Here are some data-driven recruitment examples of high recruitment volume with AI


Data-driven recruitment makes objective hiring decisions based on various data sources. These are some of the examples that generate data i.e. standard resume screening, interviews, and job offers. Data-driven recruitment process monitors the hiring process’s effectiveness using various hiring indicators and applies the findings to improve recruitment. Furthermore, gathering and evaluating data during recruitment helps you choose the best candidate for the job by removing biases and assumptions.


A critical recruitment KPI and an excellent example of using data to inform decisions are tracking the sources from which you receive applications. For example, you may follow the success of various channels, including job boards, advertisements, agencies, social media, and your career website, and determine your cost per hire as a result.

You should invest more money in the source that produces the highest quality prospects. For example, if you receive more suitable applicants from quality job boards than from Google or Facebook advertisements, you may want to increase your budget for them. Or, if LinkedIn isn’t providing you with qualified candidates, you should cut your spending there or remove this from your plan.

Central place.

1. Selection process

The selection process generates much data when recruiters select the best-suited candidates for a particular position.

These are some of the data generated during the selection process.

Verifiable and relatively accurate historical data from the job seekers like educational qualifications, work experience, and licenses. Scores from tests and examinations that measure the candidate’s skills, competencies, personality traits, and work preferences scores of the individual the individual’s responses to questions related to work experience, interpersonal skills, and cultural fit

2. Candidate experience

The applicant’s overall opinion of the employer’s hiring and onboarding procedure is also a data point that can improve the organization’s hiring process; some other data points are time invested. It considers the amount of time invested in each stage of the hiring process, from initial contact to screening, interview, and job offer, to welcoming the new hire into the company.

3. Recruitment planning

You can decide what to do with your recruitment data by mining it. These are some examples of typical recruitment challenges that relevant data will help you identify and find solutions for:

If you need to cut your expenses, focus on cost associated KPIs like cost per hire, job advertising performance, or the number of applications per source or channel.

To speed up your recruitment process, concentrate on speed-related metrics like time to fill, time to hire, and time to productivity.

If you want to have more highly-skilled employees, pay attention to quality-related metrics like source of hire, candidates per hire, new hire turnover, and new hire retention rate.


In inclusion, the data generated when hiring can provide insights on improving the recruitment process and help them track and measure KPIs associated with recruitment processes.

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