DRIVERS OF MARKETING CAREER SUCCESS

ABSTRACT

What are the success factors for careers in marketing? This important question is lacking a definite answer so far. This paper presents an empirical study of the drivers of marketing career success. First, we interviewed renowned marketing executives about the characteristics that, in their view, make successful marketers. Next, we analyzed data from a career survey among the alumni of a major business school, with career success as the dependent variable. We identified three distinctive factors that are related with success in marketing: extraversion, independence, and breadth of experience. Furthermore, our findings suggest that a style of learning from concrete experiences (learning from “what happens”) serves a marketer better than a learning style of deep reflection. Different from many other professions, we did not find a relationship between conscientiousness and performance in marketing. Interestingly, having followed a major in marketing during business school education is not correlated with later success in a marketing career. Our findings support the notion that work-integrated elements with an emphasis on experiential learning in marketing curricula contribute to marketing career success. We discuss ways of implementing this is in marketing education programs. Specific suggestions for follow-up research are given at the end of the paper.

Introduction

What makes a marketer successful? In the 1960s, at a time that marketing was just becoming a major field of management, Smith and Cooke (1967) brought up the need for research “to guide marketing educators in relating education to job performance” (p. 59). The authors deemed it important to address the question of “what talents and capabilities are required to perform the various tasks involved in various marketing jobs or positions” because “early identification of marketing talent is important” (p. 60). “If one sets about to hire a young man¹ for a marketing job, he has little or no basis of selection except an expression of interest combined with some sort of academic accomplishment” (Smith & Cooke, 1967, p. 60). In response to the Smith and Cooke paper, Banks (1967) proposed “the development of a battery of tests which can indicate the probabilities of success in various marketing business jobs or functions” (p. 62).

More than five decades later, we still do not have a definite answer to the question of what makes a successful marketer. In the meantime, several researchers have addressed this question by asking employers and marketing educators what capabilities are required for marketing personnel. For example, in a survey, Boatwright and Stamps Mirjam (1988) asked company recruiters their importance ratings of student characteristics for marketing jobs. McArthur, Krzysztof, Pang, and Alcaraz (2017) carried out a content analysis of 359 job advertisements for entry-level marketing personnel. A study by Kelle and Bridges (2005) asked both marketing practitioners and marketing educators (the demand and the supply side) about the need to incorporate the development of professional and career development skills into the marketing curriculum. The requirements for marketing employees mentioned most frequently in these studies are leadership, communication skills (oral presentation and writing skills), interpersonal skills (teamwork), and motivation. Interestingly, specific marketing expertise is not in this list. There have been two studies on the effect of a specialized marketing education on marketing career success: Hunt Shelby, Chonko Lawrence, and Wood Van (1986) and Bacon (2017). In both studies, no evidence of this effect was found.

The question of: “What makes an excellent marketer?” becomes even more relevant in the current and changing marketing context. For example, Di Gregorio, Maggioni, Mauri, and Mazzucchelli (2019) report an increased demand for digital skills for starting marketing positions.

The Present Study

The present study examines what characteristics are correlated with the performance of marketers in their jobs. For this purpose, we considered a broad set of potential drivers of career success in marketing. We included personal variables, such as Personality Traits, learning styles, and education and career development variables, such as career trajectory, experience, and organizational support for career development. One input to our study were qualitative interviews with senior marketing executives, asking them what, in their view, makes a successful marketer. The next step was to supplement this input from experienced marketing executives with insights from the literature. For this purpose, we not only used the marketing and management literature, but also work in the fields of career studies, personality, and learning. From these inputs, a questionnaire was developed that was administered to alumni of a major business school who had graduated at least five years ago.

By analyzing these marketing career data, we can address questions such as:

• Nature or Nurture? Are people born with the gifts of a marketer, or are there important roles for education, training, and experience?

• How do successful marketers learn? And what are the implications for how we educate future marketers?

• How distinctive are marketers in terms of specific capabilities, compared to other management jobs?

The answers to these questions are important for several parties: companies hiring marketers, recruitment agencies, and aspiring and current marketers who ask themselves if they are fit for a future as a marketing professional. Another important group of stakeholders is marketing educators, who design and execute marketing curricula with the purpose to deliver successful marketers.

Of course, this single study does not provide definitive answers to the big question of what makes a successful marketer. However, it offers findings with potentially important implications, especially for marketing educators and suggest directions for follow up research.

Interviews with Top Marketers

We started our research by conducting interviews with high level executives with extensive experience in the marketing profession and who can be considered successful marketers themselves. To identify such persons, we used a ranking of marketing professionals from a major professional marketing magazine in the Netherlands (“Tijdschrift voor Marketing”), which annually publishes the list of the “Top 100 Best Marketers.” This ranking is based on the reputations of these marketing professionals as seen by people in the wider business community. At the start of our research project, we took the most recent list at the time (2017) and conducted interviews with five professionals from the top of “The best marketers of the Netherlands.” These five were all executives with extensive experience in CMO positions and business jobs for, on average, 25 years. All were male, reflecting the start of marketing as a “man’s business.” At the time of the interview, three of these executives had moved to CEO or other senior positions. The business fields in which these executives had gained their experience were consumer packaged goods (CPG), retailing, finance, chemical industry, and utilities. The interviews took place in personal meetings, each with two of the three authors present, and lasted about an hour and a half.

The question that we asked the executives to reflect on was: “What makes a successful marketer?” When answering this question, the executives typically started to talk about their own careers and the careers of marketers they knew, colleagues, bosses, and subordinates.

The interviews were recorded, written out, and coded. The processing included two steps, as described by Spiggle (1994): categorization and abstraction, in a process of two rounds. First, in bottom-up way, the various words and statements were classified into categories and subsequently collapsed into higher order classes. In a second round each item was assigned to one of the classes. This qualitative part of our research served as a pre-stage for the quantitative survey. The inference process (Spiggle, 1994) took place with the quantitative data from the survey.

The result of the qualitative analysis is Table 1. The classes were given the labels as shown in the table (italics). Several of the expressions in the table (with quotation marks) are literal quotes from the experts. According to the experts, successful marketers understand customers, have a high level of curiosity, and “passive wisdom and knowledge.” Experience in different jobs helps, and also having done “groundbreaking stuff,” for example a successful product introduction or the launch of an influential advertising campaign. The experts emphasize the need for both an analytical and a creative mind-set: Intuition and sensing are deemed important. One expert warned that marketers should not be too rational: “McKinsey people and engineers are too rational.” Some experts think that a person’s genetic code determines the capability of being a successful marketer: “you either have it, or you don’t.” Others believe that marketing can be learned. According to the experts, successful marketers have excellent communication skills.

Table 1. Success factors for marketers mentioned in interviews with top marketers (““= literal quotes).

Learning & Curiosity	Communication
Understanding consumers “Boundless curiosity” “Passive wisdom/knowledge”	“Talk a lot” “Read your colleagues and the board” “Convince people in your company” Social-emotional intelligence “Need political skills”
Experience	Experimentation
“Experience helps” Different jobs “Must have done groundbreaking stuff”	“Experiment, just try” A/B testing Agility/autonomy/less hierarchy
Capabilities	Independence/determination
Analytics and creativity “Power of intuition” “McKinsey and engineers are too rational” “Gut feel”	Guts, independent minds “Be a bit arrogant” Commitment Strong in execution “Not only plan, do!”
Nature or Nurture	Support
“Need genetic code” “Marketing is an art” “Marketing can be learned” “More nurture than nature”	Mentors or role models (mentioned several times)

Effective marketers talk a lot about what they are doing, pick up signs from finance and accounting colleagues and from the board, and get their plans accepted.

In the spirit of “Big Data,” some of the experts demonstrated a strong belief in experimentation and testing. Autonomous teams should try out alternative tactics in the marketplace, for example, in product design or pricing, and find out what works best. Furthermore, successful marketers “have guts,” show commitment, and are strong in execution. Finally, our experts mentioned that having mentors or role models is a positive factor for a successful marketing career.

After collecting these qualitative insights from top marketers about career success factors, we turned to the design of our career survey among practicing marketers.

Career Survey

The purpose of our main study is to analyze, in an empirical setting at the individual level, the relationship between career success in marketing and a set of independent variables, personal variables, and career development variables. The data were collected through a survey among alumni of a major business school in the Netherlands.

Research Model

The construction of our survey and the subsequent analysis of our data were guided by Figure 1. Objective Career Success is the key dependent variable. Objective Career Success is directly observable, measurable, and verifiable by an impartial third party (Heslin, 2005). Thus, in the field of career studies, Objective Career Success is defined by verifiable attainments, such as salary, promotions, and job status. Besides Objective Career Success, the field of career studies also work with the concept of Subjective Career Success (Hughes, 1937, 1958). Subjective Career Success is defined as an individual’s evaluation of his/her unfolding career experiences. It is usually operationalized as job satisfaction (Shockley, Heather, Ozgun Burcu, Poteat Laura, & Dullaghan, 2016; Thorndike, 1934). For an employee’s well-being in his/her job, Subjective Career Success is an appropriate measure. If a marketing person is not happy, (s)he may be less productive, consider resigning, and/or switching to another job or profession. Job satisfaction is a continuing concern for companies that wish to retain high-quality marketing employees. We expect that Objective Career Success positively affects Subjective Career Success.

Figure 1. Research model.

Figure 1 includes two sets of independent variables that may affect Objective Career Success: personal and career development variables. Of course, personal variables may affect career development variables. Finally, the model includes control variables. Measurement details are provided in the Appendix.

Variables

Dependent Variables

Objective Career Success can be measured through variables such as pay (Thorndike, 1934), occupational status (Heslin, 2005), and hierarchical job level (Hall & Chandler Dawn, 2005). We used five indicators: income (value of annual pay package), hierarchical position, profit responsibility (Y/N), board position (Y/N), and number of reporting employees reporting (see Appendix). Subjective Career Success can have multiple dimensions. Shockley, Heather, Ozgun Burcu, Poteat Laura, and Dullaghan (2016) distinguished the following: recognition, quality work, influence, authenticity, personal life, growth, and development, and, as a summary measure, satisfaction, which we use in this study. We carried out factor analyses and used factor scores as dependent variables for objective and Subjective Career Success (see Appendix).

Independent Variables

We have two groups of independent variables: (i) personal variables and (ii) career development variables.

Personal Variables

Within the category personal variables, we have four variables: (a) Personality Traits, (b) Learning style, (c) Career orientation variables, and (d) Other personal variables.

• a) Personality Traits. In our interviews, the top marketers brought up the subject of nature versus nurture. This leads to the question about the extent to which people’s intrinsic personality characteristics affect their marketing careers and marketing career success. To examine this, we applied the five-factor model of Personality Traits, often called the “Big Five.” There is broad consensus that this model can be used to describe the most salient aspects of personality (Costa & McCrae, 1992; Goldberg, 1990; Judge, Bono, Ilies, & Gerhardt, 2002). Table 2 lists the five personality factors and provides a short description of each. Many earlier career studies examined the relationship between the five Personality Traits and job performance. In a meta-analysis of 117 such studies covering a broad set of different occupational groups, Barrick and Mount (1991) found that conscientiousness showed a consistently positive relationship with job performance for all occupational groups. Other traits were only important for specific groups. For example, extraversion was a valid predictor of job performance in two occupational groups, management and sales. Judge Timothy, Higgins Chad, Thoresen Carl, and Barrick Murray (1999), who distinguished extrinsic and intrinsic career success, which are roughly comparable to objective and Subjective Career Success, found that conscientiousness positively predicted intrinsic as well as extrinsic career success. Ng, Eby, Sorensen, and Feldman (2005) conducted a meta-analysis (140 studies) of the predictors of career success. They found significant positive correlations of extraversion and conscientiousness with two Objective Career Success variables, salary, and promotion.To implement the five-factor model, we used the scale developed by Gosling, Rentfrow Peter, and Swann (2003). (see Appendix)

  Table 2. The “Big five” Personality factors (Costa & McCrae, 1992.).

  Openness to new experiences (inventive/curious vs. consistent/cautious)

  Conscientiousness (efficient/organized vs. easy-going/careless)

  Extraversion (outgoing/energetic vs. solitary/reserved)

  Agreeableness (friendly/compassionate vs. challenging/detached)

  Neuroticism (sensitive/nervous vs. secure/confident)

• b) Learning style. The abilities for sensing and learning of the individual are important concepts in the management (Constance & Peteraf Margaret, 2015; Teece, 2007) and marketing literature (Day, 1994; Sinkula, 1994). The top marketers of our interviews also mentioned intuition and sensing as critical for a successful marketer. Therefore, we also address the relationship between learning style and marketing career success. Marketers continuously monitor and process all what happens in and around their markets and try to learn from it. They observe how customers respond to their offers and learn that some actions are successful whereas others are not. They study market research reports, see what competitors are doing, get feedback from resellers, and read about market and societal trends. How do marketers digest all these signals and subsequently use the lessons learned for their (future) marketing decisions? One route is deep learning, which means constructing a complete and precise mental model of all the forces in the market and their interactions. An alternative is developing a quick impression of what is going on and acting fast. To examine which ways of learning correlate with success in marketing careers, we apply the Kolb Learning Style Inventory (KLSI). Kolb’s model “is the most widely accepted learning style model and has received substantial empirical support” (Manolis, Burns, Assudani, & Chinta, 2013, p. 44). The Korn Ferry Hay Group, part of Korn Ferry, a worldwide operating firm in executive search and recruitment, uses the Kolb learning style approach in its human resource practice. Kolb’s learning theory focuses on the internal cognitive processes of the learning person, where knowledge is created through the transformation of experience. Kolb’s model distinguishes two main dimensions: active/reflective and concrete/abstract. His learning cycle comprises four stages: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE) (Kolb, 2005)). A person observes a phenomenon (Concrete Experience-CE), starts to reflect about it (Reflective Observation-RO), then develops a conceptual model about the phenomenon (Abstract Conceptualization-AC), and next tries out specific actions (Active Experimentation-AE). For different people, the four stages of the learning cycle get different weights. The combination of weights characterizes their learning styles. According to Kolb’s theory (Kolb, 1984), there are links between learning style and field of occupation. For example, individuals high on CE and AE are doers. They enjoy performing experiments, carrying out plans in the real world, and taking risks. People working in action-oriented jobs, such as sales and marketing, tend toward such a learning style. On the other hand, individuals high on AC and RO are good at understanding and creating theoretical models. This learning style fits well with individuals who work in math and basic sciences (Cherry, 2019; Kolb & Goldman, 1973). We used the Kolb Learning Style Inventory (see Appendix) to measure the learning styles of our respondents. As my be expected, there is some but limited overlap between learning style and personality (Ming & Armstrong, 2015).

• c) Career orientation variables. When examining factors that motivate careers and career success, career researchers increasingly look beyond variables such as income and position. They observed that modern managers are not just driven by salaries, promotions, and a high position within the organizational hierarchy (Vinkenburg & Torsten, 2012). Employees are also increasingly concerned about their level of independence and the balance between work and personal/family life (Lips‐Wiersma & Hall Douglas, 2007). Our study includes two career orientation variables that have received attention in the recent career literature: protean career orientation and work-life balance. Both career orientation variables affect the choices that people make in their working life. Therefore, they may also be related to the objective and subjective successes of careers in marketing.

• Protean career orientation

In our interviews, our five top marketers mentioned that marketers should have “guts” and “independent minds” and be able to work in a setting of relative “autonomy” (Table 1). These terms are related to the concept of a protean career orientation as described by Hall and Mirvis (1996). This is an orientation where the individual rather than the organization is responsible for her or his career path development. The term protean comes from the name of the Greek god Proteus, who could change shape at will (Baruch, 2014). We use the protean career orientation scale developed by Baruch (2014) to measure this variable (see Appendix).

• Work-life balance

Balancing work with personal and family life also is linked to how a person wants to organize his/her life. This construct has become critical in times when many households comprise partners with demanding jobs and when it seems increasingly difficult to truly disconnect from work. If there is no good work and private life balance, work-family conflicts may lurk, and careers may suffer. We use the scale developed by Schein (1990) to measure work-life balance (see Appendix).

• d) Other personal variables

  Gender. Several recent studies have examined the differences between male and female employees with respect to career success (e.g. Bacon (2017); Ng, Eby, Sorensen, and Feldman (2005)). The overall picture is that women are trailing behind men when one looks at Objective Career Success. However, when looking at Subjective Career Success, women and men tend to perceive themselves as equally successful.

  Nationality. We were interested whether international work experience affects a person’s career success. Ng, Eby, Sorensen, and Feldman (2005) found a significant positive correlation between international experience and salary and promotions, our objective career variables.

  Bachelor-master school switch. In most university systems, it is possible to switch universities between the bachelor and the master’s degree. Changing schools might be a sign of taking initiative and conscious steps in life. Do students who switched schools between their bachelor’s and master’s perform differently from those who remained at the same institution?

  Master’s program major. We were interested in whether having a master specialization in marketing is related to marketing career success.

Career Development Variables

Besides individual’s personal variables, a marketing career success, can also be influenced by a specific early career trajectory or assets obtained during a career affect later career success. Thus, we specifically looked at (a) steepness of early career, (b) experience, and (c) organizational support.

1. Steepness of early career trajectory. Upward mobility early in a career is widely considered as a predictor of later success. In his study of intra-organization career patterns, Rosenbaum (1979) showed that mobility early in a career has an unequivocal relationship with later promotion or demotion. We examine whether this also holds for marketers.

2. Experience. During the interviews, our top marketers mentioned experience as an important driver of career success. Traditionally, individuals came up through the ranks within the same company, specializing in one functional domain, mostly in the same geographical area. Experience in such settings was the number of years a person was working in the organization. However, nowadays career scholars speak of “boundary-crossing” careers (Arthur & Rousseau, 1996), where people cross organizational, functional, industry, and geographical boundaries (Chen, Veiga, and Powell 2011). Such boundary crossings increase the breadth of experience of an individual. We include in our study the breadth of experiences across organizations, functional domains of management, industries, and countries (For measurement specifics, see the Appendix).

3. Organizational Support. Organizations can also support their employees by offering them postgraduate education, programs, and courses. We specifically look at two types of postgraduate courses, those offered by universities and those offered by other institutions.

Several of the top marketers we interviewed mentioned the importance of mentors or role models in creating career success. Organizations can help their employees grow and develop their capabilities by matching them with supervisors or mentors (Kram, 1985). Experienced people serving as mentors can be very important for individuals (Allen et al., 2004; Wyner, 2017). Therefore, we also included a mentor variable.

Control Variables

We included two control variables: Years out and Company size. Respondents who finish university earlier and have been active in the labor force longer have more opportunities to advance their careers and thus be more successful. Furthermore, some of the indicators for career success (e.g., salary level) may also be influenced by Company size.

Data Collection

To study the relationships between the independent and dependent variables, we collected data using a survey among graduates of a major public business school in the Netherlands. This school offers a one-year MSc management program in which the students choose from a set of a majors, one of these being marketing. However, careers in marketing are not the exclusive territory for students with a major in marketing. Therefore, we included graduates from other majors in our sample and sent questionnaires to the graduates of the following majors: (i) Marketing Management; (ii) Strategic Management; and (iii) Business Information/Management of Innovation. Given the nature of today’s marketing jobs, we felt that graduates from these majors had a good chance of ending up in a marketing job. The alumni who participated in our survey and filled out a questionnaire graduated between September 1, 1988, and May 1, 2012. We took September 1, 1988, as the starting point because from that day onwards, the first alumni graduated from a new educational program that the school had started in 1984, consisting of a three-year bachelor and one-year master’s program. By taking May 1, 2012, as the end point, we ensured that the alumni in our sample had at least five years of working experience, a minimum for speaking of a “career.” The Alumni Office of the school sent out the online questionnaire on May 15, 2017. Only alumni whose e-mail addresses were available at the Alumni Office received the questionnaire (for the older alumni, this went down to about 75%). Respondents were asked to return the questionnaire within two weeks, with a reminder sent at that time. They were promised a T-shirt with the school log after returning the completed questionnaire.

Table 3 provides a summary of the questionnaire. We realized that, due to the purpose of our study, we requested quite an effort from our respondents by asking them to provide a lot of information: personal background data, facts about each job in their career, additional detailed information about their current job, scores on scales for personality, learning styles, career orientation, and a series of additional variables. Therefore, we expected a low response rate. Fortunately, our initial target population was quite large. We sent out 3217 questionnaires, 260 (8%) of which were returned. Unfortunately, not all respondents were able to complete the entire questionnaire. As our questionnaire was quite demanding, respondents had to put substantial effort in recalling and/or looking up specific details about their career. We were able to use 146 questionnaires for our analyses. This number is comparable with respondent numbers in other career studies among school alumni, for example, 116 in Reitman and Schneer (2003) and 170 in Jepsen and Choudhuri (2001).

Table 3. Summary of the questionnaire.

Intro letter

Personal background information (age, gender, study, etc.)

Summary of career, with for each job (up to ten jobs)

− job title

− year of start and end

− tasks/responsibilities

− firm/organization

− nationality of organization

− functional area

− industry

Scales for personality, learning style, and career orientation

Additional questions about the current job (position, company size, annual pay package, etc.) to determine objective career success

Scales for subjective career success

Table 4 provides descriptive information about the respondents in our study. The alumni in the dataset are predominantly male (74%), the majority are Dutch (85%), and the average age was 38² years (with the oldest respondent being 63). On average, respondents were mid-to the second half of their career, being out of school for about 12 years, and had had about three different jobs so far in their career. They had been in their current position for about three and a half years. The largest groups of respondents were majors in strategic management (47%) and marketing management (35%), and a smaller group (18%) in business information/management of innovation. These numbers reflect the actual numbers of graduates in the various majors up until our survey. Strategic management had always been one of the most popular and largest specializations among the business school alumni we surveyed. However, business information management started attracting more students recently due to the advancement of technological information.

Table 4. Profile of the respondents in our study (n = 146).

Gender	Male/Female	74%/26%
Bachelor	From same school as master’s	71%
Major Specialization
Marketing Management		35%
Strategic Management		47%
Business Information/InnovationManagement		18%
Nationality Dutch		85%
Age (average)		38 years
Years out (average)		11.6 years
Number of different jobs (average):		2.98
Number of years in current job (average)		3.53

Analysis and Findings

Method and Estimation Results

Our analysis focuses on respondents’ jobs at the time of the survey, which is the current point in a respondent’s career. We measure objective and Subjective Career Success at that point and relate it to the independent variables discussed earlier. We classified the respondents into three groups based on their current job: (i) respondents in marketing jobs (n = 52: working in marketing, sales, CRM, account management, and marketing research); (ii) respondents in management jobs (n = 55: working in planning, project management, operations manager, general management, consulting, and strategy); (iii) respondents in other jobs (n = 39: a mixed set of respondents with jobs in IT, business processes, finance & accounting, and education. The primary purpose of our study was identifying the drivers of success in marketing jobs. Possible contrasts with management jobs might help to make this picture more clear. The group “other jobs” was included it in some of the analyses when we were interested in career success in general and across domains.

Our primary analysis method was multiple regression, with career success (objective and Subjective Career Success, respectively) as the dependent variable, and personal variables, career development variables, and control variables as independent variables. We used the structure proposed in our research model (see Figure 1). The purpose of our study is discovery of relationships rather than testing. We did not formulate specific a-priori hypotheses about the relationships between our dependent and independent variables. Therefore, we decided to apply the stepwise regression method. Stepwise regression builds a model for explaining the dependent variable by adding one independent variable at a time (step-by-step). The variables are selected based on their contribution to explaining the variance in the dependent variable. Adding variables to the equation stops when the variance explained by adding another variable is below a specified cutoff criterion. Stepwise regression is a data-mining technique because it capitalizes on the specific data in the sample. This is useful for a study like ours that focuses on discovery rather than testing, and in which ideally, the results should be validated with new data. Unfortunately, our sample is too small for both exploration and validation. We used the statistical software package SPSS for the multiple regressions. We ran our analyses for three different parts of our dataset: respondents with marketing jobs, respondents with management jobs, and for all respondents (marketing, management, and other).

Table 5 presents all stepwise multiple regressions results, with Objective Career Success (left) and Subjective Career Success (right) as dependent variables. Our main focus is the difference between marketing jobs and management jobs, i.e., the results for Models 1 and 2 and Models 4 and 5, respectively. The analysis for all jobs (including the category “other jobs”) was conducted to develop insight into the overall picture.

Table 5. Results of the multiple regressions.

	Dependent Variable Objective Career Success			Dependent Variable Subjective Career Success
Independent Variables	1 Marketing Jobs N = 52	2 Management Jobs N = 55	3 All Jobs N = 146	4 Marketing Jobs N = 52	5 Management Jobs N = 55	6 All Jobs N = 146
Personality Traits
Openness to new experiences						0.243 (.00)
Conscientiousness
Extraversion	0.368(.01)
Agreeableness
Neuroticism					0.339 (.02)	0.179 (.02)
Learning Styles
Concrete Experience			0.266 (.00)
Reflective Observation				-0.351 (.01)	−0.418 (.01)
Abstract Conceptualization						0.168 (.03)
Active Experimentation
Career Orientation
Protean Career Orientation	0.292 (.04)		0.237 (.00)	0.536 (.00)		0.334 (.00)
Work-Life Balance			−0.217 (.01)
Other Personal Variables
Gender 1=male 0=else
Gender x Work-Life Balance					−0.304 (.04)
Nationality 1=Neth 0=else
Bachelors 1= Same school 0=else		−0.391 (.00)
MajorMKT 1=yes 0=else
MajorSM 1=yes; 0=else
Career development
Steepness Early Career	0.292 (.02)
# Different Organizations	0.342 (.01)
# Different Nationalities			−0.165 (.05)
# Different Domains
# Different Industries		−0.486 (.00)
Total Experience
Mentor 1=yes 0=no
Postgrad Education Univ
Postgrad Education NonUniv		−0.456 (.00)
Control variables
# Years Out		0.950 (.00)	0.336 (.00)
Company Size (# employees)			−0.232 (.00)
Extra for Subj Car Success
Objective Career Success	n.a	n.a	n.a.			0.243 (.00)
R2 -adjusted	0.484	0.656	0.384	0.466	0.376	0.453

Notes: Numbers are standardized (beta) regression coefficients, with significance (p, two-sided) in brackets.

Empty cell: variable not included in the model.

Criteria for variables selection: Pin: 0.10; Pout: 0.15; only variables with p < .05 included in regression.

When a variable is included and significant in multiple stepwise regression, this is a stronger sign of its influence than a significant bivariate correlation, because the multiple regression also accounts for the influence of other explanatory variables. Therefore, we mainly focus on the results of the multiple stepwise regressions. For a more detailed view, Table 6 provides the bivariate correlation coefficients of the independent variables with career success. An independent variable with a significant bivariate correlation with the dependent variable may be missing in the estimated multiple regression model because another independent variable takes over the effect.

Table 6. Bivariate correlations.

	Dependent Variable Objective Career Success			Dependent Variable Subjective Career Success
	1 Marketing Jobs N = 52	2 Management Jobs N = 55	3 All Jobs N = 146	4 Marketing Jobs N = 52	5 Management Jobs N = 55	6 All Jobs N = 146
Personality Traits
Openness to new experiences			0.158*	0.385**	0.420**	0.459**
Conscientiousness	−0.292*		−0.161*			0.189**
Extraversion	0.491**		0.199**	0.346*	0.341*	0.308**
Agreeableness
Neuroticism					0.370**	0.358**
Learning styles
Concrete Experience		0.394**	0.404**
Reflective Observation			−0.213**	−0.472**	−0.484**	−0.352**
Abstract Conceptualization				0.373*		0.206*
Active Experimentation
Career orientation variables
Protean Career Orientation	0.461**		0.217**	0.615**	0.282*	0.559**
Work- Life Balance					−0.294*
Other personal variables
Gender 1=Male 0=else					−0.254*
Gender x Work Life Balance				−0.252*	−0.351**
Nationality 1=Neth 0=else
Bachelors 1=Same School 0=else			0.162*
MajorMKT 1=Yes 0=else
MajorSM 1=Yes; 0=else
Career Development
Steepness Early Career		0.420**	0.287**
# Different Organizations	0.451**		0.240**
# Different Nationalities
# Different Domains
# Different Industries
Total Experience	0.355**					0.179*
Mentor 1=Yes 0=No					0.293*	0.159*
Postgrad Education Univ	0.399**		0.163*
Postgrad Education NonUniv		−0.252*
Control variables
# Years out	0.368**	0.621**	0.460**		0.295*
Company Size (# employees)		−0,281*	−0.305**
Extra for Subj Car Success
Objective Career Success	n.a	n.a.	n.a.	0.447**	0.335*	0.340**

Empty cell= no significant correlation.

+=positive correlation; -= negative correlation.

*=p < .05; **=p < .01 (one-tailed)

Findings for Objective and Subjective Marketing Career Success

Objective Career Success

Table 7 summarizes the multiple regression model estimates for Objective Career Success in marketing jobs (Model 1 of Table 5). Objective Career Success in marketing jobs is greater as respondents are more extroverted, have worked in many different organizations, have a more protean career orientation, and have a steeper early career trajectory. In the multiple regression, extraversion gets the largest beta-value (0.368). Extroverted individuals are more outgoing, energetic, assertive, and sociable (Costa & McCrae, 1992; Toegel & Barsoux, 2012).

Table 7. Summary results for Objective Marketing Career Success.

Variables in the model (with beta-coefficient and p-value)
Extraversion	0.368 (.01)
#Different organizations	0.342 (.01)
Protean career orientation	0.292 (.04)
Steepness early career	0.292 (.02)
R2 (adjusted) = .484 n = 52

Apparently, individuals with these characteristics are more successful in marketing jobs. Second, having worked in many different organizations is positively related to marketing career success (beta = 0.342). The current literature is undecided about the value of external mobility and breadth of experience. Here, we find an indication that this factor is favorable for marketers to be successful. Next, marketers who are more in charge of their own career (i.e., have a protean career orientation) are more successful (beta = 0.292). To our knowledge, this is the first empirical indication that marketers with a protean career orientation do better. This result confirms the importance of career development competences for career success (Watts, 2005). Finally, a steep early career trajectory breeds later success (beta = 0.292). This relationship has been reported in many other career studies in the literature (e.g., Vinkenburg & Torsten, 2012), and apparently it also holds for marketers.

For two variables it is particularly interesting that they are absent in the estimated model for objective marketing career success: conscientiousness and having majored in marketing. We will discuss these variables later. Also, gender, nationality, and mentoring did not have a significant relationship with career success in our study. Furthermore, none of the control variables entered the model,³

Model for All Jobs

Several independent variables did not enter the stepwise regression model estimated for marketing jobs alone (Model 1) but did appear in the model estimated for all jobs (Model 3). These are shown on the left-hand side of Table 8. The right-hand side of Table 8 gives the bivariate correlations of these variables with Objective Career Success for marketing jobs. The variables learning from concrete experiences (a learning style variable), work-life balance, and Years out are related to Objective Career Success in general. The bivariate correlation coefficients for these variables with career success in marketing are substantive and in the same direction as for all jobs. This suggests that these variables do play a role in marketing jobs but fail to become significant, because of the limited sample size.

Table 8. Independent variables appearing in the model for objective career success for all jobs.

Variables in the model for all jobs only (see Table 6, Model 3, with beta- and p- values) n = 146		Bivariate correlations of these variables with objective career success for marketing jobs
Concrete experiences	0.266 (.00)	0.205 (.11)
Work-life balance	−0.217 (.01)	−0.218 (.11)
#Different nationalities	−0.165 (.05)	0.039 (.40)
Years out	0.336 (.00)	0.368 (.01)
Company size	−0.232 (.00)	−0.102 (.25)
R^2 (adjusted) = 0.384 n = 146

Marketing Jobs versus Management Jobs

Interestingly, the set of explanatory variables that enters the model for marketing jobs is completely disjoint from the set of explanatory variables for management jobs. As we saw earlier, the model for marketing jobs (Model 1, Table 5) contains extraversion, number of different organizations worked in, and protean career orientation as significant independent variables. In the model for management jobs (Model 2) all of these are missing, suggesting that these correlates of career success are specific for marketing. Steepness of early career is also missing in Model 2, but it does have a bivariate correlation coefficient with Objective Career Success of 0.420 for management jobs (Table 6, column 2).

At the same time, the model for career success in management jobs (Model 2) contains three independent variables that do not appear in the model for marketing jobs: having a bachelor’s degree from the same university, having worked in many different industries, and having taken non-university postgraduate courses, all having a negative relationship with Objective Career Success in management jobs.

Subjective Career Success

Doing well in a job, in terms of salary, company position, and responsibilities creates job satisfaction. In a meta-analysis, Judge and Bretz (1994) reported a correlation of 0.25 between extrinsic (objective) and intrinsic (subjective) career success. For marketing jobs, we found even a larger bivariate correlation coefficient: 0.447, implying that for marketers, objective career satisfaction explains about 20% of the variance in subjective career satisfaction.

The right-hand side of Table 5 shows the independent variables explaining Subjective Career Success. Reflective observation is negatively related to subjective marketing career success beta = −0.351), implying that a learning style of reflective observation detracts from a person’s people satisfaction in a marketing job. protean career orientation has a significant positive relationship with Subjective Career Success (beta = 0.536), indicating that people who take their marketing career into their own hands enjoy it more.

We highlight two interesting bivariate correlations with Subjective Career Success in marketing (Table 6; column 4). We see a significant bivariate correlation between Subjective Career Success and openness to new experiences of 0.385. Furthermore, Subjective Career Success is positively correlated with extraversion (0.346). This shows more extrovert marketers not only perform better (Objective Career Success), but also have greater job satisfaction.

Relationships Between Independent Variables

The way a career has developed in the past, i.e., career development, may well be influenced by personal variables. This has not been taken into account so far. Ideally, to analyze the full research model in Figure 1, we should estimate a structural equations model. However, our sample size is too small for this. Fortunately, this does not seem a big problem since only very few significant bivariate correlations exist between the personal and career development variables in our dataset. Of the complete set of 153 correlation coefficients (17 personal variables x 9 career development variables), only eight are significant (p < .05), all under 0.35.

Discussion

Our findings must be interpreted in the context of the population where the data come from: Business school graduates working in marketing jobs.

Nature Factors

Extraversion

In our study, the personality trait of extraversion emerged as the most prominent explanatory factor for Objective Career Success in marketing. Extroverted individuals get energy from interacting with others, are outgoing, energetic, assertive, sociable. In addition, the significant positive correlation of extraversion with Subjective Career Success suggests that extroverts not only perform better but are also more satisfied with their careers. This prominence of extraversion as a driver for success in marketing careers concurs with the insights from our interviews with top marketers. They mentioned skills such as extensive communication, reading colleagues and the board, and the ability to convince others in the company (Table 1). Extraversion as a favorable trait for a marketer is also in line with the requirements for marketers mentioned by employers and recruiters, referred to in the beginning of this paper: leadership, communication skills and interpersonal skills. Leadership is correlated with extraversion (in a meta-study Judge Timothy, Higgins Chad, Thoresen Carl, and Barrick Murray (1999) found an average correlation coefficient of 0.31). Besides extraversion, communication skills represent an independent predictor of leadership (Mitchell, James, & Lee, 2022).

Conscientiousness

In our study, conscientiousness is not related to success in marketing careers. This is interesting because in the literature conscientiousness is positively associated with job performance in a broad variety of fields (Barrick & Mount, 1991; Judge Timothy, Higgins Chad, Thoresen Carl, & Barrick Murray, 1999; Ng, Eby, Sorensen, & Feldman, 2005). Conscientious people are efficient and well-organized and the opposite of easy-going and careless (Costa & McCrae, 1992). Top marketers in our interviews mentioned “commitment” and “strong in execution” as success factors for marketers, elements that are related to conscientiousness. One reason that we did not find an effect of conscientiousness in our data may be a sampling issue. Our population is business graduates who all have completed an academic education at the master’s level. People who are unable to organize themselves are not able to reach that stage. Therefore, the respondents in our sample have a basic level of conscientiousness and it is conceivable that from a certain point onwards, a higher level of conscientiousness does not help marketers. Being too conscientious could even work against being a successful marketer because it may constrain new thinking and hamper creative ideas. An indication of this is the negative (bi-variate) correlation between conscientiousness and Objective Career Success for respondents in marketing jobs. A too conscientious person may be less suitable for a marketing career. This is interesting finding deserves further study.

Other Personality Traits

We did not find significant relationships between either agreeableness or neuroticism and career success in marketing jobs. However, the personality factor openness to new experiences is positively related with Subjective Career Success in marketing. Openness to new experiences is associated with divergent thinking and creativity (McCrae, 1987; Feist, 1998; Kaufman & Gregoire, 2016), and creativity in turn is the lifeblood of marketing (Andrews & Smith, 1996; Levitt, 1983).

Learning Style

Our data suggests that learning from concrete experiences is beneficial for a successful career in marketing jobs. Such a learning style matches with the idea of “trial-and-error learning,” mentioned by Moorman and Day George (2016, p. 28). Our top marketers in the qualitative interviews mentioned that experimenting, trying, and daring to fail are success factors for marketers (Table 1). A learning style of concrete experiences is different from a learning style of reflective observation where a decision-maker tries to acquire deep knowledge and attempts to develop a complete mental model of all the forces in the market. We even found that a reflective observation style is negatively related with Subjective Career Success in marketing, suggesting that individuals with this learning style are less happy in a marketing job.

Managers with a learning style of concrete experiences have significantly higher tacit knowledge levels than those with other learning styles (Armstrong & Anis, 2008). Apparently, learning through concrete experience helps marketers build valuable tacit knowledge, i.e., “passive wisdom/knowledge,” as a basis for “intuition and sensing” in the words of the top marketers in the interviews. This is in contrast with the rationality of “engineers” and “McKinsey people” (Table 1).

Nurture Factors

Personality Traits and learning style are enduring characteristics of an individual. We also studied factors which people can control in their life and which may help their careers, for example education, training, and experience.

Specialization in Marketing at University Study

In our data there is no relationship between having majored in marketing and later success in a marketing career (r = −0.065). Interestingly, studies in the USA produced similar findings. Hunt Shelby, Chonko Lawrence, and Wood Van (1986)analyzed survey data from 1076 marketing practitioners sampled from the AMA membership directory. They did not find a relationship between having an (undergraduate) marketing education and career success in marketing, measured in terms of income and job title. More recently, Bacon (2017) replicated the Hunt Shelby, Chonko Lawrence, and Wood Van (1986) study, using data collected online. He found that an undergraduate degree in marketing was positively related to income in marketing jobs, but “surprisingly, respondents with some nonmarketing majors earned about the same as marketing majors in marketing jobs” (Bacon, 2017, p. 109).

All respondents (business students) in our dataset had followed marketing courses during their bachelor’s studies. Apparently, having majored in marketing during a master’s does not offer an additional (long-run) advantage for career success in marketing. Yet, a marketing specialization may well help a graduating student to secure an entry-level position in marketing (Hunt Shelby, Chonko Lawrence, & Wood Van, 1986), while this advantage disappears later in a marketing career. McArthur, Krzysztof, Pang, and Alcaraz (2017), who analyzed advertisements for marketing jobs, found that even for entry level marketing jobs, advertisements demanded a marketing degree only in half (50.4%) of the ads. This suggests that for employers in search of marketing personnel, a marketing degree is a moderate priority. Although in the marketing literature so far, no impact on marketing career success has been found of marketing degree programs compared to non-marketing degree programs, as we will see later, there are indications that marketing programs with a strong emphasis on experiential learning are a positive exception (Ikbal, 2023).

Postgraduate Training and Education

University postgraduate education is positively correlated with Objective Career Success in marketing jobs (r = 0.399; Table 6, column 1). Participating in these university postgraduate courses appears to help becoming a successful marketer, however, at the same time, there may be a reverse causal path here, namely that promising individuals have a greater chance to be sent to such courses.

Though positive effects of mentors were mentioned by the top marketers in our interviews and in the literature (Kram, 1985, Allen et al. 2004; Wyner, 2017) we did not find an effect of mentoring in our data.

Experience

Having worked at a larger number of different organizations is positively related to Objective Career Success in marketing. Apparently, exposure to a broad variety of situations leads to the accumulation of valuable marketing experience. This matches with the views of the top managers in the interviews who mentioned “experience/different jobs” as a success factor for marketers. Interestingly, we did not find this effect for (non-marketing) management jobs.

Nature or Nurture?

Is becoming a successful marketer a matter of Nature or Nurture? Our findings suggest both.

On the Nature side, we found significant positive effects on marketing career success from enduring traits such as extraversion, openness, and a style of learning from concrete experiences. On the Nurture side: An academic business education is probably useful (see Bacon, 2017; we could not test in our data since they were all from business alumni), but we did not find evidence of an added value of a marketing specialization at the master’s level. Positive nurture factors are breadth of experiences from having worked in many different organizations, and university level post-academic education.

Independence

The top marketers in the interviews mentioned “independent minds” and “guts” as success factors for careers in marketing. Our finding that success of marketing careers is correlated with a protean career orientation (taking charge of one’s own career) is consistent with this idea. Professional mobility of successful marketers is an indication of their independence. Whitler and Morgan (2017) observed a relatively high mobility of top marketers. For example, compared to CEOs, CMOs stay in office on average half as long.

The greater mobility of marketers also has a downside because, as Bidwell and Ethan (2015) found, upward career progression is much more likely to happen through internal mobility, which is favorable for people staying in the same organization. This may harm marketing professionals and may be related to Whitler (2019)’s finding that marketing is underrepresented as a background for CEOs in the Forbes F100 companies. So, it seems that mobility is a positive factor for becoming a successful marketer, but maybe harmful for further career progression. This is an interesting hypothesis that needs further research.

Distinctiveness of Marketing

Do successful marketers have characteristics that are distinctive from other management jobs? In our study we found that the “success profile” for marketers has at least three distinctive elements.

Extraversion

This personality trait, found to be the most important driver of success in marketing careers, is not correlated with success with career success in management or other professional jobs of business students graduates.

Independence

As discussed before, independence and autonomy are indicators for successful marketers. We did not find this for individuals in management and other fields.

Experience

Mobility is favorable for career success in marketing. Again, we did not find this for other job types. For management jobs we found that having worked in a larger number of different industries is negatively correlated with Objective Career Success in management.

Lessons for Marketing Educators

The findings of this study are relevant for various parties, including companies who want to select candidates for their marketing positions and individuals who ask themselves if a marketing job fits their abilities. Here we focus on the impact of our findings for marketing educators.

“Marketing operations are one of the last phases of business management to come under scientific scrutiny” (Kotler, 1971, p. 1). As marketing academics, we like to think that the field has become increasingly analytical since marketing management became a functional business area in the 1960s. By building models of marketing phenomena and using optimization techniques, marketing decisions were expected to become more and more programmable (Kotler, 1971, p. 2). Fifty years later, this is not the picture emerging from our study. Successful marketers are not persons who apply deep reflection to the events in their markets, but rather individuals who learn from concrete experiences, experimenting, trial and error, and are extroverted. As marketing educators, we have to reflect on these findings and about their implications for the way we teach marketing, especially considering the absence of a correlation between having a degree in marketing and marketing career success.

Educational systems tend to favor the personality trait of conscientiousness (Poropat, 2014). Marketing curricula are no exception. In the same academic institution of the alumni surveyed in this study, data were available for marketing majors of a given year on Personality Traits together with their grades for five marketing courses. These courses covered topics in the areas of consumer behavior, marketing strategy, and marketing research (five courses in total). The correlation between average grade and conscientiousness was 0.21 (p < .001; n = 177). The correlations of marketing course grades with the other four Personality Traits (including extraversion) were negligible. As mentioned earlier, in our study there was no relationship between conscientiousness and performance in marketing. This suggests that important capabilities that make a person fit for a marketing job are not necessarily related to success during university studies.

There is another discrepancy. Marketing faculty are academic researchers who think in terms of causes and effects, theories, and models, and probably most often have a learning style that emphasizes reflective observation themselves. This is quite different from the learning style of learning from concrete experiences that characterizes successful marketers in our study. Marketing faculty need to realize this and think about its consequences for the didactic approaches they deploy in their marketing curriculum. Marketing faculty probably prefer analytical courses, but a marketing education program should also offer students ample opportunities for learning from concrete experiences.

How marketing educators can help

Our study has potential implications for the marketing curriculum and attention for the personal development of marketing students.

Curriculum

According to Kolb and Kolb David (2009) experiential learning involves the creation of knowledge through the transformation of experiences. Ideally, experiential learning is a cyclical process learning “where the learner ‘touches all the bases’ – experiencing, reflecting, thinking, and acting – in a recursive process that is responsive to the learning situation and what is being learned” (Kolb & Kolb David, 2009, p. 44)” This means that immediate or concrete experiences are the basis for observations and reflections and these reflections are then assimilated and distilled into abstract concepts from which new implications for action can be drawn. Following, these implications can be actively tested and serve as guides in creating new experiences (Kolb & Kolb David, 2009, p. 44). Whereas many educators probably emphasize a subset of the experiential learning steps, including all four of them will be most effective.

Creating concrete experiences can be done through several approaches, for example case studies, internships in companies, (company-team) projects, business games and simulations. Ursic and Hegstrom (1985) found that among alumni, corporations, and students, internships, and cases rate highly as marketing teaching methods, much higher than lectures. Computer simulation, engaging the students in a real-life-like situation can also be an effective form of experiential learning and improve students’ decision-making techniques and skills (Ganesh & Sun, 2009). Training in communication skills (reporting and presenting) is also highly valuable in a marketing curriculum. This can be done by having students work on concrete business problems, which are presented to company representatives in class. During the course, students can then solve the case problem and present and sometimes even implement solutions during the course by applying concepts and methods that are being taught during the course or identified by students themselves after studying the problem. In such situation, faculty may be most effective when they operate as a coach, providing feedback to students.

In a recent paper, Ikbal (2023) describes a yearlong “Integrated Marketing Education (IME) Program,” an integrated curriculum design with experiential learning as an important principle. Mandatory elements of this curriculum are a quarter long internship course where a student works 20 hours per week off campus with a company and a set of three client-based consulting projects (also a quarter long). From a survey administered to both, alumni from this IME program and alumni from the regular marketing program of the same university it was concluded that the alumni from the integrated (IME) had higher starting salaries and higher long-term career satisfaction than alumni from the regular marketing program at the same university. Experiential learning can, therefore, be seen as a key success factor in marketing.

Nowadays, providing students with (big) data sets and have them use these for addressing business problems and identifying new business opportunities also is an increasingly popular way for the creation of concrete experiences. Our findings about the importance of concrete experiences does imply that marketing education should feature such experiences. This does not mean that we should replace the more analytical and conceptual materials that quite often seems to dominate many business school curricula. In fact, the combination of the two, where business school professors can be especially instrumental guiding their students in reflecting and abstracting, will most likely create the best learning experience.

Personal Development

Our findings underscore the importance of the personal trait of extraversion, independence, proactive career behaviors and communication skills. These findings imply that marketing curricula should not only focus on marketing knowledge and skills but also pay attention to personal development. Students of marketing programs should understand their personality and what advantages or disadvantages it poses. For example, while extroversion is associated with better communication skills, introverts tend to be better listeners (Grant, Gino, & HofmannDavid, 2010).

Moreover, while extraversion is an (enduring) trait and by definition difficult to instill in people, behaviors and skills associated with extrovert personality can be learned. Introverted individuals can be trained to engage in extraverted behaviors (Margolis & Lyubomirsky, 2019). If introvert marketers commit themselves to acting more extraverted (active, energetic, talkative) they still can get a lot of satisfaction from their job. Experimental work in psychology has shown that dispositional introverts when acting extraverted improve happiness (Zelenski, Santoro, & Whelan Deanna, 2012). Communication skills, also important for marketers, can be trained: “Unlike trait extraversion, communication skills are more malleable and capable of being trained and developed over time” (Mitchell, James, & Lee, 2022, p. 1526).

Personal development training in marketing programs should aid students in developing an understanding which career directions within marketing are most suitable for them. For example, while the majority of marketing jobs benefit from extraversion, some marketing jobs may favor introvert personality, for example more technical jobs such as search engine optimizers, content marketers, and marketing research analysts. Introvert managers generally are better listeners (Grant, Gino, & HofmannDavid, 2010). This is important in a time when in the social media landscape of today listening to consumers becomes more and more important. Introvert marketers are also more inclined to listen to marketing researchers and R&D people. Personal development training in marketing programs should also help to develop the protean career attitude in students, being ready to network, research career paths, pursue new career directions.

Follow-Up Research

We cannot give a definite answer yet to Smith and Cooke’s (1967, p. 60) question: “What talents and capabilities are required to perform the tasks involved in various marketing jobs or positions?” and there remains a great need for research “to guide marketing educators in relating education to job performance” (Smith and Cooke 1967, p 59). Nevertheless, in this study we obtained interesting insights, that can also serve as building blocks for further work on this topic.

Limitations

Of course, the present study has its limitations. For example, with a qualitative study of only five participants, we may have missed some underlying factors. Furthermore, in the quantitative part, we had only respondents from one business school in one country, and we only looked at marketing as a whole, whereas specific sub-areas may require specific capabilities. At the same time, these limitations constitute inspiration for follow up work. We discuss alleys for this research.

Contingencies

Similar research projects in different contexts (schools, countries) will help to broaden and deepen our insights. In future work, a differentiation in marketing jobs would also be a valuable next step.

Sub-fields of marketing

In the present study, marketing jobs encompassed a broad variety of functions, including marketing management, sales, CRM, account management, and marketing research. The desirability of traits and capabilities probably varies across specific sub-fields of marketing. For example, for marketing management learning from concrete experiences may be the best learning style, whereas for market researchers a leaning style of reflective observation might be better.

Marketing in different industries

In a similar vein, the favored profile of a marketer may be contingent on the type of industry. Are the best fitting personalities and learning styles different, for example, for marketers in consumer-packaged goods, marketers of services, industrial marketing, sports marketing or nonprofit marketing? Jaworski Bernard (2011) proposed in-depth studies to observe, record and analyze the roles of various marketers, CMOs and other marketing professionals, and in various industries, including technology. Naturally, the large samples needed to study such different settings are a major challenge.

Changing Marketing Context

The required capabilities for marketing professionals may change over time. For example, as marketing becomes more data-driven and IT-supported, the requirements for successful marketing professionals may shift in the direction of more analytical and technological skills (Schlee & Karns, 2017) and better market information capabilities (Moorman & Day George, 2016). This implies that future marketers may need a deeper, more reflective observation learning style than successful marketers than in the past. Alternatively, marketers could remain in the role of the outgoing, action-oriented decision-makers but work closely in cross-functional relationships with specialists such as data miners, web designers, and digital analysts (Moorman & Day George, 2016, p. 17). It is important to keep reexamining the question of the desirable qualities of marketers as the marketing context evolves over time.

The Marketing Curriculum

Like earlier researchers, we did not find an effect of having followed a specialized marketing program in business school on later marketing career success This raises questions about the current marketing education programs. Our findings show the important role of experiential learning in marketing jobs. In the previous section, we discussed several ways how marketing departments of business schools can deal with this issue. Systematic studies of the effects such efforts, such as Ikbal (2023) are important for the field.

Knowing which characteristics make successful marketers and how education can help, improves the quality of marketing management, and thereby strengthens the position of marketing in the company.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ At that time, marketing was considered a “man’s business,” which referred to candidates for marketing jobs as well their recruiters.

² The annual number of graduates of the school increased over time. Therefore, recent graduates were overrepresented in the data compared to older ones.

³ “Years out” has a significant bivariate correlation of 0.368 with Objective Career Success (Table 6, column), but in the multiple regression this effect is taken over by the number of different organizations the respondent had worked in (mutual correlation: 0.607)..

⁴ We thank Korn Ferry for granting us permission for using the KLSI scale.

Appendix: Measurement details

Objective Career Success

We measured Objective Career Success with the following items (Cronbach alpha: = 0.672):

• Value of annual pay package (5 categories)

• Hierarchical position on a continuum (0-100) between lowest level employee (0) and CEO (100)

• Profit responsibility: (1=yes; 0=no)

• Board position (1=yes); 2=no)

• Number of employees (directly or indirectly) reporting to respondent (5 categories)

We carried out a factor analysis and found one dominant factor with high loadings on all five variables that we called “Objective Career Success.” The factor score on this factor is our dependent variable Objective Career Success.

Subjective Career Success

We used the scale for job satisfaction from Shockley, Heather, Ozgun Burcu, Poteat Laura, and Dullaghan (2016) with the following three items (Cronbach alpha = 0.925):

• My career is personally satisfying

• I am enthusiastic about my career

• I have found my career quite interesting

The items are measured with 7-point Likert scales.

We carried out a factor analysis and found one dominant factor with high loadings on all three variables that we called Subjective Career Success. The factor score on this factor is our dependent variable Subjective Career Success.

Personality Traits

We measured the five personality factors using the “Very brief measure of the Big-Five personality domain”, developed by Gosling, Rentfrow Peter, and Swann (2003), which is a 10-item inventory, rated with Likert scales. (Example of an item: “I see myself as extraverted, enthusiastic”, with a scale running from “Strongly disagree” to “Strongly agree”). This measure has the advantage of requiring limited time, while reaching “adequate levels of convergence with the widely used Big-Five measures” (Gosling, Rentfrow Peter, & Swann, 2003, p. 504).

Learning Style (KLSI)

We used the Kolb Learning Style Inventory (KLSI) 3.1, which produces for each respondent a score for each of the four elements of the learning cycle, CE, RO AC, and AE (see Figure 2). It uses a forced-choice, 12-item inventory.⁴ Example of an item: When I learn, (1) I like to deal with my feelings; (2) I like to think about ideas; (3) I like to be doing things, (4) I like to watch and listen. The respondent is asked to rank each of these four endings according to “How well you think each one fits to how you go about learning something”.

Protean Career Orientation

The protean career orientation scale developed by Baruch (2014) consists of seven items (measured with 7-point Likert scales). Examples of items:

• I navigate my own career, mostly according to my plans

• I’m in charge of my own career

• Freedom and autonomy are driving forces in my career

  Cronbach Alpha = 0.806.

Life-Style Balance

We used the Career Orientation Inventory developed by Schein (1990) consisting of five items (measured with 7-point Likert scales). Examples of items:

• I dream of a career that will permit me to integrate my personal, family, and work needs

• I feel successful in life only if I have been able to balance my personal, family and career requirements

Cronbach Alpha: = 0.735.

Gender:1=male; 0=else

Nationality 1=Dutch; 0=else

Bachelor1= if the bachelor’s degree was obtained atthe same school as the master’s degree

0= other school

Master’s Major

Three categories: Marketing Management; Strategic Management; Other

MajorMKT = 1if major is marketing management; 0 else

MajorSM = 1if major is strategic management; 0 else

Steepness of Early Career Trajectory

We defined three job levels: Junior, Senior, and Executive

• Level 1 Junior: Trainee, junior manager, assistant manager, (assistant) product manager, project manager, trade manager.

• Level 2 Senior: Senior manager, marketing manager, project leader, area manager, brand leader, sales manager.

• Level 3 Executive: CEO, CMO, CFO, general manager, (marketing) director, vice president, board member.

Steepness of early career trajectory is computed as: 1/(number of years to get from job level 1 to job level 2). This measures the steepness of the first stage of a respondent’s career. Steepness Early Career is set at 0 if a respondent never reached job level 2.

Experience

We looked at the different working environments of a respondent. In our data, for each consecutive job of a respondent, we have the name of the organization, the nationality of the organization, the functional domain, and the industry. We computed values for the following variables:

• #Different organizations = number of different organizations where the respondent worked

• #Different Nationalities= number of different nationalities of organizations during career

• #Different Domains = number of different functional areas during career

• #Different Industries= number of different industries during career.

• Total experience = #Different organizations + #Different Nationalities + #Different Domains + #Different industries

Categories of functional domains:

• Marketing (including sales, CRM, account management, marketing research)

• Management (broad set of management activities, including planning, project management, general management)

• Strategy

• Consulting (general)

• Education (teaching, coaching, training, HRM)

• Finance and accounting

• Analytics (IT, software, business intelligence, supply chain, business processes)

• #Different industries = number of different industries during career

Categories of industries:

• Manufacturing (e.g., CPG, Consumer durables, other manufacturing)

• Consumer services (e.g., telecom, leisure, health care, media)

• Retailing

• Financial institutions

• Business services (e.g., consulting)

• Non-profit

• Other

Mentor

•  = 1 if respondent reported that one or more mentors or role models had a significant influence on her/his career; = 0 otherwise.

Postgraduate Courses

• Postgrad Education Univ.= number of postgraduate courses attended, offered by universities/academic business schools

• Postgrad Education Non-Univ.= number of postgraduate courses attended, offered by non-university organizations (parties from practice or own company)

Years Out

• Number of years since graduation

Company Size

• Number of employees (categories on a 7-point ordinal scale)