The predictive analytics market was valued at nearly $18 billion in 2024. The market is expected to increase about 14 times in size by 2037. Many industries such as finance and healthcare are all reaping the benefits of predictive modeling tools.
Like with any maturing tech, organizations are finding new use cases to implement predictive solutions. For example, what if you could streamline and optimize your current hiring process?
Today, we’re going to explain all you need to know about predictive hiring and how you can use it to recruit top tech talent.
What is Predictive Hiring?
Predictive hiring is a recruitment and hiring strategy that relies on historical data for analysis and insight. It’s an approach that combines HR tools with artificial intelligence (AI) and machine learning (ML) algorithms.
By gathering data and building predictive models, companies can make accurate predictions. Some insights include company loyalty, fit, and compensation expectations of every tech candidate. If you incorporate predictive analytics into your talent sourcing strategy, you can make better-informed recruiting and hiring decisions.
Demand is growing for expertise in newer fields such as edge computing industrial automation. Predictive hiring tools can help companies identify candidates with the necessary technical skills for cutting tech roles.
Elements of Predictive Hiring
A predictive hiring approach can vary depending on factors such as industry and business goals. However, every plan is founded on the same basic components.
- Data Collection: gather employee data, job market trends, and candidate personal details.
- Predictive Models: select, train, and test algorithms with historical data.
- Assessments: competency exams and personality tests to enrich and inform predictive models.
- Bias Mitigation: remove human or unconscious bias from the recruitment process.
The Importance of Predictive Analytics in the Recruitment Process
Mercer reports that the average employee turnover rate in the US from 2023 to 2024 was 13.5%. Tech position turnover may be higher because high performers are becoming more likely to change jobs frequently. One survey reported tech turnover rates as high as 57%.
With so much movement, how can you stay on top of your workforce and maintain business continuity?
A Statista survey of business leaders listed skills, experience, salary, and fit as the top challenges in IT recruitment.
Image sourced from Statista
Predictive analytics tools for hiring help you address most, if not all, of the challenges in tech recruiting.
Benefits of Using a Predictive Hiring Strategy
Let’s take a look at some of the benefits of applying forecasting technology to the hiring process.
- Reduce employee turnover: predict which candidates are most likely to stay for the long term. Identify employees who are likely to leave and take preventative measures.
- Identify more qualified candidates: find high performers who are a good fit.
- Source hidden or passive candidates: spot top talent primed for a move if given the right opportunity.
- Improve retention and engagement: proactively identify employee disengagement. Re-engage team members before they exit.
- Identify skill gaps: evaluate your staff for current and future skill and competency gaps. Design training programs to upskill and bridge gaps, increasing employee performance.
- Save recruiting spend: automate workflows such as call or resume screening. Reduce hiring metrics such as time-to-fill and cost-per-hire.
- Better workforce planning: spot employee and market trends ahead of time. Prevent vacancies and skill gaps that disrupt continuity and stay ahead of your competitors.
- Reduce bias: train models to remove unconscious bias from the recruiting process. You’ll hire top candidates with a more fair and diverse talent pool.
How to Build a Predictive Hiring Process for Your Business
A predictive hiring approach helps your business optimize recruitment and workforce management. An effective strategy tailors to your organization’s needs and the talent you intend to attract. Follow these steps to build hiring and recruitment driven by predictive analytics.
1. Identify your goals
Every achievement in the business world begins with an objective. What’s the target you’re aiming for? In other words, for what purpose do you wish to implement predictive hiring tactics?
Meet with leaders and key stakeholders to look at the possibilities of predictive analytics. Create realistic, collaborative objectives such as:
- Increasing workforce diversity: promote innovation and inclusion and expand your employer reach.
- Reducing time-to-hire: streamline candidate application and evaluation processes.
- Improving technical skill matching: ensure hires meet the demands of the role and any identified skill gaps now and in the future.
- Enhancing retention rates: prioritizing cultural, role alignment, and career ambitions during recruitment.
Once you have one or more primary objectives, flesh them out by making them into SMART goals.
Image sourced from indeed.com
An example of an effective project goal would be to reduce time-to-hire by 10% over the next quarter.
2. Gather data
The foundation of all predictive analytics tools is data. Models and algorithms are trained, tested, and optimized from the wealth of big data.
The types of data to gather for predictive hiring analysis include:
- Talent pool data: information from your application tracking system (ATS), candidate screening tools, and other apps.
- Employee data: internal information from your HR, payroll, and other platforms.
- Feedback: employee surveys on areas such as job satisfaction, engagement, and loyalty.
- Recruitment channels: job boards, social media, and professional networking sites.
- Assessments: data from pre-employment assessments and employee skill exams.
- Market data: job market trends, average salaries, and industry employment rates.
In general, the more data you can gather, the more insights you’ll gain about your hiring process. Higher amounts of data also mean a higher accuracy of predictions.
It can be tempting to focus on gathering as much information as possible. However, don’t fall into the trap of using bad data. Focus on collecting high-quality data sets that are clean, verified, and accurate to obtain the best insights.
3. Select the right solutions
Alright, you’ve got a destination (your goals). You’ve got the fuel to get there (gathered data). What about the vehicle that drives you where you want to be?
Many options will get you moving through the world of predictive analytics. Which one or ones you decide on depends on fit and need.
- Technical assessment capabilities: tools like HackerRank are ideal for evaluating coding and problem-solving. Other options such as iMocha provide thousands of pre-built assessments for a wider range of business skills.
- Integrations: flexible APIs are crucial for many businesses. Can you seamlessly connect an enterprise integration with your existing business tools?
- Bias mitigation features: AI-based screening, applicant anonymization, candidate assessments, and other capabilities.
- User-friendly interfaces: is the platform easy for your team to adopt and use? Look for tools with intuitive dashboards that allow hiring teams to focus on the most important metrics for your goals.
- Customization: the ability to design and automate workflow such as data collection. Your team can focus on choosing the best candidates.
4. Train predictive models
Going the predictive route means you are at the mercy of algorithms. You’ll only get as far as the models carry you. How can you ensure your predictive modeling is healthy, happy, and well-trained?
Image sourced from neo4j.com
Start by feeding your models high-quality historical data. Model training helps the AI understand exactly what you’re looking for.
Work closely with your IT specialists to identify role-specific competencies. For an engineering role for a call center in cloud solutions, for instance, success might depend on coding proficiency and experience in a similar environment.
Keep in mind that predictive models are iterative. They improve with time and as more data becomes available. Expect predictive outputs to surprise you.
Let’s look at the previous example. You may find excellent communication skills are more indicative of success than technical skills. (Insights like these are exactly why you’re implementing predictive hiring).
At the same time, rely on guidance from your subject matter experts. If something isn’t adding up, then re-evaluate and re-train the model.
5. Integrate pre-employment assessments
We’ve mentioned pre-employment tests several times for a reason. You can’t accurately predict how a candidate will perform in the role if you don’t know their skill set. Many pre-employment assessment platforms, such as Canditech, focus on technical skills.
When choosing a pre-employment assessment tool, look for a variety of the following:
- Coding tests: evaluate technical proficiency in relevant languages such as Python, Java, and C++.
- Specific technical skills: competencies such as database management, IT architecture, QA testing, or Linux server administration.
- Cognitive ability tests: measure problem-solving skills, verbal comprehension, and logical reasoning.
- Behavioral assessments: assess cultural fit and interpersonal skills.
- Soft skills: examine leadership, communication, and other soft skills.
6. Establish Success Metrics
It’s important to define “success” when implementing a new strategy for any business. How will you measure success for your predictive hiring project?
Hiring and recruiting metrics to monitor include:
- Employee retention rates: the percentage of employees who stay with a company over a specific period of time. Improvement in this metric signifies hiring is better aligned with the business.
- Time-to-fill: measures how long it takes to fill a job opening from the time it’s posted to when a candidate is hired. Working on this metric can enhance productivity by decreasing the time that positions remain vacant.
- Time-to-productivity: how quickly new hires can contribute meaningfully to their teams.
- Job performance scores: a standardized way to measure how well an employee is performing their job. Use performance reviews and manager feedback to assess predictive accuracy.
- Employee engagement: how happy and active team members are in their roles.
- Cost-per-hire: indicative of whether your hiring efforts are using resources efficiently.
These are just some examples of predictive hiring metrics. Of course, which ones you prioritize will depend on your business goals.
7. Analyze and Evaluate
Once you’ve begun implementing predictive analytics, measure the results. Are your KPIs trending in the right direction? Gain feedback from stakeholders. What does your hiring team have to say about the new process? What do managers think about the new hires being sent their way since the switch to predictive hiring?
Are candidates generally seen as weaker, stronger, or the same? Data may show a lack of skill competencies, whereas team leader feedback may provide additional criticism.
Combine qualitative and quantitative information to evaluate your current system. If you’re not happy with the current results., identify what’s missing. A tool or model may be underperforming or simply lacking accurate data.
8. Continuously Monitor and Optimize
Regardless of whether you’re getting positive results or not, there is no “mission accomplished.” You might reach your goals for several quarters and then see things start to slip. Why does this happen? There are too many variables. Candidate expectations and job markets are forever changing.
For example, since the pandemic, employee preferences for remote work have been on the rise. Some top talent may even prioritize flexible working over something like compensation. Because of this, tech companies have responded, with 56% of firms allowing employees to choose where they work.
Image sourced from linkedin.com
Optimizing your hiring strategy is a constant effort. Continuously feeding data to your predictive analytics models. Stay updated with current trends. Monitor the results and audit your algorithms periodically.
Stay on top of predictive hiring and technology trends. Consult with your technology experts and upgrade your processes when necessary.
Predictive Hiring Best Practices
You can be sure your competitors will be using predictive hiring tactics to attract star candidates. You can help your employer brand rise above the competition by following the best practices.
Cross-functional Collaboration
When building predictive hiring, it’s vital to collaborate across departments. Don’t put all your eggs in the IT basket. Likewise, don’t get all of your guidance from front-line recruiters.
Put together an implementation team that brings together experts from many different roles. Greater teamwork will ensure richer data, diverse ideas, and collaborative insights.
Support Your Team with Training
Giving your hiring teams predictive tools is one-half of the battle. They also need to understand how to use them. Team training is part of an effective predictive hiring strategy. Take advantage of educational resources provided by vendors for any tools you use. This includes access to training videos, webinars, and even in-person workshops.
Develop specific documentation to guide each role on how to use predictive tools. For example, a recruiter should understand how to analyze screening and assessment data. HR managers need to know how to gain workforce planning insights from the models and so on.
Create a Talent Roadmap
A talent roadmap is a document that guides your team on the skills and competencies needed to reach your hiring goals. The roadmap helps drive recruitment decisions and also the entire predictive hiring process.
A talent roadmap should include:
- Potential skill gaps and competency needs
- Recruitment and workforce planning goals
- Candidate assessment criteria
- Applicant screening and selection methods
- Long-term business goals and projected growth
- Employee performance metrics and assessments
Roadmaps serve as a quick reference for any team member involved in recruitment and hiring.
Use Predictive Hiring to Future-Proof Your Business
You can’t always know the future, but you can have a better understanding of what’s likely to happen. Don’t waste your resources and recruiting efforts by hiring the wrong people. Take a predictive analytics approach and improve the chances of both employee and employer success.
Identify the candidates who are most likely to excel in a role, stay with the company, and be satisfied with the job. Follow the steps in this guide and start hiring better tech candidates today!
