Analyzing Employee Attrition

In this:

• Measuring employee attrition
• Measuring employee commitment
• Carefully examining the reasons why people leave
• Creating a better exit survey
• Example exit survey

To create an above-average company, you have to do three things well:

1. acquire or attract employees capable of above-average performance (above-average relative to your competitors)
2. activate those employees you hire to a level of high performance
3. and keep more employees with above-average performance than those with average or low performance.

This is about the third, your ability to control employee attrition

Attrition refers to the number or percentage of employees who are leaving a company to work for other companies or who have decided to pursue other opportunities. The attrition rate is the measurement you use to determine the percentage of employees who have left a company in a given period.

<Tip> Some might use other terms to refer to attrition — termination rate and exit rate, for example — they all mean the same thing. When I get into measures below, I use the term exit rate because it is shorter and more comfortable to say than the attrition rate.

The effort required to find, select, and get employees to a high level of performance can be substantial. As a result, it is reasoned that the cost of replacing each employee, all factors considered, can exceed an entire year of employee pay. Naturally, the cost of losing an above-average employee in a key position may be more – in some cases, two to three more. That fact may explain why there is no more frequently discussed topic among HR professionals as employee attrition and why employee retention (the inverse of attrition) is the most commonly cited justification for HR programs and projects. And, if you need even more proof of the importance of managing employee attrition for an enterprise, it turns out that it's the most often used HR key performance indicator (KPI) — the standard for measuring how successful an HR organization is in meeting its objectives. Sometimes the standard for how well a business leader is meeting their 'people objectives' as well.

Despite the importance of controlling employee attrition rates to both HR professionals and executives, most strategies meant to reduce attrition are based on anecdotes rather than on sound logic and evidence. Many HR departments exert a great deal of effort in the hope of influencing their organization's attrition rate measure, only to see that it tenaciously remains the same. Or worse, it erratically moves up and down with little explanation. In such a world, executives keep doing what they’re doing, and HR keeps reporting the attrition rate as a KPI, but everyone's actions end up having little influence on the attrition rate. The inability to exhibit any evidence of control over the attrition KPI destroys the credibility of human resource professionals and their programs.

Fortunately, there’s a wealth of easily collectible data you can use to use to control employee attrition. You can test your assumptions about attrition through analysis considering the new information. The actions that are doing nothing to support your control over attrition can be eliminated, making room for actions that allow you better control over attrition. 

In this post, I point out common misconceptions about employee attrition, show you a better way to think about attrition, and give you the playbook for how to measure and control attrition through sound data analysis rather than guessing or blindly following whatever others are telling you to do. Before you do anything with measurement, it is important to build a foundation in sound thinking. So let's start with the misconceptions.

Getting Beyond the Common Misconceptions about Attrition

The most common misconceptions about attrition are the following:

* Attrition is only related to what you do or don’t do well as a manager or company

* People leave for only a single reason

* All attrition is the same

* All attrition should be prevented

* You can compare the attrition count of one group directly to another

* You can control employee attrition with global, one-size-fits-all efforts

Basing retention strategies on misconceptions is both costly and ineffective. The purpose of People Analytics is to provide evidence-based recommendations by determining the statistically significant relationships between the antecedents you control, employee behavior, and outcomes. Let me walk you through how evidence-based approaches to put to rest some of the misconceptions about attrition.

Misconception 1: Attrition is only a result of what you do or don’t do well as a manager or company.

Evidence-Based Perspective:

 * What other companies do or don’t do matters as much to influence your attrition rate as what you do or don't do

 * The number of job and person-specific external opportunities that are available matters

* The economy and job market matters

Misconception 2: People leave for only one reason.

Evidence-Based Perspective:

* The decision to leave a company is multivariate: There is no single cause. As with the human lifespan, many reasons contribute to or protect against adverse outcomes, like, for example, death. To ask, "What causes death?" works when you are talking about a specific case but is non-sensical when applied to a population. There are many factors associated with death, some more frequent than others. Also, some elements at the same time push and pull in different directions.

<Remember> Saying that there is a range of different things that matter is not to throw up your hands and say "stuff happens." You can determine the precise balance of each variable's contributions to attrition using a multivariate regression model that includes all of the variables you want to test to see how much each variable proportionally contributes to the employee population as a whole or for a particular segment. 

While I don't show you how to analyze multivariate problems using a multiple regression model in this post, I do so in Chapter 14 of People Analytics For Dummies: Modeling HR Data With Multiple Regression Analysis.

* Some of the factors that influence attrition aren’t a part of the conscious decision to leave, making the face-value answer to “Why?” problematic. If an existing employee gives you a single answer, it may or may not be the real reason, but it certainly isn’t the only reason. Some variables mathematically increase the probability of employee attrition but are never a part of the employees' conscious awareness. If an essential variable is not in their consciousness, they cannot possibly mention it in an exit survey.

Misconception 3: All attrition is the same.

Evidence-Based Perspective:

* Attrition in job types that have greater responsibility has more impact on company performance than attrition at lower-responsibility level job types.

* Attrition in job types that have more company-specific strategic value - determined by the company’s unique product or service value proposition - has more impact on company performance than attrition of more general job types.

* Attrition of employees with above-average performance has more impact than with employees who have average or below-average performance.

Misconception 4: All attrition should be prevented

Evidence-Based Perspective:

* Attrition creates much-needed opportunities for job movement and promotion within an organization and is necessary to bring in new talent with fresh energy.

* Most companies have pay-for-performance compensation practices and leadership succession planning programs. These programs intend to decrease the likelihood of high-performing employees leaving while letting the attrition rate of lower-performing employees remain high or even increase. It’s antithetical to these resource-intensive practices to try to reduce all employee attrition with other, resource-intensive programs. Most people don’t realize performance management programs can only work if you have employee attrition - otherwise, they accomplish little to nothing at all. (also experienced HR professionals) 

<Remember> If you intend to reduce employee attrition below that of other companies, then a performance management program that withholds rewards from average performers to provide rewards to high performers is not for you. The two ideals are not mathematically compatible.

 * What is more important than the overall reduction in attrition is control of attrition so that there’s lower-than-average attrition in the segments where you want to retain proportionally more employees (high performers in critical jobs) and higher-than-average attrition in the segments where that attrition is acceptable or desirable.

Misconception 5: You can compare the attrition rate of one job family or cost center directly to another.

Evidence-Based Perspective:

* Some job types have naturally higher attrition rates than other types. For example, highly interchangeable roles like cashier, customer service rep, or sales rep have higher average attrition rates than more specialized or technical roles.

* People in different tenure horizons (0–1 year, 1–3, 3–5, 5+, for example) have very different annual attrition rates.

* People in different geographic locations have different annual attrition rates because there are different opportunities and labor market conditions provided to them.

* It’s unfair and useful to compare the attrition rate of groups that have different team composition by job type, location, or tenure because these factors influence the overall probability of attrition as much or more than other factors.

<Remember> Unless you mathematically control the variables I describe above (Job Family, Tenure, and Location), you should never compare one leader's exit rate with another! 

Misconception 6: You can control employee attrition with global, one-size-fits-all efforts. 

Evidence-Based Perspective:

* Targeted intervention is most effective.

* Making interventions proportional to the most likely need per segment, you can more effectively reduce attrition. For one segment, a well-timed promotion within the company is a useful retention tool; for another, an above-market pay offer is the only strategy that would work; and for another, it’s correcting bad manager behaviors or providing necessary support at the team.

<Remember> The fact that the factor that matters is different by segment is not to say, "Everyone is different" and therefore do everything or don't do anything. You can use analysis to determine the best options per segment or individual.

 * Targeted interventions achieve better results because concentrating resources produces advantages.

What happens if you one hundred different people about why they think people leave their jobs? You hear some of the most common explanations repeated over and over — “People leave managers, not companies,” or pay dissatisfaction, job dissatisfaction, lack of promotion opportunities, and burnout. Indeed, it is easy to imagine how someone subjected to one of these conditions would be more likely to pursue another job opportunity. Yet if it is a simple as this, then why is that many employees, perhaps the majority of employees, endure in employment under the same conditions? If 80% of employees in the same situation stayed and only 20% left, is the variable a meaningful explainer or predictor of attrition? You may begin to see, as I do, there are problems with the conventional explanations. The common reasons may or may not be a factor in any one individual case; however, some other things are going on as well.

Some recent research from my Alma Mater, the University of Minnesota, has brought into view how critical events or shocks play a role in the decision to leave the job. The thinking goes like this - sometimes, an unexpected shock may increase a person’s likelihood to exit who may not have otherwise exhibited any of the typical characteristics or circumstances that would generally see before an exit. Examples of shocks are being passed over for an expected promotion, the announcement of a merger, a spouse, being offered a job out of town, the birth of a child, or stock options vesting at a greater-than-expected value. When studying the general non-shock related factors, you have to control for shocks mathematically. If you were not to include all of the critical factors and relevant shocks in a multivariate analysis, the omissions make it challenging to ascertain the actual proportional contribution of each of the other characteristics. This is where much of the study of employee attrition breaks down.

<Remember> Though none of the many possible explanations provided totally and always wrong, these non-evidence based generalizations are not useful. People analytics involves using data to understand the situational attrition risks that different companies, groups, and individuals experience and to help determine with more certainty what specific actions reduce attrition in those circumstances. People Analytics is more effective than guessing or trying to implement dozens of stock solutions.

Considering attrition in the context of talent strategy

Before the dawn of people analytics, most companies’ HR strategies boiled down to one idea: Do everything you can imagine possible to keep as many employees as possible.

Whereas in the past, many doctors believed that fresh air and “bloodletting” was a useful solution for illness, today they have a more focused and data-driven approach. It’s also the same with employee attrition.

Now, with the help of people analytics, you'll be able to more carefully measure employee attrition and match your efforts to a more carefully crafted strategy. With employee attrition analysis, you can identify which employee segments have the highest attrition risk and narrow in on plans to reduce attrition risk and then measure how successful your actions are.

Measuring Employee Attrition

If you’re going to analyze employee attrition, first, you have to quantify it into a measurement. Read on to find out how.

Every measurement begins with a working definition and a mathematical operator. Here’s how to operationally define exits, the base component of the exit rate. An exit is someone who was an employee that is no longer an employee. To calculate the number of exits, you extract a list of all current and former employees and count the employees within a given segment with an exit date within the period you are reporting.

<Tip> Most human resources information system (HRIS) have a preconfigured exit list report that can provide a list of all employees who have exited in a given period. These lists generally include the names of the employees, but also some basic facts about the employee, like worker ID, start date, job, manager, business unit, division, location, base pay, and gender. If you do not know how to get this list with a sufficient range of information included on it, you can ask someone from IT or HRIT to help you get a list like this.

The shorthand formula for exits looks like this:

Exits Formula: Count [Segment].[Period].Exits

Say that you want to know how many statisticians exited in 2017. In words more humans are likely to understand, you count exits in the following way:

If employee exit date is equal to or greater than January 1, 2017, and less than January 1, 2018, AND job equals ["Statistician"], then count it; if not, then don’t.

You may achieve this count using an If-Then statement in Excel or any programing language:

January 1, 2017, to January 1, 2018, represents the period. Period = [2017].

“Statistician” is the job, which represents the segment. Segment = [Statistician].

Using the shorthand method I just mentioned, you’re applying the following operation:

Count [Statistician].[2017].Exits

In practice, you would apply this operation for all relevant segments and periods to prepare a dataset for your graph output or visual dashboard.

You might also prepare this dataset as a base input for other, more complicated compound measures that combine two or more measures with more operators — for example, exit rate. The next section deals with that concept.

Calculating the exit rate

If you think about exits simplistically, you may take the position that a segment or period with more exits is worse than a segment or period with fewer exits. The problem with this position is that, because there are a different number of employees in each segment, you can’t compare the number of exits to each other directly to derive any meaning.

Let’s compare Group A to Group B in the same period. Group A in Period 1 has 100 people. Group B in Period 1 has 50 people. If ten people leave from both Group A and Group B in Period 1, that represents 10 percent of Group A and 20 percent of Group B. Although we're talking about the same number of exits, employees are exiting Group B at two times the rate of Group A.

You'll encounter the same problem if you’re comparing Group A to itself over time. Let’s say that Group A has 100 people in Period 1 and has 200 people in Period 2. If ten people leave in Period 1 and 20 people leave in Period 2, you may assume that exits are two times larger in Period 2 — but this assumption isn’t true. Exits are 10 percent of headcount in both cases, even though Period 2 had more exits on an absolute basis. (The math here is simple: 10/100 = 10%, and 20/200 = 10%.)

<Remember> In most situations, the exit rate is a far more useful figure to calculate than the exit count.

<Remember> It’s important to understand what exits mean in some relative context so that you can compare different segment sizes to each other. For this reason, report segment exits as a percentage of segment average headcount — this is the best way to calculate the exit rate. To calculate the exit rate, you divide the number of segment exits within a given segment within a given period by the average headcount of the same segment for the same period:

Exit-Rate Formula: Count [Segment].[Period].Exits / [Segment].[Period].Average-Headcount

Figure 11-1: Calculating the exit rate

For this calculation, you need to count the total number of exits from a segment for the period of analysis. Then you divide the exits by the average number of employees in that segment.

Calculating the annualized exit rate

If you know the number of exits for part of a year, you can extrapolate this information for the rest of the year — the annualized exit rate, in other words —using the following formula:

Annualized Exit Rate = ({YTD Exit Rate} x (12 / #-of-Months))

Annualization satisfies the basic problem that you cannot compare a partial year to a full year. Therefore it’s difficult to make sense of partial year data without putting it on a more commonly understood annual basis. Annualization allows you to view the partial year, if what occurred in the months that elapsed happened over 12 months.

<Remember> Annualization is a basic forecast. Annualizing based on a portion of the year may miss regular seasonal variations that may skew the results. Annualization misses any acceleration or deacceleration that may be occurring. Annualization does not provide a precise forecast — it can help you compare the data you have to annual historical data or other benchmark data expressed on an annual basis. There are other, more rigorous methods for forecasting and predicting if that is your intent.

Refining exit rate by type classification

Not all exits are the same. At the time that an employee leaves the company, someone records the exit in the Human Resource Information System (HRIS). At this time, whoever is recording the exit, either a manager or an administrator, must input some details regarding exit. Was the exit voluntary or involuntary? Was the exit regretted or non-regretted? Was the exit avoidable or unavoidable? What was the provided reason for the exit? More than one classification type framework is desirable, and these can be used alone or used together for various purposes. The classification type framework varies by company and by the desired level of logical precision.

In this list, I describe some of the most commonly used exit classification types:

* Voluntary: An exit is voluntary if the employee voluntarily exits the company of their own choice and free will.

* Involuntary: An exit is involuntary if the employee exits the company and it isn’t their choice — it’s the company's decision. If you fire an employee or lay them off in a restructuring or reduction in force, then the exit is involuntary. All exits are either voluntary or involuntary.

<Remember> If you want to understand changes in headcount for accounting or projection, you would include in your exit rate all exits. If you want to understand whether employees are “voting with their feet,” you need to look specifically at exits coded as voluntary, excluding all involuntary exits, which aren’t the employee’s choice. Combining voluntary and involuntary confounds the conclusion.

* Avoidable Voluntary: Admittedly, all exits are either voluntary or involuntary, but it’s possible to break down the voluntary exits a bit further. You can call an exit an Avoidable Voluntary if the employee voluntarily exits the company of their own choice and free will, and there was something the company could have done to prevent the employee from exiting.

 * Unavoidable Voluntary: An exit is unavoidable if the employee exits the company, and it was completely unrelated to anything the company can control. For example, if an employee exits the company because a spouse is taking a job in another city, this is an unavoidable exit. Other unavoidable reasons include exits to go to school, care for a sick relative, or raise a child, for example.

<Remember> To make an avoidable and unavoidable distinction, you need some interaction with the employee regarding his reason for the exit at the time he submits his resignation. For purposes of the avoidable or unavoidable classification, you don’t necessarily need all the details. Still, you do need to know whether the person is leaving for some reason that is entirely outside the influence of the company. 

* Regretted Voluntary: Another way to break down voluntary exits involves the organization's point-of-view regarding the exit. The terminology is admittedly strange and unsettling. In any case, you should regret an exit if an employee with an above-average performance rating leaves. You should not regret an exit if the employee's performance is average or below. The reason is that all parties benefit when an employee with below-average performance leaves. The exit provides the possibility to hire, transfer, or promote another employee who is above average.

* Nonregretted Voluntary: An exit is nonregretted if the employee voluntarily exits the company, and at the time of exit, the employee has a below-average performance rating. You might not regret losing this person because you could replace them with someone that performs better than the person leaving – meaning you are getting a performance upgrade out of this transaction.

<Remember> The regretted/nonregretted distinction isn’t intended to be rooted in cruelty. There is a fitting process. The regretted/non-regretted distinction reflects this fitting process. What is best for the company (and all other employees that work together) is that the company retains a high percentage of the employees who perform extraordinarily well in this environment and allows or even prompts other employees to go to other environments where they can achieve extraordinary performance. The company wants to have a lower regretted exit rate than non-regretted so that it is getting better, not worse, over time. Without the distinction, there’s no way of knowing which way the company is heading.

Okay, but what is unavoidable?

The person charged with making the Avoidable/Unavoidable type classification must have some information about the exit and must make a judgment call on which type to pick. Because other people providing the classification may not fully understand your definition or purposes for making this type of a decision, I suggest establishing a decision tree like the following:

    Is the person leaving for another job? (Yes, No)

    If no, then the exit is most likely unavoidable.

    If yes, is the person moving? (Yes, No)

    If yes, which scenario applies better?

      A.) Moving for the new job.

      B.) Moving for reasons other than the job.

    If it’s A, then it’s most likely avoidable. If it’s B, then it’s most likely unavoidable.

This example here is simplistic and probably misses a lot of other unavoidable scenarios. You should extend it. The bottom line is that you need some information and human judgment to code the exit properly as Avoidable and Unavoidable. It is well worth it to do so.

Avoidable or unavoidable seems like an arcane detail; however, it’s relevant if you plan to use Voluntary-Exit-Rate as a key performance indicator. For example, if I were to compare two managers with ten employees, and each had two people exit, they would both have a 20% Voluntary Exit Rate. What if one of them had two unavoidable exits, and the other had two avoidable exits? This has two very different meanings. One manager has a 20% Voluntary Avoidable Exit Rate, and the other has a 0% Voluntary Avoidable Exit Rate. The distinction is not trivial to the manager, mainly if you assess the manager's performance based on employee exits. The cleanest measure of “people who vote with their feet” is Avoidable-Voluntary-Exit-Rate.

The avoidable and unavoidable distinction can also improve the accuracy of predictive models. It works better if you design one model to predict avoidable voluntary exits and a different model to predict unavoidable voluntary exits. Once sufficient accuracy has been determined with each independent model the output can be combined again for an improved overall forecast.

Calculating the exit rate by any exit type

In practice, when calculating by any exit type or even a combination of exit types, you calculate exit rate the same way, while filtering for the type you want to report. Use the following formula to calculate [Exit-Type]-Exit-Rate:

[Period].[Segment].[Exit-Type].Exits / [Period].[Segment].Average-Headcount x 100

If you work with the example shown in Figure 11-1 — the statistician exit rate — you would use the following equation:

[2017].[Statistician].[Voluntary].Exits / [2017].[Statistician].Average-Headcount x 100

In this example, if only 35 of the 62 Statistician 2017 exits are Exit Type = Voluntary, then the Statistician 2017 Voluntary Exit-Rate is

35 / 526.38 = 0.0665 x 100 = 6.7%

The 6.7% figure means that, in 2017, 6.7 percent of the statisticians left the company for voluntary reasons.

Segmenting for Insight

Segmentation is the practice of categorizing employees into different groups based on common characteristics. This process takes on great significance when it comes to the quality of your people analytics. If you have too many segments, you end up with thousands of ways of looking at the same metric. That, of course, leads to long reports, dashboards that require a lot of user filtration, and alert-fatigue. If you have too few segments, you can fail to see any meaningful insight.

Rightly segmenting the dataset is what can make the difference between the success and failure to engage your audience and demonstrate useful insight. You can segment employee exit rates in business units, job families, jobs, job tenures, locations, demographic segments, behavioral segments, attitudinal segments, or any other meaningful segment.

Figure 11-2 illustrates some of the common categories of segmentation in people analytics.

Figure 11-2: Segmentation categories

Rather than focus on the entire company at once, use segmentation to break down the company to see what’s going on in specific niches. Segmentation provides numerous advantages. Here are the most significant advantages:

* Tailor-made dashboards: Through segmentation, you get to personalize your reporting to different audiences instead of having a generic, watered-down dashboard for everyone. And when you focus on specific groups, traits, and characteristics, you’re more likely able to bring to the foreground what executives should focus on, what matters, and what to do about it.

* Identifying evidence-based learning opportunities: An analyst can study the exit rate by segment and then compare it to the company average or forecast benchmarks to understand how different (either high or low) the segment is. Segments with significant statistical differences from average (either high or low) represent excellent research opportunities!

* More effective retention efforts: Simply put, segmentation helps you better understand your employees' needs so that you can do a better job of solving problems because segmentation refrains from treating all people the same.

* Optimum use of productive resources: Because you aren’t wasting resources trying to influence the behavior of everyone, segmentation helps to reduce costs and increase the effectiveness of your resources. It allows you to concentrate your resources, money, time, and effort on the segments and problems that have the most value. Rather than apply watered-down efforts across all people, you can apply more concentrated resources to a targeted need.

So, what kinds of things might you discover by segmenting the employee voluntary exit rate? With segmentation, you might learn that women and men are equally likely to exit — which would call for one type of retention strategy — or you might learn that women are more likely than men to exit or vice versa (which would call for very different strategies). If there are differences, you may decide to look further to determine what is the cause of those differences.

With segmentation, you might learn that, on average, employees have a 10 percent voluntary exit rate in their first year, 5 percent in their second year, 20 percent in their third year, and 5 percent each subsequent year. With this information, you may decide that by the end of the second year, someone should communicate with all employees about their next career opportunity at the company.

With segmentation, you might learn that sales reps are, on average, twice as likely as statisticians to voluntarily exit — 20 percent versus 10 percent, regardless of manager — and that this has remained true over time regardless of the overall company exit rate. With that information, you might not want to compare the exit rate of managers in charge of sales reps with managers of statisticians.

With smart segmentation, you might learn that people who agree with the statement, “I know my next career move at the company," are one-third less likely to exit in the next 12 months as someone who disagrees with the statement.

Also, segmentation may show you if there is or isn't a significant difference in exit rate between people in a similar job who are paid different amounts of money, for example.

Much more detail about segmentation found in my book People Analytics for Dummies. More of my thoughts concerning the value of segmentation in developing insight found in these three posts: Segmenting for Perspective, Finding Useful Insight in Differences, and Making Sense of HR Metrics.

Measuring Retention Rate

Employee retention rate is the opposite of the exit rate — the other side of the coin, in other words. The retention rate is the percentage of employees who start a period and then make it to the end. To truly understand, predict, and control employee attrition, you have to have a theory of not only why people are leaving but also why people are staying. One thing you can be sure of is that if you can increase employee retention, you reduce employee attrition. Though retention and attrition are quite similar, in some contexts, the retention rate is a better metric than the exit rate.

As with most metrics, there’s more than one method to calculate the retention rate. The easiest method combines several other metrics that easy to understand and that you already know how to calculate easily:

[Segment].[Period].{Headcount-SOP} = the count of employees in a defined segment on the first day of a defined period

[Segment].[Period].{Headcount-EOP} = the count of employees in a defined segment on the last day of a defined period

[Segment].[Period].{Hires} = the count of employees hired in the segment in the period

<Tip> When analyzing a segment it’s generally best to exclude the people involved in transfers in and out of the segment from the entire retention analysis because these situations add unnecessary complexity. Remember to remove the people involved in transfers from the Start of Period Headcount and the End of Period Headcount and ignore the transfer activity. Analyze transfer activity separately.

The shorthand Retention Rate formula looks like this:

(([Segment].[Period].{Headcount-EOP} - ([Segment].[Period].{Hires}) / [Segment].[Period].{Headcount-SOP}) x 100.

To calculate the segment retention rate, you first count the number of people with a defined segment classification at the end of a defined period. Then you count and subtract the number of new hires with a defined segment classification in the defined period and then divide the result by the number of employees classified with the defined segment at the start of the defined period. Finally, you multiply that result by 100 to get the segment retention rate.

<Tip> If you are the type of business that has many hires that do not last an entire year, e.g. most retail organizations, then the simple method of calculating retention I provide here will not be accurate. It may result in a near-zero or negative retention rate, when in reality, more employees may have been retained than the retention rate reflects. If you have a lot of hires that exit and are replaced in less than one year, then you will need to use a more advanced retention calculation technique. Feel free to reach out to me by LinkedIn or email for more detail on how to do this.

Measuring Commitment

Fortunately, you don’t have to wait until people exit the company if you want to find out where you stand and in what segments you can work to improve employee commitment to reduce attrition.

Commitment is a measure of psychological attachment to the company. Committed employees feel a connection to the company, feel that they fit in, and feel that they share the goals of the company. As a result, committed employees are more loyal to a company and less likely to leave it. In short, commitment is a measurement of the bond between an individual and a company.

Organizational scientists have developed many nuanced definitions of commitment, as well as numerous survey scale options to measure it. The scientists have also demonstrated that these survey-based measures of commitment predict actual work behaviors such as attrition, organizational citizenship behavior, and job performance.

In this section are ten questions selected to help you calculate a composite index of commitment, which you should offer as statements with a Likert agreement scale, which I discuss in Chapter 12. These questions can be used together, in a smaller batch, or as independent items. That said, indexes that consist of multiple items tend to perform better than single items — the composite index covers a broader range of issues and is a more reliable predictor of future behavior.

First, here's the scale you should be using:

Likert agreement scale:

(1) Strongly Disagree

(2) Disagree

(3) Neither Agree nor Disagree

(4) Agree

 (5) Strongly Agree

And here are the statements:

For each statement, respondents indicate how much they agree with the statement by choosing a value from the Likert scale, just described:

* I am thrilled being a member of <Company>. * I believe in the mission of <Company>.

* <Company> has a great deal of personal meaning for me.

* I would be delighted to spend the rest of my career with <Company>.

* I enjoy discussing <Company> with people outside it.

* I feel like I’m "part of the family" at <Company>.

* I feel a strong sense of belonging to <Company>.

* It would be tough for me to leave <Company> right now, even if I wanted to.

* I feel that I owe this organization quite a bit because of what it has done for me.

* <Company> deserves my loyalty because of its treatment toward me.

Commitment Index scoring

Each item can either be reported independently by each Likert selection choice or combined into an indexed scale. If you use all ten items, you can define the Commitment Index as the cumulative response total, where you assign 1 point for Strongly Disagree, 2 points for Disagree, 3 points for Neither Agree nor Disagree, 4 points for Agree, and 5 points for Strongly Agree. Applying this method, those who take the survey can achieve a possible Commitment Index score between 10 and 50 points.

Commitment types

You may take the Commitment Index one step further and categorize all survey responses as one of three commitment types:

* High: Scored 40 to 50

* Uncertain: Scored 30 to 39

* Low: Scored less than 30

Calculating intent to stay

Though the method is less robust than the 10-Item Commitment Index, you can also simply ask employees a single item representing whether or not they intend to stay. This item also uses a Likert agreement scale.

* If I have my way, I will be working for this organization one year from now.

I have identified a strong correlation between the response on this Intent to Stay item and an exit over the next 12 months. Admittedly, the intent to stay is not a perfect individual predictor, but it is a great predictor of exits by segment (for example, by business unit). By this, I mean that the lower the average response to this item in a business unit, the more exits you see in that unit. It provides a signal indicating where you should go to work.

<Warning> The Intent to Stay item does have a few built-in flaws. Most employees have no idea what will happen in the next year — they may have no plans at the moment to leave, but this may change later. For those who plan to leave, you’re getting a reasonably accurate predictor of their future behavior if presented with an opportunity; however, if no desirable opportunity presents itself, such individuals are still employed at the end of the period you’re reviewing.

These flaws tell you as much about how to analyze the problem as accurate predictions. We now know that the problem is, at minimum, one part intention and one part external opportunity. What you need to understand two conditions:

a) what the employee wants, and b) how likely another employer is to take them.

Identify conditional logic to refine your understanding of the problem to improve how you collect and model data in the future to make more accurate predictions.

<Tip> You may question whether employees respond honestly to any direct questions you pose. The degree to which employees will provide you with accurate information on surveys is influenced by the decisions you make. The most important decision here is to have your survey professionally administered by a third party who can help you work with the sensitive information that employees provide in a manner that provides for their individual safety and confidentiality.

Calculating the benefits of measuring commitment and intent to stay

I want to stress that the Commitment Index and the Intent to Stay item are both helpful when it comes to comparing segments with each other to identify which segments are more likely to experience high attrition in the future before it happens. Both the Commitment Index and the Intent to Stay item can be trended over time to see whether the attrition risk is increasing or decreasing.

Not all high commitment employees stay — not all low-commitment types go. Still, if you hold on to the survey data and one year later report exit rate segmented by the recorded commitment type you recorded one year before, you find that low-commitment employees are at least two to three times more likely to exit than employees on average, and that high-commitment employees are two to three times less likely to exit. If your average exit rate is 10 percent, this could mean that low-commitment employees could have an exit rate of 30 percent or more, though high-commitment employees could have an exit rate of 5 percent or less. I encourage you to collect commitment data on surveys and try this analysis yourself. In any case, my research and the research of many other professionals have validated the fact that measures like Commitment Index and Intent to Stay are useful in predicting employee exit. It may not be the only factor predicting the likelihood to leave, but it is useful.

Finally, if you have included the Commitment Index and the Intent to Stay item in a more extensive annual survey requesting opinions about a range of other topics (managers, pay, leaders, company prospects, and so on) you can correlate the responses to all topics with a commitment to see which items best explain commitment. What you’re looking for are the items where you see the most significant difference between high-commitment and low-commitment employees. Though correlation doesn’t imply causation, those items that correlate with the Commitment Index and the Intent to Stay item can help you identify the likely drivers of employee exit and retention.

<Tip> This is referred to as “Key Driver Analysis.” To find out more about Key Driver Analysis, see Chapter 15 in People Analytics For Dummies.

Understanding Why People Leave

The Streetlight Effect (also known as the Drunkard's Search principle) is a type of observational bias that occurs whenever people search for something only where it’s easiest to look. Both names refer to a well-known joke circulated among data professionals. The story goes like this:

A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys, and they both look under the streetlight together. After a few minutes, the policeman asks the man if he is sure that he lost them here, and the drunk replies no — he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "This is where the light is."

The drunkard looking under the light is also how you go astray when analyzing employee attrition. You are scrutinizing employee attrition to see what's broke, what is causing it, and what to do about it. Unfortunately, unless you put careful forethought into collecting the relevant data beforehand, from a broad sample of people, some who stay and some who go, then you will be like the drunk hopelessly looking for the keys in the wrong place.

In the next section, I share the ways that things go wrong with exit surveys, talk about what information you need to collect in an exit survey, and then provide an example of what a good exit survey looks like.

Creating a better exit survey

An exit survey represents the last opportunity you have to ask questions before the employee moves on. Exit surveys can help you classify exits, identify talent competitor threats, understand how talent competitors are winning your people, and learn what you can do better to keep people. If you’re serious about wanting to gain control over attrition, you need to do exit surveys. Unfortunately, a lot of lousy exit surveys are out there, and not many companies are getting much use from them. In this section, I explain what can go wrong and then provide you with an example of a more effective exit survey instrument. Table 11-1 spells out the problems posed by many exit surveys.

Dealing with the Problems of Exit Surveys

Problem 1: Your survey has a low, non-random response rate

Most companies achieve less than a 30 percent response rate on their exit surveys. In this scenario, the most substantial categorial reason that employees leave is "Unknown." (70 percent). How then does anything on the exit survey represent any useful information? In theory, a small sample can represent useful information about leavers if the sample is collected randomly, however since those providing feedback on the exit survey are not selected randomly, then the responses of the 30% are misleading. To be clear - if you have no collected a random sample and you have less than a 70% response rate, I would suggest you are better off not to use the data at all! The underlying cause of the low response rate is generally poor execution. The survey isn't requested in time before the employee leaves - there wasn’t an appropriately motivated interest in the employee. The result is too few responses for useful analysis.

Problem 2: Your survey lacks confidentiality

Another problem is created when you decide to use managers or HR representatives to collect feedback at exit rather than a professional third-party administrator. The lack of investment in a safe third-party survey provider telegraphs a lack of seriousness about getting candid feedback and is "Amateur Hour."

Employees have little to gain by digging deep to provide their best answers. They may think, “I have some feedback I can share, but at this stage, it’s water under the bridge for me, so it's not worth the effort."

 If you have a third-party agent collect the data for you and provide assurances that they only share data back with the company in aggregate, then people feel safer to speak their mind.

Problem 3: Poor design: You have errors in question logic that bias responses

Many exit surveys are designed by people who may have taken a survey or two before but have no professional training in survey design, so they make errors that bias response and confound interpretation. A typical example is asking directly "Why did you leave?" — and then providing a list of options to choose from, where several of the options on this list are duplicate or overlapping, and other essential options are omitted. Of course, in the circumstances, the person is forced to choose one option from the list provided. The questions asked, and the response options frame the entire analysis.

Furthermore, the requirement to provide one reason implies that there’s only one reason when, in fact, there may be several. Also, the biggest influencers of behavior are often inaccessible cognitively. They could be inferred indirectly with a careful design but cannot be provided by the employee directly.

Problem 4: Poor design: You aren’t asking the questions necessary to get the essential information you need at the time of exit

More important than the details of the confounding "Why are you leaving?" question, other questions are often missed.

For example, "Where are you going?" "When precisely did you decide to leave?" and "Did any critical incidents influence your decision?" would be equally important questions to ask.

What you don’t ask is often more important influence on your success or failure than what you do ask, because there are so many more things left out than put in and omission is irretrievable.

Problem 5: You’ve muddied the waters by including together data from regretted and nonregretted exits

You have employees that you want to keep and employees whose exit you wouldn't mind at all. If you don't distinguish between the two, the data you collect pulls you in the wrong direction. For an ill-fit or otherwise low-performing employee (nonregretted exits), you want the company experience, pay, or manager relationship to be uncomfortable. If you lump these together with regretted exits, then you have made the reasons regretted exits leave blurry, particularly if you have more nonregretted exits than regretted.

If you collect data for both regretted and nonregretted exits, it is o.k. But you should partition these so you can separate the information you collect from these very different exit types at the time you report the data.

Problem 6: You have errors in analysis and interpretation

A common mistake is interpreting the reason for exit from only exit survey data, without a point of reference. There is a severe logical flaw if you review exit data without a point of reference and use it to interpret the reason for people are exiting. A better analysis is to compare the response to the same survey questions between stayers and leavers. 

For example, let’s say you asked all employees to rate their satisfaction with managers, pay, and career opportunity on a scale of 1 to 10, and on average, employees responded with an 8 to a career opportunity, 7 to managers and 5 to pay. The five doesn’t sound good, especially when compared to the other items, but you are not sure. When employees leave, you ask employees to rate the same three things again. On average, among employees who leave, the responses were 7 to managers, 6 to career opportunities, and 5 to pay. Again, this result seems to indicate pay is responsible for exits since employees are most negative about pay (5). However, you need to think more carefully about this. Since the stayers and leavers evaluated pay the same way - and more people stayed than left - then there is a problem with the logic of the conclusion that pay is the reason for the exit. If the response to the pay item is the same on average between “stayers and leavers,” and there are more stayers than leavers, then the pay item cannot explain the exit. Looking at the other items, you notice that the only item where people who left responded less favorably than the people who stayed was career opportunity. The perception of career opportunity is a more plausible explanation than any of the other items, even though it is rated higher than the other items on average.

It is important to compare the responses of stayers vs. leavers to the same (or similar) questions to determine the real reasons for leaving.

You cannot determine what questions differentiate between stayers and leavers only using exit surveys because the stayers don't take exit surveys! Better analysis for determining why people leave is to survey all employees on a wide range of issues some periodic consistent basis and then report response to these issues comparing the responses to stayers and leavers over time. It is the items that are different between the stayers and leavers that is the most plausible explanation (and future predictor) for the exit. To do this analysis, you need to collect survey data in a manner that you can hold on to it for a while and join it with exit data to do this analysis. Many professional employee survey services, including my own company, provide this capability.

Example exit survey

Enough of the problems! On to a more effective exit survey instrument. The exit survey example below was carefully constructed, taking into consideration many of the problems described in the table above. The design of the example exit survey included below is intended to take advantage of the opportunity you have to collect some data at the time employees leave, while attempting to steer clear of some of the most apparent logical flaws noted above. In any case, hold the information you obtain from any exit survey loosely. It is a single data point from a broader range of data you have the opportunity to collect and analyze to validate or invalidate theories. The logic of your analysis is just as important (or more important) than the calculation. If you use all of the opportunities you have to collect data, think systematically, and be careful with the logic of your analysis you arrive at a better answer then if you view each data source independently.

Below are a set of exit survey questions designed to get you the information you need. I have included a variety of notations that explain the choices I made and that provide instructions about how you should set up the skip logic and response options. This detail should not appear in the survey - it is for your eyes only.

Sample Exit Survey Questions

After you leave <Company>, will you immediately be working for another employer? (Yes, No)

<Remember> Replace <Company> with your company’s name.

<Remember> In the exit survey are not trying to collect all possible exit types, which in their variety will occur too infrequently to provide value to know. The first thing you are looking to achieve is to determine the skip logic in the survey, so the person gets survey questions that are relevant to them. The second thing you are looking to achieve is to determine if this is an Avoidable or Unavoidable exit. Most will answer "Yes," they will be going on to another employer, so they will skip to the next set of questions. If they answered the "No" and select one of these personal endpoints, then the survey mechanics should skip all of the questions about the new job, which will not apply.

<If no> 

 Will you tell us more about what you will be doing: 

 (Select one)

 * I don't want to say

 * Retiring

 * Caring for children or significant others

* Going to school

 * Other

<If yes>

* Is your new job in the same industry? (Yes, No)

* Is your new job an increased level of responsibility? (Yes, No)

* What is the name of your new employer? (Open Ended)

<Remember> Here above, you know the employee is going to a new employer, and now you want to be able to report, filter, and analyze exits by the yes/no questions to reveal important patterns in questions below in the exit the survey. We also ask the name of the new employer - this is to begin to accumulate a running list, which could help you identify companies actively targeting your employees and to identify companies that may be presenting a better value proposition for some reason. 

For your new employer, do you expect to gain or lose in the following areas?

<Scale> 1. Lose a lot. 2. Lose a little. 3. Neither lose nor gain. 4. Gain a little. 5. Gain a lot.

* Overall quality of the company/organization

* Quality of leadership

* Manager relationship

* Peer relationships

* Quality of work

* Learning and development opportunity

* Level of responsibility

* Long-term career opportunity

 * Expected value of total compensation package (base, bonus, & stock, if applicable)

* Benefits (health & retirement, if applicable)

* Perks (food, onsite services, and fitness, for example)

<Remember> Here above you provide a spectrum of items that characterize the nature of the employee and employer relationship, and a scale so as to determine on balance how your company stands versus its talent competitors, and if any of these broad categories may be pushing or pulling employees. The nature of the question is easy for the employee to respond to. They simply scan the list and select a number between 1 and 5. The structure of this set of items provides for the possibility of analyzing multiple influences, without over-taxing the employee to reconcile complex logic. I suggest that you provide this same set of questions to employees you have acquired from other organizations to compare the responses of those coming to your organizations with those leaving your organization. The comparison of the responses from those acquired versus those leaving will be an interesting and useful visual. From that comparison, you will see overall what motivates people to leave jobs across any talent competitor and where on balance, you are winning and losing in your talent market.

On balance, how would you characterize your decision to leave <Company> 

(Select one)

* Mostly for reasons within <Company>’s ability to address.

* Mostly for reasons outside of <Company>’s ability to address.

If this applies, please describe anything that prevented you from being as successful as you would have liked to have been at <Company>: (Open-ended)

Did any recent actions or events affect your decision to leave <Company>?

(Select all that apply)

* Actions or inactions by your manager

* Actions or inactions by the leadership team

* Actions or inactions by peers in your immediate workgroup

* Actions or inactions by others at work not in your immediate workgroup

* Work-Related Other

* Personal changes or events

If you would like to clarify or qualify any of the choices you made above, please do so here: (Open-ended)

Is there anything we could have done differently to keep you longer? (Yes, No)

<If yes> Please tell us what we could have done differently to keep you longer: (Open-ended)

Is there anything <Company> can do now or in the future to get you back? (Yes, No)

<If yes> Please tell us what we can do to get you back? (Open-ended)

This is an excerpt from the book People Analytics for Dummies, published by Wiley, written by me.

Don't judge a book by its cover. More on People Analytics For Dummies here

I have moved the growing list of pre-publication writing samples here: Index of People Analytics for Dummies sample chapters on PeopleAnalyst.com

You will find many differences between these samples and the physical copy in the book - notably my posts lack the excellent editing, finish, and binding applied by the print publisher. If you find these samples interesting, you think the book sounds useful; please buy a copy, or two, or twenty-four.

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