Article: Bringing together ATS and predictive analysis

HR Technology

Bringing together ATS and predictive analysis

The real-world results of using an ATS are compelling, but the need of the hour is to club it with predictive analysis
Bringing together ATS and predictive analysis

The need of the hour is to bridge the gap between ATS and Predictive Analytics. Scenarios like automatically predicting the joining probability of an applicant or making the recruiter aware of the skill gap of a candidate are now being explored


Enterprises are no longer the only head hunters. Well-funded start-ups have now entered the fray of attracting top talent and social recruitment is a fast mover, with the use of social professional networks growing in popularity for key positions. About 44 per cent of talent acquisition leaders in India say that social professional networks are responsible for fulfilling key positions as opposed to 38 per cent from their global counterparts. This plays a major role in recruiting passive candidates as well.

For regular and bulk hiring, internet job boards continue to dominate the landscape in India with TA leaders attributing 56 per cent to job boards and 47 per cent to internet resume databases. This is in stark contrast to the overall global consensus of 26 per cent. With quality of hire outrunning time to hire as the most important metric in companies, specialized assessment providers are able to carve a niche market for themselves, offering 40-60 per cent in accuracy of assessments.

Helping to automate this tedious task of recruitment, Applicant Tracking Systems started out as workflow tools, tracking a candidate’s progress from submitting an application till being hired or rejected. ATS was able to do scheduling of interviews, have branching logic and route candidates through different departments. Today, an ATS is able to post requirements to job boards and social networks automatically, track applicants, send out invites, engage with candidates, acts as the bridge between the candidate and the recruiter and also assess applications to a level. ATS also helps to look for an internal resume pool, rather than have them scattered across hundreds of recruiters’ systems. But, it has still remained constricted to mainly workflow engines, triggering actions through different stages. Making a business case and talking about the ROI, the real-world results of using an ATS are compelling. For instance, the hiring cycle at a company dropped from 115 days to 37 days when the company implemented an ATS solution. The time saved reduced the cost per hire, which could be as high as $10,847 came down to as low as $3,300.

Thus the following can be the advantages of an efficient Tracking system:

• Streamlined applicant tracking process

• Reportable recruitment analytics

• Simplified communication and scheduling

• Enhanced employment brand

• Reduced staff and staff time on basic tasks

• Automation of applicant selection to a degree

Even with the ever growing use of ATS, I feel that Social analytics is supremely important for one reason—aspiration. Today’s HR system captures the personality of a candidate or an employee on the basis of what he does within the organization, but it fails to capture what his aspirations are. Social analytics can make that available to recruiters and the workforce management groups, fostering a culture where people are in jobs they can potentially love. But that can only happen in a world of democratic data sharing within the dominant professional social networks.

The need of the hour is to bridge the gap between ATS and Predictive Analytics. We are exploring scenarios like automatically predicting the joining probability of an applicant based on different slices of historical data, automatically making the recruiter aware of the skill gap of a candidate and provide a curated and timed learning path even before the candidate joins. We are even looking at predicting as to how long he will stay in the organization. All of this is based on data models and Artificial Intelligence, looking at everything from previous salary, education, marital status, skill and certifications, even distance from proposed office.

This involves a lot of the proverbial Big Data Analysis, but something that is not being looked at extensively today is the mash up of data that exists within the organization with data that exists without. A simple example such as analyzing the number of traffic signals and congestion on the route from the residence of the employee will help in gauging how soon he will tire out leading to loss in productivity and increased stress levels. This apart, the current package and designation of the candidate will also help the organization learn and research the industry sweet spot package that he will be ready to join at in the future. The possibilities are endless. 


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Topics: HR Technology, #HRMetrics

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