IT/Tech/DigitalTalent Technology3 Reasons why Predictive Analytics is your most valuable Ally in Recruitment

For the fourth year in a row, the number one global business challenge is Human Capital. However, for many HR professionals, this does not come as a surprise. The war for talent is more intense every day. Big Data has also started playing a consequential role in Human Resources and talent management, becoming a true game-changer for the industry. A strategic tool called predictive analytics is now used in human resources everywhere. Organisations can use it to...
Catenon World10 months ago5219 min

For the fourth year in a row, the number one global business challenge is Human Capital. However, for many HR professionals, this does not come as a surprise. The war for talent is more intense every day. Big Data has also started playing a consequential role in Human Resources and talent management, becoming a true game-changer for the industry. A strategic tool called predictive analytics is now used in human resources everywhere. Organisations can use it to understand and forecast where talent is plentiful, scarce, or when it leaves. Talent management expert John Boudreau has shared insights on the role of predictive analytics in optimising talent decisions in a complex world.

 

Predictive Analytics – What is it? Why is it important? 

Predictive Analytics is the branch of advanced analytics which is used to make predictions about unknown future events. It uses techniques like data mining, statistical modelling, machine learning, and artificial intelligence to analyse current data and make predictions about the future. Accurate predictions are the cornerstone of effective workforce and productivity in companies. By forecasting how many employees are likely to leave, you can plan how many new employees to hire accordingly. Better predictions thus make it easier to match forecasts with reality. Even a small increase in accuracy means saving. 

 

3 Ways in which it is Useful:

 

  • Retention analytics and attrition risk:

Knowing which employees are most susceptible to leave is crucial information in order to accurately predict attrition risks of other employees. A predictive analytics program could do so by considering factors such as demographic data, information about compensation, commutes, performance, attendance, and hundreds of other attrition variables. Then, by cross-referencing this data with things such as historical trends, determines which indicators are most relevant.

Automating the study of these statistics can allow companies to know which candidate profile is most susceptible to have a high turnover rate. It can then rank them from most adapted to work in their business to least. This means you can scale your hiring efforts accordingly: reducing empty desk time, panic hiring and lowering costs.

 

  • Leadership Potential Assessments:

Great leaders are what separates average organisations from great ones. Being able to predict which new hires, based on their profile, have potential is advantageous for any business. Indeed, finding the right talent and nurturing it to create the next generation of great leaders is one of the main challenges of human resources and recruitment. There are different strategies to nurture that talent and develop leadership: broaden knowledge, experience, enhance skills – unfortunately, it is financially impossible for all organisations to provide everyone with such opportunities.

Talent analytics can help spot the people with an adapted leadership profile, representing the best return on investment. By determining the characteristics leaders have in common, whether it be academic credentials, career progression or engagement level. The processing of information then pinpoints the hidden correlations predicting leadership. Based on actual data coming from their own environment, the organisation thus makes decisions.

 

  • Quality of hire analytics:

The employment market for top performers is fiercely competitive. It is imperative to hire quality employees and fast. Studies by Bersin by Deloitte show that top candidates only last on the market 10 days, while the average recruitment process takes about 52, leaving recruiters under pressure to act quickly, often sacrificing the quality of their hire. This can hinder employee engagement rate, increase recruitment costs, and drop overall productivity. 

Quality of hire analytics identifies the pivotal roles in organisations and builds hypotheses as to why some new hires perform better than others. Looking at performance assessments, demographic data, survey results and more factors, quality of hire analytics can create optimal hiring profiles most adapted to an organisation.

 

It is important to remember that however efficient it may be, predictive analytics cannot and will not replace human intervention. It can, however, give you the deep insights needed to make the best possible decision based on facts. This is the power of HR Big Data: increase recruiter efficiency based on data and accurate predictions. By filtering out the less productive work, you’ll allow your recruiting team to focus on the top candidates. The return on investment that matters: getting the right people in the right roles in the most effective way.

 

 

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