Predictive analysis tools for HR

Predictive analytics is a tool that allows you to anticipate employee behaviors and future events within the company.

Predictive analysis: what are the objectives?

The objectives of predictive analytics are to assist in decision making, to adjust and to develop strategies adapted to real situations. To do this, it relies on data mining and statistics. It works with automation and parameterization solutions. In concrete terms, using an identified database, this methodology consists of cross-referencing predefined variables with variables taken at a given point in time.

In this way, predictive analytics can be used to identify performance indicators that can then be interpreted by human resources departments. However, to extract usable indicators, the predictive method is based on different working modes:

• the use of predictive modeling consists of studying past trends to predict future events;
• Descriptive analysis consists of analyzing the data to organize them into categories;
• As for decision analysis, it requires collecting all the information necessary to understand how a decision was made.

The application of predictive analytics as a whole has many undeniable advantages for the company and, in particular, allows human resources management to fully exploit a multitude of HR data. With such an HR analysis tool, the benefits are numerous:

• HR departments can exploit a large amount of data;
• HR analysis solutions are powerful and scalable tools;
• all HR data is centralized in one place;
• Process automation allows HR departments to have more time to deal with strategic issues concerning the company’s human capital (talent management, skills development, etc.).

Predictive analysis applied to human resources

HR departments use predictive procedures because they represent an indispensable working tool for anticipating, analyzing and understanding events and behaviors in the company.

In terms of improving the recruitment process, this tool helps to find the best profiles corresponding to the job offers. If this HR solution is complemented with artificial intelligence, machine learning can study all the CV information from big data and professional social networks, to extract the right candidates for a particular position. The recruitment process is thus optimized and more efficient, both in terms of time and cost.

For talent management, the company’s human capital can be enhanced by combining personal data with the results of assessment interviews. This approach can highlight training and skills development needs. HR departments can set up customized training plans or closely accompany employees in the acquisition of new skills. Similarly, the results of this analysis tool can be used to guide human resources management towards the actions needed to exploit the wealth of human capital.

The improvement of the company’s performance is therefore closely linked to all the human resources actions upstream: data analysis, recommendations to be applied, etc.

Moreover, thanks to the predefined performance indicators, it is possible to identify the strong points or the factors of failure, which offers a decision making process more adapted to each situation. This makes it possible to adjust HR strategies and anticipate events that could destabilize the company’s productivity.

In order to optimize the turnover rate, which includes the rate of turnover and absenteeism, human resources management must succeed in identifying the reasons that generate turnover and absences. Thanks to data and the intervention of machine learning and artificial intelligence, it is possible to anticipate a resignation or to evaluate the social climate in a company, etc. With the results obtained, concrete solutions can therefore be implemented quickly.

What predictive analytics tools are available to HR?

The digitalization of human resources consists in using powerful HR solutions and big data to exploit the available data. However, in order to find the data analysis tool that best suits the expectations of human resources departments, the needs must be clarified.

What will predictive analytics be used for? Will it evolve over time? For example, with new strategies to be put in place, the tool’s capabilities may need to be expanded in the medium term.

Several analytical techniques exist among many others, with specificities:

• Data mining is the extraction of relevant HR information from a multitude of data.
• Text mining allows for semantic content analysis. It is relevant for reviewing potential candidates on social networks, where professionals can detail their experiences and share their portfolio.
• Data visualization is a tool that organizes all the data collected to make it usable and visible. It is ideally suited to descriptive analysis.