The trajectory of spends on job boards can be symbolised by a single direction - upward. With approximately 30 million new graduates entering the job market every year and a persistent skills gap, the Indian jobs market exhibits a fundamental imbalance. Furthermore, the unprecedented churn witnessed in FY-22, characterised by the Great Resignation, widespread startup layoffs, and a staggering 90 million job changes, has further accentuated the role of job boards. They have emerged as silent beneficiaries in this evolving landscape, and their pricing strategy mirrors this phenomenon.
Recruiting the right candidate has always been a challenging task, and in recent times, it has become increasingly expensive. However, while the cost cannot be entirely eliminated, it can be effectively managed.
Many companies fail to capitalise on the untapped potential of their own candidate data. Over the years, every recruitment company, agent, or freelancer amasses a significant pool of candidate data through sourcing efforts. Given that data collected a few years ago might not be "fresh" (current candidate willingness to switch). Moreover, this data often remains disorganised and scattered across various platforms, such as Google documents, Outlook emails, or Excel sheets. Consequently, organisations continue to rely heavily on job boards, even though they have access to many profiles that are "almost there" candidates. The challenge lies in navigating past this reliance on job boards and utilising the existing candidate data effectively. While Applicant Tracking Systems (ATSs) exist, they were primarily designed to track the application process rather than facilitate efficient data search and outreach.
Searching for the right candidate profile from a stack of resumes often feels like searching for a needle in a haystack. This challenge arises because most Applicant Tracking Systems (ATSs) primarily function as workflow automation systems, relying on string match searches. For example, searching for a "front-end developer" typically requires the exact term in the underlying resume for a match to surface. As a result, there is a lack of contextual understanding within the search process.
Resumes are highly personal artefacts - they express the purpose that each person words as intimately as possible. Unlike standard documents, resumes are distinct and akin to fingerprints, capturing the essence of each candidate. Consequently, deciphering this expression of purpose becomes crucial in finding the best match for the hiring manager's requirements.
But picking up context is challenging. Resume parsers have been around for years, but accurately matching candidates based on inferred skill sets remains difficult. Inferring whether a profile is a perfect fit for a "front-end developer" role based on mentioning "Worked on React" can be tricky. The complexity deepens when it comes to the frontline workforce, as standardised job roles are scarce. A sales representative can be referred to by various titles such as demand executive, business development executive, customer executive, or salesperson. These titles all encompass individuals with the skill set to make customer contact, inspire trust, and effectively communicate the product proposition - be able to "sell" in short.
Exciting developments are reshaping the recruitment landscape. Deep learning models and Natural Language Processing (NLP) engines have made remarkable progress in comprehending the underlying skill sets described in resumes. This breakthrough heralds a transformative era where 'context' becomes the game changer for matching candidates and predicting their potential to meet the requirements of specific roles.
Once recruiters extract relevant profiles from their database, the next challenge is determining if the job opportunity is compelling enough to entice candidates to switch. This represents the second part of the equation: ascertaining candidate interest. Unfortunately, this is where recruitment can start to resemble telemarketing operations. The focus shifts from finding the right candidate who contextually matches the job requirements to prioritising the first candidate who broadly fits the criteria.
To address this challenge, a comprehensive solution is needed—one that offers a centralised lead database, contextual matching capabilities, and real-time multi-channel outreach to potential matches. By leveraging these capabilities, recruiters can ensure that the shortlisted candidates are promptly informed about the job opportunity and engaged in meaningful conversations regarding its prospects. This approach significantly reduces the Time-to-Fill (TAT) while keeping the total hire cost at a fraction of traditional methods. With a centralised database that provides visibility across the organisation, recruiters search internally for suitable candidates before venturing out externally. This approach optimises utilising existing resources and reduces dependence on expensive job boards. Forward-thinking Talent Acquisition (TA) organisations are already partnering with us to enhance their recruitment processes and maximise the value of their data. Data is the new oil, or so they say - but you ought to let your data fuel your business better. And that would manage the spending on the job boards with time.