“What gets measured, gets improved.” – Peter Drucker

Data is a new form of currency across all industries. In the world of recruitment, this currency is paramount for creating and optimizing competitive hiring strategies. Smart recruiters incorporate data analysis into every part of their processes in order to remain ahead in the talent wars and achieve maximum ROI.

Data Analytics for Recruitment: The Basics

Recruiting teams that utilize data analytics benefit from a broad overview of all hiring efforts. This is especially important in optimizing the recruitment funnel within organizations looking to hire quality candidates at a high volume, or on a short timeline. 

With data in the picture, recruiters must ensure their analytics strategy is set up correctly – complete with quality data sources and fully functional analytics models. 

A good concept to remember when working with complex data sets is Garbage In = Garbage Out (GIGO). Incorrect or poor input data only leads to meaningless (or worse, detrimental) results. 

All too often, teams use poor quality or inadequate sources of data on which to base key decisions. Data should come from valuable points along the recruitment funnel and provide a basis for meaningful decision-making. For example, recruiters might utilize data sources such as the number of users present at the beginning vs. the end of the recruitment funnel; the time an average user spent on each step; the drop-off rates at each step; etc.

Equally as important as the input data is the model used to analyze it. Proper tracking via multi-touch attribution is necessary in most modern hiring campaigns, as recruiters manage multiple sources of applicant data. Multi-touch attribution – including linear attribution, time-decay attribution, and positional attribution – helps recruiters identify which aspects of their funnel are converting the most applicants.

The Goals of Recruitment Analytics: A Closer Look

Recruitment analytics always begins with one or more questions – the answers to which allow recruiters to optimize their processes and better understand their applicants.

A couple key questions a recruiter might ask before setting up an analytics strategy are:

• Where are the hiring bottlenecks across the recruitment funnel?
• Which are the best candidate sources for recruiting? 

Recruitment analytics can help solve issues regarding sourcing and job ads, branding, usability, application processes and job offers. 

While it’s common for recruiters to focus on Cost-per-Hire (CPH), this isn’t the be-all-end-all metric. The end goal of hiring campaigns shouldn’t be to hire more cheaply – rather to hire better, which can be measured beyond the hiring stages via the ELTV (Employee Lifetime Value) metric. This idea drives successful optimization of recruitment funnels using data analytics.

To gather such insights, data must be properly collected and analyzed first.

Gaining Insights: Relevant Data and Actionable Insights

Not all metrics are created equal, and neither are they universally valuable for all recruitment campaigns. Organizations need to focus on the data that directly affects their desired results. 

Input data that may prove valuable for hiring organizations includes:

• Volume of applicants. This number informs recruiters about the total applicants entering into top-level stages of the funnel. It’s important in both for measuring effectiveness of sourcing and examining the recruitment funnel as a whole. 
• Volume of applicants per funnel stage. Totals and/or changes in this value help to identify friction points in all stages of the recruitment funnel, including sourcing, applications, interviews, selection/offers and onboarding. 
• Volume of hires. The goal of any recruitment campaign is to achieve an optimal number of hires with a high retention rate.
• Marketing investment. It can be easy for companies to overspend their resources in the wrong areas, or miss potentially lucrative areas of focus. Data helps to avoid these otherwise costly mistakes.
• Time. Measuring how long it takes candidates to move through various stages of the recruitment funnel can inform high-level, time-based output insights such as recruitment velocity.

The above data sources, when utilized in the right analysis models, bring an array of actionable insights to recruitment teams:

• Cost-per-Lead (CPL). The cost of an applicant landing in the recruitment funnel. 
• Cost-per-Applicant/Action (CPA). The cost of an applicant performing a specific action within the funnel, such as submitting a contact form or uploading a resume. 
• Cost-per-Hire (CPH). This is the total recruitment cost divided by the number of hires per given time frame. Recruitment costs include both internal (HR staff, capital, organization) and external (vendors, partners) sources. 
• Lifetime Value (LTV). A metric that’s truly unique to each business, LTV essentially refers to the quality of hires. Some common data metrics used to calculate LTV are Retention Rate and Time to Productivity. 
• Recruitment Velocity (Time to Start). The length of time between ad placement and onboarding – or, in simpler terms, how fast a company is hiring. This measurement takes into account factors such as conversion rate, number of leads and a window of time to gauge the effectiveness of a recruitment pipeline.

The Bottomline: Recruitment analytics keep hiring efforts competitive and efficient.

Hiring organizations must ensure they’re utilizing best practices in order to maximize effectiveness of their campaigns. Recruitment analytics can be a key driver for optimization, since it’s directly informed by campaign-specific behavioral data. Today’s smart recruiters are turning toward comprehensive and powerful Demand-Side Platforms to achieve quick and effective results.

An insight-driven hiring campaign is one of the most valuable assets a growing company can cultivate today. It’s important not to overlook the essential foundation afforded by comprehensive and customized recruitment analytics.

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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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