Name Your (Defection) Price: What Does It Take to Lure a Marketer to a New Company

Summary of Findings

• The average marketer requires an increase of 43.2% to be lured away from their current job to a similar one elsewhere. This number suggests that marketers are relatively content in their roles.
• Director-level marketers have a relatively higher Defection Price than individuals in other organizational levels.
• As marketers climb the corporate ladder, their defection price increases, and equity increases that defection price at every level but the VP level, where having equity has no effect on defection price.
• Marketers in Tech & Software companies have a lower Defection Price than their peers in other industries.
• Remote employees also have a lower Defection Price than in-person employees.
• Publicly traded company marketers have a lower Defection Price than employees of companies in other funding categories.
• The variables in our study explain about 28% of the reason why individuals’ Defection Price may differ from the average of 43.2%.
• Relationships, working conditions, and personal considerations are among many factors that also influence defection decisions, but are not captured in this analysis.

Introduction

Following the recent “Great Resignation” period of 2021 and 2022, during which time we read about (and experienced) employees leaving their jobs en masse to pursue greener pastures, it occurred to us to ask marketers how much of an increase, if any, was required to entice them to make a change.

Of course, people leave jobs to take promotions elsewhere, and that is natural. But in many cases, we have heard about people leaving their current role to go to another company to do the same thing – horizontal moves. If people were moving simply because they didn’t like where they were, we reasoned that they might make such a move for little or no increase in pay. On the other hand, if people required large increases to justify such a move, perhaps it really is just about the money.

When Is The Defection Price Right?

To measure how much of an increase in pay would be required to inspire a horizontal move from one company to another, we asked respondents to name the percentage increase in total annual compensation that would be required to entice them to leave their current company. We called this a person’s “Defection Price.” 

In our sample we found that the average respondent said they would require an increase of 43.2% to be lured away from their current job to a similar one elsewhere. This number seems to suggest one of two explanations. First, it may be that most marketers were relatively content in their roles at the time we conducted our survey in Spring 2023. After all, a 43% increase in compensation for doing the same job is substantial. It may also suggest that most marketers feel they are being paid substantially less than they ought to be, and hence feel a next career move would have to bring with it a substantial increase. 

One test of the latter possibility lies in how respondents rated their satisfaction with their overall compensation, as well as each component of it. If marketers are satisfied with their compensation, that would suggest people are generally happy where they are and so would require a substantial increase to consider defecting. 

In our sample, respondents reported an average overall satisfaction rating of 3.5 on a 5-point scale, indicating that marketers are somewhat more satisfied than dissatisfied with their total compensation. 

This result argues against the explanation that marketers would require a lot more in a lateral move because their current pay is much too low.

A Survey of Potential Influences on Defection Price

In our survey, Defection Prices ran from a low of zero to a high of 100%. Clearly some people would leave their job for the same pay they are making today, while others would require double the pay to make such a move. The object of our research, then, is to understand the factors that influence Defection Price to be lower or higher.

We first explored whether various groups differ in their willingness to switch companies. For instance, do individuals in certain industries, marketing disciplines, levels within an organization, gender, or other such groups, have higher or lower Defection Prices?

Organizational Hierarchy Appears To Influence Defection Price… But It’s Complicated

One of the factors that most strongly influences compensation is a person’s level within an organizational hierarchy. To test the relationship between organizational level and Defection Price, we conducted an analysis of variance (ANOVA) test (see the appendix for more information on ANOVA).  

The results in the chart below suggest that director-level marketers have a relatively higher Defection Price than individuals in other organizational levels. Manager levels also appear more demanding than individual contributors, but equivalent to vice president (VP) levels. VP and individual contributor levels are statistically equivalent.

The Influence of Equity

To understand why vice president level respondents would have a lower Defection Price than Directors, we first examined the simple correlation between a person’s level and their defection price (individual contributor = 1 to vice president = 4). Here we found a small positive correlation, suggesting that as one rises through the organizational ranks, one’s Defection Price tends to rise. 

One of the more common ways organizations attempt to influence employee retention, particularly among those in upper levels of the organization, is by offering equity in the company. In our survey, approximately 48% of respondents reported having been awarded equity. To understand how equity influences Defection Price, we asked respondents to tell us how many months of their current salary that equity was projected to be worth when fully vested. We also captured the vesting period. Where individuals did not receive equity, their monthly value was set to zero.

As the chart below shows, a person’s Defection Price tends to rise steadily as they rise through the organization. Those with equity have a statistically reliably higher Defection Price from Individual Contributor through Director level. For VP and above, however, that pattern disappears. While those without equity continue to see a gradual increase in their Defection Price, VP levels with equity experience a substantial drop in their Defection Price.

Tech & Software: Easy Come, Easy Go

In our recent report on marketer compensation, we found that marketers in Tech & Software make less than their peers in other industries. Tech & Software marketers also stand apart when it comes to the increase in pay required to lure them away into a new job. Where it would take an increase of 47% to lure the average non-tech marketer to a new job, the average Defection Price for Tech & Software marketers was just 34%, representing a statistically reliable and substantial difference. Given the stark difference between Tech & Software and other industries, and the fact that all other industries were statistically equivalent, we will simply compare Tech & Software to all others going forward.

The Roaming Eye of Remote Marketers

The next grouping we tested was based on whether a marketer worked remotely, was hybrid (remote and in-office), or full-time in office. One stood out as being substantially less expensive to lure away from their current roles: remote employees. Remote employees required only 38% more than their current pay to defect, which was reliably and meaningfully lower than either hybrid (45%) or in-person employees (48%). 

The combination of being both a Tech & Software marketer and a remote worker resulted in a Defection Price of just 31%, compared to 45% for everyone else.

Lower Defection Prices for Public Employees

Next, we examined the relationship between the funding type of respondents’ organizations and their respective Defection Prices. Here, the average marketer in a publicly traded company had a substantially lower Defection Price (35%) than did employees of companies in other funding categories, which were not statistically distinct from one another and had Defection Prices that were 9 to 11 points higher (44% to 46%). This difference exists even when controlling for how much equity a marketer has been awarded, further accentuating the distinction between marketers at private companies and others.

Defection by a Thousand (Or a Few, at Least) Cuts

In addition to the factors just discussed, there are a number of other factors that are quantifiable and therefore can be assessed through correlation and regression analyses (see the Appendix for a description of these tests). Results of that analysis revealed a variety of factors that have small-to-medium sized correlations with Defection Price.

Table 1: Factors tested for the prediction of Defection Price

Factor Type	Factors Associated With Defection Price	Influential?
Categorical	Organizational level	VP or above have a lower Defection Price, all else being equal, but other factors, such as how much equity one has been awarded, tend to negate this impact to some extent.
Categorical	Remote or not	All else being equal, remote marketers do not have a lower Defection Price than others.
Categorical	Industry	Marketers in Tech & Software companies have a lower Defection Price than others, all else being equal.
Categorical	Funding Type	All else being equal, public company marketers have a lower Defection Price than others.
Quantifiable	Bonus Percentage	As bonus percentage increases or decreases so too does Defection Price.
Quantifiable	Company Financial Performance Rating	Rosier assessments of one’s company’s funding lead to higher Defection Prices.
Quantifiable	Equity – Monthly Pay Equivalent	The amount of equity a person receives does not increase Defection Price, when considered in context of all the other factors we examined. Those who did not receive equity were assigned a value of zero.
Quantifiable	Salary	One’s salary is not an influence on Defection Price.
Quantifiable	Current Company Tenure	One’s tenure with a company is not influential on Defection Price.
Quantifiable	Satisfaction – Salary	Satisfaction with one’s salary is not influential on Defection Price.
Quantifiable	Satisfaction – Bonus	Satisfaction with one’s bonus is not influential on Defection Price.
Quantifiable	Satisfaction – Equity	Satisfaction with one’s equity  benefit is influential on Defection Price. Higher satisfaction with equity leads to a slightly higher Defection Price.
Quantifiable	Satisfaction – Healthcare	Satisfaction with the healthcare  benefit is not influential on Defection Price.
Quantifiable	Satisfaction – Other Benefits	Satisfaction with other benefits is not influential on Defection Price.

Correlation analyses reveal simple, one-to-one relationships between any two variables – whether changes in one variable are reliably associated with changes in another. Each of the quantifiable variables in Table 1 has a statistically reliable, positive relationship with Defection Price, such that an increase in Defection Price is reliably associated with increases in each.

However, all of the factors in Table 1 are at work in the real world at once. As we saw in the case of organizational level and having equity, two or more factors often work together to influence a variable of interest, such as Defection Price.

Often, the reason two variables seem to be related is that one or more other variables influences them both. In the case of a factor such as Defection Price, it seems reasonable to assume that many factors are influential. Simple correlation analyses cannot detect this, nor can it help identify the relative strengths of factors that co-occur. Multivariate statistical techniques such as multiple regression can.

To test the relative influence of each variable in the context of all the others, we conducted a multiple regression analysis, entering each of the 10 quantifiable variables listed above. We also added the four categorical variables described above.

Of course, there are many variables that bear on whether employees might stay or leave a job, only some of which we have measured here. Among those are critical factors such as relationships with coworkers and superiors, working conditions, and family and lifestyle considerations that may impact how an individual feels about a given company and job. We would therefore not expect to create a model that predicts all or even most of the influence on why a person would have a higher or lower Defection Price. 

Indeed, the variables in our model accounted for 27.9% of why an individual’s Defection Price would differ from 43.2% – the average Defection Price across all marketers we surveyed. Our multiple regression analysis revealed that most of the variables that appeared to be highly correlated with Defection Price exhibit a small but reliable influence on Defection Price when considered in context with the other variables in the model. Model parameters can be found in the Appendix.

The most influential variable in the model is the indicator that contrasts Tech & Software company marketers with others. All else being equal, being a Tech & Software marketer tends to lower one’s Defection Price. Likewise, being a public company marketer also tends to reduce Defection Price, as do both being at the VP level or above. 

In contrast, the financial performance of one’s company, one’s bonus percentage, tenure in the organization and amount of equity received all exert an upward influence on Defection Price. Interestingly, the amount of equity one receives is a small but reliable upward influence on Defection Price, but satisfaction with one’s equity is a slightly stronger upward influence.

Table 2: Categorical Variables and Defection Price

Categorical Variable	Influential in Model?	Defection Price If Variable Is Applicable	Defection Price If Variable Is Not Applicable
Publicly Traded	yes	35%	46%
Remote	no	25%	43%
Tech & Software	yes	34%	47%
VP and above	yes	41%	44%

We were surprised to find that salary was not an influence on Defection Price when considered in the context of the other variables in the model. Likewise, none of the measures of satisfaction with other elements of compensation was found to be influential. Being full-time remote also does not influence Defection Price. And, while being a remote worker appears to lower Defection Price when considered in isolation, in the context of all the other factors that influence Defection Price, it does not turn out to be a meaningful factor. This mirrors our findings concerning the relationship between compensation and remote working.

Implications

In our survey, we found that when considering all the available factors together, the value of the equity a person has been granted exerts a relatively small upward pressure on Defection Price. This suggests that the actual value of equity matters.

One’s equity, however, was not nearly as important in determining Defection Price as the financial performance of the company. Companies that are doing well financially will be somewhat less likely to lose marketers to better offers elsewhere. Strong financial performance may provide marketers a sense of security and the prospect of greater financial and career opportunities in the future, thereby raising the price other companies would need to offer to lure them away.

Surprisingly, while tenure had a small positive influence on Defection Price, how much one is being paid in salary is not a significant influence on Defection Price. The asking price for luring marketers away from their current job is high at 43%, but it is not a marketer’s current salary, or even how satisfied they are with it that raises or lowers that price. 

What our results clearly show is that working in Tech & Software and in a publicly traded company are both downward influences on Defection Price. Even after removing the influence of financial performance, bonus percentages, and equity, marketers in Tech & Software and publicly traded companies still appear to be easier to lure away from their current jobs than others. Why this is the case is a matter for future research.

While VP-level marketers in our survey have Defection Prices (see Table 2) that are similar to non-VPs, such that the two are not statistically different when compared to each other in isolation, the story changes when all factors in the model are considered. Being a VP does have an influence on the model, whereas being remote or not does not. This is despite the fact that remote marketers in our study had a substantially lower average Defection Price than all others. Clearly other factors associated with those remote workers caused the difference we observed in Defection Price.

What’s clear is that the great resignation is over, and that marketers will be inexpensive to lure away to a new job. Providing marketers equity will help maintain a higher defection price, but that stops working for more senior marketers, whose experience with equity in prior roles may lead them to discount the future value of equity.

Appendix

Statistical Terms Used in This Paper

Analysis of Variance (ANOVA)

ANOVA, a statistical method, is used to compare averages from three or more groups. For example, in our research on the B2B Buying Experience, we looked at buyers from six industries and their interactions with sellers. With ANOVA, we discovered that buyers in Manufacturing interacted less (12.8 times) than those in Professional Services (16.7), Business Services (16.2), Tech (16.9), and Financial Services (16.9). This analysis helped us make sure that the patterns we found are true for B2B marketers in general, not just for the more than 900 marketers we looked at.

Multiple Regression Analysis

Multiple regression, also known as multiple linear regression, is a statistical method used to understand how various factors together influence an outcome. For example, to predict the time it takes for a typical B2B buyer to research and purchase a solution, one could use multiple regression to consider different influences, such as the price of the solution, the number of vendors being evaluated, and how crucial the solution is to the buyer. Hence, regressions are considered predictive models. In the example alluded to here, we used multiple regression to see how these three factors (price, number of vendors, solution importance) help explain the differences in how long the buying process takes for different buyers. Multiple regression also provides a measure of how important each factor is to the model’s prediction. For example, price was found to be a bigger driver of buying cycle length than solution importance.

Correlation Analysis

Correlations measure whether changes in one factor are reliably associated with changes in another. The easiest correlations to think about are those where one measurement increases, and then a second one also consistently increases. For example, in our research on the B2B Buying Experience, we found that when buyer’s increase the number of vendors they evaluate, their buying process takes longer. That is a positive correlation. In contrast, we found a weak but reliable negative correlation between how important a buyer rates a solution and the length of the buying cycle. More important solutions have reliably shorter buying cycles.

Statistical Reporting

Finding	Statistical Test	Statistic	Significance Level	Effect Size	Sample Size
Independent from other factors that may influence defection, each reported level of fiscal health was found to be associated with an average 3% increase in Defection Price.	Regression Analysis	F=15.084	p<.001	0.175	649
n isolation – without other factors that may influence defection – the presence of equity in one’s compensation package raised the average Defection Price by 6%.	Regression Analysis	F=15.084	p<.001	0.175	649
Independent of other factors that might influence Defection Price, those who work for public companies reported, on average, a 3% lower percentage increase needed to consider switching companies compared to their counterparts in PE-backed, VC-backed, and private organizations	Regression Analysis	F=15.084	p<.001	0.175	649
Good company financial performance has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=0.308	p < .001	Moderate	652
Position level has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.095	p=.01	Small	652
Equity has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.241	p < .001	Moderate	652
Bonus percentages have a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.376	p < .001	Moderate	652
Tenure at one’s company has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.230	p < .001	Moderate	652
Satisfaction with one’s salary has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.174	p < .001	Modest	652
Satisfaction with one’s bonus percentage has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.183	p < .001	Modest	652
Satisfaction with one’s equity has a positive correlation with an increase in Defection Price.	Pearson’s Correlation	r=.314	p < .001	Moderate	652