What is HR predictive analytics? Types, benefits and strategies

Explore the transformative power of HR predictive analytics: Learn about types, benefits, and how to implement them for a data-driven future in HR management.

Alfie Young

HR predictive analytics: Underused yet crucial, HR predictive analytics helps you to anticipate future trends for improved decision-making.

What is HR predictive analytics? - It uses statistics and modelling to anticipate future organisational outcomes, analysing current and historical data.

Top 5 types of predictive analytics - Decision trees, linear regression, neural networks, cluster models, and time series modelling.

Benefits of HR predictive analytics - Enhances engagement, productivity, and diminishes turnover risks, transforming HR into a strategic asset.

Implementation challenges and solutions - Challenges include identifying quality data sources, integration with systems, managing change, and bridging talent gaps. Solutions involve phased rollouts, fostering trust, and leveraging user-friendly tools.

Future trends - HR predictive analytics will become more accessible and democratised, reshaping HR strategies with data-driven decisions.

The future of your organisation

Recent technological innovations have inspired organisations to continuously improve the way we work. Amidst the buzz around Large Language Models (LLMs) and other AI advancements, it's notable that many organisations are still not fully leveraging AI models that have been around for years, such as predictive analytics. 

This is highlighted by research showing that only 42% of HR leaders agree that their people analytics processes and/or technology deliver actionable insights. This comes as no surprise considering the same research indicated that only 34% of these organisations make moderate use of predictive analytics. 

The reality is that many of us either overlook the potential of predictive analytics or lack the necessary knowledge and training to implement HR predictive analytics tools effectively. 

What is HR predictive analytics?

Have you ever wondered how to predict what will happen and how well your organisation will do in the future?

Predictive analytics is the use of statistics and modelling techniques to forecast future outcomes and performance. This is done by examining current and historical data patterns to determine the likelihood of recurrence. 

Often mistaken for descriptive analytics, which addresses “what has happened?”, predictive analytics instead answers “what will happen?”, showing how past events can determine what comes next. Predictive analytics is invaluable for understanding where your current trajectory will take you. This ability to predict future trends and behaviours enables you to anticipate problems that lie ahead, and make more informed, data driven decisions, increasing efficiency and productivity. 

In the context of HR and the workplace, predictive analytics can serve as a strategic tool, almost like a crystal ball, empowering you to better anticipate and shape the future of your workforce. It allows for effective planning, the identification of opportunities for improvement, and the proactive addressing of potential issues before they arise. If your organisation fails to grasp this chance, you run the risk of falling behind. 

The top 5 types of predictive analytics used today 

Decision trees

Decision trees simplify complex decisions into a tree-like model, breaking down data into smaller subsets and making them ideal for quick decision-making.  These methods include random forest and gradient boosting machines. They are particularly effective in understanding non-linear relationships for example revenue prediction and employee behaviour.  For instance, decision trees can assist you in analysing factors that influence employee engagement, such as satisfaction, wellbeing or alignment. 

Linear regression 

Linear regression is probably the most simple of predictive algorithms to use. It is usually the best for when relationships between variables are expected to be clear.

Neural Networks

Neural networks, mimicking human brain functions, excel in handling complex data relationships using AI and pattern recognition. For instance, neural networks can pinpoint some of the key traits of your top salesmen or behaviours that lead to increased turnover risk.

A molecular structure with interconnected spheres, symbolising neural network concepts in HR predictive analytics.

Cluster models

These models group similar data points together, which is highly effective for segmenting employees based on shared characteristics like skills or performance.  This can be particularly useful for engineering teams, allowing for the creation of tailored development programs. Each engineer can receive personalised training and project assignments that align with their unique skills and areas for growth, ensuring that the team as a whole is well-rounded and highly skilled.

Time series modelling

Time series models assess data over time, crucial for identifying patterns like seasonal variations in workforce productivity. This model helps in effective project planning and resource allocation by predicting future trends based on past patterns.  

A hand points to a chart showing monthly sales and productivity, illustrating HR predictive analytics with a time series model.

The best way to determine the optimal algorithm is to run controlled tests on your data. Comparing performance metrics of different algorithms highlights the best approach for your specific business case. It's also worth testing ensemble methods that combine multiple algorithms. 

For example, a random forest combines predictions from many decision trees to improve accuracy. Ensembles leverage the strengths of different algorithms. No single machine learning algorithm works best for all data. The optimal approach depends on factors like the size of historical data, types of variables, expected relationships, and the desired model interpretability. Thorough testing is key to selecting the right machine learning method for your predictive needs.

Why should HR departments and leaders care about HR predictive analytics? 

HR predictive analytics enables you to foresee and tackle potential issues before they escalate, as well as capitalise on opportunities to enhance workplace dynamics and culture.

Improve engagement

By leveraging HR predictive analytics to analyse factors like autonomy, collaboration, and alignment, you can gain profound insights into future employee engagement levels. This advanced approach tracks the course of engagement across different teams and departments and alerts you when this is trending downward, or when it is likely to in the future. 

Predictive analytics, in conjunction with correlation analysis, can reveal how varying levels of engagement may impact your key goals and objectives. These capabilities allow for timely interventions in areas of low engagement, such as burnout or limited autonomy, ensuring a proactive approach to maintaining a highly engaged workforce. 

Improve productivity

By detecting complex patterns in large datasets, you can make better decisions to help you improve productivity. For example, you can utilise scenario modelling and understand answers to questions like: 

  • How will sales training from supplier ACME impact next quarter’s revenue?
  • What impact will a 1% reduction in absences have on costs next year?
  • If we increase engagement by 20%, what will that mean for reducing turnover?

By simply tweaking inputs like the type of sales training, absence cost %, or engagement %, predictive models can rapidly churn out forecasts for different scenarios. This enables you to make data driven plans that improve productivity.

Reduce turnover risk

Considering the high costs associated with employee turnover, which ranges from 30-50% for entry level, 150% for mid level and 400% for high level employees, predictive analytics is invaluable. It leverages both historical, and real time-time data to identify factors contributing to employee turnover, such as career growth opportunities, compensation, work-life balance, and management quality. Essentially, HR predictive analytics can help you identify churn risks and allow you to proactively devise targeted retention strategies. 

HR predictive analytics is not just a tool; it's a strategic asset that empowers HR departments and leaders to make smarter, data-driven decisions. It enhances various aspects of HR management, from engagement and productivity to turnover, ultimately contributing to the organisation's success and sustainability. 

However, for a successful implementation of predictive analytics, it's essential to not only recognise its broad benefits but also proactively address the common challenges, ensuring a smooth and effective rollout without early stumbling blocks.

Common implementation challenges of HR predictive analytics

Identifying right data sources and ensuring data quality

One of the primary challenges when implementing predictive analytics is firstly identifying the right data sources and also ensuring the quality of this data. Many organisations have an abundance of data, yet a large portion may not be appropriate for predictive analytics. It's crucial to focus on data that provides a solid foundation for predictive models.

Once these sources are identified, the next critical step is to guarantee the quality and cleanliness of the data. Data quality encompasses several key characteristics: accuracy, completeness, validity, consistency, and timeliness. Failing to meet these data quality standards can result in unreliable and inaccurate predictions and insights, undermining the effectiveness of the predictive analytics initiative.

Solution: Clean, complete data and focus on continuous improvements

Cleansed, standardised data with sufficient history is vital for accurate models. This may require an investment in data infrastructure. This also involves refining algorithms, data, and processes continually rather than one-off projects.

Integration with existing systems

In order to be effective, predictive analytics needs to be integrated with existing systems. If in-house systems consist of multiple applications, in which some of these are out of date, it will be very challenging. 

Solution: Implement a phased rollout

Start with one department or action area to prove the value of HR predictive analytics and then expand gradually. This approach helps to prevent stakeholder fatigue and allows for adjustments based on initial feedback. 

Change management

When implementing predictive analytics it may be met with some resistance by employees who are sceptical about using their data to drive insights. 

Solution: Foster trust, not blind faith

You must interpret predictions reasonably, combining data-driven insights with human judgement. Setting clear organisational goals through OKRs (Objectives and Key Results) and KPIs (Key Performance Indicators) is also crucial. As is dedicating resources to change management and fostering a data-driven culture through employee education is vital. 

Team engaged in change management, reviewing HR predictive analytics on a screen, discussing data-driven workforce strategies

Talent gap

Despite implementing all the processes above, a common issue among organisations is a lack of training and knowledge of how to adopt predictive analytics and implement it into the workforce. If done independently, predictive analytics requires a team of skilled data scientists and analysts who can develop and deploy the models effectively. Without a skilled in house data scientist, finding and retaining talent can be a challenge and often very expensive. 

Solution: Leverage analytics tools

Whilst training current employees on applying HR predictive analytics is an effective solution, it can be costly. Instead, adopting a user-friendly analytical tool that requires less technical expertise can reduce dependency on highly specialised data science skills. 

By addressing each of these challenges with their respective solutions, organisations can effectively harness the power of HR predictive analytics, leading to more informed decision-making and improved overall performance. 

By addressing each of these challenges with their respective solutions, you can effectively harness the power of HR predictive analytics, leading to more informed decision-making and improved overall performance. Our consultancy, backed by data expertise, is equipped to guide you through every step of this process. We can help you tackle these issues, ensuring that you make the most of HR analytics in your strategic decisions. If you're interested in transforming your HR processes with data-driven insights, click here to get started with our expert team.

Future trends

HR predictive analytics will become democratised

Traditionally the complexity of predictive analytics was confined to its use by specialists. Now looking forward to the future, HR predictive analytics is set to undergo a significant transformation as it becomes more democratised and accessible.

This shift is making predictive analytics not only accessible but also affordable, spurring its widespread adoption among organisations. The result is enhanced business value, using predictive analytics to inform their HR decisions. This democratisation presents an exciting opportunity for organisations to redefine their HR strategies. By leveraging HR predictive analytics, you can make more data-driven, informed decisions that help you achieve your goals faster.

Don’t miss out on this opportunity to redefine your HR strategy. Our expert team is here to guide you through every step, making the complex process simple. 

Contact us now and lead the change!