October 16, 2023
Predictive analytics software can improve human resources operations in an organization, from candidate procurement to employee churn analysis. Itransition experts leverage predictive analytics capabilities to enhance talent acquisition and retention strategies.
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
the average budget per user for HRIS software or $210 per user per month
believe providing useful people data and analytics is the key goal of their HR tech stack
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: semanticscholar.org - Employee’s Performance Analysis and Prediction Using K-means Clustering & Decision Tree Algorithm
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.
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:
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.
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.
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.
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.
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