Recruiters who use data are more likely to perform well and save money than those who don't. CognitionX estimates that the cost of hiring candidates could be reduced by up to 71% and recruiters' efficiency improved up to threefold.
Several types of recruitment technologies use data, such as applicant tracking systems (ATS), recruitment CRMs, people analytics, and recruitment marketing software. Each performs a different function, from candidate management to managing the candidate application system.
In its simplest form, data is used in ATS to filter candidates. ATS manages the application process digitally, automating candidate filtering based on their attributes. These attributes include previous work experience, location, tangible skills in various technologies, and the likelihood that they will accept the offer.
Using data to deploy data-driven recruitment, you can effectively sort through many applications that fulfill your criteria and easily streamline the recruitment process. This will reduce the manual time required from HR professionals, allowing them to allocate it to more valuable tasks. According to our ATS latest statistics, 94% of recruiters agree that ATS has had a positive impact on their organization’s hiring processes.
Recruiters who stick to data-driven recruitment use tangible facts and statistics to inform their hiring decisions, from selecting candidates to developing recruiting strategies.
How Does Data-Driven Prospecting in Recruitment Work?
Data-driven prospecting in recruitment entails gathering, measuring, collating, and analyzing candidates, as well as employee data, in order to efficiently hire the candidates best suited to your firm. According to LinkedIn, talent acquisition teams with mature analytics are 2x more likely to improve their recruiting efforts by reducing time and cost.
To effectively recruit, you need to determine traits that reflect your ideal candidate. This might be 3-5 different characteristics, including but not limited to previous experience, skill set, and personality.
To determine these characteristics, you can look internally to your best employees to determine ideal traits. This will largely help standardize your recruiting when seeking in house human resource departments or externally outsource your recruiting to a recruiting company during seasonal periods of high volume hiring.
Here are some ideal candidate traits that you can seek to set:
- Location - If not a remote position, geography can be important in determining the probability of the candidate accepting your job. Additionally, location can be reflective of a specific subset of knowledge. For example, if you were seeking lawyers who were specifically well-versed in US laws.
- Skillset - The skill set of individuals can be indicative of how well an individual performs in a role. This is particularly relevant for highly technical roles.
- Time in role - Knowing the time a candidate has been in a position can be a great indication of how likely they are to accept a new position. Holding a position for longer (12+ months) greatly increases the likelihood that they would switch.
- Job titles - Titles are reflective of the experience an individual holds.
- Previous work experience - Previous work experience can assist in validating their ability to do transferable tasks, qualification for the job, and basic understanding of the work environment. Looking at your existing employees and their previous work experience can also help determine which organizations you may have better success sourcing from.
Technology the candidate has used in previous work - Knowing the technology the candidate has previously had experience with can be indicative of the skills they have. Again, this is particularly important for jobs that work with highly technical and sophisticated technologies.
As a recruitment firm, setting these ideal candidate traits will only help your current portfolio of clients. To improve your opportunities of getting new clients, you need to search for businesses that are hiring roles that you have a portfolio of strong candidates for. This way, by being more intentional, you can bring on more clients by making more placements.
Further, data such as where your client usually sources from, live jobs data and hiring trends will all help you gain an edge over the competition.
What is more important than the data itself is being able to react in real time when information is available so that you can act first. Given the volume of data that exists on the web and the different sources you need to pull, it can be very difficult to collate together. Thus, there are different recruitment platforms out there that assist in pulling all of this information and alerting you when your specific criteria is met, allowing you to reach candidates first.
Vet Your Data Before You Trust It
Not all data is created equal, and the traits above are only as reliable as the pipeline feeding them. Before rolling out a data-driven tool, Bussing recommends finding out where the underlying data came from, confirming that whoever sourced it understood what they were doing, and checking that it isn't stale, since data has a shelf life and outdated signals about skills, titles, or technology can skew your rankings. She also advises working with vendors who can demonstrate that they actively clean, govern, and test their data, and who give you visibility into how it's used and whether it feeds back into a larger system beyond your own organization.
Benefits of Data-Driven Recruitment
Historically, recruiting was heavily reliant on job boards, careers pages, social media and referrals. This left a very large talent pool that was not necessarily targeted.
There are many benefits of data and how it can help your recruiting team:
Make more informed decisions - When choosing between candidates, using data can help remove hiring bias. Specific bits of data will place one candidate ahead of another such as previous technology their firm adopted that might make the transition to a new job easier. Recruitment data will help you holistically rank your candidates to make unbiased decisions
A caveat: data mitigates bias; it doesn't erase it. It's thus worth noting that data-driven tools don't automatically remove bias; they reflect it. As employment attorney Heather Bussing of Bussing Law explained on a recent industry webinar, AI-driven scoring systems are built on patterns drawn from historical hiring data, and that data carries forward the recruitment biases of the humans who generated it. In her words (paraphrased), you can't extract bias from these systems the way you'd pull cherries out of a pie: it's more like trying to get the shortening out of a baked crust, because the bias is mixed in rather than sitting on top. Her recommendation is to treat "data-driven" as a way to manage bias, not as a guarantee that it's gone, and to build in ongoing outcome audits rather than a one-time check of the tool.
- Reduces bad hires - Data on previously qualified candidates can help you improve the quality of hire going forward. Research shows that 79% of recruiters who use an ATS have improved the quality of candidates they hire.
- Improves ROI - Bad hires can be extremely costly. The U.S. Department of Labor estimates that the average cost of a bad hiring decision is at least 30 percent of the individual's first-year expected earnings. Tracking data on the cost of acquiring candidates for your hiring pipeline will quickly reveal which channels provide the best ROI.
- Better manage timing in the hiring process - With data-driven prospecting, stakeholders such as your HR team, candidates, and management will better understand timelines. This is because they have better decision-making frameworks and forecasted workloads. Estimated times for different processes will be able to be calculated. Overall, this will improve your candidate experience when getting new hires. Thus, this will in turn improve the acceptance rates from candidates.
- Make future forecasts - Understanding what the future pipeline of talent is will significantly help smooth your recruiting efforts. It will allow your organization to properly prepare budget, allocate time, resources and frequency. Using the correct data points can help your team choose the correct recruitment channels and improve the overall hiring funnel.
- Better employee retention - Data such as predictive analytics can be used to determine culture fit. Assuming that this data is used, culture fit can help improve employee retention, as the fit extends beyond just a job description, reducing turnover rate. Retention will also ultimately help improve your overall employer branding and reduce future hiring costs.
- Know who's making the decisions - Data-driven recruitment usually focuses on scoring candidates, but Bussing argues it's worth turning the lens the other way too. She recommends tracking demographic data on who is actually making hiring decisions, not just who is being evaluated, since patterns like these can reveal issues that candidate-side data alone won't show. Her reasoning: once that data exists, employers are generally expected to know what it shows them, so collecting it is only useful if it's paired with a real commitment to act on what you find.
Future of Having a Data-Driven Recruitment Strategy
Prospecting in recruitment is a very complex activity that requires various tasks at different levels and stages. Operating in a data-driven environment can greatly enhance the process, specifically during prospecting. If they manage to unlock the potential data has for the recruitment space, firms may hire better quality candidates, reduce costs, and hiring time.
Data tools filter volume; they don't replace the interview. It's tempting to see data-driven filtering as the fix for rising application volume, especially now that candidates increasingly use AI to tailor and mass-submit resumes. Torin Ellis, a consultant and strategist who explores talent management methodologies, points out that high application volume isn't new and that filtering technology alone won't resolve it. In his view, what still matters most is who sits across from the candidate at the interview stage: a well-prepared, experienced, and diverse panel is what distinguishes a polished document from a candidate who can back it up. His recommendation is to treat data tools as a way to manage volume, not as a substitute for that human evaluation.
Build accountability into your rollout - As you build out a data-driven strategy, it also helps to build in habits of ongoing governance. Ellis recommends asking who is missing from the room before evaluating or deploying a new tool, since a narrow group of decision-makers is more likely to miss blind spots than a genuinely diverse one. He also advises getting it in writing exactly which features you're turning on or off with a given vendor, since vendors continually update the underlying models and you want a documented, revisitable decision rather than a one-time setup step.
Data will be a great benefit to any hiring team, even one that is comfortable making decisions based on intuition. Data will assist them in seeing what worked and what didn't in prior hiring processes so that they may improve future hiring decisions.
Sources
1. Torin Ellis, Consultant & Strategist, webinar panelist, "Maintaining Ethical Fairness in AI Recruitment and HR"
2. Heather Bussing, Employment Attorney, Bussing Law, and co-author of "Get Pay Right: How to Achieve Pay Equity That Works", webinar panelist, "Maintaining Ethical Fairness in AI Recruitment and HR"
