Predictive Analytics in Recruitment

HR as a function is evolving rapidly. Usage of technology has become a key enabler for HR. Many companies have also started exploring HR Analytics as the next big lever to improve the performance of the organization. However, the success of such initiatives has been uncertain. Further, the adoption of HR Analytics in the recruitment sub-function of HR is even less evolved. Here is an attempt to understand the challenges and the road that lies ahead of us.
Analytics has three levels of sophistication:
1) Hindsight: Data-collection and reporting
2) Insight: Making sense of the data and developing tangible actionable insights
3) Foresight: Develop predictive models to predict future behaviour or success ratios

While the organizational HR Analytics covers all sophistication levels, we have found that the Recruitment Analytics is typically restricted to ‘Reporting’ and ‘Insights’. In very limited cases, analytics is being used to predict success of recruitment drives. Predictive analytics in HR can significantly improve the conversion ratios, reduce cost and improve the predictability of recruitment. Wouldn’t it be great if a recruiter could predict the likelihood of getting a candidate on-board early in the recruitment cycle? Knowing the preference of the candidate without asking would significantly reduce the wasted effort spent on chasing uninterested candidates. The recruiter can then focus his/her energies on the candidates that are FIT and are more likely to join the organization. Wouldn’t it be great if the recruiter gets alerted when a passive FIT candidate is thinking about a new job, even before he/she updates the resume? These insights can be derived from the behavioural data of the candidates. E.g. Which jobs does the candidate apply for, which interviews does he attend, what mails does he open and click, what kind of articles/blogs does he read, how frequently he visits the job boards, does he respond to the call-back CTA, etc.? While HR Managers appreciate the value that Recruitment Analytics can bring to the organization, there are some serious problems that they face:

1) Data Collection Challenges: Unlike other HR Analytics where the behavioural data of employees is required, the data required for Recruitment Analytics is of candidates who are currently outside the system. There is no easy way to collect this behavioural data in the outside world all by itself.
Banking and financial services have solved a similar problem by setting up a credit bureau. They share the loan repayment data with each other. This helps them in predicting the likelihood of loan default.
Google collects traffic data of all websites by providing a free service called Google Analytics to the webmasters.
If there was an organization/agency that could collect data from various employers offering some value in return, employers would be able to have access to data to perform advanced analytics. Alternately, this intermediate organization could precook the insights for instant consumption.

2) Identity Management
Assigning and managing identity to each candidate when he is not signed up with the company is extremely difficult. In the absence of this, what do you track the behavioural data against? Again, a unique ID management system could potentially solve this issue. Adhar based validation and tracking of data could be the next frontier.

3) Fire-fighting Mode:
Recruiters are usually stretched and find it hard to dedicate time to futuristic projects. Hence, the system/solution has to be pre-cooked and easy to eat. Unfortunately, current Recruitment Analytics can predict outcomes only at an aggregate level (e.g. Cost of recruitment, Overall success ratios). Candidate-specific insights are missing. Therefore, it has limited value during execution. Limited actionable insights emerge. It is crucial to collect rich behavioural data of the candidates (active as well as passive) and use it to predict fit and intent for a specific job of the employer. It is important to assess not only the relevant skills of the candidate, but his preferences and behavioural patterns in past jobs/interviews. This form of disruptive recruitment analytics can help predict and streamline the hiring process to a great extent, thereby saving hiring time and costs significantly. Moreover, hiring the right candidates who are perfectly suited to the job drastically brings down the attrition rates and helps enhance the productivity of the company.