Predictive analytics in HR:
use cases and implementation advice

Predictive analytics in HR: use cases and implementation advice

October 16, 2023

Predictive analytics in HR market stats

expected sales of workforce analytics market by 2028

SkyQuest Technology Consulting

of organizations believe workforce analytics is critical to their success

SkyQuest Technology Consulting

of HRs say they have objective behavioral and cognitive data on candidates

Predictive Index

the average budget per user for HRIS software or $210 per user per month

Software Path

believe providing useful people data and analytics is the key goal of their HR tech stack Research

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Top 6 use cases of predictive analytics in HR

Employee performance optimization

HR predictive analytics can provide a wide range of insights into the way employees are achieving their performance goals and the company’s objectives. For example, ecommerce enterprises can use transaction and sales data collected by their POS systems to determine the most productive employees and the best sales channels. Analyzing factors that impact team productivity and the overall job satisfaction level can help HR specialists predict how different process and motivation schemes will influence employee performance. Moreover, driving insights from performance metrics, training records, engagement surveys, and teams’ feedback, predictive models distinguish correlations and patterns that trigger underperformance and high performance among team members.

Simple machine learning methods, such as K-Nearest Neighbors, are highly applicable to performance analysis and prediction. K-Means Clustering and Decision Trees, which are two of the oldest methods of machine learning analysis, can also be used to devise predictive analytics systems capable of performance evaluation. Using these insights, HRs can classify employees by groups for analysis and predict the team’s performance capacity in the future.

A K-Means cluster identifies top-performing employees based on prior metrics, and simple graphing translates the results into slide-friendly graphics.

Scheme title: K-Means cluster analysis of employee performance

Data source: - Employee’s Performance Analysis and Prediction Using K-means Clustering & Decision Tree Algorithm

Candidate sourcing, filtering, and head-hunting

AI-based analytics, along with RPA in HR, offers more proactive methods for head-hunting prospective employees, so-called passive candidates, 'in the wild'. Predictive analytics tools in HR are capable of scouring the web for individuals suited to your organization, with a growing number of companies offering AI-based 'broad sweep' candidate retrieval.

When companies cast an initial net for possible job candidates, AI and predictive analytics models analyze numerous factors, including education, skills, experience, and relevant domain expertise, to help HRs filter, rank potential candidates, and predict who is likely to be a good fit for a particular position.

Comprehensive analytics insights help HRs streamline candidate sourcing, meaning less time spent rejecting unsuitable job-seekers. Moreover, such systems can help reduce hiring costs: AI-enabled models automatically process enormous amounts of potential employee data, and companies can allocate fewer human resources to these manual tasks.

Employee attrition projections

HR departments can struggle to timely identify employees at a high risk of leaving the company. Beyond analyzing employee performance, predictive analytics systems factor in engagement level, sentiment, and satisfaction, as well as career development milestones.

Having analyzed all the data sources, predictive analytics models can provide insights about the employees at risk of resigning. If spotted in due time, HR can mitigate the flight risk and take proactive measures to motivate employees to stay, using targeted retention techniques and personalized consultations. Timely analysis of attrition rates will help to cut down employee turnover and assist HR leads in reevaluating their human resource management strategies.

Succession planning

Instead of hiring new star specialists and managers from the outside, predictive analytics can help HRs to identify top talents and prospect leaders in-house. By analyzing employee experience, past career trajectories, skill sets, and competency upgrades, predictive analytics models can help HRs spot and analyze employees with the potential to become managers and occupy leadership positions within an organization.

Predictive analysis can become a game changer in building succession plans and adopting targeted programs to groom skilled, high-performing employees for future career development.

Talent acquisition planning

HRs are responsible for maintaining an organization’s workforce, preventing employee shortages or overstaffing. Predictive analytics can help HR specialists forecast an organization’s talent requirements and workforce needs in advance.

Data-driven timely planning will enable HR leaders to reevaluate their hiring decisions, employee training and development initiatives and elaborate new recruitment strategies to maintain the availability of skilled personnel exactly when and where needed to support business outcomes.

Budgets and compensation planning

Predictive analytics models can help HR specialists define and approve HR budgets with top management by providing data-driven insights and projections based on particular market trends, employee turnover, and company growth objectives.

Predictive analytics can ensure market-based compensation planning, enabling HR departments to analyze industry salary benchmarks and market trends to keep the company’s compensation packages competitive. Predictive models can suggest appropriate salary ranges for various positions, taking into account employees’ experience, performance, market condition, and particular business goals.

Real-life examples of predictive analytics in HR

The Utah-based HR tech company HireVue performs text analysis on candidate interviews, combined with various other signifiers of suitability developed by the company's psychological research teams. The company uses proprietary machine learning models that analyze video-based interviews to predict the candidate’s future job performance. Most interestingly, the platform can assess non-verbal cues like facial expressions, eye movements, body language, and even personal style to enable companies much deeper insight into a candidate's personality than any other form of an interview. The effectiveness of this AI-powered system can be proven by the caliber of its clients: Deloitte, Goldman Sachs, Amazon, and Unilever. For example, using the HireVue platform, Unilever achieved a 90% faster hiring process, resulting in £1m of annual cost savings.

The immense advances of LinkedIn’s machine learning research are unsurprising since the company has one of the richest datasets in the world that grows exponentially day by day. LinkedIn Recruiter helps hiring managers and recruiters streamline talent sourcing and improve hiring processes and overall future outcomes. When a recruiter makes a candidate search request, a predictive analytics system ranks candidates that match a specific position based on their work experience, skills, job posting time and location, etc. LinkedIn Recruiter uses Gradient Boosted Decision Trees (GBDTs), among many other intensive machine learning approaches, to calculate 'non-obvious' factors that may align a potential recruit with the interests of an employer.

LinkedIn Recruiter

Scheme title: LinkedIn’s recommendation system architecture
Data source: — The AI Behind LinkedIn Recruiter search and recommendation systems

Textio is an augmented writing platform that uses predictive analytics and NLP to help HR professionals craft messages that resonate the most with existing employees and potential ones. For example, recruiters use Textio to reveal business jargon, age and gender bias phrases that turn off candidates and receive suggestions on what language will appeal the most to a certain audience. Importantly, Textio augments writing based on the language stack that a company already uses, allowing organizations to stay consistent in terms of style and branding across all communication channels.


Image title: Textio interface for employee performance management
Data source:

Remesh is an audience intelligence platform that allows enterprises to better understand their audiences, be they employees or consumers. With their predictive analytics-powered platform, Remesh helps companies to capture authentic employee feedback, which can be used to make data-backed decisions about employee health and benefits management, change management, diversity and inclusion, and cultural transformation. To employees, Remesh’s platform looks like a combination of a simple messenger app and a survey platform, where an HR professional asks both closed- and open-ended questions. Remesh analyzes employees' responses in real time, segments them into groups, and provides actionable insights. Given that the platform provides anonymity to the employees' responses, there is a high probability of authentic answers.

Video title: Remesh audience intelligence platform
Data source:

The AI meeting analysis framework Headroom, which recently raised $9 million in venture capital, can automate note-taking and uses emotion recognition algorithms to determine what impression a presentation is making on the 'silent' listeners in the room, providing the speaker with real-time feedback in a console window of their screen. The system draws on many factors, including pupil dilation, eyebrow disposition, mouth shape, and other groups of facial landmarks.

Human-centered AI is the key to the future of remote collaboration…We need co-presence: the sense of “being there;” not just seeing and hearing another person, but understanding and remembering them in the context of your shared virtual environment. Human-centered AI can greatly aid this understanding and memory to increase collaboration effectiveness and lead to higher productivity by teams.

Andrew Rabinovich

Andrew Rabinovich

Co-founder and CEO, Headroom


Image title: Headroom’s AI-powered in-house video conferencing
Data source: Headroom Inc.

HR predictive analytics implementation best practices

Predictive analytics streamlines decision-making within HR departments. So how to implement it properly to process vast volumes of HR data, enhance employee engagement, and improve your business’s bottom line? Here are the top 3 principles to consider:

HR predictive analytics implementation best practices

Data is the cornerstone for the accuracy of a predictive analytics system. You can always start building a predictive analytics model by leveraging in-house historical data, containing years' worth of reports and logs to analyze. However, you should ensure the data is accurate and consistent. Depending on the data quality, you should be ready to gather additional data or even start gathering brand new data sets to make sure your predictive analytics model gives you accurate insights. Also, pay attention to the necessary equipment and methodologies that constitute a value-driven business intelligence pipeline.

HR predictive analytics systems should also contain relevant metrics. Depending on the business objectives, HR predictive analytics adopters should develop meaningful benchmarks of success that positively impact the company. For example, you can measure the profit growth percentage or increase in sales after investing in training your sales representatives. Certain industries have intrinsic metrics (such as social media shares obtained in PR organizations, traffic fluctuations in SEO operations, or units produced in industrial settings) that can be input into an employee's predictive analytics profile. But where productivity is more subjective, companies would need to make an effort to implement other accurate metrics.

It's not always necessary to expensively train a large, curated corpus of data to run machine learning-based predictive analytics on internal company data. So long as your dataset is consistent and old enough to yield meaningful year-on-year statistical trends, many of the 'lighter' approaches will suit the majority of companies. One of the most favored algorithms is K-Nearest Neighbors (KNN). KNN is known as 'the lazy learner' since it traverses the entire dataset for a 'nearest neighbor' prediction instead of actually training a machine learning model to make predictions based on historical data. For example, IBM used this algorithm to predict employee attrition and performance. Other lower-impact approaches can be developed by similarly 'established' machine learning analysis techniques. For example, the Academy of Entrepreneurship Journal published a study into an ML-based employee retention framework using basic components such as Support Vector Machine and Random Forest. Using these algorithms, the authors established retention probabilities based on factors such as academic qualifications, contract type, department placement, and even the employee's degree major.

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Implementation challenges of predictive analytics in HR



Talent shortage


When trying to implement predictive analytics, the majority of HR departments in companies face a lack of skilled talent. While most applications of predictive analytics in HR are user-friendly, they call for substantial data analysis skills, which is a rarity among HR professionals.



Adopting a predictive analytics solution, you deal with two aspects: technical implementation and changes in business processes. First, depending on your goals and available budget, you can either hire internal technical specialists or trust the implementation of a predictive analytics solution to external predictive analytics and machine-learning experts who can help you implement and tune the predictive analytics model. Anyway, to realize the full potential of the technology for your human resource management practice, you should invest in HR specialists' training to facilitate the adoption process and guarantee the system's smooth operation.

Insufficient IT resources


Running predictive analytics and data analytics in HR is a very demanding process from the IT perspective. Smaller companies with limited cloud infrastructures will have the hardest time implementing these analytics programs at scale.



Outsourcing the implementation of a predictive analytics solution into your HR practice can be a viable option. A reliable partner will help you choose an out-of-the-box SaaS solution, customize it or build a fully custom system according to your particular business objectives. Using the scalable cloud infrastructure facilitates the adoption process and eliminates the burden of maintaining complex IT infrastructure.

Regulatory compliance and privacy


Predictive analytics can raise a number of regulatory challenges regarding data privacy, bias, and employee monitoring.



From a legal standpoint, it's essential to stay on top of the turbulent regulatory environment surrounding employees' rights regarding workplace monitoring and AI systems and, if necessary, to take professional advice about protecting the company from legal exposure. However, to eliminate the risk of regulatory compliance issues, predictive analytics models usually use obfuscated data for training and delivering predictions, guaranteeing personal and sensitive data safety.

Augment your HR practice with predictive analytics

Predictive analytics solutions are an excellent addition to the digital toolkit of recruiters and talent managers. Also, thanks to abundant historical data, enhanced data governance practices, and continuously enlarging datasets, machine learning-based forecasting is becoming more accurate than ever before. To seamlessly implement predictive analytics into your human resource management practices, contact our professional AI/ML experts.

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