Attraction: Quantifying Talent Acquisition

In this:

• Improving the talent acquisition process with data analysis
• Making better hiring decisions with data analysis 
• Gaining insight with better metric context
• Using the Critical Incident Technique to develop a Behaviorally Anchored Rating Scale for measuring hiring quality (and other vices)

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 above-average performance
3. and keep more employees with above-average performance than those with average or low performance.

This is about the first, acquiring or attracting employees

Any company that competes on a product-and-service level needs, at minimum, to acquire talent, coax good work out of those people, and hold on to its most productive people. If the company cannot acquire the quantity and quality of talent it needs, it’s reflected in diminished productivity relative to competitors.

Attraction represents the force of the organization to draw in or attract talent for its purposes, whatever that purpose may be. Attraction's principal importance to company performance is straightforward: To grow, a company must acquire new people to perform work for the company. The company’s current and future success is determined by the company’s ability to acquire a sufficient quantity and quality of talent to design, produce, and sell more products. If the company cannot attract the people it needs to operate at a level above that of its competitors, none of your other management strategies or systems matter.

Introducing Talent Acquisition

When a company is just starting, the work of talent acquisition often is performed by a key founder or ends up being shared by everyone on the team. As a company grows, the demands of talent acquisition become more daunting and complex. If everyone is involved in hiring all the time then no work will get done. Eventually, the company must hire people to take responsibility for the work of acquiring more people. Historically, this highly specialized role within an organization has been called either Staffing or Recruiting — increasingly; it's being called Talent Acquisition.

Whatever you call it, it isn’t unusual for a growing company to have dozens (if not hundreds) of people doing this work. I have worked in some companies — Merck and Google are two prominent examples — that have over 300 recruiters. Talent acquisition is like a business within a business. And, with its high volume of activity, the inputs and outputs are more difficult to see, manage, and control anecdotally but are much easier to see, manage and control using mathematics.

The fact of the matter is that talent acquisition, like sales or supply chain management, is a production-oriented function for which there are straightforward ways to measure success — there are, in other words, clear inputs (applicants) and clear outputs (hires) and start and end timestamps. In this respect, you’re dealing with a classic throughput funnel (see Figure 9-1), where a sizeable initial pool is whittled down to a relatively small final result.

Figure 9-1: Talent acquisition professionals find candidates and then work those candidates through stages until a hire is made.

As Figure 9-1 illustrates, first, you have a lot of activity, and eventually, a hire is made — and it's this activity that needs to be managed correctly. Talent acquisition measurement isn’t limited to the number of hires that come out on the other side of a funnel. You can use a variety of metrics and analysis to wrangle better control over what is going on in that funnel. Relevant measurement categories include volume, efficiency, speed, cost, quality, and the experience of candidates and hiring managers.

<Remember> Measurement helps you see what is working well, what isn’t (and why), and how to make it work better. In some cases, you'll have to use measurements to justify making the best decision possible under the circumstances.

Making a case for talent acquisition analytics

The design and day-to-day running of a company involve a lot of decisions — not just decisions made by the CEO but also those countless decisions made every day throughout the command structure of an organization. The aggregate quality of these decisions determines success or failure. Talent acquisition is a job function that facilitates decisions that have enormous consequences for companies.

Making the right decisions means asking the right questions. For example: How do you attract to your company the best candidates in each field or discipline? How do you determine what "best" even looks like? Where do you find these stars? How do you get them to agree to leave where they are and come to you? How much should you offer? Should you pay for quality and let the pros do their thing, or should you hire upstarts for less and bring them into a system that makes them high quality over time? When you need to defend your hiring decisions, how can you convince others that you made the best choices?

The answer to these questions determines whether your company consists of the best band of people out there who are committed to excellence or is a mismatched collection of mediocrities just trying to muddle through the best way they can. Measurement and analysis are designed to help you systematically improve your chances of getting the right answers and thus improving your decision-making process. And what is it that can be measured and analyzed when it comes to talent acquisition? I thought you'd never ask.

Seeing what can be measured

Analytics can be applied to an array of decisions from within the talent acquisition function. The following examples show the types of decisions that can be made better with data:

* Priorities: Which jobs and candidates should you focus resources on, in what order should you focus on them, and how much of your funds should be directed to each one?

* Goals: Should you optimize the talent acquisition process for speed of hire, cost of hire, quality of hire, candidate experience, or a balance?

* Resources: There are substantial options for applying resources (money, time, materials) to talent acquisition strategy and tactics. Where and when should you invest resources (and which ones) in talent acquisition channels, staff, technology, training, incentives, new selection techniques, and other supports?

* Candidate characteristics: Which candidate traits should you favor in the talent acquisition process (generally and per job) to produce higher-quality hires, stimulate a more efficient process, support company culture, or help a hiring manager solve a specific problem on a team?

* Screening and selection instruments: Which screening and selection instruments (methods of thinning applicant pools and rating candidates) should you apply?

These are some examples of frequently used selection instruments:

Unstructured interviews: In an unstructured interview, the format and the questions asked are left to the direction of the interviewers.

Structured interviews: A structured interview uses a predetermined list of questions that are asked of every person who applies for a particular job. For example, a situational interview focuses not on personal characteristics or work experience, but rather on the behaviors needed for successful job performance.

Sample job tasks: These tasks can include performance tests, simulations, work samples, and realistic job previews that assess performance and aptitude on particular tasks.

Personality tests and integrity tests: These assess the degree to which a person has certain traits or dispositions (dependability, cooperativeness, and safety awareness, for example) or aim to predict the likelihood that a person will engage in certain conduct (theft or absenteeism, for example).

Cognitive tests: These assess reasoning, memory, perceptual speed, and accuracy, skills in arithmetic and reading comprehension, as well as knowledge of a particular function or job.

Criminal background checks: These provide information on arrest and conviction history.

Credit checks: These provide information on credit and financial history.

Physical ability tests: These measure the physical ability to perform a particular task or the strength of specific muscle groups, as well as strength and stamina in general.

Medical inquiries and physical examinations: Such exams could include psychological tests designed to assess current mental health.

<Remember> All these “people decisions” add up and, over time, impact the long-term success or failure of every company. Superior talent acquisition can lead to competitive advantages. If your company had an attrition rate of 25 percent per year and its talent acquisition efforts produce below industry average hires, it will take only two years for 50 percent or more of employees at your company to be below the industry average. 25% turnover may be an extreme example, but even with a 10% turnover rate in 5 to 10 years, any company can go from great to below industry average if they don’t have hiring quality figured out. Conversely, in the same scenario, if the talent acquisition function produced exceptional hires, it could quickly change the talent profile and trajectory of the company in a short time as well.

Getting Things Moving with Process Metrics

Talent acquisition, as I mentioned earlier in this chapter, is best thought of as a process of inputs and outputs. The job of talent acquisition is to take inputs, or applicants, and produce outputs, or high-quality hires. (Refer to Figure 9-1.)

Talent acquisition can be measured, managed, and improved by targeting four distinct areas: speed, cost, quality, and experience.

In this section, I walk you through the measures of talent acquisition from a process standpoint. I start with the output of the process in mind first and work my way backward from there.

Answering the volume question

The goal of talent acquisition is to produce hires. As you might expect, you have an easy way to gauge success here — count the number of hires made. Of course, one guy standing there next to the water cooler doesn’t fully represent all the work that occurred to hire that guy. Behind the scenes, you need to measure a lot more to have a complete appreciation for what it takes to hire another guy like this one.

Here's a quick peek at the essential numbers here: number of initial candidates, number of phone screens, number of onsite interviews, and number of offers.

<Remember> Most executives don’t care how many phone screens you make as long as the company gets the number of hires they expected. Yet counting these activities is essential for analysis; remember that the goal of talent acquisition is to produce more hires, not to perform more activity. The volume of activity at each phase of the talent acquisition funnel is data you need, though it may not be the data you show to executives. Talent acquisition funnel data is more useful behind the scenes for purposes of evaluating activity and isolating where the company can be more efficient.

To evaluate how successful you are at talent acquisition, you should understand that companies have different headcount growth needs and targets and that your own company’s growth needs and goals will change over time. Successfully hiring 100 people sounds good, but if you needed to hire 200 people to achieve the company’s objective, 100 isn’t so good. Conversely, if you needed only 100 and you hired 200, that would also be bad.

The way to address this problem is to compare the volume to a need or plan. Did your talent acquisition team produce enough hires to meet the company’s headcount objectives? The previous statement cannot be measured by directly measuring the number of hires made — it requires understanding hires in the context of headcount and headcount plans.

In this section, I spell out some of the primary metrics necessary to measure talent acquisition output as it relates to headcount and headcount plans by segment.

Headcount measures

Let's get some basic terminology out of the way. First and foremost, you have three ways to express what I've been calling headcount

* Use the headcount value at the beginning of a period.

 * Use the headcount value at the end of a period.

* Take an average of the beginning and ending headcount values.

<Remember> Situationally, you need all three numbers for different reasons and different calculations, some of which I describe later in this chapter.

In the HR data world, you can find a variety of rather strange terms, such as the ones defined in this list:

* Active: An active person has a record in the database and is working with the company in some way in the period of the report focus.

* Terminated: A terminated person has a record in the database and is no longer working with the company after the date provided. (You should be excited to hear that Terminated doesn’t mean the company hired an android that looks like Arnold Schwarzenegger to go back in time to destroy that person. Though the person’s future self may or may not be in real trouble, the historic self should be just fine.)

* Employee: An employee is someone who works for the company with a specific wage or salary and has an employment contract (written or implied) with the company; the company controls the work and how it will be done.

If you had to evaluate all these criteria each time you attempted to count the number of people employed by the company, the exercise would become tedious and riddled with error. Fortunately, all mature companies have a database, known as a human resources information system (HRIS), in which each person who is an employee is recorded as an employee with other personal details as well as any employment-related transactions that occurred. For this reason, HRIS is often referred to as the system of record: By definition, a person is not an employee if the system of employee record doesn’t have a history of her as an employee. This definition sounds redundant but is, in fact, accurate.

<Remember>When it comes to identifying who is an employee for people analytics, the HRIS is the primary source of truth — the judge and jury. It is the system of record.

* Non-employee: A non-employee is a person working for your company who isn’t an employee — that is, the relationship between the two parties is between two businesses, one of which is providing a service to the other. The non-employee may be self-employed or could be the employee of another company that has a contract with your company. I tend to call these folks contractors, though your HRIS may have many worker classification types. Contingent workers and board members are two examples of non-employees. Though the distinction may seem petty, the difference is critical because of the legal and tax implications (which are not petty). To further complicate the matter, a single individual can move between different worker types over time. Fortunately, the HRIS records these changes.

To keep things simple, I discuss just two types of workers here, employees and non-employees, because I’m primarily interested in who is an employee on a given day or range of days. Because many names can be used to designate a non-employee, you should establish a filter to include only active employees on a given date, while excluding anyone with any other classification on this date.

<Remember> Extracting information from a specific database to determine the answer to a question can be complicated, and hinge on several important details, even if the problem is as fundamental as “How many employees did we have on this day?” I'm not in a position to give a detailed step list for carrying out a database query on your database, not knowing what you have done with it, so I keep my descriptions general (yet still helpful, I hope). For this discussion, assume that everything you do with headcount is filtering for employees and excluding non-employees. Therefore, what you find here is a blueprint you can abstract up from for a general design framework, not as a legal document.

End-of-Period Headcount (Headcount.EOP)

Let’s look at how you arrive at end-of-period headcount (Headcount.EOP). If you define Headcount.EOP as a count of active employees in a particular segment on the last date of a particular period. Here is the shorthand expression:

{Headcount.EOP} = Count of [Active].[Employee].[Segment].[Period].[Last Date of Period].[plus any other necessary qualifier].

For simplicity's sake, I refer to it as

{Headcount.EOP} = Count of [Segment].[Period].[Last Date of Period]

You should take [Segment] to include any qualifiers you add to get to the segment of the overall population you want to count, even if those qualifiers are numerous. I put [Segment] in the formula to represent where that logic will occur so that you can move forward without endless distracting detail.

I calculate this shorthand expression using source data, extracted from the HRIS. (Table 9-1 provides a tongue-in-cheek version of such source data, using a company that seemingly hires only former presidents of the United States.) I use curly brackets {} to denote that the result is a record set or a list of values. The use of square brackets [] refers to a filter or dimension of the data. The underlying data and values within the filters determine the form of the output. After computing the Headcount.EOP expression, you could end up with no result, a single value, or multiple values.

Table 9-1: Headcount.EOP Detailed Active Employee List: Report Dates: 9/30/2017, 10/31/2017

For purposes of the example, here’s the shorthand expression of the {Headcount.EOP} definition restated to filter to a set of records that represent all employees in the East region on the last day of October 2017:

{Headcount.EOP} = Count of [Segment:Region=East].[Period=2017-10].[Last Date of Period =10/31/2017].

In this example, I have provided a distinct instruction for each filter that, when combined, will result in all records that exist that match the filter criteria.

<Tip> To identify headcount on a specific date, you have to account for the changing status and the associated dates. Without adding great complexity, one way of doing this is to extract separate reports for each date you want to look at. You can extract reports for every day, for just the end of each week, or the end of each month, for example. Other segmentation details regarding each individual can be added in columns to the right of Region, such as Division, Manager, Pay, Job Function Category, Job, or Survey Responses.

Now let me walk you through the shorthand expression:

{Headcount.EOP} = Count of [Segment:Region=’East’].[Period=2017-10].[Last Date=10/31/2017]

It turns out that, given the source data as specified in Table 9-1, only George Bush and Barack Obama meet the filter criteria. The result of the shorthand expression is a value of 2:

Count of [Segment:Region=’East’].[Period=2017-10].[Last Date=10/31/2017] is 2

Because the result could be multiple outputs, if you were to formulate the segment categories and segment value filters differently, you typically show the output in a format that works no matter what the definition is. Described in table form, it would look like Table 9-2.

Table 9-2: Headcount.EOP: Output Table (with Filter for East)

Start-of-Period Headcount (Headcount.SOP)

Whatever you do for End-of-Period Headcount, you can also do for Start-of-Period Headcount. Because the periods in the example are months, you would run reports and extract a list of employees as of the first day of the month. However, feel free to base the period of analysis on quarters, which means that Start-of-Period is the first day of each quarter. If you want to live dangerously, you can use years as the period of analysis; then Start-of-Period would be the first day of the year.

The shorthand expression for Start-of-Period Headcount (Headcount.SOP) will look like this:

{Headcount.SOP} = Count of [Segment:Region].[Period].[First Date in Period]

Average-Headcount

I have shown you how to calculate headcount at the start of a period and at the end of a period. Sometimes, however, you might need the average headcount. In fact, Average Headcount is used in the denominator of many of the HR metrics that you’ll actually care about. Two examples that come to mind that use average headcount are exit rate and hire rate.

There's more than one way to calculate average headcount — ways that have varying degrees of precision and varying degrees of practicality. A basic method is to add together the Start-of-Period headcount and the End-of-Period headcount and divide the sum by 2. A more precise method is to calculate headcount by segment for every day and then take the average of all days in the period. Constructing a daily average increases the amount of computing that’s necessary, and so is less practical in most situations. For a compromise, calculate headcount by equal intervals in the period. For example, if you’re calculating average headcount for a year, you might calculate headcount by segment at the end of each week, the end of each month, or the end of each quarter over one year and average this by segment.

Average-Headcount-Basic

Here’s the shorthand expression representing the way you calculate the average number of employees in each segment employed during the selected period, calculated with beginning and end divided by 2:

Average-Headcount-Basic: [Segment].[Period].Headcount.SOP + [Segment].[Period].Headcount.EOP / 2

Average-Headcount-Daily

Here’s the shorthand expression representing the way you calculate the average number of people employed in each segment during the selected period using daily values:

Average-Headcount-Daily: [Segment].Headcount.Day1 + [Segment].Headcount.Day2 + [Segment].Headcount.Day3 + [Segment].Headcount.Day4 + [Segment].Headcount.DayX . . . (until last day of period or sample) / Number of days in the period.

Whichever way you go, you end up with an employee list for each day in the period you want to use, which you then combine into a single combined data extract.

You will add a variable to the extract for counting — labeled, appropriately enough, the counting variable. When conditions are met for what you want to count, the counting variable contains a value of one (1); when conditions are not met, the field contains a value of zero (0). In the example, if the individual is an active employee on the date specified, a one (1) is applied in the counting variable; if the individual is not active and/or is not an employee on that date, the variable contains a zero (0). This allows you to apply a simple, repeatable methodology of summing the counting variable across all dates in the data set and simply dividing by the number of distinct dates found.

<TechnicalStuff> In statistics, a binary (one/zero, 1/0) counting variable is called a dummy variable.

If you examine the calculation to produce average headcount using the start-of-period headcount and the end-of-period headcount, you’re creating an average by summing the segment headcount using records from the first day of the month and records from the last day of the month. Because two distinct dates are found for each monthly period, producing two records per individual, you’re summing the headcount for each segment in each period and dividing by 2. If you had a record for each day of the month, you would be dividing by 28, 30, or 31, depending on the number of distinct days in each monthly period. Because you have only beginning and end days in the example, you’re dividing by 2.

Hires

A hire is, by definition, someone who was not an employee that became an employee. To calculate the number of hires, you extract a list of all active and terminated employees and count the employees within a given segment with a start date squarely within the period you're interested in.

The shorthand hire expression looks like this:

Hires: Count [Segment].[Period].Hires

Hire-Rate

The hire rate is the number of hires during a period, expressed as a percentage of average headcount in that period. To calculate the hire rate, you divide the number of hires within a given segment within a given period by the average headcount of the same segment for the same period:

Hire-Rate: Count [Segment].[Period].Hires / [Segment].[Period].Average-Headcount

Figure 9-2 illustrates a hire rate example.

Figure 9-2: Calculating the hire rate.

Headcount-Growth

Headcount growth is the increase in the number of employees from the start of the period to the end of the period. To calculate headcount growth, you subtract Start-of-Period Headcount for a given segment in a given period from the end-of-period headcount for the same segment in the same period:

Headcount-Growth: [Segment].[Period].Headcount.EOP – [Segment].[Period].Headcount.SOP

Headcount-Growth-Rate

Headcount growth-rate is the increase in the number of employees from the start of the period to the end of the period as a percentage of headcount at the start of the period. To calculate Headcount-Growth-Rate, you divide the headcount growth within a given segment within a given period by the Start-of-Period Headcount of the same segment for the same period:

Headcount-Growth-Rate: [Segment].[Period].Headcount-Growth / [Segment].[Period].Headcount.SOP

Figure 9-3 shows a (rather unimpressive) growth rate calculation.

Figure 9-3: Calculating growth rate.

Headcount-Plan-Achievement-Percent

Most company leaders have plans for how many people they want to have as a whole and in different segments by a future date — I call this a headcount plan. Headcount-Plan-Achievement-Percent is a particular segment's headcount on a particular date expressed as a percentage of that segment's headcount plan on the same date:

Headcount-Plan-Achievement-Percent: [Segment].[Period].Headcount-EOP / [Segment].[Period].Headcount-EOP-Plan

Figure 9-4 graphically illustrates actual achievement versus the headcount plan.

Figure 9-4: Somebody needs to step up their game.

<Remember> If time, money, quality of hires, and the experience of people were no object, you could stop all this math homework at the number of hires. Unfortunately, speed, cost, and quality all matter, so you need to measure more things than just the volume of activity.

Answering the efficiency question

You don’t go from a single call with a candidate to making an offer in one step. You talk to lots of people, and you use many steps to refine the list of people you’re talking to until you arrive at the end with the hire.

The movement from one step to the next can be measured by the talent acquisition funnel metrics. The rest of this section looks at the metrics you can use.

Requisitions

A requisition is a request for applicants to fill an open job. To calculate the measure I call requisitions, you count the number of requisitions in a selected segment in a selected period:

Requisitions: Count [Segment].[Period].Requisitions

<TechnicalStuff> Requisition is the technical term for an open job –these terms are used interchangeably. What name you use depends on either what you prefer or what people in your company have used in the past. If nobody at your company has heard of the word requisition before you might prefer to tell other people you are counting the number of open jobs, rather than tell them you are counting the number of requisitions.

Candidates

Candidates are people who are considered for open jobs. Here’s the shorthand expression:

Candidates: Count [Segment].[Period].Candidates

Applications

An application is a formal request by a candidate to be considered for an open job (a job requisition). To calculate applications, you count the number of applications in a selected segment in a selected period:

Applications: Count [Segment].[Period].Applications

<TechnicalStuff> While the words applicant and candidate may often be used as synonyms, there is an actual technical distinction reflected in the databases that are used to track talent acquisition activity. Anyone who has been considered for any job has a candidate record. If you are using a unique identifier, all candidates should have just one record in the talent acquisition database regardless of how many different jobs they have applied for at your company. This is to be contrasted with the word applicant. A candidate may have applied to multiple job openings, and so a candidate may have multiple application incidents, each of which would have a unique identifier, known as the applicant ID.

Interviews

An interview takes place when the people who will participate in the hiring decision formally assess a candidate for a decision either by phone or in-person:

Interviews: Count [Segment].[Period] Interviews

Offers

An offer takes place when a candidate has been selected, and a formal invitation has been given to the candidate join to join the company:

Offers: Count [Segment].[Period].Offers

Offer-Accepts

Offer-Accepts is the number of candidates with offers who have accepted those offers:

Offer-Accepts: Count [Segment].[Period].Offer-Accepts

Funnel-Stage-Pass-Percent [Pass%]

Here’s the percentage of applicants that pass from one stage to the next stage, by segment, by period:

Pass-Percent: [Segment].[Period].[StageX+1 Applicants] / [Segment].[Period].[StageX Applicants] x100

Funnel-Stage-Fail-Percent [Fail%]

Here’s the percentage of applicants that do not pass from one stage to the next stage by stage, by segment, and so on:

Fail-Percent: [Segment].[Period].[StageX+1 Applicants] / [Segment].[Period].[StageX Applicants] x 100

Funnel-Yield-Percent [Yield%]

Here’s the percentage of applicants who make it all the way to offer-accept, by segment and so on:

Yield-Percent: [Segment].[Period].Offer-Accepts / [Segment].[Period].Applicants x100

Figure 9-5 shows what a yield-percent calculation would look like.

Figure 9-5: Getting to 8.

<Remember> Talent acquisition funnel metrics contain essential insights about how successful you are at effectively thinning the pool of applicants as you work through the selection process. The less you thin at one stage, the more thinning you have to do at the next step. However, if you thin too much at the top of the funnel, the applicant pool may get too small to produce someone to pass to the next stage to deliver a hire.

Figure 9-6 shows two funnels: B takes twice as much effort to produce the same number of hires as A, so in this comparison, A is far better than B based on volume.

Figure 9-6: A tale of two funnels.

In scenario A, you get six hires from 100 applications, 50 phone screens, and 25 interviews. In Scenario B, you also get six hires, but you had to screen 50 more applicants by phone and have an interview round with 25 more people. If each interview is 1 hour long, and each applicant in the interview round must have conversations with five people, you have spent 125 additional hours (25 x 5 x 1 hour) interviewing. If the average pay of the interviewers is $50 per hour, the inefficiency of scenario B cost the company at least $6,250 more. Also, time was lost unnecessarily.

Producing more candidates gives you more options to choose from, but having more isn’t a good thing if it just requires you to do more work to provide the same number of hires. If, however, having more candidates can provide more hires and/or higher-quality hires, the increased volume can be justified.

If you suspect that you’re in Scenario B or if you can see that your funnel is taking more work or taking longer than it had before to get a hire out the funnel, consider increasing the number or difficulty level of criteria applied to screen candidates at the phone screen stage. By setting a higher bar in the selection standard earlier, you let fewer candidates through; because those candidates are of higher quality, more of them will become hires, which increases the hire yield on the work conducted at each stage.

When you calculate these funnel metrics, you can:

* Compare a funnel for the whole company over time to see whether you’re improving or getting worse.

* Compare the funnels from different divisions, locations, recruiters, or other creative ways of segmenting your data to derive perspective or answer a question.

* Derive how much activity needs to occur in order to produce a set number of hires in a set time frame and/or forecast how many hires the current funnel will create over the next quarter based on what you have achieved in the past.

<Remember> Understanding what’s going on in the talent acquisition funnel can help you work toward correctly balancing the volume, time, cost, and quality that you want to achieve in talent acquisition as a company. Results vary.

In addition to measuring the shape and overall yield of the funnel, you can use the funnel metrics to estimate how many recruiters, how many applicants, and how much action are required in order to produce a hire in a given time frame. (See Figure 9-7.)

Figure 9-7: Looking at talent acquisition efficiency.

Average-Hires-per-Recruiter

You can calculate the average number of hires made per recruiter in a given period using the following approach:

Average-Hires-Per-Recruiter: [Segment].[Period].Hires / [Segment].[Period].[Recruiter].[Average-Headcount]

Average-Phone-Screens-per-Hire

Here’s the shorthand formula for calculating the average number of phone screens it took to make a hire in a given period:

Average-Phone-Screens-Per-Hire: [Segment].[Period].Phone-Screens / [Segment].[Period].Hires

Average-Interviews-per-Hire

The shorthand for getting the average number of interviews it took to make a hire in a given period looks like this:

Average-Interviews-Per-Hire: [Segment].[Period].Interviews / [Segment].[Period].Hires

Interview-Offer-Percentage

To calculate the number of offers extended as a percentage of distinct candidates interviewed during the selected period, use the following: 

Interview-Offer-Percentage Formula: [Segment].[Period].Offers / [Segment].[Period].Interviews

Knowing, on average, how many recruiters and how much activity is required in order to produce a certain number of hires is important so that you know how to scale resources up or down to meet a changing hiring plan over time. It’s also helpful to know how long it will take.

Answering the speed question

Another way of looking at the funnel is to measure the difference in time between the day each job was first opened for applications and when they were filled (Time-to-Fill) or measuring the gap of time between when each candidate started in the process and when they start as an employee (Time-To-Start).

Hiring speed is critical because it:

* Helps your company develop an advantage over slower talent competitors (usually larger companies) by moving much faster than they do to get to an offer-accept

Every day that a candidate waits in the process increases the chance that she will have a conversation about an opportunity with another company that you then will have to compete with at the offer stage. If you move faster, the candidate is less likely to receive an invitation to interview with other companies before getting your offer. The worst-case scenario is that, by the time you get around to making an offer, the candidate has two different offers to choose from. In this scenario, your overall probability of getting the candidate to accept has dropped to 33 percent. You may also enter a bidding war, which means that you’ll have to pay more money to the candidate. If you offer this candidate more than you pay your existing employees, you will have to increase their pay, too, at the next annual pay review to stay out of trouble.

* Communicates to the candidate that when you reached out, you were serious — you mean business!

 Compare a crisp process to a process where the candidate has a conversation with a recruiter, ends the call, and then waits weeks to hear from anyone. Someone who doesn’t hear from you for a long time may conclude that the company isn’t interested in hiring her or may believe that she isn’t the company’s first choice. Speed is part of the experience. There’s something exhilarating about getting something you want quickly.

* Adds value to the company

The less time it takes for you to fill a position, the less time jobs remain vacant. Vacant positions reduce productivity and put a strain on co-workers, increasing their likelihood to leave as well. Finally, if you can move faster to fill jobs, you will be able to do more things in a given quarter or year, regardless of whether you use that time to produce more hires or do something else with it.

You can use two metrics to measure time in recruiting: time-to-fill and time-to-start. Though I haven’t called attention to it as a metric, the average time between each stage can also be measured. Examples of stages are Application, Pre-Screen, Onsite Interview, and Offer. Understanding the time between each step can help you diagnose where you’re losing time to reduce the overall time.

Time-to-fill

The average time it takes to find and hire a new candidate, measured by the number of days between publishing a job opening and the candidate’s acceptance of a job offer, is the time-to-fill, or the time it takes to fill a job for the company. If you were to open a job today, how long is it likely to take to fill that job? How does this vary by job function or level? You should be able to answer these questions. If you can’t, measure time-to-fill:

Time-to-fill: Sum of [Segment].[Period].Days between job post date and offer accept/ [Segment].[Period].Offer-Accepts

Figure 9-8 illustrates a time-to-fill calculation.

Figure 9-8: Calculating time-to-fill.

Time-to-start

Time-to-start measures the average number of days between the moment a candidate joins the process and the moment the candidate starts the job. In other words, it measures the time it takes for a candidate to move through the hiring process after they’ve applied.

To calculate the average number of calendar days from the date a job requisition is approved to the date a new hire begins work, follow this shorthand formula:

Time-to-start: Sum of [Segment].[Period].Days between job post date and employee start date / [Segment].[Period].Hires

Figure 9-9 illustrates a time-to-start calculation.

Figure 9-9: Calculating time-to-start.

Time-to-start: Candidate view

This formula calculates the average number of days elapsed between the application start date and employee start date.

Time-to-start (candidate view): Sum of [Segment].[Period].Days between application start date and employee start date / [Segment].[Period].Hires

Answering the cost question

Counting how much money you spend is always a useful exercise in self-reflection that can lead to productive restraint. Cost is a particularly important measure in business.

It’s useful to understand your overall hiring costs to monitor whether you’re getting increasing productivity from the spend or whether your expenses are increasing with each hire. It’s also useful to make sure you’re getting an adequate return on your investment.

The first and most traditional measure of recruiting cost is cost-per-hire, as I explain next.

Cost-per-hire

Cost-per-hire measures the economic cost of the effort taken to fill an open job. Given that filling an open job is in no way, shape, or form a uniform task, you can imagine that a multitude of factors come into play when determining the real costs of each new hire. Generally speaking, you can divide them into these two types of costs:

* Internal

• Additional management costs (the time necessary for additional management involvement in recruitment marketing events, interview, and selection meetings)
• Employee referral incentive
• Nonstaff costs (office costs, for example)
• Other, internal staff cost overhead for government compliance
• Talent acquisition staff costs (salaries, benefits, and training, for example)
• Relocation and immigration fees
• Sourcing staff costs

* External

• Advertising and marketing
• Background check, eligibility to work, drug tests, and health screens
• Campus talent acquisition activities
• Career site development and maintenance (costs related to building and maintaining the site and keeping it populated with fresh, relevant content)
•  Consulting services (including EEO consulting)
•  Contingent fees
• Immigration expenses
• Job fairs and talent acquisition events
• Recruitment process outsourcing (RPO) fees (for prescreening and assessing candidates)
• Relocation
• Sign-on bonus
• Social media (the time involved in planning and creating social content and engagement with prospects along with the cost of any sponsored content)
• Technology costs such as LinkedIn Recruiter licenses, applicant tracking systems (ATSs), background-check software subscriptions, onboarding applications, and any other technology costs to support talent acquisition such as some of the new sourcing tools, like Entello
• Third-party hiring agency fees
• Travel costs

Most of these costs are accumulated and recorded by accounting, generally, not specific to each hire. To calculate cost-per-hire, you add up all of these costs and divide by the number of hires. The level of aggregation of financial data restricts the range of options you have for segmenting cost-per-hire data, which in turn restricts the level of insight you can produce – for this reason, financial measures are not a very precise tool. Cost-per-hire is sort of like putting your finger out to feel the direction of the wind- you get a sense of the force and direction of the wind, but it is not very precise.

<Tip> For all the painful details on calculating cost-per-hire, check out the Society for Human Resource Management's cost-per-hire standard at

www.shrm.org/ResourcesAndTools/business-solutions/Documents/shrm_ansi_cph_standard.pdf

<Remember> Cost-per-hire doesn’t account for the value produced from making investments to increase hiring volume or quality; it only focuses on the expense.

Deriving useful insight from HR Metrics is different than knowing how to calculate them. For my thoughts and advice on this, see Making Sense of HR Metrics and Finding Useful Insight in Differences.

Hiring ROI

As I mentioned earlier in this chapter, cost-per-hire is the most traditional and frequently encountered measure for looking at talent acquisition spending. Cost-per-hire is nice to know and trend; however, the problem is that it doesn’t take into consideration the difficulty of filling different types of jobs or the value produced by those jobs. When these perspectives are lacking, it would be impossible to interpret increasing or decreasing cost-per-hire. It may be that costs are growing, but that more difficult and higher-value-producing hires are being made.

A new measure that puts the cost of hiring into the context of value is hiring ROI. Hiring ROI requires that you first estimate the total Employee Lifetime Value (ELV) of the hires the costs are associated with. For instructions see Chapter 6, Estimating Lifetime Value, in my book People Analytics For Dummies.

When you calculate cost-per-hire, you divide all talent acquisition costs in a period by the number of hires made in that period. When you calculate hiring ROI, you divide the ELV of all hires in a period by all talent acquisition costs in the period. When using the hiring-ROI metric, you measure the dollar output of each dollar input into the recruiting process. When using the cost-per-hire metric, you measure the number of hires produced for every dollar input into the recruiting process. They’re both measures of efficiency, but hiring ROI has some advantages.

Hiring ROI is a new concept that allows you to evaluate your spending for talent acquisition relative to the value the hires are producing for the company. This is important because the difficulties involved in creating the output the company needs will vary over time and will vary between companies. If you’re evaluating performance based on cost-per-hire and not considering the generated value, you may find your resources unnecessarily constrained when you are chasing after unusually valuable talent, which reduces your ability to effectively produce high-quality candidates in a provided time frame. In the past, evaluated costs were based on the number of hires produced; however, the difficulty of sourcing, selecting, and hiring isn’t equal by job or the quality of candidates, so reporting costs by the number of hires is misleading and fraught with peril. For example, it costs much more money to source and hire an executive-level job than a lower-level job, and it can cost more to source and hire for a technical job than for a general job, like a cashier. It’s much better to align resources with value and focus on where you want to spend your time and money to produce higher quality.

Answering the quality question

When you hire people for your company, you like to believe that they will prove to be the best hires you’ve ever made, but in reality, you won’t be right as often as you might think.

To find out whether you got it right, you have to follow your choices through to the future to see what happens. You can’t see the future, but if you go back in time, you can see how well the company has done in the past. If you do this, you will find some successes and many failures. Each situation by itself seems unique, but if you put them together in aggregate form, you can see overall rates of success and failure that can be used to measure the quality of hires produced by the selection decision process.

If hiring quality is vital to you, here are some of the foundational principles you need to use when measuring hiring quality:

* Measure the percentage of success and failure of the decision process over a large number of selection decisions over a long period. Do not be satisfied with short-range assessments.

* Implement a method of measuring success and failure that is separate from the methods used in making the selection decision itself.

* Come up with clear definitions of success and failure as they relate to on-the-job performance.

* Develop a rubric to classify strong success (above-average performance), moderate success (average performance), and failure (below-average performance) for each job family and level. A rubric is essential to increase objectivity and to detach the quality evaluation from a typical process of performance evaluation that is subjective and may use a scale that, unfortunately, isn’t useful when measuring hiring quality.

<Remember> A rubric is a scoring guide. Scoring rubrics are used to delineate consistent criteria for measuring performance. A scoring rubric allows managers, employees, and recruiters to communicate and evaluate performance criteria, which otherwise can be elusive, complex, and subjective. A scoring rubric isn’t intended for only one task — it can provide a basis for self-evaluation, manager review, peer review, hiring decisions, and documentation of job requirements. The goal is to produce as much of an accurate and fair assessment as possible while also fostering a common understanding. This integration of performance measurement and feedback by way of rubrics is called ongoing assessment or formative assessment.

Using the critical incident technique

The critical incident technique is an investigative tool for capturing critical incidents — stories of past events that involve highly effective and highly ineffective job performance in a job for purposes of developing a rubric for evaluation. The goal of carefully scrutinizing critical incidents is to examine past experiences that identify the knowledge, skills, abilities and other characteristics that are necessary to produce successful job performance in the present or future. In particular, you should be most interested in identifying the knowledge skills and abilities that differentiate good from poor performance.

<Remember> Because the repetition of knowledge, skills, abilities and other characteristics is tedious, I will refer to them as KSAOs or collectively as competency. Competency represents the characteristics that are necessary to perform some job function successfully. Competency is a catch-all phrase that doesn’t care whether the necessary characteristics are knowledge, skill, or ability or whether the trait is developed over time or innate.

In the current context, I am suggesting you use the critical-incident technique to develop a scoring rubric you can use to measure hiring quality through the performance evaluations of employees in their first 90 days. If you apply the rubric consistently, then you can measure changes in hiring quality over time.

The rubric can also be used to develop valid pre-hire assessment tools for screening candidates, for measuring ongoing performance on the job, and to facilitate ongoing conversations about performance. The rubric should be continuously evaluated and improved over time by correlating previous quality scores using the rubric with other job performance measures, peer feedback, and objective productivity measures. You also should periodically go through the group critical incident technique exercise to re-visit and improve the performance rubric you are using.

Figure 9-10 illustrates a performance rubric for listening, in the form of a behaviorally anchored rating scale. This scale is based on an example provided in an ETS Research Report by Harrison J. Kell and other authors.

Citation: Kell, H. J., Martin‐Raugh, M. P., Carney, L. M., Inglese, P. A., Chen, L., & Feng, G. (2017). Exploring Methods for Developing Behaviorally Anchored Rating Scales for Evaluating Structured Interview Performance. ETS Research Report Series, 2017(1), 1-26.

Figure 9-10: Behaviorally Anchored Rating Scale (BARS) for Listening

Figure 9-10 is an example of a behaviorally anchored rating scale for a factor we refer to as “Listening.” This particular BARS example provides a scale from 1 to 7. By definition, a BARS rating scale will provide statements that can be used in juxtaposition to each other statements to determine where someone may fall. Imagine if you were asked to rate someone on listening using a scale of 1 to 7. How would you know what a 1 is versus a 3, versus a 7? You may guess, but your guess would be different than mine. The statement provided in BARS is used to generate a greater degree of reliability in the measure. The BARS rubric for listening allows the rater to look for the statement that best describes the relative complexity level of listening (either described or directly observed). This measurement of listening will be better than one made without a behavioral anchor.

Example 9-10 is an example rubric of one factor, listening, you may be looking for in either candidates or employees. The first purpose of the critical incidents technique is to identify the factors that differentiate between the best and worst performers in each job, which will vary. The second purpose of the critical incident’s technique is to develop the rank order list of behavioral anchors you will use to measure each factor. The total rubric will include several factors, each of which will have BARS.

You can use a variety of ways to capture critical incidents, including interviews, surveys, written reports, and facilitated group discussions. The people involved in developing the critical incident material should be chosen from a pool made up of people who do the work, managers of those people, and others who know the job —customers, vendors, consultants, and subordinates. The information captured when the critical-incident technique is applied isn’t intended to evaluate current performance or to assign individual credit or blame for past performance — it’s used to develop a criteria to evaluate good and bad so you can identify the competencies (knowledge, skills abilities, and other characteristics, KSAOs) used to produce exceptional performance so those competencies can be searched for in the talent acquisition process. Because the rubric is based on the critical incidents of good performance in contrast to critical incidents of poor performance, everyone can agree on the competencies measured by the rubric are useful to the company.

<Remember> The conversations to develop the rubric should never be punitive, or else you will not receive accurate information.

At a high level, the critical-incident technique captures three items:

* The circumstances in which the job behavior occurred

* The job behavior itself

* The positive or negative consequences of behavior

These reports of critical incidents often highlight instances of poor performance and outstanding performance as well as personal characteristics perceived to be related to the behaviors and outcomes. A single incident isn’t of much value, but dozens or hundreds of them can effectively help you identify the pattern of behaviors that are most likely to lead to a good result. The list will be made into a rubric that can be used to critically evaluate observed or described actions and behaviors to assign a level of quality.

Figure 9-11 is an overview of the workflow to apply the critical incident technique to develop behaviorally anchored rating scales (BARS).

Figure 9-11: Critical Incident Technique Workflow Overview

Figure 9-11 shows a high-level four-step outline of the critical incident technique, working left to the right. Imagine you are standing in a room with other people, and you have a blank wall or whiteboard.

1.    Your first task as a group, represented by the number 1 (Incident Queue) is a list of positive or negative incidents that associate to positive or negative job performance incidents for a particular job and a description of what it is believed the employee did or did not do that resulted in this outcome. The little colored boxes are intended to represent an index card, which you can imagine you might use in real life. The purpose of the figure is to understand the process, not get lost in the specifics, so I have made the index cards small. At this stage, the incidents may vary substantially as will the descriptions of the behaviors each person providing the critical incident think matter the most.

2.    Underneath the number 2, the second task is to take each incident read them out loud and use the group to sort them into groups that I refer to as factors. These are capabilities: maybe they are knowledge, skills, or abilities, or perhaps they are behaviors more difficult to define in traditional categories. You may find any number of factors - for example, 3 factors or 20 factors. Figure 9-11shows that all incidents in the fictitious case break down into three factors (Listening, Customer Service and unnamed Factor 3)

3.    Once all the critical incident cards have been sorted into factors, the next task, 3 (Reduce & Write as Behaviors), is to look for critical incident redundancies that can be reduced. At the same time, the group can work together on writing or re-writing the behaviors that associate with the remaining critical incidents crisply. 

4.    The final task, 4 (Order as Behavior Anchored Rating Scale), demonstrates that you will align the behaviors associated to each factor to a standard scale in order from bad to good. In the example provided, it is a 7-point scale with 1 representing the behaviors related to the worst performance and 7 representing the behaviors associated with the best performance. Notice each column now represents this exercise for each factor. If you have more factors, it will span further to the right. If you have gaps in the scale for a factor, then you will work with the group to find an example that will fit.

The four-steps depicted in Figure 9-11 is just an overview. Below is a more detailed outline of how to perform the critical-incident technique for identifying job success factors and create an associate behavior anchored rating scale:

1. Create a group of subject matter experts.

Identify, invite, and gather subject matter experts in a group setting.

2. Record critical incidents.

After making introductions and providing an ice-breaker, have the subject matter experts individually document specific positive and negative job performance incidents on a form or index cards. Somewhere on the form or card, have the participants write the individuals' behaviors or attributes that are perceived to be the key to producing the unusually positive or negative experience. The first write-up may be rough. Have a facilitator read each scenario described on a card, and as a group, confirm understanding of the incident and the associated behaviors or KSAOs; edit and condense as needed.

3. Sort similar cards into groups.

Have the subject matter experts move the index cards into groups of similar concepts. You can call these groups of statements factors — either success factors or failure factors. Have the subject matter experts refine how you’re organizing the index cards until you arrive at what you believe are the core factors related to extraordinary job performance and the best organizing framework in which to place each statement. At the end of the exercise, you should have some factor headers, and the detailed examples supporting those concepts will be on index cards pinned or taped below.

4. Order the incident index cards.

Sort cards containing the examples into positive and negative categories within each group. Then further arrange the cards on a scale from 1 to 5, with the examples related to the highest level of performance at 5 and the examples of the worst performance at 1. A moderate example should be put in the middle.

<Tip> You can modify the scale smaller or larger, depending on how much material you have: 1 to 3, 1 to 5, 1 to 7, or 1 to 10.

You can accomplish this sorting task in different ways, either individually or together as a group. However, I suggest that you have each subject matter expert assign a score and then average across all participants. If there is a wide disagreement on the position of an index card, you can have a discussion as a group to find out what the reason was and come to a consensus on where to place this card.

<Tip> If you can’t come to a consensus on an example, it’s best to exclude it.

5. Fill in the gaps.

After all the cards have been placed under a factor and sorted on the scale you've chosen, look for the gaps, and request additional incident examples that can be put into any holes. Having some of the puzzle pieces in place will aid recall of situations that fit above, below, or between the categories. With the whole in place, you can go back to ask now whether anything is still missing.

 6. Review the factor scales.

You should use subject matter experts to review the completed scales and weigh in on the scale’s relevancy and accuracy for the job you want to apply it to.

7. Save the work of the meeting.

You can take pictures or have someone in the meeting type up each factor and each card, and then record the relative position of the cards electronically. Eventually, you’ll have a clean electronic copy of the factor scales, with crisp examples you can use to get the opinions of others who were unable to participate in the group exercise. Reconcile feedback until you can reconcile no further. Again, if there is broad disagreement among a sample of stakeholders, it may be best to remove whatever the stakeholders can’t agree on and go with what they can.

8. Finalize into a behaviorally anchored rating scale (BARS), apply, and analyze.

After you have thoroughly vetted the scale, you have a completed assessment framework that represents a theory of how behaviors are associated with job performance. As a result of the preceding steps, you’ll have a series of factors and a 1-to-7 scale with an example for each rung on the scale to help an assessor decide what number to choose when evaluating someone for that factor.

The scores from the various factors should be combined into a final combined index. For example, if there were three factors, each rated on a 7-point scale, each person can be evaluated to receive points between 0 and 21.)

You can assess performance using BARS in a variety of different time frames for a variety of different purposes. For example, you can assign each candidate a score using BARS following a structured behavioral job interview. You can also use BARS to measure job performance once a hire has been made. In the context of the interview, the interviewer must evaluate the interviewee based on the interviewee’s own description of their past behaviors in situations that relate to the interviewer’s question. In the context of using BARS to measure job performance after an employee is hired, the rater (managers or peers) is evaluating the employee based on their actual observations of job behavior.

The best way to measure hiring quality is to applying BARS to the observation of actual on-the-job performance following hire. You can create a preliminary measure of hiring quality based on the application of BARS in the interview process, but this will always be inferior to the measurement of observations of actual job performance. If you hold onto your pre-hire assessment and correlate it to ongoing performance assessment, then you can continuously evaluate the overall predictive power of the rubric you use in the interview process and scrutinize each component of the rubric you use at the same time.

<Remember> If you measure something in the interview process that you find out is not correlated to on the job performance, you should stop measuring it. Conversely, if you do not measure something in the interview process that you find out is related to job performance, then it should be added.

The choice of where to put people on the BARS scale will always be subjective, but the critical-incident technique can at least help you develop scale anchors that provide a consistent point of reference rooted in real experiences.

The scales you have developed through the critical-incident technique facilitate a transfer of qualitative information into a numerical measurement framework, which allows it to be combined with other data to be analyzed as a model. The scales will never be perfect, but they are better than having no anchor, and through repeated use and analysis, the scales can be learned, communicated, and improved.

<Remember> A talent acquisition process measures are the foundation of making decisions about talent acquisition process design and about whom to hire. All the techniques covered in this chapter should be judged in light of that primary goal. Make the methods work for you.

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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