If your organization does not employ predictive analytics in recruitment you’re missing out on a virtual gold mine of data. In today’s talent acquisition market, a data-driven approach to recruiting is essential.
Especially if you want to employ the finest applicants regularly and effectively. So, we’ll teach you how to get started with it in this article. Using data and predictive modelling, recruitment analytics allows companies to employ more quickly.
The methodologies that go into analytics are as varied as the tools accessible to the recruiting sector. Predictive analytics has been demonstrated to save up to 23 hours per week in manual work, mostly of applicant shortlisting and pre-screening.
What is Predictive Analytics in Recruitment?
The technique of leveraging previous data to develop predictions about future recruiting actions and applicants is known as predictive analytics.
It all comes down to gathering and analyzing data using statistics, machine learning, and modeling approaches to best forecast what could happen in given circumstances.
The collecting of massive amounts of data from several platforms enables this. Because these vast data sets exist, complicated predictive algorithms may be used to anticipate future outcomes.
An ATS, for example, can collect information about a candidate from external sources such as their CV, cover letter, social media, and so on.
It varies from standard hiring methods in one important manner. Instead, the more usual “go with your gut” approach allows for virtually complete impartiality in your decision-making.
Recruiters may remove their personal biases from the process by basing recruiting decisions mainly on data and algorithm-based projections. As a result, they may provide more consistent and superior results.
Benefits of Recruitment Predictive Analytics
- Gives objective insight into the effectiveness and worth of your efforts through a recruitment funnel.
- Allows you to keep track of high-potential individuals and actively develop them as prospective recruits.
- Allows you to build a comprehensive talent pool or a permanent record of all applicants to which you may refer back.
- Enables you to gain insight into and enhance your employment and hiring processes.
- It also enables recruiters to make proactive hiring decisions and make personnel choices more quickly and effectively.
- Allows you to forecast which prospects will perform well and which would be poor recruits.
How to get started with predictive analytics?
Let’s talk about how to use predictive analytics in your own firm now that you’re aware of what’s feasible and the benefits it may give.
Step 1: Choose your tech stack
There are many HR systems and data analytics solutions to pick from. On the market, Your predictive analytics software stack should ideally be linked to your applicant tracking system (ATS), or wherever you gather and store candidate data.
Choosing an ATS with predictive analytics capabilities allows you to keep all of your raw and output data in one place. Whatever platform you select, be sure it allows you to track all key KPIs efficiently.
Step 2: Choose your KPIs
The next step is to work with your team to establish what you want to improve and which recruiting metrics are most critical to attaining that objective. Creating a recruiting matrix is a fantastic approach to get started with this.
These are basic spreadsheets that break down your most important areas for improvement and the key metrics that pertain to each. Then they weigh the value of individual measures vs. others, and they do it to determine high, medium, and low priority KPIs.
Step 3: The predictive analytics lifecycle
Once you have your tech infrastructure in place and have worked out a plan for what to measure and why the next step is to initiate the predictive analytics lifecycle. This cycle consists of the following stages:
- Collecting data
- Pre-processing the data
- Establishing an analysis type
- Training the model
- Performing predictions
- Acting on insights
The important parts for you to focus on are collecting the data, making sure it’s clean and accurate, and acting on the predictions and insights from your platforms.
Your tech stacks will be able to correctly manage the grunt work of processing data, identifying patterns, and reporting on potential improvements or flaws. After that, keep an eye on your data inputs and predicted outputs.
Step 4: Set up reporting with analytics tools
After you’ve started your predictive analytics engine, you’ll need to establish a recruiting KPI dashboard to track your progress. This data dashboard should be simple to use and contain only the most important data.
One thing to consider at this point is the simplicity with which you can report on your KPIs. Your boss or executives will most likely want to know what the metrics indicate. Also, they want to know the outcomes of various process adjustments.
Step 5: Continuously track the success
Seeing and reporting on KPIs is useless until you improve who you hire and how you hire. Overall, you have to track the hire performance. It also doesn’t signify anything if you configure it once and never modify your recruiting data or reporting metrics. Tracking regularly will help you improve the quality of hire.
Inherently, recruitment data analytics is a game of change and gradual development. It makes predictions based on the data it has access to and also the results of your actions. Hence, to get the most out of these platforms, you should follow their advice regularly.
As previously said, the data you feed it and the actions you take with the results will determine how effective your predictive analytics model is. When used correctly and regularly, predictive analytics in recruiting by recruiting teams may significantly impact the quality of your employees. Consequently results in a better recruitment process.
Hiring managers may use data analytics to understand better candidates’ talents and appropriateness for certain firms and job posts. It can also put them in the direction of the top applicants.
At Thinklytics, we help you utilize predictive analytics in your recruitment journey to receive better outcomes. Hence, helping you in finding the top talent for your company.