Predictive Analysis: Buzz Word or Does it Really Improve the Recruitment Process?

Recruiters have used data to refine their approaches to hiring, recognizing, and promoting employees over the years. HR managers may now make effective use of advanced resources like Predictive Analytics because of the rise of data-driven HR planning.

Successful examples of companies incorporating recruitment and predictive analytics into their hiring procedures include Google, Deloitte, and Cisco. Predictive analysis can help in many ways, including providing clearer insight, keeping tabs on promising employees, and building a more robust pool of talent.

Here, find out how predictive analysis is helping 50% of companies worldwide enhance their hiring efficiency.

What is Predictive Analysis?

A subset of data analytics known as "predictive analytics" aims to build models from collected data in order to make specific predictions about the future. While it's impossible to predict the future with certainty, predictive analytics can shed light on what to expect in light of historical patterns.

Having the ability to develop such predictions allows you to cut down the time spent on hiring and find top talent from the piles and piles of candidate data. This helps businesses quickly find the best people for open positions and make them an offer before their competitors do.

Time-to-hire and quality-of-hire are boosted for businesses, while applicants have a more positive experience and are more likely to accept offers as a result.

Why is Predictive Hiring The Way Forward?

Most people know that it can take a lot of work for recruiters to quickly and effectively screen resumes. Statistics show that when a post opens up in a corporate world, there are often around 250 applicants applying for that one post. And recruiters often have multiple openings to manage at once. Imagine managing 750 candidates and looking through their data if three positions are open.

Sounds terrifying, right?

The massive amount of energy wasted by recruiters manually sorting through unfit CVs is a direct result of the fact that the vast majority of applicants do not meet the fundamental job requirements. One of the ways in which predictive analytics can make the hiring process easier is by enhancing this very area.

Nonetheless, there are additional upsides to integrating predictive hiring into your recruitment strategy:

1) Predicting Performance

2) Strengthening the Hiring Process

3) Keeping employees from leaving

4) Keeping your hiring practices uniform

5) Intensifying efforts to streamline hiring

6) Enhancing your employer brand and providing a positive experience for job seekers.

How Can Recruiters Leverage Predictive Analysis To Prevent Recruitment Losses?

Hampers The Employee Turnover

Studies demonstrate that it is possible to avoid 75% of the causes of employee turnover.

Companies can use predictive analytics to foresee the financial consequences of staff resignations and retirements, allowing them to allocate resources better. Predictive analytics can aid in the development of focused retention and hiring strategies by providing insight into the factors and elements that may lead to each prospective resignation from the workforce.

Additional read: Why Candidates Reject Job Offers? 10 Reasons You Must Know As a Recruiter or TA

Predictive Hiring

A company's people are its most valuable resource; thus, selecting the best candidates requires careful consideration and the use of advanced predictive analysis tools.

Predictive hiring software analyzes a company's existing workforce to determine which applicants would be the best fit for open positions. As a result of AI and predictive analytic algorithms, only the most qualified prospects are presented to the hiring manager for further consideration.

The hiring manager and the business will save money and time as a result of this. Prototypes that incorporate many indicators, such as production efficiency, worker lifecycle information, attrition statistics, and engagement survey responses, can also be used to forecast an applicant's future success.

Reduced Time to Hire

Both the time to fill and the time to hire are critical criteria that greatly affect the recruitment process. Time to fill is the amount of time it takes from when a job is posted until it is filled. Time to hire is a metric used to measure how long it takes to hire a candidate from initial contact until acceptance is provided.

Because of predictive recruiting, HR departments can now do thorough analyses to figure out how long it will take to fill a position and make the necessary changes. Because of this, the hiring process is more refined and streamlined than ever before. This makes it easier to find the best candidates quickly.

Better Talent Sourcing

Streamlining procedures, closing knowledge gaps, and reducing complexity are all priorities for businesses. Human resource managers use employee data from social media, health markers, and even stock changes to make a prediction about who is most likely to stay stop working soon.

Additional read: 20 Ultimate Candidate Sourcing Tips For Quality Hire

How To Start With Predictive Analysis?

On average, you cost the company 30% of the employee’s first-year expected salary by making the wrong recruiting decisions. In the case of bad hiring, for example, if the company hires someone with a 50,000$ pay package, the average cost to that firm will be $15,000 dollars.

So, how do we avoid this?

By using the techniques, we are going to tell you right here!

Select The Technologies That Will Power Your Business

Predictive analytics tools should be integrated with your applicant tracking system or human resource management system. As in where you keep records of potential employees. Having the ability to perform predictive analytics inside of an ATS is a great time saver, as it allows you to store both your raw data and your final results in the same place.

For instance, RippleHire’s ATS lets you keep track of all of your applicants in one central location and use powerful tracking, reporting, and analytics tools to draw meaningful conclusions from your data.

Shortlist Your KPIs

The next stage is to have a group discussion on what needs fixing and which recruiting metrics are most crucial to getting the job done. As previously noted, analytics platforms will only collect the data you specify. To narrow in on the most important key performance indicators, though, you'll need to have some sense of what it is you're seeking to enhance.

Establishing a recruit matrix is a useful first step in this process. These are basic spreadsheets that list your top improvement areas and the most important metrics related to those areas. High, medium, and low-priority KPIs can then be set by assigning relative relevance to the various measures.

Here are some examples of possible focus areas and corresponding key performance indicators:

1)Source Quality

2) Time to Hire

3) Cost per Hire

4) Interviews to Hire

5) Offer Acceptance Rate

6) First-Year Turnover Rate

7) Performance and engagement scores

8) Qualified candidates per position

At this point, you should have a selection of key performance indicators (KPIs) that can assist you in anticipating future results.

Time To Draw Your Predictive Analysis Lifecycle

The next step in the predictive analytics lifecycle is to put your infrastructure in place, as well as your strategy for measuring what and why.

In a predictive analytic lifecycle, you'll find the following stages:

1) Define the requirements

2) Explore the data

3) Pre-processing (cleaning) the data

4) Establishing an analysis type

5) Develop the model

6) Deploy the model

7) Validate the results

8) Acting on insights

You should prioritize data collection, data quality, and the implementation of platform-generated forecasts and insights. This happens when the right key performance indicators (KPIs) are used, and only exact and relevant information about applicants and processes is used as input.

Set Up Tracking and Reporting Using Recruiting Analytics Tools

Following the activation of your predictive analytics machine, the next stage is to construct a hiring KPI dashboard to evaluate the outcomes. This display should be easy to use and show only the most important information about your key performance indicators (KPIs).

Keep Track By Continuously Measuring Success

However, if you don't use this information to change who and how you recruit, seeing and reflecting on KPIs is meaningless, setting it once and then never adjusting it based on new data from recruitment or new metrics from reporting is likewise pointless.

Using predictive analytics in hiring is fundamentally a game of iteration and refinement. It analyzes the information and results of your actions to make predictions. You can get the most from these platforms if you follow the advice they give you on a regular basis, try out some experiments with different approaches and see what happens, and make sure your data is representative of the real world.

Bottom Line

Recruiters should integrate predictive analytics into their comprehensive technological solution and talent acquisition strategy. We would end the discussion by saying that we should use the strength of data to recruit better and faster to get an edge in a competitive talent market.