Predictive Analytics in Recruitment: How Data is Shaping Hiring

Without a doubt, the hiring process has become an albatross for companies with high rates of turnover, which itself is becoming a more common occurrence. Corporations are doing their best to address employee engagement challenges for retention, striving hard to meet employees' ever-changing expectations and demands.

Yet, the cost of a bad hire can impact companies in terms of finances and resources. Even outsourcing the recruitment process to established talent acquisition agencies does not guarantee the best hires who stay in a company for a long time.

So, how can companies prevent hiring mistakes? By learning from their past mistakes, of course.

But who is going to pinpoint these mistakes during the hiring process?

Enter predictive analytics in recruitment pipelines.

Let’s take a look at how data analytics helps refine recruitment decisions, assists seasoned recruiters in preventing past mistakes, and embraces change to attract and retain the best talent.

What is Predictive Analytics in Recruitment?

Predictive analytics refers to continuous learning over a huge amount of data using several statistical and machine-learning algorithms to leverage artificial intelligence in a particular domain.

Predictive hiring analytics is focused on learning from the following:

• The hiring patterns in a company
• Knowing about an employee’s career history and each job tenure
• Reasons for an employee to leave the job, and much more

These patterns are then used to deduce hiring mistakes in recruitment, observe similar exit patterns among employees, and predict corrective measures.

In short, predictive analytics can help recruiters and HR managers hire the best talent without any rush or bias, aid in retaining the best talent of the company by suggesting engagement and motivational strategies, thus preventing costly turnover and hiring mistakes.

How Predictive Analytics Works in Recruitment?

There is no guarantee that a person who made a hiring mistake will be present in a company's future hiring process. So, there is a chance the same mistake can occur again through another recruiter.

A robust system or recruitment platform, powered by insights from predictive analytics, helps aid any recruiter by suggesting measures to avoid occurrences of past hiring mistakes. Regardless of who spearheads any stage of the recruitment process, the company can be assured that it will not get a misfit candidate for the job role.

Here is how a company can build its own AI assistant for predictive analysis with the help of recruitment and engagement software platforms:

Step 1: Collecting Data

The very base of predictive analytics in talent acquisition is the massive data relating to every minute hiring detail of all company employees, past and present. Such data includes:

• Resumes of all candidates who applied for different job roles
• Candidate assessment data from screening and evaluation through different online portals
• Candidate progression data from the Application Tracking System (ATS)
• The performance of selected employees over the years
• Their tenure in the company

This data set is huge and crucial to analyzing all possible hiring pathways that have yielded the best talents and bad hires for the company since its inception.

Step 2: Training Predictive Models on Historical Hiring Outcomes

Any data analytics framework or tool involves generating a learning or training model that learns from the given dataset. This process involves deducing an algorithm based on association rules, decision trees, or classification clusters to understand what will work best and what won’t at every stage.

The recruitment process has several possible decision branches at each stage and associated rules relating to a candidate’s skills and job relevance. Candidates are classified based on eligibility for further assessments.

Therefore, predictive analytics involves rigorously training predictive models by feeding different edge cases for every possible association rule, decision branch, and candidate classification in the company’s hiring history.

Step 3: Generating Predictions

Learning from a massive hiring dataset's association rules, classification clusters, and decision branches leads to deducing a set of rules or predictions.

Suppose the predictive model sees a pattern that employees with a certain skill excellence tend to switch jobs frequently. It will predict that they can become a bad hire for the company.

Additionally, candidates who do not possess this skillset were found to join the company, receive training on the same skillset, and perform exceptionally well while remaining in the company. Now, if a similar candidate appears for the same job role in the future, the model assures recruiters that they can be good hires.

This example is a very simple decision branch. However, in reality, the model considers many other factors to generate predictions.

Step 4: Interpreting and Using Results for Hiring Decisions

An interesting aspect of predictive analytics in recruitment is that the model never stops learning. This means that the data related to every new applicant and onboarded employee goes into the system for analysis and assessment, leading to more accurate and up-to-date predictions.

Recruiters and HR business partners can change, and new members who are unaware of past hiring mistakes can surely repeat them. Predictive analytics helps such professionals during each step of the hiring process by providing accurate insights based on actual hiring decisions made in the past.

Real-World Applications of Predictive Analytics in Hiring

Predictive analytics is now an undeniable part of talent acquisition. From monitoring the scope of filling possibly empty job roles to providing a bird's-eye view of a candidate’s value profile, categorizing their eligibility, and suggesting ways to reduce the time-to-hire, talent acquisition analytics provide it all.

Here are some practical applications of predictive analytics that help recruiters and HR professionals in the hiring process and in retaining the best talent.

1. Forecasting Future Hiring Needs: Recruitment tools and platforms powered by trained predictive models can accurately deduce the job span of an employee in any given role in the company. Knowing this retention duration can help companies plan for hiring candidates beforehand or devise sufficient engagement measures to improve the retention of such employees.

2. Identifying Top-Performing Candidates: Associating an employee’s history, hiring performance, and current performance in the company can give accurate patterns of top performers in the company. Such associative patterns help recruiters filter similar candidates and perform similar hiring processes to get the best candidates in the future.

3. Predicting Employee Turnover and Retention Risks: Predictive models also generate statistical charts and trends showing high turnover and retention trends. Such patterns also show company policies and hiring decisions that have directly influenced employee turnover, predicting the conditions and decisions that can risk the retention of existing employees.

4. Spotting Passive Candidates Ready for a Job Change: Most recruitment platforms maintain a database of potential candidates from a huge talent pool in one or more domains. When companies partner with such platforms, they receive proactive predictions of potential candidates who can fill their vacancies or roles that may become vacant in the near future.

5. Matching Candidates to Roles Beyond the Resume: Predictive models scan through a candidate’s resume to deduce their skill profiles rather than just matching resume skills with job descriptions. Such models learn and help match candidates to prospective job roles in suitable teams or companies that they may not have noticed while applying, providing them with more opportunities.

6. Speeding Up Hiring and Reducing Time-To-Fill: Analyzing past hiring scores and candidate performance patterns helps provide predictions that enable recruiters to make quicker decisions or skip certain unnecessary hiring steps. Such actions speed up hiring and attract candidates for critical job roles without delay.

7. Enhancing Candidate Experience With Smarter Engagement: Predictive analytics provides holistic insights to all stakeholders in the hiring process, including applicants. Intelligent engagement through chatbots and website assistants helps candidates understand the job requirements more clearly while also learning their progress in the hiring pipeline.

Benefits of Predictive Hiring Analytics

Predictive analytics for hiring is a rich source of knowledge that aids recruiters in making efficient hires quickly. Additionally, the hiring process is continuously refined to accommodate the collective interests of the candidates and recruiters' expectations to meet the job requirements.

Here are some proven benefits of using predictive analytics in recruitment:

Increased Recruitment Efficiency

Learning from the past hiring data gives interesting insights on the number of applications received for a job role, the time taken for screening and subsequent processes, the filtering association rules, and the selected candidates’ performance metrics.

Such data helps refine and improve the existing hiring process. For instance, if candidates selected in a certain round show similar performance metrics to candidates hired in the past, the recruiters can speed up the subsequent rounds. As a result, the company gets quality hires in a short span of time.

Reduced Hiring Costs and Errors

In case of candidates who perform equally well in all rounds, it can be difficult to make selection choices without bias. This can mean including more evaluation rounds that can lengthen the hiring process.

Predictive analytics models can retrieve details of past performances and skill profiles to provide insights on who can be better performers. Such insights save companies time and costs while also avoiding hiring errors.

Make Quality Hires With Better Retention

Analyzing the duration of candidates' previous jobs tells a lot about how committed they will be to their new job role. Predictions about how long a candidate stays in a company can help prevent hiring frequent job switchers and save the company’s finances.

Also, predictive analytics provides insights on the parameters and engagement measures that helped previous companies retain such employees. Overall, insights on an employee’s lifecycle in a company can help future recruiters find the best matches for companies that align with their policies.

Reduction in Unconscious Bias and Greater Diversity

Introducing unconscious bias in the hiring process can be unfair to some candidates applying for a particular job role. Such biases are more common in the initial rounds of screenings, where most profiles are rejected because of the screening professionals’ innate bias.

Tools powered by predictive analytics eliminate biases related to gender, educational backgrounds, alma maters, ethnicity, and much more, paving the way for the association of candidate profiles and skillsets with the job descriptions. Also, their association rules relating to the company’s DEI initiatives ensure that the filtered candidates come from diverse backgrounds.

Strategic, Forward-Thinking Talent Planning

Monitoring the timeline of past and present employees in a company provides insights into the exact periods of high turnover and job vacancies in a company. Such insights can predict the occurrence of similar incidents in the future. Thus, companies can be more proactive in their hiring when they anticipate such massive turnover.

Predictive analytics play a crucial role in helping companies plan a strategic and talented workforce. Team performance analytics can help predict downsizing and upskilling requirements in existing teams, create new job roles, and match candidates from vast talent pools.

Ethical Considerations and Limitations

Companies that use external platforms and software for hiring must ensure that such platforms consider the local labor laws and company policies when considering candidates.

For instance, candidates may belong to different time zones and be willing to work at their convenience hours. Talent acquisition tools must ensure that such candidates are matched to job roles that satisfy their work expectations while meeting the company’s goals and policies.

AI-powered models driven by predictive analytics are unbiased in their insights. They also consider the company's adherence to several labor laws, legal advisories, and ethical considerations.

How Revaluate180 Helps With Predictive Analytics in Recruitment

The world of analytics can be overwhelming. What starts as a small snowflake of data can increase multi-dimensionally to become a huge snowball consisting of several layers of patterns and learning modules.

However, recruiters need not fear the training models and algorithms behind some of the powerful AI-based recruitment tools that provide predictive analytics.

Revaluate180 offers value-driven hiring and organizational services. We use data-driven analytics to help companies recruit talent that aligns with the role and the team's dynamics. Our approach aims to improve hiring decisions, team synergy, and long-term retention. Additionally, we provide individual development services to help employees have a great career in their current company.

In the worst case, when employees have to leave a company, we provide smooth severance services and mandatorily collect their feedback to improve our models consistently. Our wide client base is proof of our accurate behavior assessment and pattern predictions to improve hiring and retention.

Final Thoughts: Why Predictive Analytics is No Longer Optional

The future of predictive analytics in recruitment is undeniable in almost every industry and company. Those who fail to leverage analytics in talent acquisition will see past hiring mistakes repeated, leading to company losses.

In an era when even hiring managers frequently change jobs, it can be challenging for companies to keep track of all their past hiring patterns. Predictive models have made it easier for companies to be notified of patterns similar to their past hiring mistakes, thus preventing hiring the wrong candidates or those who switch jobs frequently.

So, companies must use one or more recruitment tools and platforms that provide accurate hiring suggestions based on predictive analytics.

Contact us if you would like to learn more about how predictive analytics provides insights about employees and a company’s employee retention strategies.