Mis-hires can drain your resources, finances, and even team morale. More often than not, they’re caused by not having enough information during the hiring process. Your candidate may have a strong resume and give good answers to all your interview questions but still prove to be unsuitable for the role for one reason or another.
Fortunately, there’s a wealth of data you can collect from candidates during the recruitment and hiring process and use to help improve outcomes and ensure better hires. Personality tests and job-specific assessments, for example, allow you to gather information that can significantly reduce the risks of making a mis-hire – even from as early on as the application stage.
In this article, we'll explore what big data is and the different types of data you can collect. We’ll also look at how you can use it to refine your recruitment strategy and build a successful team.
Big data refers to the immense volume and variety of data collected from candidates during the application process. This includes structured data – like candidate profiles, resumes, and test answers – and unstructured data – such as social media posts and email exchanges.
With a traditional approach to hiring, candidates are typically asked to provide a resume and a few references before undergoing an interview. These are important steps, but they only enable you to learn so much about a candidate before hiring.
Big data enables you to take things to the next level. The information you collect from your candidates can help you understand them better and make data-driven decisions. For example, job-specific skills tests allow you to measure and compare your candidates’ knowledge, critical thinking, and experience in a standardized way.
It’s important you collect information that’s relevant to the role and will help you make decisions. Most of the data will be supplied by candidates themselves, but you can also use data in the public domain, such as LinkedIn profiles and social media presence.
In addition, you’ll need to comply with local laws and regulations around data collection. For example, if any of your candidates are European, you must comply with the GDPR (General Data Protection Regulation). For candidates from California, it’s the California Consumer Privacy Act.
Here are some of the most common types of data employers use to improve their hiring process:
This includes typical hiring data such as resumes, cover levers, contact details, work experience, and education. One highly effective use of application data is to refine job descriptions and the application process itself: Which job descriptions and applications have resulted in the most successful hires?
Here’s where you can collect a wide variety of highly relevant data points for assessing candidates for your role and company and comparing them easily.
TestGorilla offers more than 300 pre-employment tests, including:
Cognition and reasoning. Evaluate logic, numerical aptitude, problem-solving, and attention to detail.
Personality and culture. Understand candidates’ behavioral traits, work preferences, and compatibility with company culture. Our Culture add test helps you decipher candidates’ ability to add value to and transform your team.
Technical skills and job-specific knowledge. Assess a wide range of programming languages, tools, and frameworks, as well as skills for marketers, salespeople, managers, and more.
Working style. Behavioral assessments help you understand candidates' interpersonal skills, teamwork capabilities, leadership potential, time management, and adaptability.
TestGorilla’s test library provides recruiters and hiring managers with a comprehensive suite of tools to collect various data types during the candidate assessment process.
You can use this data to compare candidates fairly and without bias. Later, you can use hiring and performance data, as mentioned above, to determine which tests are most effective for your company.
Public data from platforms like LinkedIn, Facebook, or even personal blogs can provide insights into a candidate's professional network, interests, endorsements, publications, and more.
This data can be cumbersome to collect on a large scale, though, and requires special tools. There are also ethical concerns, such as whether a person’s personal life should influence their professional prospects.
In addition, there are strict laws that govern how this data can be used. In the US, for example, this includes discrimination laws, the Fair Credit Reporting Act, and the National Labor Relations Act.
These can be difficult to obtain in any concrete fashion from current candidates, but you can learn a lot by comparing the performance of existing employees with their application.
You can correlate performance reviews, project outcomes, sales figures, and the like with pre-employment tests to determine which aspects of those assessments are the best predictors for your company.
This allows you to prioritize candidates who do well on those in the future and further refine your application and hiring process.
As prospects travel through the recruitment process, they’ll interact with your company in various ways. How candidates interact with job postings, their responses to certain situations, their communication patterns, and notes from video or live interviews, can prove insightful.
Your data collection efforts aren’t limited to your own candidate pool. Industry trends, competitor analysis, salary benchmarks, and current events can all influence your hiring strategy, decisions, and candidate pool. For example, an increase in demand for a specific role may mean having to provide a more competitive or attractive offer.
Big data offers numerous benefits that can enhance the entire hiring process. Here are six ways you can use big data to improve your recruitment strategy:
As mentioned, refining your recruitment process is one of the best ways to use recruitment and hiring-related data at scale.
You can analyze the data you’ve collected to determine which of your job descriptions, candidate experiences, assessments, job ads, recruitment platforms and channels, and managers have resulted in the most successful outcomes. Look for trends that suggest a particular approach worked well or poorly, and then adjust accordingly.
For example, you can use tools like ChatGPT to assess the style and tone used in job descriptions, as well as their contents, and identify language and descriptors that have attracted top talent.
You can also track which job platforms candidates have applied through and then prioritize those that have provided the most attractive candidates.
With all the data at your disposal, you can compare candidates across various factors. Of course, the most valuable of these will be pre-employment tests, especially role-specific tests.
With TestGorilla, for example, all your candidates’ data is presented in a simple layout that makes it easy to filter and rank candidates quickly. You can drill even further down by exporting candidates’ responses to specific questions and tests. See, for example, our article on the data you receive from skills assessments and what you can do with it.
Beyond enabling you to compare candidates more quickly and effectively, big data also helps you reduce bias in your hiring process, which is essential to ensuring you select the most suitable candidate for a role.
You can also correlate all this data with performance data further down the road to determine whether specific tests – or even specific questions – have proven especially predictive of future performance.
For example, you may notice that your most successful hires scored a similar percentage on your assessments. Adjust the passing criteria to require candidates to score the same or higher. This improves the quality of your hires by helping you identify top talent.
There are several ways you can use the data you collect to personalize and improve the candidate experience in several ways.
First, you can evaluate how candidates interact with your recruitment platform and pipeline and identify bottlenecks and frustration points on the one hand and successes on the other.
Asking candidates for feedback during and after the process is a great way to collect this data. There are a variety of AI tools available today for analyzing unstructured data like this and finding trends.
Also, you can use big data to improve and further personalize communications “at scale.” You can segment candidates based on age, location, skills, experience, and interests, then adjust your communication accordingly to ensure your language and tone are appropriate and effective.
Likewise, time-stamped data you collect can help you better understand how long each part of the recruitment process takes and set clear expectations among your staff on when they are likely to need to respond to candidates or provide them with assistance. This can significantly reduce wait times – which both improves the candidate experience and makes for quicker hires.
In addition to the data you’re collecting from candidates, there’s no doubt a ton of data your company generates each day. You can use this to understand what kind of talent your company will need in the future.
For instance, if your company's sales data indicates steady growth in a particular product category, you might foresee a need for more specialists in that area.
Likewise, understanding trends in your company's historical turnover rates can guide proactive hiring to compensate for anticipated departures or retirements. (This data will also help you to manage and significantly reduce turnover.)
All this allows you to be proactive in hiring new talent and prevents last-minute scrambles.
Analyze the performance of different sourcing channels and invest in those that yield the best results.
For example, if Glassdoor’s job board provides more suitable candidates than other platforms, improve your ad budget for that platform to reach a wider audience.
You can also check for other trends, which could be seasonal, bound to the market, impacted by current events, or influenced by technological changes.
Well after hiring, data continues to add value. You can track the performance of hired candidates and analyze whether the skills and qualities assessed during the recruitment process are in keeping with actual job success.
Gather and consolidate all the relevant data from various sources such as surveys, resumes, and the candidate’s actual performance. Then, use data analytics tools such as Power Bi or Tableau to identify and observe any patterns that develop or to extract any insights you may find.
This information helps you refine your recruitment strategies over time and improve the alignment between candidate attributes and job requirements. For example, if you found that most unsuccessful coding employees lack a particular certification, you could use this to screen out future candidates who also lack it.
Big data has opened up unprecedented possibilities for transforming the recruitment landscape. With it, you can better understand candidate behavior, refine your recruitment strategies, and make informed decisions that lead to more successful hires.
TestGorilla provides access to a diverse range of assessments that allow you to collect meaningful data across various skills, traits, and attributes. This data-driven approach enhances the decision-making process and helps you select the best-fit candidates for your team.
Learn more from our quick product tour, and sign up for a free plan to get started with TestGorilla today.
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