Predictive analytics in HR management

Knowing the future has been a human desire since ancient times. Oracles, Nostradamus and crystal balls have all trended at some point in history. Technological advancements have brought us less obscure and more reliable solutions for foreseeing the future, like predictive analytics, a method which relies on big data to offer accurate valuations of trends. This tool is already popular in a lot of activity sectors including finance, healthcare, marketing and now it’s making its way to HR. Here are some predictive analytics use cases to become more accustomed to its capabilities.

Since for most organisations, their human resource is their most valuable one; it makes sense to use this new method for each step of the HR process. The goal is to take an informed decision which is based on the information from various sources, and it is not affected by the bias a human recruiter may have.

Why use predictive analytics for HR?

The value of using predictive analytics for HR comes from its power to combine information from diverse data sources like CVs, recommendation letters, social media profiles, personality test results and offer a comprehensive result which can even compare the fitness of the candidate with the team and the company’s culture.

It’s a matter of using all the information we have gathered to take the best decision for the future. By utilising the power of numbers, the HR process becomes more efficient, and we can measure an increase in quality and accurately predict some outcomes.

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This article offers relevant examples of the link between efficiency, effectiveness, and outcomes. The first one is evaluating the number of openings in the near future, the expected quality of the hires and the length of their employment. Another one is the average cost per hire, cultural fit and the contribution to quality.

Predictive analytics applications in HR

Looking at the entire hiring and talent retention process, there are numerous points where predictive analytics could make a difference for HR. We will discuss each of these in the following sections.

Selecting the right talent

An excellent employee is not only a good specialist, but it is also someone who is a good fit for the team they will work with, either as a colleague or a leader. It is also someone who is aligned with the values of the company hiring them, you can’t work for a petrol company and support green causes after hours. The right piece for the talent puzzle is someone who also shares a similar background and future aspirations with their peers.

Predictive analytics can go beyond the traditional qualifications and background checking to assess if an applicant is satisfactory for a particular position. By including broader sets of data, like public posts from social media profiles, it creates a personal profile, not only a professional one.

Although it seems secondary, sometimes the cultural fit is more important than actual qualifications, as even the most competent employees don’t feel motivated in an environment with different values, and sooner or later frictions arise.

Boost performance and increase productivity

Almost half a century before predictive analytics, the work of Deming and the Six Sigma and lean philosophy revolutionised production. The same number-centric principles can now be refined and reused to increase the productivity of modern employees.

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It’s all a matter of numbers and meeting KPIs. Analytics is just the right tool to help managers assess if the staff is on track by closely monitoring progress. Keeping a close eye on daily performance metrics also helps prevent slips before they become dangerous for the entire organisation.

A thorough analysis can also help organisations develop personnel fluctuations models, prevent seasonal gaps and schedule extra staff when necessary to avoid overcrowding and customer dissatisfaction.

Prevent high employee turnover rates

One of the most significant HR expenses in replacing top specialists. Estimations show that the total cost of finding a suitable replacement go as high as twice the annual salary, which makes this one of the costlier decisions.

Predictive analytics can identify the probability that an individual employee will leave the organisation based on behavioural markers collected from the same and similar industries. When such red flags appear, the management can take timely actions and keep the high-potential employees or start a recruitment campaign to prevent a void in a critical position.

Workforce planning

In 2017, it was said that 85% of future jobs hadn’t been invented yet. In this situation what can a young person do? What can governments do to prevent unbalances like unemployment? Again, predictive analysis can offer a satisfactory solution. By looking at long-term trends, it can provide a list for potential gaps in the workforce and direct youngsters and those looking for a more satisfying career towards those workplaces which are secure in the years to come.

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Increase employee engagement

Not everybody is keen on team buildings and casual Fridays. Some people would just love more free time to spend with their loved ones and maybe a bonus to take that road trip they are dreaming about. With predictive analytics, you are just taking a step further than those employee engagement surveys and looking more in-depth at what motivates people and what makes them stay with the same company for years to come.

This tool work in a similar way to Amazon’s recommendation engines which look at the users’ profiles and strive to make personalised recommendations based on individual behaviour and cohort preferences.

Final thoughts

Although selecting the right employees seemed more like a job for psychology graduates it is becoming more and more a number-driven task for machines. Of course, the human factor will not be eliminated; hiring managers will still be in charge of taking onboard new employees, performing regular evaluations and even firing people. Yet, doing all these tasks with some insight from the predictive analysis will help them be more accurate and prevent unnecessary losses which could be easily avoided by looking at the patterns which escape simple observation.

Written by Sophia Brooke, a tech enthusiast and a writer

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