Recruitment has traditionally been based on feelings and instincts. Companies reviewed resumes and relied on interviews for insights about candidates. However, many hiring managers now use an alternative approach known as data-driven recruiting. HR managers and recruiters use data to understand how their hiring process works and where they can improve it.
Data-driven recruitment has even become a buzzword. But does it live up to the hype? And more importantly, how can you implement it in your organization without being overwhelmed? This blog post describes the four stages of data-driven recruiting, explains how data-driven recruiting can help your business, and shares insights about relevant HR technologies.
What is data-driven recruiting?
It’s an approach to hiring new talent where HR managers base hiring decisions on specific measurable skills and job-relevant criteria. Practicing data-driven recruiting means using facts and ideas from your current and previous hiring processes to formulate an improved method of working. Factors to consider include:
- Your current rental price, or the fair market value of the rented real estate at the time of lease signing
- Average hiring time
- Resources offered by the most suitable candidates
- Conversion rates (candidate interviews, offer to hire, time to hire, etc.)
Many leading organizations such as Uber, Robinhood, and Facebook/Meta use data-driven recruiting to screen resumes and consistently identify the best candidates according to skills rather than educational background.
Benefits of data-driven recruiting
Data-driven recruitment uses technologies, techniques, and data to analyze a large pool of talent and identify candidates with skills, experience, and ideas to help organizations achieve their goals.
Using data in the hiring process improves the quality of hires. Data-driven recruiting helps you:
- Allocate your budget. For example, to spend your budget wisely, you can determine which recruitment channels attract the most qualified candidates.
- Increase productivity and efficiency. 68% of HR managers claim they need better-recruiting metrics and technologies. For example, you can track the number of emails your hiring team members exchange with candidates to see if there are specific steps you can take to speed up hiring.
- Stay diverse. Analyze application form conversion rates to determine if you need to adjust your questions or change the design of your career page. Check candidate demographics to see if you inadvertently discriminate against protected groups.
- Better control the hiring process. 77% of HR managers claim that recruiting is more effective if they better understand the market. For example, the rate of return on hiring can show you how many applicants you usually need for one hire.
- Make more objective (and legally sound) hiring decisions. For example, choosing the best candidate based on the results of a structured interview is an effective hiring method.
- Give arguments in favor of improving the recruitment process. 94% of HR managers say that recruitment software improves their hiring process. For example, if your company invests in a referral program, you can provide data showing this method’s effectiveness to support your arguments.
How to manage data when hiring
“HR will not be replaced by data analytics, but HR who do not use data and analytics will be replaced by those who do.” ― Nadeem Khan, Introduction to People Analytics: A Practical Guide to Data-driven HR
Data-driven recruitment may seem like an obvious concept for talented professionals, but such a strategy doesn’t appear overnight. Here are four steps for embedding data into your workflow.
Step 1. Choose your metrics
Choose metrics based on your company’s capabilities and the amount you want to invest in tracking data. Pro-tip: pick what you’d like to analyze based on what you think is most important to track when recruiting.
Make sure you obtain quantifiable information. For example, when tracking reasons people leave your company, include specific categories of reasons so you can determine the percentage of employees who leave for related reasons rather than getting a list of unique reasons entered in a free-form text field.
Step 2. Collect your data
Once you determine what data you want to track, determine how you want to collect it. Suppose you want to track your careers page conversion rate to see how many people move from viewing job openings to applying. Website analytics can help you see how many people view your general careers page as well as how many look at individual vacancies compared to the total number of applicants.
Step 3. Analyze the numbers
By analyzing positive and negative feedback from candidates, you can understand your recruiting strengths and weaknesses.
If you find that you’re wasting time and energy on part of the process that doesn’t work, it’s time to rethink the situation. You may find that the part of the puzzle you find slow and difficult gives the best results. You may be able to identify an obvious bottleneck that slows you down. Thus, data-driven recruitment can resolve many issues. It’s time to listen to the numbers and make changes.
Step 4. Use your data to plan your next steps
Some data you can respond to immediately, while other data can help you make changes over the long term. You can use this term to re-evaluate your entire hiring process.
For example, you can model future labor supply and demand with data to identify and analyze future labor risks. You might do this if you want to design your workforce for specific scenarios or model workforce scenarios over the next five years (for example, model the development of labor supply by assessing the consequences of employees leaving or retiring).
You can consolidate, analyze, and monitor gaps in your recruiting process with the help of data until your plan is reached. To do this, you can use an analytics tool. And if your plans change, you can easily adjust settings to quickly assess the impact of new conditions on your strategy.
The right tools will help you analyze data and provide meaningful information for your company.
HRForecast tools that can help with data-driven recruiting
The current job market requires hiring professionals to be experts in research, sales, and marketing to effectively attract the best talent to their organizations. Therefore, you need tools to conduct market research, identify relevant candidates, and follow employment trends.
For example, Market Intelligence is a people analytics platform that gives you insights into future job roles and skills so you know what kinds of people you need to have in your company. You can then make sure you have these people through upskilling or hiring.
smartPeople is a talent marketplace that helps you analyze skill gaps in your company strategically and plan how to close them. For example, you can determine how many people with which skills you will need to hire over the next five years. You can close skill gaps through internal recruiting, external recruiting, employee development, and upskilling.
smartPlan is a strategic workforce planning platform that detects skill gaps. The difference between smartPeople and smartPlan is that the latter works at the operational level, allowing you to see skill gaps for individuals and among groups. As an employee, you can use smartPlan to see what skills you are missing; as a department head, you can see which skills your team lacks. Then you can take concrete steps to close the skill gaps. The goal of smartPlan is to help you with upskilling and support internal recruiting, so you don’t need to search for new people.
Examples of data-driven recruiting in practice
Machine learning (ML), artificial intelligence (AI), the next generation of industrial automation, intelligent analytics, and quantum computing are the latest HR trends. Big data analysis can reveal future HR trends and on which platforms you can find the right talent without wasting time and money.
Here are four examples of how HR tech solutions can make recruiting more data-driven:
Take macroeconomic data into account to achieve your goals, define your audience, and attract the right talent. For example, Deutsche Telekom followed significant changes driven by evolving technologies thanks to data from multiple sources.
Deutsche Telecom started collecting macroeconomic data and improving information with the help of AI and ML algorithms; they also analyzed competitors in their industry and improved risk management.
As a result, the company was able to identify future trends and the development of workforce skills, understand how trends translate into skillsets, and detect technological white spots, such as the use of AI and people analytics or self-service tools.
Strategic skills management
Work with a broad database, identify skill gaps, and create an ongoing employee skills inventory. For example, Lufthansa Systems Hungary faced rapidly changing skills in the IT industry. Therefore, the company implemented a strategic skills management project to ensure the transparency of existing skills and future skills requirements. This has revealed skills gaps and enabled the company to take measures to address them.
As a result, Lufthansa Systems Hungary gained essential insights into the structure of workforce skills and hidden skills as well as essential insights into recruiting trends. They also increased transparency in terms of availability, competition, and costs in the local labor market.
Use data to develop a strategy or program for employee development. For example, Deutsche Telekom AG sought new opportunities to develop their workforce with skills they project they will need in the future, supported by a practical data-driven approach.
Thanks to automated market screening, Deutsche Telekom AG analyzed job postings and detected trends and changes in job requirements.
Using HRForecast AI technology, Deutsche Telekom AG discovered the skills they’ll need to fulfill targeted job roles, a transparent path to acquiring them, and how much investment would be needed to close skills gaps (in terms of time and money).
You can use succession management data to develop a reliable map of leadership and talent criteria according to your company’s strategies. For example, Deutsche Bahn has recognized the importance of preparing for future challenges. With the help of HRForecast, Deutsche Bahn tried to answer the question: “What do current Deutsche Bahn vacancies look like, and how should they be optimized for the future?”
With the help of automated market screening, they identified new trends and adjusted their strategy accordingly to profit from external perceptions of competitors’ strategies, white spots, and skills gaps. That’s how they created individual recruitment and development strategies, as well as more attractive and future-oriented job postings.
Use data to assess the past and plan for the future
Even if your hiring teams are used to making decisions based on intuition, they can find a vital ally in data. Data-driven recruiting will help your HR team see what worked and what didn’t in past hiring processes and improve their future hiring decisions.
Data-driven recruitment helps businesses:
- Track each candidate’s progress from the first time a candidate sees a job advertisement through the onboarding process.
- Better understand candidates. What prompted a candidate to apply for the job? On which website did they find the job? You can use data to better understand where your target audience is getting information from.
- Better understand what messages work. You can find out which messages work for potential candidates in order to adapt your messages to your audience’s wishes and needs. For example, how can you keep your audience’s interest and find out what potential candidates want from their employer?
- Expand your presence in particular areas. You can try to expand your presence in a particular talent market when looking for a new specialist or a team of professionals.