If you are a recruiter and you've ever had to fill an open position, then you know how stressful it can be. You are constantly looking for candidates but can't find the right fit for your role. No matter what stage of recruitment you are in, there is one thing that's true: better data leads to better decisions.
What is predictive data?
Predictive data is the process of using analytic techniques to predict future behaviour, outcomes and trends based on historical data. For example, if you have a group of 1,000 candidates who applied for a job in the last year, you can use predictive analytics to determine how many will apply again this year and what their chances are of getting hired.
The key difference between traditional recruitment and predictive recruitment is that with the latter you're looking at past performance rather than simply relying on gut instinct or intuition. With this approach you can make informed decisions about which candidates are likely going to be successful in your organisation, which means less time spent interviewing candidates who aren't right for your company and more time focused on finding ideal matches.
Predictive analytics can help you improve your recruitment efforts by providing insights into historical recruiting data...
Predictive models are used to predict the likelihood of an event occurring or a person taking a particular action, based on their past behaviour and other relevant factors. In this way, the model learns from past behaviour and makes predictions based on what has happened before. It's important to note that predictive analytics is not just about predicting which candidates will be successful in an organisation; it also helps companies understand how they can improve their hiring process overall--from identifying high-value candidates earlier in the process all the way through on-boarding them successfully once they're hired.
Artificial Intelligence improving the recruitment process?
Artificial intelligence and business intelligence software can provide several benefits to recruiters.
AI helps recruiters make better decisions based on their existing data sets, while business intelligence software allows them to easily access those past decisions and use them as examples when making new ones moving forward.
How large organisations leverage the power of predictive analytics to improve their recruitment process...
Large organisations have been using predictive analytics to improve their recruitment process for years. To understand how you can use predictive analytics in your organisation, let's take a look at some of its key benefits:
Employee turnover
The average employee turnover rate in the UK in 2022 was 33.6% and it is estimated a rate of 35.6% in 2023. Employee turnover is harmful to company culture, and this is why lowering employee churn is top priority for HR professionals. Using this predictive data for the year ahead, HR professionals can supply talent that best fits the company and therefore improve retention of a positive culture.
Identity candidates with best skills for the job
Knowing how to get candidates to join a company is essential - you must be able to identify the best talent and skills on the market. Many companies use predictive hiring to understand what makes a strong job fit. This ensures that HR have access to quality talent to improve their talent pool.
Expand talent pipelines with better talent sourcing
The source of candidates (such as LinkedIn) has a huge impact on your hiring process and finding the best talent on the market. Predictive analytics can improve where you find the best talent and help you understand what platform has the best talent.
Implementing Predictive Analytics
The first step in implementing predictive analytics is choosing a tech stack - this is the software and tools you'll use to collect, store, and analyse data.
The most common recruitment metrics include:
Time-to-fill - the amount of time it takes to fill an open position
First year turnover rate - the percentage of employees who leave within one year after hire
Dropout rate - the percentage of applicants who don't get through all steps of your process before dropping out
Once you've identified which metrics are most important for your organisation's needs, the next step is collecting relevant data from past hires and existing employees to time-to-hire and average training costs.
Use artificial intelligence with the information above, as well as any other available variables such as location or industry experience level required for each role to predict whether someone will be successful in their role at your company based on historical data points, such as whether they stayed with their previous job longer than one year before leaving.
Use insights from predictive data and take action to implement them into your processes.
Tracking and measuring success is the best way to ensure that you're making the most of the predictive analytics.
We believe that predictive analytics is the next step in the evolution of recruitment. It's a powerful tool that can help you find more candidates who are likely to succeed in your organisation - which is so important to retain a positive culture and a productive and inclusive workplace.
We hope this article has given you some ideas about how you might use predictive data in your own business, as well as some information on how large organisations are currently doing so successfully today.
Read more blogs from us: https://www.nufuture.co.uk/cm/blogs
Connect with nufuture today to find out how we can help you.
Manchester phone: 0161 791 0237