanding of the terms being used in Questionnaire, a glossary of terms has been attached at the end of questionnaires being sent through e-mails. This step has been taken after consulting 12 Marketing and finance managers from Warid telecom, Wateen telecom, Bank Alfalah, Dewan Motors and Faisal Bank, MEI-PAK and 6th sense Consultation.

Sample

The sample size for collecting data regarding this study was decided to be a figure around 150 observations while all the persons being surveyed were marketing and finance executives. And as it is a managerial sample, observations around 150 were reliable for generalization of results. The data was collected through mixed Random sampling techniques in which Snowball sampling was used to collect data from different managers through referrals from the previously interviewed managers, while stratified sampling technique was implemented to group finance and marketing managers according to their nature of industry.

Data Analysis

The statistical techniques to be applied depends on the structure of collected data. Before description of the findings of the survey, the profile of the respondents and comparative tests have been applied, in order to describe the sample and determine its degree of external validity. The external validity is “ the extent to which research findings can be generalized beyond the immediate research sample or setting in which the research took place” (Gill and Johnson, 2002). According to Hussey and Hussey (2003), low response rates and missing values may bias the data and thus may not be the representative of the population. To do so, a screening process was conducted to remove the survey questionnaires with missing values and thus 132 observations were obtained.

Analyzing the profile of respondents according to the sales of their companies, the results are seen in Table 1, show that there is a balanced representation of all the turnover ranges,

Table 1

Observed N

Expected N

Residual

Over 1 Billion

9

18. 3

. 8

501 million-1 billion

26

10. 0

-1. 0

101-500 million

31

19. 1

-. 1

10-100 million

35

15. 5

-. 5

Below 10 million

28

11. 8

-. 8

Total

102

Chi-Square(a)

. 978

Degree of freedom

7

Asymptotic Significance

. 995

Table 1: Total Sales Chi square Test

In order to identify whether there are differences in the perception of importance of marketing measurement between marketing and finance managers a t test has been conducted. The results reveal (see Table 2) that there is a difference in the perception between the two groups of . 43 on a 5 point Likert scale. Finance managers perceive marketing measurement to be less important then marketing managers; this difference is supported by a statistical significance (2-Tailed) of . 074. Therefore, there are only 7. 4% of possibilities to have obtained this result by chance. Apart from slightly greater concerns of large companies, there were no significant differences between the importance attributed to marketing measurement and the respondents from the different companies.

Mean

Std. Deviation

Sig. (2-tailed)

Marketing Department

4. 19

1. 09

Finance Department

3. 76

1. 26

. 074a

Difference

. 43

-0. 17

a) equal variances assumed

Table 2: t Test on Importance of Marketing Measurement.

To measure Marketing productivity metrics being used, the respondents were asked to indicate, on a 5 point scale, the importance of the metrics for assessing marketing productivity. Table 3 ranks the results for marketing productivity metrics according to their average score for importance.

MARKETING METRICS

PRODUCTIVITY

Type

% rating as Very Useful

% rating as very Mean Standard important deviation

1) Profit of the company F 98. 00% 77. 78% 4. 74 0. 52

2) Turnover expressed in value or volume

F

94. 50%

67. 31%

4. 62

0. 60

3) Gross contribution margin

F

89. 10%

60. 78%

4. 51

0. 71

4) Loyalty/retention

C

89. 10%

35. 19%

4. 28

0. 93

5) Number of consumer complaints

C

89. 10%

22. 22%

4. 12

0. 90

6) Awareness of a brand or company

C

80. 00%

25. 93%

4. 11

1. 11

7) Advertising Awareness of Brand

C

69. 10%

23. 08%

4. 01

1. 12

8) Customer satisfaction

C

90. 90%

31. 48%

4. 67

1. 16

9) Distribution/availability at point of sale

C

69. 10%

33. 98%

3. 91

1. 04

10) Products awareness

C

70. 90%

23. 08%

4. 87

0. 90

11) Price competitiveness

M

90. 90%

37. 74%

3. 79

1. 17

12) Market share of company, brand

M

91. 70%

50. 00%

3. 70

1. 29

13) Total number of active customers

C

63. 60%

26. 92%

4. 21

1. 22

14) Marketing expenditure

F

96. 40%

24. 53%

3. 66

1. 35

15) Perceived quality

M

81. 80%

35. 29%

3. 84

1. 28

16) Total sales

M

89. 90%

29. 63%

3. 91

1. 91

Table 3: Marketing Productivity Metrics

A second stage of analysis has been conducted grouping the metr

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