The market has a growing number of sourcing tools in addition to LinkedIn. They address one of LinkedIn’s main drawbacks – its limited data on talent. When LinkedIn falls short on finding and engaging with talent, new tools that search beyond LinkedIn come to recruiters’ rescue. Such tools also offer additional ways to contact candidates that LinkedIn does not, such as e-mail and phone numbers.
However, these tools do not mitigate the biggest sourcing challenges. That is, they are still based on manual selection of and engagement with candidates, one by one. The pool may be wider, but the work is still cumbersome.
Talenya’s AI-powered sourcing platform, by contrast, offers a comprehensive solution to all of these challenges by automating the sourcing process and significantly widening the candidate pool. Translation: more candidates, less work. Here’s how Talenya makes recruiting easier:
Little or no work on creating a search
With a traditional sourcing tool, recruiters must manually enter keywords to find specific skills and job titles. With AI, the search is built automatically using the recruiter’s job description and an analysis of the company’s employee profiles. Assuming that the company is looking for similar people for the same role, AI uses hundreds of keywords to create a much more granular search without any extra effort on the recruiter’s part. Many hidden candidates are uncovered thanks to AI’s ability to infer and specify talent that a human couldn’t. Furthermore, Talenya does not require the recruiter to upload the job description. By integrating with the most common Applicant Tracking Systems (ATS), it can automatically use the job description in the ATS to create and conduct its search.
Learns and Predicts Recruiter Preferences
With just a few calibrating questions, Talenya can tailor its search process to specific recruiter preferences. Recruiters are shown a number of relevant candidates and asked to give them a thumbs up or thumbs down. The search is refined accordingly, using the platform’s machine learning algorithms. This process helps account for implicit preferences of the recruiter which may not appear in the job description.
Automates and optimizes engagement
Traditional tools may widen the search beyond LinkedIn, but they still require recruiters to contact each candidate manually. Instead, Talenya’s platform selects and engages with hundreds of qualified and diverse profiles, without any effort on the part of the recruiter. The engagement process itself is also optimized with respect to Talenya’s prediction of which candidates are most likely to leave their current job in addition to meeting requirements. Using multiple channels such as e-mail and LinkedIn, the AI will invite candidates to interview with the employer. Those who show an interest are pushed into the user’s Applicant Tracking Systems for follow-up. Thus, recruiters can use their valuable time screening and interviewing many more candidates rather than monotonously searching for a few people to contact.
Boosts diversity in search results
Many candidate search tools, aside from LinkedIn, can filter search results by diverse identities. While boosting diversity is always a good thing, simply filtering a search without altering the search terms won’t necessarily find you the best candidates. Talenya’s platform addresses hiring disparity instead with a couple of patented processes. Specifically, Talenya’s AI can predict and add missing skills to a candidate’s profile. This is important because women and minorities often use different or fewer terms in their online profiles, making them harder for recruiters to find. These small changes and additions serve to level the playing field for a diverse range of talent without showing any less-qualified candidates.
At the engagement stage, Talenya can also be programmed to engage only with diverse talent if diversity hiring is your goal.
Balances between talent pool size and quality
One of the most common practices of traditional search techniques is to add or delete keywords and see the impact on the talent pool quality and quantity.
This is a very labor-intensive process of trial and error which relies on a lot of guesswork. Talenya’s platform uses AI to optimize the balance between these two criteria. It will tighten requirements when the pool is big and relax them when the pool is small. Recruiters do have some control here, though: during the calibration process, the AI will ask the recruiter to approve search changes before making them.
So, what do all these features amount to? In short, Talenya’s platform is meant to be used from the get-go, rather than after LinkedIn proves inadequate. It’s a fully automated system that not only saves recruiters valuable time and energy, but intuits search qualifiers and expands the candidate pool much wider than manual search ever could.
