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3️⃣ Talent Acquisition
Jessie Walsh avatar
Written by Jessie Walsh
Updated over a week ago

🚇 On this page: Index of metrics contained in the Talent Acquisition chapter.

Related pages:

⚠️ Note: Talent Acquisition metrics are only available if you connected your ATS with People Analytics. For more info, see the integration guides.

Overview

It’s a free market and people job hop. What matters is whether the revolving door is draining or topping up your in-house talent pool. That will determine the future of your organization, and the metrics in the Talent Acquisition chapter will help you gauge if it’s a high or low tide.


Access the Talent Acquisition Chapter

You can access the Stories page through the link in the global navigation bar at the top of every page. You can also use the search box to find a specific metric or story chapter.

The top of the Stories page will display all the available Chapters so you can click the one you want to see.


Talent Acquisition metrics

Sourcing

Source of Hire

A metric within the Talent Acquisition story chapter.

Definition

Source of Hire is a metric that shows how many new hires (out of total hires) came from each individual channel.

Insight to

It is a visualization of how successful is each source and where you should continue, stop or increase investment of your budget. Referrals can be a good indicator of employee engagement and can be compared to eNPS data.

How we calculate this

Source of Hire(T) = Distribution (Sources of new hires came during the selected time range T)

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Gender

  • Location

  • Race

  • Recruiter

  • Role

  • Team

Number of Postings

A metric within the Talent Acquisition story chapter.

Definition

Number of Posting provides an overview of all job postings and their statuses.

Insight to

It is an indicator of how fast the organization is growing, how big the workload on the recruitment team is, and what currently active vacancies the organization is looking to fill.

How we calculate this

It counts job postings with an active, draft, and closed status for selected time range T.

Source data

Extracted from ATS:

  • Job postings publish date

  • Job postings close date

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Location

  • Recruiter

  • Role

  • Team

Offer

New Hires Ratio

A metric within the Talent Acquisition story chapter.

Definition

New Hires Ratio represents the ratio of new hires compared to an average number of employees for the selected time range. If the value is greater than 100%, your headcount at least doubled during the specific period.

Insight to

A high New Hires Ratio often results in a lower organizational output/productivity due to additional workforce effort to onboard new employees.

How we calculate this

New Hires Ratio (T) = Number of new hires at time T / Average number of employees for selected time period T

Average number of employees for selected time period T = (Headcount at the start of the period T + Headcount at the end of the period T)/2

Source data

Extracted from HRIS:

  • Employee’s employment start date

  • Employee’s employment end date

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Age

  • Department

  • Division

  • Race/Ethnicity

  • Gender

  • Generation

  • Location

  • Manager

  • Role

  • Team

  • Tenure

Offer Success Rate

A metric within the Talent Acquisition story chapter.

Definition

This metric shows the number of accepted, rejected, and pending job offers that await for candidates’ feedback.

Insight to

It represents your organization’s ability to attract and get desired candidates on board. Offer Success Rate indicates whether candidates had a positive experience that compelled them to accept your offer and whether your job offers are attractive enough for the best people in your talent pipeline.

How we calculate this

The height/length of the stacked bars represents the total count of the offers sent during the specific month. Meaning, if an offer is accepted, rejected or pending later than the month of sending the offer once resolved, it will be visible in the stacked bar in the month of sending the offer.

Accepted = Number of accepted offers

Pending = Number of pending offers

Rejected = Number of rejected offers

Acceptance Rate = Number of accepted offers divided by the total number of offers sent

Pending Rate = Number of pending offers divided by the total number of offers sent

Rejected Rate = Number of rejected offers divided by the total number of offers sent

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Location

  • Role

  • Team

Offer Acceptance Ratio

A metric within the Talent Acquisition story chapter.

Definition

This metric shows the number of accepted and rejected job offers grouped by the date of acceptance or rejection.

Insight to

It represents your organization’s ability to attract and get desired candidates on board, similar to our Offer Success Rate. The difference between these two metrics is that Offer Success Rate counts the resolution of the offer (accepted or rejected) in the month when it was sent, compared to Offer Acceptance Ratio, which shows the offer resolution in the month when the feedback from the candidates is received, regardless of when the offer was sent (e.g. Offer sent in June and accepted in July will be counted as accepted in July).

How we calculate this

The height/length of the stacked bars represents the total count of the offers accepted or rejected during the specific month.

Accepted = Number of accepted offers

Rejected = Number of rejected offers

Offer Acceptance Ratio = Number of accepted offers divided by the total number of accepted and rejected offers for the specified time period Offer Rejection Ratio = Number of rejected offers divided by the total number of accepted and rejected offers for the specified time period

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Location

  • Role

  • Team

Time to offer, fill, and start

A metric within the Talent Acquisition story chapter.

Definition

Measuring time that candidates have spent in the hiring process is one of the essential recruitment metrics and an indicator of hiring process efficiency.

Insight to

Time to close a vacancy and have an employee start with the company directly impacts the company’s bottom line, through resources spent in the talent acquisition process (cost of hiring) and time to productivity of new hires (Quality of Hire) resulting in ROI. Track this metric to understand the efficiency of your hiring process and the impact that candidate experience — influenced by time spent in the hiring process — has on your employer brand.

How we calculate this

For all successfully hired candidates, we calculate the median in days for:

  • Time to Hire = time between the first contact with the hired candidate and the moment the candidate accepts the offer

  • Time to Fill = time between the creation of a posting within the ATS and the moment the candidate accepts the offer

  • Time to Start = time between the creation of a posting within the ATS and the first day of work of the new hire

Source data

Extracted from ATS

Available filters

  • Department

  • Gender

  • Location

  • Race

  • Recruiter

  • Role

  • Team

Selection

Candidate Journey (soon to be Hiring Funnel)

A metric within the Talent Acquisition story chapter.

Definition

Candidate Journey visualizes the path your candidates take to get hired. The size of each stream flowing from stage to stage represents the number of candidates.

Insight to

We tend to think of our recruitment process as a funnel. That's the ideal stage where we would want it to be - it allows us to deliver consistent candidate experience and equal assessment possibilities. If your candidate journey shows inconsistency across different organizational units, the shape of the graph will deviate from the standard funnel appearance.

How we calculate this

Nodes represent the stages you defined in your ATS, while streams are simple counts of candidates moving through recruitment stages. The width of each stream represents the number of candidates in the flow.

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Gender

  • Location

  • Race

  • Recruiter

  • Role

  • Team

Time in Recruitment Stage

A metric within the Talent Acquisition story chapter.

Definition

Time in Recruitment Stage tells you how long the candidates are staying in each step in your selection process.

Insight to

This metrics is here to help you discover potential hiring bottlenecks. Think of the ideal process and how much time you'd like your candidates to spend in each stage of the selection process. In the "all" view, you can explore if some stages keep your candidates waiting longer than the others. By changing views, you can highlight differences between recruitment stage velocities in particular departments, teams, or roles.

How we calculate this

Each application goes through the stages of your hiring pipeline(s). We measure the time individuals spend in each stage and calculate the median, minimum and maximum value in days.

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Gender

  • Location

  • Race

  • Recruiter

  • Role

  • Team

Candidate Drop-Off Rate

A metric within the Talent Acquisition story chapter.

Definition

Candidate drop-off rate tells you how many candidates drop from the recruitment process during a specific selection stage.

Insight to

This metric helps you evaluate your hiring process by presenting information on number of drop-offs and reasons behind them. Reasons for drop-offs might indicate why are some stages too strict or not selective enough. Additionaly, our statistical procedures will surface insight if some stages have a particularly high number of drop-offs initiated by candidates.

How we calculate this

We measure how many applications are rejected at each stage of your hiring pipeline(s) in absolute and relative terms. We also take into account who initiated the termination of the recruitment process and what was the reason behind it. Additionally, we utilize statistical procedures to extract potential insights.

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Race

  • Gender

  • Location

  • Recruiter

  • Role

  • Team

Candidate experience

Feedback Rating Distribution (soon to be revamped)

A metric within the Talent Acquisition story chapter.

Definition

Interview feedback ratings allow you to see if there are any systematic differences in the way you score candidates across different teams, positions and demographics.

Insight to

It can be an indicator of biases that exist in the recruitment process

How we calculate this

This is a simple distribution of ratings your team gives to candidates over time.

Source data

Extracted from ATS

🚑 Problem with source data? See Data Health 101 for remedies.

Available filters

  • Department

  • Location

  • Role

  • Team

Each entry includes a definition, calculation method, the origin of source data, and a list of available filters.


Source of metrics

Metrics for the Talent Acquisition chapter are extracted from your ATS and HRIS.

The metrics will only show up if you connected the required external data sources to your People Analytics account. If the data required to construct a specific metric is missing, a placeholder is displayed instead:

☝️ Example of a placeholder pointing out a missing metric

If you need help, follow the instructions to connect external data sources to your People Analytics account.

🚑 To get accurate and reliable metrics and filters, make sure your source data is in good shape. See more in the Data Health 101 guide.


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