In 2024, recruiters have a wealth of useful data at their fingertips.
For years, ‘gut feeling’ was all we had to go off when it came to strategic decisions — but instinct alone can’t compete with cold hard data.
And with the sector increasingly shifting towards data-driven recruitment, those who fail to effectively harness analytics face being left behind.
Indeed, recruitment analytics themselves are evolving.
In this guide, we’ll take you through how to establish effective recruitment analytics, from laying solid foundations to gaining stakeholder buy-in.
Understanding the Need for Recruitment Analytics
With the recruitment market struggling in the economic downturn, it’s more important than ever to achieve optimal performance through data insights.
But it’s not always as easy as it sounds.
Recruitment agencies currently struggle with a lack of visibility into their data and insights into their performance. This is largely due to outdated systems, from recruitment CRMs to spreadsheets, and overly complex tools with steep learning curves.
Not only does this mean your decisions aren’t backed up by data, but it’s a huge time sink, with hours wasted building manual reports and attempting to find significant insights across a wide-ranging tech stack.
However, by leveraging recruitment analytics, agencies can significantly enhance their recruitment strategies, hiring decisions, and business development. Analytics empower you to collect the relevant data for the hiring process and organise it more productively and efficiently.
There are seemingly endless benefits, from reduced onboarding costs and less time to hire, to improved quality of hire and enhanced ROI.
When do you need to start your recruitment analytics journey?
Yesterday.
The development of tech and software surrounding recruitment analytics means more businesses, agencies, and recruiters can tap into this data and seamlessly make analytics a part of their processes.
Simply put, professionals can’t afford to avoid data analytics in recruitment any longer.
So what is recruitment analytics in 2024?
The Evolution of Recruitment Analytics: Old vs. New Approaches
Well into the new millennium, recruiters depended on classified adverts in newspapers, job boards, word of mouth, and headhunting to tap top talent and make optimal hiring decisions.
The advent of the internet and social media were game changers, but as a sector, recruitment was slow to capitalise on the data this online shift supplied.
It’s not that recruitment analytics are a new phenomenon; recruiters have always attempted to record and measure basic analytics. But this process used to take a huge amount of time (and money) — and it was nearly impossible to achieve precision. These methods were vulnerable to human error, while old-fashioned interfaces meant attempts to spot patterns and trends in data could be nightmarish.
But now, an intelligent approach to data analysis offers you endless opportunities to improve all your recruitment processes, quickly and painlessly.
It’s now possible to generate data-based forecasts such as projected time-to-fill, the probability of interview acceptance, future candidate performance, and candidate tenure. With data backing you up, it suddenly becomes much easier to uncover top talent, improve your processes, and manage stakeholder expectations.
You can track niche data points too. Analyse candidate and employee data to pinpoint hiring bottlenecks, identify the qualities your best hires bring to the job, or create a more cost-effective recruitment process.
Ultimately, access to this data and cutting-edge analytical tools empowers you and your team to accurately measure your recruiting efforts and to make effective, data-driven strategy decisions.
Laying the Foundation for Recruitment Analytics
Before diving headfirst into the world of data-driven recruiting and HR analytics, you must lay robust foundations.
The recruitment analytics tools you use must:
- Have the capabilities to track your key metrics
- Display data in a transparent, easy-to-understand way
- Be user-friendly and match the technical skills of everyone on your team
- Include integrations that work with other tools and systems you are currently using or plan to use
Choosing the Right Recruitment Analytics Tools For You
There are several factors to consider when it comes to identifying the best analytics tools for your purposes.
1. Real-time Data
Whether you’re in a strategy meeting, reporting to the board, or conducting one-to-ones with your team, there’s little point in working with out-of-date information.
Indeed, using old data can make it very difficult to manage stakeholder expectations and to properly understand how your team is performing.
In a worst-case scenario — it can lead to bad business decisions.
The best tools in 2024 all offer real-time data, accessible at the touch of a button.
Leading recruitment analytics software OneUp, for example, empowers users to report on real-time data, complete with beautiful dashboard visualisations. This enables recruitment leaders to drill down into the activities behind the metrics — as they’re happening.
What’s more, with your real-time data from all your systems in one place, it’s easy to visualise performance for any metric over different periods.
2. Custom Dashboards
The ability to build custom dashboards is non-negotiable in 2024.
No two recruitment teams are the same — and the metrics you’ll track reflect this.
Building custom dashboards for your specific metrics means you can focus your team on the targets that matter and get an overview of performance at a glance. What’s more, you’ll always have key figures on hand in meetings, complete with clear, illustrative visualisations to support your point.
With older recruitment analytics tools, painstakingly building each custom dashboard could take a huge amount of time, not to mention advanced technical skills. But with tools like OneUp, you can create reports in minutes rather than hours — freeing you up for important tasks, such as data analysis.
3. Automation
As the old saying goes, time is money.
But thanks to the dawn of AI, it’s never been so easy to recoup it.
Automated tools are a must-have in 2024: they eliminate the time-consuming manual work of collating, measuring, and analyzing data, leaving you to get on with the activities that make a difference.
If you’re sinking your time and your teams’ time into tedious tasks such as data entry, you’re already losing out. Why? Today’s teams are increasingly turbocharged by automation.
Imagine sitting down at your desk to start your day, and finding all the data you need already in your inbox.
OneUp makes this a reality.
You can also automate your daily activity update, weekly summary, or monthly leadership report, as well as progress updates.
4. Team Motivation Tools
Once you’ve defined your metrics and set your team targets, the next step is engaging your employees with their performance.
The importance of this should not be underestimated.
Traditional barriers to achieving employee engagement with data included a lack of data transparency, a lack of access to data, and a lack of analysis skills.
By employing modern user-friendly software, this obstacle becomes obsolete.
Some recruitment analytics tools such as OneUp go the extra mile when it comes to engaging your team. Tailor custom dashboards to track individuals’ progress towards their targets, and then give them access. Run your one-to-ones and team meetings based on real-time progress against targets. And, using OneUp, set your rep's missions, challenges, and targets that focus them on the metrics that matter and align with your business goals.
By being transparent with your teams about their targets and progress, you can empower self-management and drive accountability.
Best Practices For Data Collection and System Integration
1. Begin With Business Outcomes
To avoid drowning in data, and confusing your insights, it’s important to ensure your analytics efforts are aligned with your agency’s goals — and those of your clients.
Consider the following:
- What are your agency’s goals? For example, are you in growth mode? Do you want to focus more on recruiting into a specific sector? Do you want to position yourself as an agency that delivers exclusive candidates, or has a super-fast turnaround rate?
- How does your recruiters’ performance align with your business goals? What areas do your recruiters need to improve in? Where are they strongest?
- Typically, what are your client’s overarching business goals and priorities? How can progress towards these goals be tracked, and how well are you currently meeting them?
When reflecting on the above points, it’s vital to interview senior stakeholders in the agency, as well as recruiters. Don’t forget to look at client feedback too. This helps to ensure you wholly understand the goals, priorities, and pain points that you can address with recruitment analytics.
Now decide precisely what data must be collected to track progress towards these goals and objectives, and what data you need to collect to answer your burning questions.
An example.
If one recruiter’s job offer acceptance rate is consistently low compared to recruitment industry standards, this suggests a communication breakdown.
Is your recruiter failing to fully investigate client briefs? Are their job descriptions unclear or missing important information? Maybe your recruitment agents aren’t asking the right questions of their candidates? Or simply failing to manage their expectations?
Perhaps they’re even putting candidates up for roles who they know won’t be the best fit because they’re struggling to fulfil tough briefs?
In this scenario, you’ll first want to establish a job offer acceptance target for the recruiter in question and set up tracking on this metric.
Next, implement various performance tracking measures along the lines of communication, such as the length of recruiter-candidate calls or the number of calls recruiters make to candidates ahead of an interview. You may also want to track the quality of hire.
The aim is to identify the issue within the workflow that is causing this low job offer acceptance rate — and then take action to rectify it.
2. Audit Your Data Sources and Implement Data Quality Management
Where is your data coming from? And how is it processed?
Data quality needs to be evaluated before integration, during the integration, and after the integration is complete.
When integrating your data, it’s vital you truly understand the data you’re integrating – and are confident in its quality.
To detect any flaws in the system before you begin your recruiting analytics journey, it’s essential to conduct a full audit of your data sources and workflows. Poor-quality data that is inaccurate, incomplete, or inconsistent needs to be highlighted for further action. Data that can’t be fixed must be deleted.
In some cases — such as where data collection is only just beginning or you’ve implemented robust data governance from day one — you should be able to tackle this task yourself.
In more complex cases, your Operations team should be able to help with this process. If your agency doesn’t have access to an Ops team, it may be worth investing in a data consultant to help you through this exercise.
Remember: where employees are involved in data entry or analysis, implement training to ensure human error is at a minimum.
3. Consolidate Your Data Processes
For many businesses, data has not historically been a priority.
This often means that the data processes that are in place are somewhat messy.
What does this look like?
Data is stored across a variety of different tools, platforms, and spreadsheets that only a handful of people have access to. Different recruiters track overlapping data from different, flawed sources. Recruiters fail to follow data entry instructions, leaving you with incomplete data sets.
You get the picture.
If this seems familiar, it’s time to burn it down and build a cohesive data workflow from the ground up. This process should be designed to track progress towards your business goals, with defined stakeholders at every stage.
4. Futureproof Your Plan
As technology continues to advance, you’ll need to add more tools to your tech stack.
Bake flexibility into your strategy during the data integration phase to allow for the addition of new sources of information and new technologies down the line. Your data integration techniques will also require regular reviews to realign them to shifting business objectives.
Think adaptable tools with plenty of integrations, and flexible frameworks.
Levels of Recruitment Analytics
So what are recruiting analytics? There are three levels:
- Operational Reporting
- Advanced Reporting
- Strategic and Predictive Data Analytics
Each level is more advanced than the last, requiring higher volumes of data and more complex data processing.
Companies new to recruiting analytics will typically start with operational reporting, before progressing to advanced, and then eventually mastering strategic and predictive analytics.
However, even the most data-literate organisation will need to continue to monitor the first two levels on an ongoing basis to get the answers they need.
Operational Reporting
It’s within operational reporting that you’ll discover the core recruitment metrics.
These could include:
- CVs sent
- Interviews Books
- Placements Filled
- Revenue
- Time to fill
- Time to hire
- Cost per hire
- Source of hire
- Quality of hire
- Hiring manager satisfaction
- Applicants per opening
Tracking these recruitment metrics is useful for measuring the performance of your strategy, your team, and individual recruiters — and subsequently taking action to improve performance.
These recruiting metrics are very simple to track in real-time with software like OneUp and rarely require any additional calculations.
Advanced Reporting
Advanced reporting requires the combination of different data sources to produce results.
One example is mapping your candidate experience. To do so, you must blend qualitative research from candidate surveys with metrics from every stage of the recruitment cycle, including the rate of drop-offs during the candidate journey, where candidates are dropping out, recruiter productivity metrics, and the retention rate for successful candidates.
Advanced recruiting metrics are useful for making data-backed recruitment decisions. Your insight from the example above may be that while the beginning and end of the candidate experience is effective, it slumps in the middle. You may then decide to install more tangible milestones and recruiter-candidate contact at that part of the journey.
Strategic and Predictive Analytics
What is predictive analytics in recruiting?
Predictive analysis can include segmentation, statistical analysis, and the creation of people models. It helps recruitment agencies forecast likely outcomes given various strategies and scenarios.
Examples can include predicting time to hire or cost per hire, auditing and optimising the recruitment process, creating the ideal candidate profile, or segmentation of candidate data.
Big Data Analytics Recruitment: Key Areas to Track in Recruitment Analytics
Wondering how to use analytics in recruitment?
To get a holistic overview of performance within your organisation and achieve optimal analytic recruitment, it’s crucial to implement tracking at every stage. What’s more, analytics tracking must be aligned with organisational goals for effective talent acquisition.
Here’s an overview of crucial analytics areas at the different stages of recruitment:
- Defining the role
- Attracting applicants
- Managing the application and selection process
- Making the appointment
- Post-appointment
Stage 1: Defining the Role
You may think you know what sort of employee your organisation needs — but predictive analytics could present a surprising picture. So start with a skills gap analysis.
You can then employ predictive analytics to build an ideal candidate profile. The tool will analyse historical data such as past employee performance to create candidate screening models and identify effective candidate sourcing models.
Predictive analytics in recruitment can also be used to plan optimal timing in the hiring process, boost candidate engagement with the process, and promote diversity and inclusion within the hiring journey.
Stage 2: Attracting Applicants
From sourcing to screening, the ‘attracting applicants’ stage of the process is one of the most prolific in analytics recruiting.
This makes it a very useful time to track individual performance metrics.
This could mean tracking the overall number of emails sent, LinkedIn messages sent, or calls made.
You can also dig deeper.
How fast are recruiters responding to enquiries from potential candidates? What is the open rate and response rate to their emails? What communication channels are they using? What percentage of the potential candidates contacted are interested in the role? Are they effectively communicating key information?
Furthermore, to optimise the ‘attracting applicants’ stage, use a solid applicant tracking system and historic data to reveal recruitment channel analytics, as well as which tactics and job ads have worked best for you in the past.
Which specific actions during the attraction phase led to a large applicant pool, high-quality candidates, a high level of recruitment funnel conversions, satisfied hiring managers, and a high retention rate?
Perhaps it’s the sourcing channel, the format of the job ad, the salary bracket, or specific keywords used within the advert?
Stage 3: Managing the Application and Selection Process
Applicants per opening, application completion rate, and recruitment funnel conversions are three useful metrics that reveal how effective this stage of your hiring process is.
Recruiting analytics examples here include recruitment channels' cost against recruitment channel conversions to determine ROI. Go granular to find out which channels are worth the investment.
There are also handy performance metrics such as several interviews conducted, and the quality of candidates. It’s also useful to look at when candidates drop out of the process. Your applicant tracking system will come in very handy here. Not only do these metrics help you stay on top of operations, but down the line, they can shape your strategy going forward.
Don’t forget to harvest qualitative data during this phase too. Send out candidate surveys for feedback on your candidate experience and how it can be improved.
Stage 4: Making the Appointment
Operational metrics to track during the making the appointment stage include:
- Time to fill
- Time to hire
- Offer acceptance rate
- Source of hire
At this stage, it’s also prudent to survey the hiring manager and successful candidate to gain their feedback on the process, including any pain points in the recruitment journey.
Stage 5: Post-appointment
The proof is in the pudding.
Stay in contact with both the hiring manager and the successful candidate after an appointment has been made to measure the success of the hire.
Time to productivity and cost of getting to optimum productivity level are two of the first metrics to track at this stage.
In the longer term, you’ll want to record first-year attrition and investigate both hiring manager satisfaction and candidate job satisfaction at semi-regular touchpoints.
After the candidate has passed their probationary period, you should also interview their manager to gain feedback on the quality of the hire.
Stakeholders and Their Interest in Recruitment Analytics
Stakeholders are all looking to get something different out of their involvement with the recruitment process.
The recruiters – Recruiters will likely be most interested in operational data that empowers them to improve their process, such as when candidates drop out of their funnel. They’ll also benefit from custom dashboards to track progress to targets such as Interviews Booked, Placements Filled, and Revenue.
Recruitment team leader – A recruitment manager will be interested in all hiring analytics, for different purposes. Tasked with making strategic decisions, optimising the recruitment funnel, and developing recruiters, they need an easy-to-interpret overview of all analytics.
Clients – Clients will want to know everything about the candidate. Where did you source the candidate? How closely do they match the ideal candidate profile? How well are they expected to perform? How long are they projected to remain with the company? Earlier on in the process, it’s also useful to keep your clients informed of predictive analytics that could be helpful to them such as the projected time to fill.
Operations Director – Your Director of Operations will need to ensure the hiring process has been fair, legal, and followed best practices. This means they need access to operational metrics such as source of hire, quality of hire, selection ratio, candidate diversity, and applicants per opening.
Agency CEO/MD – They want to know they’re getting bang for their buck, so focus here on finance-related metrics. For instance, how much is the client paying vs how much time recruiters are spending on the project? How much revenue is in the pipeline? What’s the forecast for next quarter?
Communicating With Stakeholders
Communicating with stakeholders can be tricky — they often want to know the information but don’t necessarily have time to hear it.
It’s important to pull out the stats that are relevant to each party (as described above), so they won’t be forced to wade through endless data to find the parts that interest them.
Next, adopt a belt and braces approach to communication.
Meetings with stakeholders should always be followed up with emails that contain all the key stats from your presentation.
Don’t fill their diaries with meetings either: stakeholders are typically busy and this could disengage them from the analytics. Instead, schedule quarterly or half-yearly meetings to go over key findings and communicate progress toward business goals.
Remember, you will need to communicate more frequently and in more detail with your recruiters than you would your CEO.
Make consultation and data transparency a big part of the process too.
What burning questions do your stakeholders have? What do they need to know to improve their performance?
Create custom dashboards for each stakeholder on OneUp, and make it clear your door is always open to discuss analytics.
Overcoming Common Barriers in Recruitment Analytics
Common barriers in implementing recruitment analytics include:
- Data scattered across multiple systems
- Low adoption of tools by employees
- Stakeholders do not trust data
- Lack of data literacy within the organisation
- Lack of resources
- Inaccurate, inconsistent, or incomplete data around the recruiting process
- Compliance concerns
Almost all of these obstacles can be overcome by gaining team buy-in, leaning on experts within your agency, and investing in the right software.
Getting Team Buy-in
To ensure team buy-in, give them the “why”.
Put data at the heart of your hiring process and explain why you’re doing so. Lay out exactly how using data could improve individual performance and then give them the training they need to effectively harness recruiting analytics. Emphasise that to thrive in today’s recruitment landscape, they must be data literate.
Then, consistently follow up with your team, and make using recruiting analytics an intrinsic tenet of their personal development plan.
Utilising Experts Within Your Organisation
Address any concerns around compliance with your Director of Operations and legal team.
Concerned about data security or unclean data? Take it up with your IT team.
Choosing the Right Software
It will be a lot easier to gain buy-in from around your organisation if employees like using it.
OneUp’s recruitment analytics software, for example, simplifies the analytics process with a user-friendly interface, custom dashboards that can be created in minutes, and clear data visualisations.
Business Benefits of a Well-Defined Recruitment Analytics System
How can data analytics help in recruiting?
Developing a well-defined recruitment analytics system takes the guesswork out of improving performance in the recruiting process.
The tangible business benefits include:
- Better strategic decision-making based on hard data
- More precise recruitment funnel optimisation
- Outperforming competing recruitment agencies that don’t have strong analytics systems in place
- Boosted team and individual performance
- Enhanced recruitment efficiency
- Elevated quality of candidates
- Accelerated business growth
- Improved forecasting across all key recruitment metrics
Here’s an example.
Using strategic and predictive analytics, you can build an ideal candidate profile. This is crafted from historical data surrounding the top performers you’ve placed in similar roles before — e.g. successful candidates who score highly on candidate quality, hiring manager satisfaction, job satisfaction, promotion, and retention.
By matching applicants with this data-backed ideal candidate profile, you should be able to boost the quality of candidates you’re sending to interview.
Final Thoughts
In 2024, if you’re choosing not to use big data analytics in recruitment, you’re impeding your performance and that of your team.
But by adopting robust recruiting analytics, you’re gifting yourself a superpower. After years of guesswork, it’s now easy to gain actionable insights and make precise, data-backed decisions to grow your business, improve performance, or overcome obstacles.
With OneUp, harnessing the power of recruitment analytics is finally simple. It delivers key insights at the touch of a button and makes it easy to optimise your hiring process.
