The Beginner’s Guide to Recruitment Analytics
HR is under increasing pressure to justify the ROI of recruiting today. As HR embraces the idea of data-driven decision-making, the recruiting function stands the most to gain. After all, very few HR functions have the same kind of bottom-line impact as recruitment. However, traditional recruitment metrics often fail to demonstrate the real-world business impact of recruitment. This is because recruitment is still considered somewhat a dark art. It’s not a perfect science – it combines technology, messaging, human interactions, and human judgment. Measuring the efficacy of all these moving parts in the recruitment process becomes difficult using conventional metrics and tools.
Enter big data analytics. By applying the principles of big data analytics to recruitment, HR and talent acquisition leaders can easily measure the business impact of recruitment. Recruitment analytics is quickly emerging as a key focus area for high-performing HR teams. Let’s look at the definition of recruitment analytics, and how you can get started on your recruitment analytics journey today.
What is Recruitment Analytics: Definition, Scope, and Examples
Recruitment analytics is defined as the application of statistical analysis and modeling to the recruitment process to improve business outcomes. Recruitment analytics essentially informs HR leaders and recruiters of the present and future performance potential of their recruiting processes and workflows. It enables you to answer critical questions about your recruiting process. For example:
- How much does it cost to fill a vacancy?
- What share of candidates apply to your organization from job boards?
- What is your quality of hire?
- How close are you to achieving your diversity goals?
- How many applications do you receive per opening?
These questions can only be answered using recruitment data. Recruitment analysis takes into account recruiting data from multiple sources and candidate touchpoints to answer all/any questions you have about your recruiting process.
Recruitment analytics is a subset of HR analytics that focuses only on the recruitment cycle. As recruitment becomes a driver of competitive advantage for organizations in a tight labor market, recruitment analytics becomes the fuel that drives this advantage. It helps you identify gaps in your recruiting strategy and process, and also tells you about the areas you already do well.
Recruitment analytics is usually offered as a part of the larger HR analytics suite or as a standalone product. Most HCM and recruitment technology providers offer some version of recruitment analytics to track key metrics like time to hire or cost per hire.
Predictive Analytics in Recruitment
As technologies like artificial intelligence (AI), machine learning (ML), and deep learning, make inroads into the world of business, HCM and recruitment technology vendors have responded by integrating these technologies to offer predictive capabilities across recruitment solutions. Predictive analytics leverages new and historical recruitment data to forecast behavior, trends, and outcomes of specific processes. A great example of predictive analytics in your daily life is the recommended products list that you see on Amazon. Or your recommended playlist suggestion on Spotify. These companies use predictive analytics to make hyper-personalized recommendations based on your previous choices of products or music.
In the recruiting process, however, the real promise of predictive analytics is its ability to bring objectivity based on actual data rather than relying on subjective human judgment. For example, it can help you:
- Projected time-to-fill
- Estimate future candidate performance
- Interview acceptance probability
- Predict candidate tenure
These are just a few ways predictive analytics can improve your recruitment outcomes and guide better decision-making.
Getting Started with Recruitment Analytics
Integrating analytics into your recruitment process does not have to be complicated. So, before we delve into how you can start applying recruitment analytics, let’s look at how it really works.
A recruiting analytics solution typically captures recruitment data from all digital touchpoints which could include your careers page, your recruiting CRM, your applicant tracking system (ATS), mobile app, application form, interview scheduling software, video interviewing software, social media handles or your offer management software. So, it is crucial to ensure that all of this data is accurate. Any analytics solution uses the principle of GIGO: Garbage-in, Garbage-out, which means your input data quality will determine the quality of your output. Therefore, flawed data-sets lead to flawed insights.
If you already use an end-to-end HCM system or recruiting platform, data quality shouldn’t be a problem. However, if you’re using discreet systems for specific HR tasks, you might need to manually collate all your recruiting data in a single platform/destination in the absence of related APIs. Now, when you collate this data, it is important to cleanse it to eliminate any duplications or repetitions. That way, you can be sure that your analytics output data is pristine.
The last part of the analytics puzzle is dashboarding and visualization. Data visualization allow you to make sense of all the data by creating context around a single metric or graphic. Ideally, your recruitment analytics dashboard will get updated in real-time to help you expand your understanding of process performance and outcomes. It visually tracks your KPIs and key data points to provide a single location to help you monitor and analyze your HR department’s impact on strategic business goals.
Succeeding with Analytics in Recruitment
Deploying a Recruitment Analytics Solution:
Assuming you decide to deploy analytics in your recruitment function, the next step is to consistently monitor your analytics workflow to weed out any inefficiencies, errors and risks, and implement structural changes to avoid them in the future. Now, what are the risks associated with an analytics system? Well, one of the biggest concerns around the deployment of smart technology in the recruitment process is its ability to ingest and amplify existing human biases.
If you’re planning to build an AI analytics solution in-house, check and double-check the data-sets your analytics solution will be trained on. Are the data sets skewed in terms of sample sizes? Do your historic hiring patterns display a clear preference for a specific sex, age demographic, or ethnicity? If so, your data might be unsuitable for training your analytics algorithm.
On the other hand, if you’re looking at deploying a SaaS-based analytics solution, try asking potential vendors questions about the development of their analytics solutions. What kind of data was used to train the algorithm? Does the vendor perform regular audits for adverse impact? Has the solution been successful for organizations of your size? These questions are critical to ensure that you’re able to achieve your intended goals with your analytics solution.
Building an Analytics Strategy:
Deploying a data-first recruitment strategy is as much about technology as it is about the organizational mindset. So, change management is what will truly position you to unleash the real power of your analytics solution. Here are a few essentials to succeeding with recruitment analytics:
- Creating a data-first culture: Having the right technology solutions is just one part of the puzzle, the second and the most important part is encouraging a data-first culture. The key to improving the performance of your recruiting processes is identifying what’s working and what’s not. A data-first culture focuses on building accountability, driving analytics adoption, and encouraging responsible use of data. This requires a top-down approach for execution, where talent acquisition leaders, the C-suite, and other HR executives champion the cause of data-based insights for decision-making.
- Developing the right skills: Effective use of your analytics solution relies largely on the skills of the people using it – your recruitment team. While you don’t need to be a data scientist to work with recruitment analytics solutions, some data skills are desirable. Workshops, training programs, or even e-learning courses on working with recruitment data can help you bring your team up to speed on recruitment analytics swiftly.
- Linking metrics to business outcomes: Crucial to your analytics’ initiative success is your ability to link recruitment metrics to clearly defined business goals. For example, demonstrating the value of ‘quality of hire’ (QoH) will be challenging, especially when you look at it in terms of measurement. However, when you break-down QoH into related and measurable components like new hire retention rate, new hire performance, and time to fill, you will be able to show the direct business impact of this metric.
Technology is transforming recruitment for the better. And data holds the key to leveraging this technology to create real business value. Recruitment analytics is no longer just a good-to-have tool but is imperative for organizations looking to turn their talent acquisition function into a competitive advantage.