Activating GPS in Decision Making: How to Implement Data-Driven Recruitment in Your Company

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“It seems we don’t have enough resumes.” “We could hire faster.” “We are losing candidates because of the complicated hiring process.” These opinions may be true, but they lack rationale because they are not data-driven. This approach helps to strengthen opinions, make informed decisions, and save both money and nerves. Consequently, according to the Harvard Business Review Report, 51% of leaders believe that a data-driven culture is a critically important part of corporate strategy.

The professionals from ITExpert explain how to make data-driven decisions in recruitment to improve the process for both candidates and hiring managers.

What is data-driven recruitment and why you should restructure your hiring now?

Recruitment in every company goes through a stage of intuitive decisions and subjective assessments like “our benefits package is weak” or “let’s look at more candidates.” However, not every business progresses to the next level — a data-centric approach.

Data-driven solutions are a necessity in modern recruitment, not just a trend or a buzzword. This term refers to the approach of making management decisions based on big data and objective factors, which minimizes errors and allows you to pay attention to important insights that are often overlooked in day-to-day operations.

In recruitment, the data-driven method helps to:

• Identify bottlenecks in processes: pinpoint issues at the level of individual specialists and the entire company.

• Enhance stakeholder management effectiveness: justify the need to increase the salary for a DevOps position or renegotiate requirements if there are no more interested candidates.

• Reduce cost-per-hire: determine which tools deliver results and which do not, optimize recruiters’ work, reduce overall expenses, and more.

• Improve the quality of hiring and funnel conversion, and therefore accelerate recruitment.

• Forecast: what is a realistic time-to-fill indicator; how to plan interviews by understanding the manager’s workload; determine if the hiring plan is truly achievable.

• Enhance your employer brand: as a result, there will be no more lost candidates or weeks-long feedback delays, while personalized and targeted offers will be accepted more often.

• Set KPIs and SMART goals for the recruitment department that will bring business value. Quantitative and qualitative metrics become the basis for bonuses and commissions that increase the motivation of recruiters. According to a study by People First Club involving over 3,000 professionals, 64% of recruitment specialists’ compensation schemes include a bonus component based on the level and profile of filled positions and the achievement of personal goals.

Maria Sydoryshyna

Technical Recruiter at ITExpert

“The data-driven approach is highly effective for developing a search strategy. To illustrate this, when I began working on .NET positions, I noticed that a significant number of specialists were open to considering job vacancies because they actively responded to offers on LinkedIn. If I sent approximately 25 messages, around 5-7 candidates would express interest. In such cases, you can save time on sourcing by posting the vacancy on job portals. On the very first day, I received about 20 applications, was able to screen the candidates’ experience, and scheduled technical interviews.

In comparison, only 1–2 people (or sometimes none) might respond to a DevOps engineer position. Such vacancies, in my experience, need to be filled with targeted and personalized offers, which take much more time.

Another insight from my experience with the data-driven approach is that IT specialists tend to be more responsive on LinkedIn, whereas C-level executives prefer email communication. Therefore, it is important to always analyze which platforms and channels work for your vacancy. This analysis allows you to adapt your sourcing strategy and allocate your efforts effectively.”

In addition, data-driven recruitment (DDR) is about creating standardized processes that are easy to replicate and scale. If the team does not exceed 30 people and only a few new hires are added every six months, you may be able to work without a detailed job description or analytics at each recruitment stage. However, if you aim to increase your headcount to 150 specialists, data-driven methods become indispensable.

What data can be used for a data-driven approach in recruitment?

Understanding the number of offers sent, candidates at various stages of hiring, and offers accepted is just the first step towards adopting a data-driven approach. What other data should you pay attention to?

👉 Recruitment speed:

• time from filling out the job brief to receiving the first resume;
• number of days to decide on inviting a candidate for an interview after receiving their resume;
• time required for a candidate to progress to the next stage of recruitment;
• the number of days spent deciding whether to make an offer;
• total time-to-hire and time-to-fill.

👉 Quality of the process:

• percentage of responses to recruiters’ emails,
• the most common reasons for candidates’ refusal to proceed with the recruitment/ hiring manager’s rejections of resumes;
• conversion rate from interviews to offers or the number of resumes needed for a successful hire;
• OAR (Offer Acceptance Rate);
• fill rate (percentage of successfully filled vacancies for the period);
• percentage of new hires passing the probationary period and how long they remain with the company.

👉 Sources and cost:

• sources of high-quality resumes (applications per channel);
• sources/platforms on which specialists who received offers responded;
• cost of obtaining a resume from a platform and the overall cost-per-hire;
• expenses on the additional promotion of vacancies.

👉 Talent market data:

• the number of specialists with the required stack or skills;
• market salaries for similar positions;
• attitudes towards the field of your product development;
• important perks and benefits for IT employees, as well as other trends.

Top tools for data-driven recruitment

To enhance your recruitment process, consider using the following tools:

1. ATS with advanced recruitment analytics or the ability to create customized reports: People Force (PeopleRecruit), Breezy, HURMA, Zoho, CleverStaff, and others.

2. Tools for designing dashboards: Tableau, Looker Studio, or Power BI.

1. Detailed analytics in advanced versions of LinkedIn Recruiter. You can view data on response rates, recruitment team activity, or funnels. Moreover, the Talent Insights platform on LinkedIn provides more facts for strategic decision-making, such as demand for talent, market capacity, talent mobility, and potential donor companies for poaching.

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LinkedIn Talent Insights report for Java development positions in Ukraine

1. Email tracking tools. For example, Mailtrack shows you when and how many times your emails were opened, sends notifications about email activity, and offers daily analytics.

2. Tools for collecting feedback from candidates: Trustcruit, SurveyMonkey, and others.

3. Tools for monitoring mentions of an employer online. For example, MediaMonitoringBot.

How to make data-driven decisions: tips and case studies

Unfortunately, 82% of recruiters had to deal with unrealistic expectations from a hiring manager. Data-backed proposals can help you justify the need for change and shape accurate perceptions about recruitment. Here are some tips to make your ideas data-driven:1

1. Select the data in advance that will help you make or justify decisions.

To do this, ask yourself the following questions:

• What would you like to know about the hiring process?
• What data do you already have available for analysis?
• What data would help you be more productive in recruitment?
• What problems and bottlenecks do you see in the funnel? Which data can help identify them?
• What sourcing channels and approaches do you use? Which ones do you avoid? How can you measure their effectiveness?
• What data is important for the hiring manager? What do they pay special attention to when making decisions?
• What does a successful and high-quality hiring process look like to you? How long should it take, and how should communication be structured? What are the characteristics of a poor process?

Sometimes, obtaining the necessary data is not as easy as you’d like. That’s why it’s important to set up analytics tools in advance to gather crucial information.

2. Pay attention not only to the quantity but also to the quality of the process.

Maria Kutsevol

Recruitment Team Lead & Account Manager at ITExpert:

“Data-driven approach reflects the seniority. An experienced recruiter knows that more candidates isn’t always better. When analyzing any position, you must consider not only the number of emails sent, candidate responses, and interviews conducted, but also metrics like time, cost, source of hire, interview-to-offer ratio, and OAR (Offer Acceptance Rate). With experience, it becomes obvious that these metrics will differ from location to location, even for seemingly identical jobs. Insights based on recruitment analytics help predict outcomes, identify problems in the hiring process, and determine how to fix them.

Data is our GPS on the way to filling a position. It allows us to make decisions objectively: taking into account the recruitment funnel; bypassing bottlenecks and potential problems; understanding where to go next to present candidates. Without proper preparation, you might find yourself in unpleasant situations or take the wrong path, waste hours in traffic, or even fail to reach your destination at all.”

3. Work as a team. A recruiter cannot operate in isolation from the business. A data-driven HR manager is an essential ally in the hiring process, contributing to forecasts and well-founded decisions. A simple example: the company is aware of its employee turnover rate, team expansion plans (new locations or products), and internal mobility. Using this data, you can create an annual hiring plan. By working together, recruiters and HR professionals can accurately plan and forecast job openings, new hires, and onboarding processes. This teamwork helps the company achieve its necessary targets and milestones.

4. Compare the collected data not only with internal analytics but also with market benchmarks, such as:

📎Response Rate. According to Salesflow, candidates respond to 20% of emails on average. Short texts and personalization can increase the rate.

📎 Applicants per Opening. On average, 36 professionals apply for a Fullstack position. You can view data for other roles in the interactive section on Djinni.

📎 Cost-per-Hire. The average cost of filling a position is $4.7K, but it can reach up to 3–4 months’ salary for a specialist, according to research by SHRM.

📎 Time-to-Hire. According to Workable, it takes 27–29 days to successfully hire a candidate for IT and engineering positions. At the same time, The Josh Bersin shares insights: Tech and media companies take up to 20 days to hire, while service businesses (including IT outsourcing or outstaffing) take up to 47 days.

5. Don’t jump to conclusions. When using data, remember that it doesn’t tell the whole story. You need to dive deeper into the “why” behind the numbers. For example, if 80% of your offers come from one resource, it doesn’t mean you should abandon others. New positions might require a more specialized tech stack and may necessitate access to niche search channels.

Let’s look at practical examples of how a data-driven approach can help successfully fill a vacancy.

Case 1: The company is rapidly scaling and needs to hire 15+ high-level specialists, while the talent pool for this tech stack is limited. 

Solution: Before starting the search, the recruiter informs the hiring manager about the number of candidates they can expect to interview during the first few months and within six months of the search. The recruiter understands the average conversion rate in the company’s recruitment funnel and the nuances of communicating with technical managers and, therefore, discusses the necessity of hiring while conducting 20% fewer interviews and suggests alternatives, such as hiring specialists with similar technologies and reskilling them.

As a result, the manager understands the market constraints and does not reject candidates for subjective and trivial reasons.

Case 2: Candidates are responding to the recruiter’s emails, but the job offer doesn’t interest them. The posted job openings aren’t attracting enough applications.

Solution: The recruiter sought feedback on the job description from their network within the industry. It turned out that the text was dry and formal, failing to catch the attention of professionals who are already overwhelmed with offers. As a result, the recruiter created several versions of shorter descriptions written in a friendly tone that highlighted the benefits of working for the company. After A/B testing, the best version was selected, which increased the conversion rate by 25%. Without this research, the recruiter wouldn’t have received more responses from candidates.

Case 3: One of your team’s recruiters has a significantly lower OAR (Offer Acceptance Rate) compared to others.

Solution: The analysis showed that the low OAR wasn’t related to the team or the complexity of the positions, so it was essential to delve into how the recruiter handles candidate objections. Colleagues helped review the recent offer rejections and recommended several useful resources. While reaching a 100% offer acceptance rate may require more time and practice, the recruiter’s communication with the next candidate already showed improvement. Without this analysis, the results would have remained low, and the recruiter might have been at risk of termination.

Even if your recruitment teams and hiring managers are used to making decisions based on intuition, data will be a great help. Data-driven recruitment will show you what worked in practice and what didn’t, as well as how to predict future results. Start by collecting the right information, setting up analytics, and paying attention to the numbers that show the effectiveness of your approaches. Before you know it, you’ll have built a data-driven hiring process!