How to Build a Better Talent Acquisition Analytics Function

Shot during the hundred days leading up to the Mr. Universe and Mr. Olympia competitions, Pumping Iron is a 1977 American blockbuster about the world of professional bodybuilding. The film revolves around the lives of bodybuilders and contrasts each man’s personality with the environment he trains in. As a result, Arnold Schwarzenegger and Lou Ferrigno became household names. 

In 2016, during an interview with Graham Bensinger, Arnold reflected on his Champion approach during the Pumping Iron days, describing how he would look in a mirror to find weak points in his physique, after which he’d implement appropriate exercises. 

We can extend the same approach to talent acquisition through analytics. They are the mirror into which we can look to help us transform the chinks in our armor. 

Here’s what to consider as you build your TA analytics function.

Excavation: Data Discovery

“We are drowning in information but starved for knowledge,” said best-selling author John Naisbitt.

The ability to collect an extraordinary amount of information on everything from hiring patterns to marketing campaign efforts is tremendous. Data is everywhere and scattered across various systems and platforms within an organization. 

Therefore, when setting up an analytics function, the initial hurdles to overcome entail gathering data from all sources (across the entire organization) and identifying what is statistically relevant. A visionary leader who bridges across functions and silos can pave the way forward.

The Foundation: Building Master Data

Once you have your data, you’ll need a massive cleanup exercise to organize and structure it effectively. Data extracted from multiple sources needs to be transformed and loaded into a consistent format. This would serve as the master data, which you can then use to create basic reports in a primary stage (like creating pivot tables in Excel to monitor progress, recruiter productivity, and utilization). Investing in the right technology here can help in structuring and cleansing data. Additionally, your ATS or ERP can help ensure consistent structure.

The Superstructure: Visualizing Data

Building the right visualizations can bring life to data in ways that make it easier to identify trends and patterns. A robust data visualization tool can be leveraged to build powerful dashboards and insights, particularly when presenting information to senior executives.

You can look at various dimensions to examine key metrics and KPIs. Some extremely powerful visualization tools are Tableau, Power BI, Qlikview, Infogram, and FusionCharts. This is the stage in which you can draw comparisons between various geographies, business units, roles, sources, etc., as well as identify problem areas and root causes.

Finishing: Predicting the Future

You can now use statistical techniques like regression, correlation, and hypothesis testing to build predictive analytics capability. This is where machine learning can help to enhance prediction and accuracy on an ongoing basis in real time. The more the data, the better the chances of getting accurate predictions. And here again, there are a plethora of AI tools to help with building this capability. (You’ll want to invest in coding capability at this stage, especially in languages like R and Python, to reap the benefits of machine learning.)

A Layered Analytics Approach

Using a three-layered analytics framework can help define value metrics in a more organized manner. The below illustration depicts the framework along with examples of some key metrics in each layer (keep in mind that you must build layers sequentially, as they have strong interdependencies):

Future Wave 

A growing area of analytics in the recruitment space is digital analytics, which consists of data and metrics related to the visitor traffic to the careers’ website, jobs posted on various platforms, and overall engagement on social media platforms. More and more companies are leveraging this data to run targeted campaigns to attract the most relevant talent groups for specific jobs in the most optimized manner possible. Such data can sit on top of your recruitment funnel and expand your outreach in a far more efficient manner. 

For instance, you might compare dropout rates across sourcing channels and even from within sections of your company website. All of which can help answer questions like: Where should I place a particular image or a hyperlink on my website? What color schemes are most attractive? How do I improve visibility of my jobs on a particular job board? Why did a particular campaign do better than others? 

By building the right kind of TA analytics function, you’ll position your business to make informed decisions that help steer it toward greater success. 

Karan Grover leads the operations, analytics ,and technology segment within the talent acquisition team at Infosys and has been with the organization for more than eight years. He is responsible for providing intelligence and insights to help recruitment leaders govern recruitment operations globally and implement relevant technologies to enhance the effectiveness of the recruitment function. He calls himself a "recruitment enthusiast" and likes to explore new and emerging technologies in this space.

He earned his PGDM in human resources from Institute Of Management Technology, Ghaziabad, India, and holds a Bachelor's Degree in Civil Engineering from BITS-Pilani, India.

Vivek is a practice lead with Infosys and heads the recruitment for engineering, IOT, and digital experience business segments for the U.S. region. He is responsible for establishing best practices, lean processes, overseeing projects designed to maximize return on investments and stakeholder delight. He has a keen interest in the technology and analytics space. Vivek completed his PGDM in human resources from St. Joseph Institute Of Management, India.

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