How to Ensure a Data-Driven Recruiting Strategy to Survive the New Normal

Companies are facing new pressures in talent acquisition that impact both processes and technology. Talent acquisition and HR leaders can no longer afford to rely on a reactive or traditional approach to recruitment. They must look to the future and rely on data to meet the needs of the business and the changing expectations of candidates, recruiters, and hiring managers. 

67% of talent acquisition and HR professionals are not providing hiring managers with the right data to make decisions, and only 32% of senior leaders are confident in the data that they have available to make decisions. Recruiting Enablement provides recruiters with the tools they need to be successful in attracting, recruiting, and hiring quality talent. These tools automate the administrative tasks of talent acquisition and allow recruiting and hiring teams to make data-driven decisions focused on quality. According to Aptitude Research, companies that use data to automate decision-making are twice as likely to improve quality of hire.

The business case for data-centric recruiting

Just making data available will not drive meaningful adoption. You need to embed it into your operational processes to make it easier to understand and drive adoption by recruiters. Recruiters are busy and will not seek out data. Therefore, it is vital that you help them put data to work to identify key trends such as:

• What is going on with diversity in recruiting
• How decisions are being made today 
• Who top candidates are
• How to avoid adverse impact on candidate selection
• How to improve key metrics, such as diversity, time to hire, quality of hire, etc.

The key to success lies in Recruiting Enablement - Leveraging data & automation to achieve new efficiencies and better hiring outcomes. Recruiting Enablement technology does not impact just one area of talent acquisition. It extends from recruitment marketing efforts to onboarding. 

This leverages data and automation throughout the entire candidate experience and provides intelligent workflows to move the right candidates along in the process in a more effective way - from attracting talent to onboarding. 

Yet, interestingly, only 27% of enterprise companies are only planning to automate more than 50% of their talent acquisition processes this year, according to Aptitude Research, and the majority of those companies are just automating the application process

To be successful with the use of data in decision making, it needs to be embedded into the process. Hardwiring those insights is key. Studies have shown time and again that candidate selection is influenced by human bias. 

Objectively, recruiters may screen candidates based on “hard data” like educational background, past employers, and location. While these criteria seem unbiased (“this is what has worked before, so let’s keep doing it!”), in fact they can sustain selection biases that negatively impact diversity in hiring. 

Subjectively, recruiters may screen candidates on keywords that the recruiter feels indicate a fit for the role, and on biases related to factors such as name, nationality, gender, and age. Whether conscious or unconscious, this also limits the diversity of your workforce over time. 

Hardwire insights, not bias, into candidate selection 

The use of artificial intelligence and data science can provide a way to widen the pool of applicants considered, while also removing bias from the selection process. Intelligent selection uses data science, applying machine learning to historical data — such as the resumes/CVs of past applicants coupled with outcomes like hires, retention, performance, etc. — to score candidates.

Done well, using algorithms that have been checked and validated, intelligent selection helps companies consider a wider, more diverse pool of candidates, and increase diversity in hiring, without introducing adverse impact. 

This can even extend to concepts such as a ‘virtual screener panel member’ service that supports your recruiters and tackles bias and fatigue. Users can use the tool to review bias-free scoring of competency question responses alongside individual sifter (or panel member) scores and adjust for any inconsistencies or bias.

AI can leverage machine learning and text analytics to score candidates’ responses to competency questions, and assess the range of experiences, abilities, strengths, behaviors, and technical/professional skills required for different roles. Natural Language Processing can also be used to analyse text structure and content to determine the quality of response with reference to historical data from your past hiring decisions and  be de-biased in the process.

Done well, AI can also score free text answers based on how they match the competencies the question is looking to address and enable performance-based assessment using inter-rater reliability techniques as well as de-biasing procedures. Automated scores can be compared to manual ones to ensure accuracy and consistency.

4 key steps to become data-driven

At Oleeo, we encourage four steps towards becoming a data-driven recruiter:

1. Ensure you are leveraging (sometimes new) data where we didn’t before
2. Apply data science to automate steps/tasks
3. Become evidence-led instead of intuition-led in decision making
4. Facilitate a culture shift in recruiting / HR

As Peter Drucker once put it: “If you can’t measure it, you can’t improve it.”

So, bring together your relevant data for a complete picture and track how they are changing over time. Know your metrics… and your analytics - Metrics measure one data point, like the percent of female applicants for a role, answering the question 

Understand the WHAT (what’s going on) and then connect and measure multiple data points to answer the question WHY. For instance, an analytic may show the attributes driving selection of a candidate -- in other words, the factors driving selection of a candidate for a role. 

This could be used to answer questions like, why are more white males being selected for a specific role?  Analytics like these could show there is a bias towards a certain educational background, or when a certain hiring manager is involved.

Share the insights with the business to gain buy-in and drive change. Leverage data-driven insights to help business leaders understand what’s happening and how to address it or achieve results. 

DDI, The Conference Board, and EY completed a global leadership survey, of nearly 28,000 business leaders across industries. The survey found that, today, only 11% of business leaders trust HR to use data to anticipate and help them fill their talent needs. 

So, to conclude employers with job openings have an incredible opportunity now to win great talent - it is vital to enable your (smaller) recruiting teams with data to automate processes & inform and improve decision making. Watch this webinar recap to learn more!