Data analytics for HR: How to Make Effective Recruitment.

In his book Drive, management author Daniel Pink talks about the disconnect between “what science is telling us and what businesses do”. He refers to employee motivation and how organisations use various incentives, especially monetarist, to motivate people. Conversely, there is a body of research that shows that such ‘extrinsic motivators’ actually have an adverse effect on employee motivation.

This disconnect between what we intuitively think is right versus what science tells us equally applicable to another critical component of the human resources function: the recruitment process.

While there are a range of psychometric tools employed by domain experts in the selection process, it is not too unfair to say that a vast proportion of recruitment decisions are still made on the basis of what one “feels”.

However, data analytics and Big Data are beginning to shape the evolution of the recruitment process. With availability of vast amounts of data aggregated from multiple sources—especially social media channels where prospective candidates usually leave their digital ‘thought prints’—and the ability to transform all that information into intelligence using powerful algorithms, recruiters now have the opportunity to rely more on facts than on intuition before they issue job offers.

This new realm of “people analytics” or “talent analytics” – which refers to the use of data analytics for making people-decisions—is now contributing to the hiring process including recruitment marketing, filtering prospective candidates, identifying outliers, planning interview questions, and determining who to retain and promote etc.

Top & bottom line impact

Companies often repeat the cliché that people are the most valuable asset of their organization. It is only logical therefore that people-decisions have a significant impact on the performance of companies. Consequently, there is a strong business case to invest in analytics-driven tools and systems for talent acquisition process just as companies would in marketing or product development.

Thankfully, this is already happening as more and more companies are coming out with tangible results of the impact that data analytics has had on their business vis-à-vis recruitment.

For example, Josh Bersein writes in an article on Forbes.com of how a client improved sales performance by $4 million in 6 months simply by implementing a new candidate screening process based on insights gathered from data the company always had. Similarly, Xerox apparently reduced attrition rate at its call centres by over 20% by using Big Data tools in hiring for its 48,000+ department.

It is clear, as Josh Bersein writes that, “If we can apply science to improving the selection, management, and alignment of people, the returns can be tremendous.”

Applications aplenty

“It is not a replacement for all other tools; it’s a new tool to be added to the decision-making toolbox,” writes David Bernstein. “By leveraging Big Data, recruiters can transform their image from “reactive,” responding to the “just-in-time” talent needs of the business, to a “proactive” business partner that has the foresight to make better and faster talent acquisition-related decisions.”

In his article, David lists several specific applications and benefits of data analytics that hiring personnel can use to improve their processes, some of which are listed here:

1. Tune hiring policies

Data available from a company’s internal systems and external sources such as demographic information, public transportation, compensation surveys, and social media can reveal patterns that will help a company determine where to hire from and at what cost.

2. Focused recruitment marketing

Online job listing sites capture volumes of data that can give insights into what is likely to elicit the best qualitative and quantitative response.

A recruiter can crawl or crawl data off such job sites and run analytics or work with one to determine the likelihood of being able to fill a particular position in a specified location based on historical data patterns. Moreover, information like what day of the week to post certain types of jobs or specific factors that influence prospective candidates to respond to a job ad can be used to better use recruitment marketing resources.

3. Evaluation of prospective candidate’s skills based on analysis of “public” work

Recruiters in the IT space, especially when looking for programmers, now have access to online services that leverage the power of Big Data and analytics to grade programmers based on code and contributions to online technical communities.

This provides recruiters a much deeper insight into the skills of the prospective candidates, which can be used in conjunction with other information to determine the extent of fit of the candidate, both from a skills’ perspective as well as a culture perspective.

4. Proactive hiring

Large companies which have multiple teams scattered in different locations can benefit enormously with the insights into talent needs that Big Data can offer. For example, by using data from sales and billings together with data from the company’s staff database, the HR department can get a good idea of the areas that will require shoring up of resources and areas that might need trimming.

HR can also use this information for ongoing talent management by clearly determining the training and development needs of individuals and teams. The Indian IT software services sector, which often needs to hire resources in advance in anticipation of outsourced projects, can certainly be one of the main beneficiaries of such proactive hiring.

The element of guesswork can easily be eliminated by embedding necessary analytic insights to the hiring process. Predictive analytics is an invaluable advantage to recruiters and takes the ‘I think this is our guy’ to ‘I know this is the guy’ whilst recruitment.

5. Recruiters are still important

People analytics faces some challenges before its widespread adoption. These challenges are mainly people and skills-related considering that data analytics requires competence in multiple disciplines: data analysis, statistics, visualisation and problem-solving. Most HR professionals currently lack these skills, and finding such individuals and getting them to work on HR data becomes imperative.

Companies should also remember that they cannot rely on data analytics alone for the recruitment process. Recruiters still remain vital to the process: data analytics can only function as a tool that can help improve success rate.

It’s not all ‘Moneyball’ and to hope that data scientists alone will help solve talent challenges is naïve. Data-based insights can only act as indicators by themselves. Domain experts like hiring managers, HR professionals, and recruiters involved must be able to identify the problem and ask the right questions before applying analytics.

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