Are you just tracking basic hiring numbers? Or are you truly leveraging the power of recruitment data?
In this article, freeC Asia will guide you through different levels of recruitment analytics and explore effective solutions to maximize their potential.
We will break down recruitment analytics from two key perspectives: a practical three-level approach and a more detailed five-level model.
While both offer valuable insights, freeC will focus on the three-level framework, as it provides the most practical and applicable structure for businesses looking to enhance their hiring process.
What is Recruitment Analytics?
As we discussed in our comprehensive guide to recruitment metrics, recruitment analytics is the process of collecting, analyzing, and interpreting data related to your hiring process. It’s about moving beyond gut feelings and making data-driven decisions to improve efficiency, quality of hire, and overall recruitment ROI.
There are many more insights and information about Recruitment Analytics in the that article, you shouldn’t skip it.
And now, we’re gonna tap into the recruitment analytics levels.
3 Levels of Recruitment Analytics – A practical approach
This 3-level model provides a clear and actionable framework. For easy absorption and application, you think of it as a progression, with each level building upon the foundation of the previous one.
Level 1: Operational reporting
Operational reporting focuses on tracking the core, day-to-day metrics that provide a snapshot of your current hiring activities. This level is primarily descriptive, telling you what is happening in your recruitment process.
The primary benefit of operational reporting is that it provides a baseline understanding of your recruitment performance, helps identify obvious bottlenecks, and allows you to track progress towards basic hiring goals.
Here are some key metrics that you can track in this level:
- Time-to-fill
- Time-to-hire
- Cost-per-hire
- Source of hire
- Applicants per opening
- Hiring manager satisfaction
- Number of interviews conducted
For example, you might generate a report that:
- Showing the average time-to-fill for open positions over the past quarter.
- Track the number of applicants received from various job boards.
- Calculate the cost-per-hire for different departments.
Talking about the tools, most Applicant Tracking Systems (ATS) offer built-in reporting capabilities for these basic metrics, and spreadsheets can also be used, although they become less manageable as data volume grows.
Level 2: Advanced reporting
Advanced reporting takes you beyond simple tracking and delves into the why behind the numbers. This level involves combining data from multiple sources to gain more nuanced insights and identify specific areas for improvement within your recruitment process.
Some key metrics including:
- Candidate experience
- Cost of recruitment channels
- Cost per qualified candidate
- Recruitment funnel effectiveness
- Metrics related to employer branding
For instance, to apply in practice you might:
- Analyze candidate survey data to pinpoint pain points in the application process.
- Compare the conversion rates of different sourcing channels to determine which are the most effective and cost-efficient.
- Calculate the cost per qualified candidate to optimize your recruitment budget allocation.
Your notice about the tools is that, this level typically requires integrating data from your ATS, candidate relationship management (CRM) system, survey tools, and potentially other HR systems. Business intelligence (BI) tools like Tableau or Power BI can be particularly helpful for visualizing and analyzing this more complex data.
Level 3: Strategic & Predictive Analytics (Future of hiring)
Strategic analytics and predictive analytics helps you move beyond analyzing past performance and begin to use statistical analysis and modeling to predict future outcomes and inform long-term strategic decisions. This level shifts the focus from “what happened?” and “why did it happen?” to “what will happen?” and “what should we do?”.
Key things you can forecast in this level might include:
- Predicting time-to-hire
- Forecasting cost-per-hire=
- Identifying ideal candidate profiles
- Optimizing for A/B testing
- Segmenting candidate audiences for more targeted advertising campaigns
For detailed examples:
- You can use historical data combined with machine learning techniques to predict the time-to-fill for a specific role, taking into account factors like seasonality and current market conditions.
- You might develop a model to identify the characteristics of high-performing employees and using that to screen candidates more effectively.
- Or run A/B tests on different job descriptions to see which version attracts more qualified applicants.
Achieving this level typically requires advanced analytics platforms, and potentially incorporating AI and machine learning capabilities. Statistical software packages like R or Python may also be employed.
This analytics level can bring to you some significant benefits, such as: enabling proactive workforce planning, providing a competitive advantage in attracting top talent, and allowing for truly data-driven optimization of your overall recruitment strategy.
A more granular view: The 5 levels of Recruitment Analytics
While the 3-level model provides a practical framework, some experts use a more granular 5-level model, often seen in academic or highly technical contexts.
This model breaks down the concepts further, but it’s important to remember that both 2 models are essentially built upon the same foundation.
Level 1: Descriptive Analytics
Descriptive Analytics corresponds closely to Operational Reporting in the 3-level model.
Descriptive analytics provides a basic understanding of your recruitment activities but doesn’t dig deeper into the reasons why things happened.
Level 2: Diagnostic Analytics
Diagnostic Analytics aligns with the core principles of Advanced Reporting in the 3-level model. It goes a step beyond simply describing what happened and begins to investigate why it happened.
For example, if you notice a sudden increase in time-to-fill, diagnostic analytics would help you pinpoint the cause – perhaps a bottleneck in the screening process, a slowdown in interview scheduling, or a change in the external job market.
This level involves combining data from different sources to gain a more comprehensive understanding, then analyzing data to identify trends, patterns, and anomalies that can explain past performance.
Level 3: Predictive Analytics
Predictive Analytics is a key component of the Strategic & Predictive Analytics level in the 3-level model.
At this stage, you begin to use historical data and statistical modeling to forecast future outcomes. This allows you to move from a reactive to a proactive approach to recruitment.
Examples include:
- Predicting the number of hires you’ll need in the next year based on projected business growth.
- Estimating the likelihood of a candidate accepting a job offer based on their profile and the offer details.
- Or forecasting the future cost-per-hire based on current trends and anticipated changes in the market.
Level 4: Prescriptive Analytics
Prescriptive Analytics builds directly upon Predictive Analytics. Not only does it forecast what might happen, but it also recommends specific actions to take to achieve desired outcomes.
This level leverages sophisticated algorithms and optimization techniques to guide decision-making.
For instance, if predictive analytics forecasts a shortage of qualified candidates for a particular role, prescriptive analytics might suggest increasing investment in specific sourcing channels, adjusting compensation packages, or streamlining the interview process to improve the candidate experience and increase offer acceptance rates.
Level 5: Cognitive Analytics
Cognitive Analytics represents the most advanced level of recruitment analytics, incorporating AI and machine learning to automate tasks, personalize the candidate experience, and continuously improve the recruitment process.
This level goes beyond simply recommending actions, in some cases, it can execute those actions autonomously. Some practical applications you can think of are:
- Using AI-powered chatbots to screen candidates and answer their initial questions
- Employing machine learning algorithms to identify ideal candidate profiles from vast datasets and match them to open positions.
- Using natural language processing (NLP) to analyze candidate resumes and identify key skills and experience.
freeC recommendation
While the 5-level model provides a more detailed breakdown, the 3-level model is generally more practical for most organizations to implement. So our suggestion is you ought to start with the 3-level framework and gradually build your capabilities over time.
Recommendations from freeC
While a 5-level model provides a more detailed analysis, the 3-level framework remains a more practical approach for most organizations when implementing recruitment analytics.
Therefore, we recommend starting with the 3-level model and gradually enhancing your analytical capabilities over time.
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