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Recruitment Analytics: How Data Helps Achieve Better Results

Data-Driven Intelligence, Programmatic| Views: 1103

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 can be a powerful tool to achieve better results across the entire talent acquisition process.

This article will explore:

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

Analytics: A Valuable Business Tool

Data analysis, or analytics, allows us to capture and interpret large data sets to guide business decisions. A proper implementation of analytic techniques and tools can boost business performance by putting useful information in front of decision-makers and improving profitability as a result. In the field of recruitment, data analytics offer important benefits to HR practitioners, such as:

  1. Reviewing hiring process performance in detail
  2. Analyzing conversion rates along the application funnel
  3. Identifying well-performing applicant profiles
  4. Uncovering areas for improvement

Key Principles: Best Practices for Working With Data

It can be difficult to work with complex data sets, especially when lacking a clear focus. A key concept is “garbage in = garbage out,” or “GIGO”: If the collected data or analytical model is incorrect, the information retrieved from it will be meaningless, leading to false conclusions or no conclusions at all.

For recruiting campaigns, a proper dataset and model might be:

– Data: Number of users across 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 kinds of relevant recruitment data and models that give recruiters the ability to perform the right analyses, gain better insights, and make well-informed decisions during 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

First proposed by Elias St. Elmo Lewis in 1898, the purchase funnel is a conceptual model of the different stages of customer interaction with a product, a process also called “the customer journey.” The stages of this journey are often referred to as 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

The funnel-like shape of this model, moving from the widest point to the narrowest, stems from the fact that every stage of the journey involves some amount of drop off. Not every customer who is aware of the product will be interested; not every interested customer will desire to purchase the product; and not every customer who desires the product will take action to buy the product.

In terms of hiring, there is also a recruiting funnel in which candidates follow similar stages from awareness to hiring:

Stages of the recruitment process [own figure]
Stages of the recruitment process [own figure]

Each of these stages has its own relevant metrics related to the drop off or churn between steps, such as conversion rates of visitors to applicants, applications to interviewees, interviews to offers, and offers to hires.

As with other funnels, an important consideration when working through the recruiting funnel is that friction needs to be minimized between stages. “Friction” is anything that keeps the candidate from making progress along the application process, including sourcing problems, poor employer branding, technology usability issues, ineffective hiring processes, and unattractive offers.

Both the user experience (UX) and the candidate experience need to be streamlined and optimized in order to increase recruitment effectiveness. The aim 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 number of qualified candidates, the business will improve costs and optimize the recruitment process.

Key Metrics: Numbers to Inform Decision-Making

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

The right tools can help manage data. A programmatic recruitment platform, for example, can analyze data to make better buying decisions. These insights can be leveraged to optimize job ad placements to reach the right audience and attract the right type and amount of candidates.

Some metrics to consider when looking to improve recruiting processes include:

  1. Volume of Applicants: Number of applicants entering the top-of-funnel stages. This number can yield insights about the effectiveness of the application website, employer branding efforts, and sourcing initiatives.
  2. 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.
  3. Volume of Hires: This refers to the number of hires actually joining the organization and starting to work. The ultimate goal of the recruitment process is to achieve an optimal volume of hires with a high retention rate.
  4. Cost per Lead (CPL): Cost of a job seeker landing in the application funnel
  5. Cost per Applicant (CPA): Cost of a job seeker performing some sort of action within the application funnel (e.g., submitting contact info or a background check)
  6. 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.
  7. 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 commonly used by almost all businesses, including performance levels, time to productivity of a new hire, and retention rates, among others.

By carefully tracking and optimizing these metrics, talent acquisition teams can gather the information they need to consistently improve their recruiting processes.

Recruitment Analytics Is a Competitive Advantage

HR professionals need to have thorough understandings of their organizations’ business goals, as this will allow them to optimize their recruitment processes in service of meeting those goals. Choosing a recruitment platform with programmatic recruitment capabilities and a focus on the needs of recruiters (rather than the needs of publishers) can give an organization a deeper understanding of data across the entire application funnel. The insights can help to achieve better results and reach recruitment goals while improving the bottom line.

About Spencer Parra

Spencer Parra is the VP of Product Management for advertising and data products at Radancy. In that capacity, Spencer and his team of product managers, program managers, data scientists, and data analysts work to develop products in a data driven mindset. As the leader of Advertising products, he works to bring a holistic full funnel approach to Radancy’s advertising technology stack with Programmatic Jobs at its foundation. Through data products, he tells the story of media performance via Radancy’s Metrics Gateway and helps ensure data is democratized through Radancy’s unified platform. Spencer came to Radancy from the Perengo acquisition in mid 2019 where he served as Lead Product Manager and a member of the founding team. With Perengo, he worked towards the vision of leveraging the same rigor and concepts from ecommerce advertising technology to the recruitment advertising space. Prior to Perengo, Spencer launched and supported in-app advertising products at Criteo as a solutions engineer. Spencer holds a B.S. in Aerospace Engineering from the Massachusetts Institute of Technology.

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