Recruitment Analytics: How Data Helps To Achieve Better Results

What gets measured, gets improved — Peter Drucker

Not long ago, the concept of “data analytics” was foreign to the field of recruitment. Nowadays, it is a crucial skill for hiring managers and recruiters as it provides powerful tools to improve results across the entire talent acquisition process.

This post will explore:

  • The importance of recruitment analytics in the current HR landscape
  • Common practices for making better hiring and business decisions
  • The application funnel as a tool to understand and optimize the candidate experience
  • The most important metrics to look at in recruitment analytics

Analytics: A Valuable Business Tool

Data analysis, or analytics, allows to capture and interpret large data sets to guide business decisions.

A correct implementation of analytics techniques and tools can boost business performance by putting useful information in front of decision-makers and improving profitability as a result.

Correct implementation of data analysis is key [source: http://bit.ly/2ozoROX]

In the field of recruitment, data analytics offer important benefits to HR practitioners, such as:

  • Reviewing the hiring process performance in detail
  • Analyzing conversion rates along the application funnel
  • Identifying well-performing applicant profiles
  • Uncovering areas for improvement

Key Principles: Best Practices for Working with Data

It can be difficult to work with complex data sets, especially if there is no clear focus.

A key concept is “Garbage In = Garbage Out” or GIGO: if the collected data or analytical model are incorrect, the information retrieved will be meaningless as well. It will also lead to false or no conclusions.

A simple truth in data handling: “Garbage in, garbage out.” [source: http://bit.ly/2ozjnnH]

Organizations need to ensure that their data collection efforts are not misused by gathering unimportant metrics. Another major source of “garbage” data are inconsistent inputs: data may be relevant, but it’s not being collected effectively (incomplete data sets; incorrectly labeled data; etc.).

Useful data for recruitment campaigns would be:

  • Data: Number of users across the several steps of the recruitment funnel; time spent on those steps; drop-off rates across these steps; etc.
  • Model: Proper tracking via multi-touch attribution

These are the kind of relevant recruitment data that give recruiters the capability to perform the right analyses, gain better insights, and make well-informed decisions on the talent acquisition process.

Recruitment analytics should always produce actionable insights to improve campaign performance and optimize recruitment and hiring processes.


Application Funnel: A Recruiter’s Optimization Problem

The purchase funnel is a concept that has been in use in marketing since the late 19th century. It is used to model the different stages of customer interaction with a product, a process also called “the customer journey.” It was first proposed by Elias St. Elmo Lewis in 1898 and depicts these stages after what has been called the “AIDA” model:

  • Awareness: Customers first learn about the product’s existence
  • Interest: Customers express an initial interest in the product
  • Desire: Customers actively want to buy the product
  • Action: Customers finally take action and acquire the product
A funnel usually has three sections: top, middle, bottom [source: http://bit.ly/2oYbHw7]

The funnel-like shape of this model stems from the fact that usually, at every stage — from top to bottom — there is a drop-off going from awareness to action, as not every customer will be interested. Then another set of these will not develop a desire to purchase it. Finally, yet another group of customers will drop off before buying the product.

In the recruitment field there is the application (or recruitment) funnel, which describes the candidate journey through the hiring process in five stages:

An example of a recruiting funnel [source: http://bit.ly/1fYPGnu]

Each one of these stages has relevant metrics related to the different ratios of drop-off or churn between steps, such as visitors to applicants, applications to interviewees, interviews to offers, and offers to hires.

As with other funnels, an important consideration when working through the application funnel is that friction needs to be minimized between stages. Friction is anything that keeps the user from making progress along the application process:

  • Sourcing problems
  • Websites with poor branding
  • Usability issues
  • Ineffective hiring process
  • Unattractive offers

Both the user experience (UX) and the candidate experience need to be streamlined and optimized in order to increase effectiveness. The goal of the HR department is to achieve recruitment goals with the best possible ROI. This is done by obtaining the right volume of hires through an optimized funnel with reduced drop-off rates. In other words: by hiring the right amount of qualified candidates, which improves costs and optimizes the recruitment process.


Key Metrics: Numbers to Inform Decision Making

Effective recruitment analytics require a proper data collection, an application funnel with a logical flow and optimized stages, as well as a focus on the right numbers. Not all metrics are equally valuable, some of them might be even misleading. Organizations need to focus on data that will let them make better decisions and improve ROI.

The right tools can help managing data. A programmatic recruitment platform helps to analyze data to implement better buying decisions. These insights are leveraged to optimize job ad placements for the best performing recruitment channel mix for the particular organization.

The following metrics can help improving results:

  • Volume of applicants: Number of applicants entering the top-of-funnel stages. This will inform about the effectiveness of the application website, branding and sourcing initiatives.
  • Volume of applicants per funnel stage: Observing the number of candidates per stage will help to identify possible friction points and opportunities for optimization across the process.
  • Volume of hires: Amount of hires actually starting to work. The ultimate goal of the recruitment process is to achieve an optimal volume of hires with a high retention rate.
  • Cost per Lead (CPL): Cost of a job seeker landing in the application funnel
  • Cost per Applicant (CPA): Cost of a job seeker performing some sort of action within the application funnel (e.g. contact info submit; background check submit; etc.)
  • Cost per Hire (CPH): Defined as the sum of all recruitment costs (internal and external), divided by the total number of hires during a certain period. Internal costs are all the costs of the recruitment process inside the company (HR staff, organization, capital), whereas external costs are all the expenses related to external vendors involved in recruitment.
  • Lifetime Value (LTV): This metric is very specific to each business as every organization has its own definition of quality of hire. However, some measurements are persistently used such as performance levels, time to productivity of new hire and retention rates, among others.

By carefully tracking and optimizing these metrics, the recruitment process will constantly improve, helping organizations meeting their business goals.


Bottomline: Recruitment Analytics Is a Competitive Advantage

HR professionals need to have a thorough understanding of their organization’s business goals. This will let them define their application funnel and optimize the recruitment process as a whole.

Choosing a recruitment platform, with true programmatic recruitment capabilities, can give organizations a deeper understanding of data across the entire application funnel. The resulting insights can help to achieve recruitment goals while improving the bottom line.


If you have insights and opinions about this topic please share your thoughts.

Thanks, Team Perengo


Perengo is a programmatic recruitment platform focusing on large-volume recruitment needs for blue-collar, hourly, and labor positions.

This post originally appeared on the Perengo Blog.

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