Nowadays, companies handle significant amounts of big data about their customers, products, and job seekers. They also have increased access to information about their candidates and recruitment performance metrics. But data is meaningless if you don't know how to evaluate it and convert it into actionable insights. Emotional predictions, guesses, and convictions based on prior experience are not enough to garner relevant information and recommendations from the reports, documents, and input you compiled. Today, AI technology allows you to eliminate gut feelings, hunches, and unverified approaches in collecting and assessing data. Thanks to that, you can base all your operations, strategies, initiatives, and decisions on precise information and facts. Those are necessary for ensuring data-driven recruitment, which is vital for stellar candidate experience, quality of hire, and team success. Here's everything you should know about it and how to accomplish it.
What Is Data-Driven Recruitment?
Data-driven recruitment is about using verifiable and tested stats and facts to inform and drive the entirety of recruitment decisions, starting from candidate sourcing to choosing the most compatible job applicant. Hence, this approach helps you optimize the hiring process and base every step and strategy on tangible data and reports. By letting data guide recruiters and hiring managers in the decision-making process, it ensures that you won't decide about candidates' potential and position in the pipeline depending on superficial and irrelevant criteria but on their skills, experiences, and merit.
For example, you can use data to assess a substantial talent pool and multiple job applicants simultaneously and identify those who match the non-negotiable criteria for a job opening. You can also analyze your previous recruitment processes to determine bottlenecks, what works well, and what you can improve to establish better hiring practices.
Besides enhancing your strategies and initiatives, you will also boost your team's performance. Data-driven recruitment helps recruiters reduce hiring costs, be more efficient, and grow their success.
How to Incorporate Data into Your Data-Driven Recruitment
1. Choose the Right Data and Metrics
The foundation of data-driven recruitment lies in selecting the right data and metrics that align with your strategic hiring objectives. So, focus on metrics that directly impact the quality and efficiency of your hiring process, such as source of hire, quality of hire, time-to-fill, cost-per-hire, and employee turnover rates. These metrics should be tailored to your organization’s unique context and should help you understand not just who you’re hiring, but how your hiring process is performing.
2. Collect Data Efficiently
Efficient data collection is pivotal for a streamlined recruitment process. Leveraging technology, such as an Applicant Tracking System (ATS), can automate the collection and storage of recruitment data. This not only saves time but also ensures accuracy and consistency in the data collected. Integrating your ATS with other HR systems can provide a comprehensive view of the candidate journey and employee lifecycle, enabling you to make informed decisions based on a holistic data set. So, make sure that data is collected consistently and accurately across all recruitment channels.
Using an ATS like Manatal can help you with collecting hiring data as well as the quality of your recruitment in the long run. With Manatal, you can easily store and manage data in a single database. Through Manatal’s Reports & Analytics feature, you’ll have full visibility on all metrics of your hiring pipeline, from time to hire, cost of hire, reasons for rejection, to recruitment performance ratios, and more. Ensuring you make the most of data-driven recruitment.
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3. Act on the data
Collecting data is only half the battle - you also need to act on the data to drive meaningful results in your recruitment process. Analyze the data collected to identify trends, patterns, and insights that can help you optimize your recruitment strategy. Use data to inform decision-making, such as adjusting job descriptions, tweaking recruitment campaigns, or refining the candidate selection process. By acting on the data, you can continuously improve your recruitment efforts and achieve better outcomes.
4. Know the Limitations of Data
While data can provide powerful insights, it’s important to recognize its limitations. Data can be subject to biases, both in the way it’s collected and interpreted. It’s important to approach data with a critical eye and consider qualitative insights alongside quantitative data. Understanding the limitations of data will help you avoid over-reliance on metrics and maintain a balanced approach to recruitment.
5. Use Data to Evaluate the Past and Plan for the Future
Historical data analysis is key to understanding the effectiveness of past recruitment strategies and can guide future planning. By examining trends over time, you can identify what has worked well and what hasn’t, allowing you to refine your approach. Predictive analytics can take this a step further, helping you anticipate future hiring needs and talent market changes. This forward-looking use of data ensures that your recruitment strategy remains dynamic and responsive to the evolving needs of your organization.
How Large Brands Use Data-Driven Recruitment
Did you know that data-driven organizations are 23 times more likely to reach new customers and six times more likely to retain them? They're also 19 times more profitable than companies that don't foster data-driven recruitment. [2] Here are some examples of big brands that achieved data-driven recruitment.
The search engine titan made an effort to eliminate bias in recruitment by leveraging data. Google strategically screens candidates via its structured interview method, where candidates are asked the same set of questions in the same order. The goal is not to find the correct answer, but rather, to evaluate candidates’ thought processes. Google also understands that unconscious biases can creep in at any moment for many reasons. So, the company puts a safeguard on via a grading rubric during your post-interview evaluation process. Within your rubric, the company gives illustrative examples of what poor, mixed, good, and excellent answers might look like to candidates equally against a set list of expectations, rather than judging one candidate’s answers against another’s. [3]
Amazon
Amazon is another good example of a successful data-driven recruitment. The leading logistics service provider utilizes Machine Learning to identify the right role for its candidates. Especially for Amazon interns and fresh grads who look for full-time roles, the company uses the Machine Learning system to automatically recommend positions that match their interests, skills, and experience, based on natural language processing of the candidates' resumes. During the process, Amazon scientists and the recruiting team closely monitor the data to ensure fairness and accuracy of the results. [4]
Johnson & Johnson
Johnson & Johnson took a self-reflection before making a hiring decision. The American pharmaceutical, biotechnology, and medical technologies company found many biases in its JDs that have been recycled over a long time. So, the company relies on an augmented writing platform to scan job descriptions for phrases that could turn off potential candidates. Then the company adjusts the writing and makes a decision based on the scores of how successful a certain job posting is, compared to similar job posts in the same location. As a result of Johnson & Johnson’s aim to welcome diverse candidates with arms open, the company saw a 9% increase in female applicants. [5]
Conclusion
Data-driven recruitment is an important aspect of finding the right candidates. By relying on data rather than gut feelings, you can make more efficient, accurate, and fair hiring decisions. Data analytics and metrics provide a comprehensive evaluation of candidates based on their skills, experience, and qualifications, leading to a more objective selection process. The approach not only improves the quality of hires but also ensures that you attract top talent that aligns with your values and goals.
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Citations:
1. wired
2. McKinsey
3. Google
4. Amazon
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