The Rise of the Data-Driven Talent Acquisition Leader

It wouldn’t be an exaggeration to say that data has transformed HR over the last decade, especially the talent acquisition (TA) function. The introduction of better analytics solutions and AI-powered tools, processes and metrics have enabled TA leaders to uncover efficiencies in recruiting, onboarding and training new hires.

This shift has brought about the rise of data-driven talent acquisition leaders who have access to richer and more accurate data than ever before. With the explosion of business apps in the workplace, access to large high-quality data sets is no longer a challenge. At least 10% of organizations use over 200 business apps.

Gartner predicts that organizations’ investments in enterprise technology will hit $4.4 trillion in 2022 despite talks of a looming recession, job cuts andor ongoing geopolitical issues.

In this article, we explore how the role of the data-driven talent acquisition leader has evolved and how analytics and emerging technologies have fostered this growth.

The 5 Key Skills of Talent Acquisition Leaders

The responsibilities of talent acquisition leaders have evolved and become more diverse over the years. It’s no longer just about hiring the right fit; TA professionals must ensure that they comply with larger business goals. For instance:

1. 1. Make hiring processes consistent with the organization’s diversity and inclusion (D&I) goals
   2. Optimize candidate journey continually
   3. Ensure that the talent acquisition efforts are meeting the set metrics and benchmarks

Successful data-driven TA leaders who have cracked the code of meeting these goals often have the following traits:

Synthesize Insights from Data

Working with analytics tools means you are swimming in an ocean of data. Effective TA leaders know how to pull data from multiple sources and use it to make critical decisions. For instance, metrics such as offer acceptance rate, time to productivity, cost of getting to optimum productivity level, resignation rate and turnover rate offer valuable information.

Experienced TA leaders take it a step further and thread them together to establish relationships between, say time to productivity and resignation rate to build a stronger retention strategy and improve the onboarding process.

Maintain a Robust TA Tech Stack

Top companies have a wide range of software applications in their HR tool stack that play well with each other. Apps like the ATS, learning experience platforms, core HR solutions (like payroll, HRMS and HCM), social media platforms and performance management are key pillars of a modern TA tech stack.

In most instances, data from each system flows into other systems in the stack, forming a complex mesh of data exchange.

A data-driven talent acquisition expert knows how to keep this ecosystem healthy so that data itself doesn’t become a bottleneck for HR. To put it succinctly, they know how to optimize their tool stack and not make it a frankenstack.

Build a Robust Talent Acquisition Analytics Ecosystem

The TA analytics ecosystem is made up of people, processes and technology. In addition to collecting HR-centric data, successful TA leaders are able to derive meaningful insights from data across other business units like finance, sales, customer success and marketing. They thread this data together with HR data to develop a narrative around cause, effect and outcome. 

Effective TA leaders also understand that it’s invaluable to have people on the team who understand statistics, machine learning and data science – analysts or data scientists are crucial to developing the narrative. They are an indispensable part of the modern HR team. Thesey valued individuals who can translate numbers into language, becoming the interpreter between the technical team and the C-suite leaders.

Marry Strategy, Data and Decisions

TA leaders use data to establish benchmarks that can help them improve the strategic direction of their talent acquisition efforts.

Data analysis skills enable talent acquisition professionals to measure where their efforts rank compared to the benchmarks and decide how they can do better. This entails getting data from the right sources, visualizing it for easier comprehension and making decisions based on the insights derived.

Have a System for Everything

Working with large data sets means you need to have a set process in place every step of the way. A data-driven TA leader uses data to solve problems with varying complexities. A slight error in data handling, and you risk getting inaccurate results. Therefore, TA leaders focus on taking a systematic approach (an assembly line, if you will) towards solving problems to make data collection, wrangling and interpretation efficient.

The Role of Analytics in the Talent Acquisition Function

TA analytics leverages data and analysis techniques to talent acquisition outcomes. To implement better recruitment strategies and improve employee performance, among other things, organizations rely on the following four types of TA analytics:

Descriptive Analytics

Descriptive analytics is the foundational layer of analytics that answers “what happened?” It presents past data and information as it is, which is then used to identify trends, patterns and outliers in the data. While descriptive analytics is great for understanding how current processes and activities impact outcomes, it doesn’t help make predictions.

For example, metrics like time to hire, time to fill, cost per hire, first-year attrition, etc. describe what happened in the past. But they don’t necessarily predict or diagnose anything.

Since this data forms the basis for future analyses, it needs to be cleaned, visualized and communicated with the stakeholders clearly.

Diagnostic Analytics

Since descriptive analytics tellsays what happened, the natural progression to this question is to uncover “why did it happen?” And that’s what diagnostic analytics aims to find out. Similar to descriptive analytics, diagnostic analytics also uses historical data.

If you have identified two patterns in your data, you can begin to link patterns to a probable cause. For instance, if you notice a high attrition rate or low offer acceptance rate, you need to delve into the employee experience data, find out potential causes, and address them.

Predictive Analytics

Predictive analytics is where analytics starts getting a bit complex because you are now using past data and present trends to forecast future events. It answers the question, “What might happen in the future?”

Here’s where TA leaders work with experts such as data scientists and statisticians to make predictions. Using predictive analytics, you can predict if a candidate would be a good cultural fit, perform well and how long they’ll stay if they’re hired.

For the high attrition rate scenarios, the trend of Great Resignation could be one of the drivers. So, now that you have this information, you can predict that the high attrition rate will continue until you address the key problem areas.

Prescriptive Analytics

Prescriptive analytics is the closing of the analytics loop. It answers the question, “What should we do next?” It consolidates insights from the previous three analytics types and makes recommendations. This form of analytics also relies heavily on data science, machine learning and statistics.

To address the high attrition rate, you may notice that fixing the problem areas will take some time and a change in the strategic direction. In the meantime, using prescriptive analytics, you can optimize your hiring efforts so that your employee strength isn’t off-balance.

How AI Is Changing the Talent Acquisition Landscape

Artificial intelligence has facilitated a monumental shift in the talent acquisition function. Speed, efficiency and scalability are the visible benefits of AI, but it contributes on a granular level to the way candidates are hired, onboarded and trained. Let’s look at the five ways AI improves the talent acquisition function.

Candidate Sourcing

There are countless avenues to source candidates for a single job role. Recruiters have to sift through job boards and social media sites, compile a candidate list and engage with them during the process.

Manually, it would take many hours to find the right match. AI can increase the efficiency of sorting through thousands of results to identify top matches for a role. AI also lets you build a composite view of candidates by unifying their relevant information (such as their Github repository for tech profiles) under a single view.

Candidate Screening

With the qualified talent pool ready, you can use AI-based features in combination with the recruiter’s human intelligence to efficiently and fairly find the best candidates for the role. By combining the job description, the recruiter’s expertise and machine learning algorithms, the best fit candidate can be identified quickly. 

Also, you can clone profiles based on the top employees, and the AI algorithm will find the closest fits based on their skills and experience.

Diversity Hiring

AI helps organizations stay on track with their D&I goals. Using machine learning, recruiters can discover large pools of underrepresented candidates. And when reviewing candidates these tools can reduce unconscious hiring biases by redacting names, photos, salaries and other details that might unfairly influence the hiring decision. AI can help find a representative talent pool and ensure each candidate is assessed fairly.

Candidate Engagement

Staying in touch with multiple candidates simultaneously can get taxing, plus you need to reach them at the right time. SeekOut’s  AI-powered email personalization allows you to make a strong first impression. Use customizable templates with messages automatically scheduled at the best time to send, and information from the candidate’s profile to reference in your email to increase response rates. 

And that’s not all. Build relationships with candidates using automated, multi-step email campaigns. Increase engagement over time and view response analytics to learn how to improve your outreach strategy and improve response rates.

And that’s not all. Leverage candidate relationship management (CRM) integrations to get a unified veiew of the candidate journey and determine where they land – prospect pools or job pools.  

Onboarding

The employee onboarding process requires a lot of paperwork and lengthy processes that can be exhausting. Thankfully, new AI features can offload a lot of this tedious onboarding work. For example, employees can interact with chatbots to learn about organizational policies. Similarly, chatbots can gradually source in forms and documents to make the process less daunting.

AI-powered assessments can gauge the employee’s skill level and craft a customized learning path to get the new employee up to speed on their work responsibilities.

Closing Thoughts

Having the right talent on board is critical for organizations to meet their objectives. Considering how wide the talent pool has gotten, recruiters must pick the right talent as efficiently. With the rise of data-driven TA leaders and better analytics and AI solutions, screening, hiring and onboarding top talent has become easy while prioritizing the D&I initiatives and keeping hiring biases at bay.

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