Data analytics is becoming increasingly important in the recruitment process. With large amounts of data available on candidates, recruiters can use analytics to identify the best potential hires for their organizations. Analytics helps improve recruitment efficiency, quality of hires, and long-term retention.
How is recruitment analytics used?
There are several key ways data analytics is applied in recruitment:
- Identifying high potential candidates – Data from previous hires,internal employees, and external databases are analyzed to build profiles of ideal candidates. Recruiters use these profiles to proactively source suitable candidates.
- Assessing candidates – Analytics helps assess candidates beyond just resumes. Online assessments, video interviews, and other data points are used to evaluate candidates’ skills, cultural fit, and future performance.
- Predicting hiring metrics – Data on previous hires is used to predict key hiring metrics like time to hire, cost per hire, and retention rate. This allows recruiters to set realistic goals.
- Forecasting hiring needs – By examining factors like business growth, employee turnover, and retirement eligibility, data models can predict hiring needs across the organization.
- Improving sourcing strategies – Analytics identifies the best sources for qualified candidates, like specific job boards, social media platforms, and employee referrals.
- Optimizing job postings – Data on previous job post views, applications, and clicks helps recruiters refine job post language, structure, and targeting to attract more suitable candidates.
- Streamlining screening – Analytics tools like chatbots, online assessments, and resume parsers help automatically screen applicants against required qualifications.
- Identifying biases – Data is analyzed to detect biases in existing hiring processes related to factors like gender, ethnicity, age, etc. This allows organizations to address these biases.
- Tracking metrics – Key recruitment metrics around cost, time, quality of hire, and diversity are tracked using data analytics to identify areas for improvement.
What are the benefits of using recruitment analytics?
Using data analytics in recruitment provides the following key benefits:
- Improved efficiency – Analytics streamlines recruitment processes like sourcing, screening, and candidate relationship management to make hiring faster and less costly.
- Better quality of hire – Data insights help attract, assess, and select candidates that are a strong cultural and skills match for the role and organization.
- Reduced bias – Analytics identifies biases that can skew hiring decisions, leading to more objective, merit-based recruitment.
- Data-driven decisions – Recruiters rely on data insights rather than gut instinct when assessing potential candidates and making hiring decisions.
- Proactive hiring – Predictive analytics enables organizations to forecast hiring needs and gaps proactively.
- Improved long-term retention – With better cultural and skills fit, employees hired using data analytics are more likely to thrive and have longer tenures.
- Enhanced recruiter skill – Data provides recruiters with tangible insights to improve their sourcing, screening, interviewing, and closing skills.
What kinds of recruitment data is analyzed?
Recruitment analytics examines both internal and external data sources. Some key data types include:
- Previous hiring data – Information on past job postings, candidates hired, time to fill roles, etc.
- Current employee data – Performance ratings, retention rates, engagement levels, and other workforce metrics.
- Job posting data – Number of applicants, views, clicks, applications submitted, etc. for current openings.
- Sourcing data – Application and hire volumes from different sources like job boards, social media, and employee referrals.
- Candidate data – Information submitted by candidates via resumes, applications, prescreening surveys and assessments.
- Screening data – Notes, scores, and assessments gathered during recruitment stages like phone screens, interviews, tests, and reference checks.
- External labor market data – Insights on availability, costs, and competitiveness of talent from various industries and geographies.
- Business data – Company growth plans, budgets, turnover metrics, and other internal data relevant to recruitment.
What are some recruitment metrics that can be analyzed?
Recruiters can analyze a wide range of recruitment metrics using data analytics. Some examples include:
Metric | Description |
---|---|
Time to hire | The average number of days between job posting and offer acceptance. |
Cost per hire | The average expenditure involved in making each new hire. |
Source of hire | Tracking the recruiting sources generating the most applicants, interviews, and hires. |
Applicants per opening | The average number of applicants attracted for each job posting. |
Offer acceptance rate | The percentage of job offers accepted by candidates. |
Application completion rate | The proportion of applicants successfully completing all application steps. |
Assessment/screen pass rate | The percentage of applicants clearing various screening stages. |
Quality of hire | Metrics indicating new hire on-the-job performance, cultural fit and retention. |
Recruiter productivity | The number of hires and time-to-fill per recruiter. |
What are some examples of using data analytics in recruitment?
Here are some examples of how companies leverage recruitment data analytics:
- LinkedIn uses data science algorithms to recommend relevant job openings to its users based on their profile, network, and activity. This helps connect quality passive candidates with relevant opportunities.
- Procter & Gamble developed an analytics model using internal hiring data that identifies top candidates likely to be strong performers and long-term fits. Its recruiters use this to focus sourcing efforts.
- Hilton filters hospitality job applicants through a data model that tests for empathy and personality traits that indicate likelihood to deliver superior customer service.
- Netflix built an analytics tool that highlights biases in reviewing written applications, helping recruiters become aware of unconscious biases.
- Cisco’s data analysis showed referrals were a top source of hire. It expanded its employee referral program and built a customized referral portal to attract more candidates.
What are some data analysis techniques used in recruitment?
Recruiters rely on a variety of data analytics approaches, including:
- Descriptive analytics – Used to summarise and categorise historical recruitment data on key metrics like quality of hire, time to fill, and cost per hire.
- Diagnostic analytics – Examines recruitment data to understand why certain outcomes occur and identify root causes.
- Predictive analytics – Uses statistical models and machine learning algorithms to make data-driven forecasts about future recruitment needs and outcomes.
- Prescriptive analytics – Goes beyond predicting outcomes to recommending optimal recruitment actions and decisions.
- Text analytics – Uses natural language processing to extract key insights from unstructured resumes, notes, and comments on candidates.
- Statistical analysis – Applies statistical techniques like correlation, regression, and hypothesis testing to recruitment data.
- Visualization – Transforms recruitment data into interactive dashboards, charts, and graphs to provide insights.
What recruitment tasks can be automated using data analytics?
Many repetitive, manual recruitment tasks can be automated using algorithms and data science techniques, including:
- Resume screening
- Matching resumes to job descriptions
- Shortlisting candidates that meet qualification criteria
- Scheduling interviews
- Sending rejection emails to unqualified applicants
- Ranking and scoring candidates
- Transcribing interview notes and comments
- Populating applicant tracking systems
- Responding to frequent applicant queries
- Analyzing social media profiles
This enables recruiters to focus their time on higher-value strategic tasks like candidate relationship building, interviewing, negotiation, and closing.
What are some tools used for recruitment analytics?
Top tools and technologies used by recruiters for data analytics include:
- HRIS and ATS like Workday, iCIMS, Jobvite – Contain applicant tracking data
- Talent intelligence software like Entelo and TalentNeuron – Aggregate external talent data
- Assessment platforms like HireVue and Modern Hire – Generate candidate testing data
- Recruitment marketing tools like SmashFly and Yello – Provide sourcing insights
- Video interviewing software like HireVue and SparkHire – Analyze candidate responses
- CRM systems like Bullhorn and Avature – Manage candidate relationships and pipelines
- Analytics solutions like Google Analytics and Looker – Turn recruitment data into insights
- Machine learning libraries like TensorFlow and Scikit-Learn – Power predictive models
- Data visualization tools like Tableau and Microsoft Power BI – Create interactive reports and dashboards
What are some potential challenges with using recruitment analytics?
While analytics offers many benefits, recruiters should also be aware of some potential pitfalls:
- Overreliance on data over human judgement during hiring.
- Unethical use of candidate data and analytics.
- Biased algorithms that replicate existing prejudices in hiring decisions.
- Poor quality and incomplete data rendering analytics meaningless.
- Viewing recruitment as a metrics optimization exercise rather than human-centered process.
- Breaches of candidate privacy and confidentiality.
- Assuming correlation equals causation without statistical validity testing.
- Lack of analytical and interpretation skills among recruiters.
How can recruiters use analytics effectively and ethically?
Recruiters should adopt the following practices for effective, ethical usage of data analytics:
- Balance data-based insights with human judgement during candidate selection.
- Test algorithms extensively for bias before deployment and regularly audit them.
- Anonymize candidate data and obtain consent wherever possible.
- Align analytics with organizational values and culture.
- Ensure adequate data accuracy, quality control, and security.
- Provide transparency to candidates on how their data is used.
- Conduct statistical testing like significance testing when deriving insights.
- Invest in upskilling recruiters on using data responsibly.
Conclusion
Data analytics is transforming modern recruitment. By harnessing the wealth of talent data available, organizations can identify better-matched candidates efficiently, reduce biases in hiring, and boost long-term retention. However, recruiters should take care to use analytics in an ethical manner by balancing data with human evaluation, testing for algorithmic bias, and securing informed consent from applicants wherever possible. With the right focus on people as well as data, analytics can be used to create an optimal, candidate-centric recruitment process.