A large talent pool can be a good and a bad thing for recruiters. It…
Sourcing talent in recruiting can be a lot like sleuthing, especially when there's so much competition to find great talent. It has often required creative and exhaustive research skills, and finding talent through social networking sites or job board resume databases-and even harder to reach places like conference participant lists or alumni associations. There are so many places to look, how do you know you're finding the best candidates? How do you know you're looking in the most effective places? In order to create a strategic sourcing strategy, recruiters must cast a wide net and leverage multiple channels at once to find the best talent. This takes a lot of time and can bleed recruitment campaign dollars when time and money is spent on less effective sources.
AI takes over where your skills can't
Enter the brains and skills of AI recruiting. Part of the job of a recruiter is instinct and intuition-and this may work well while interviewing candidates and determining successful matches between a company and a new employee, but it's harder to rely on a gut-check for strategic sourcing. This is where the data analytics can transform the process. Even the best practice just a few years ago has already changed and talent acquisition organizations need to keep updated on how candidates experience their job search-whether this means making applications accessible by smartphone or incorporating SEO best practices into job requirements. A strategic sourcing process will keep up with the continual flux, incorporating new data with the old.
AI uses big data to automatically source the candidates you need
Ultimately recruitment teams can't sleuth the whole internet with a magnifying glass or predict the next big innovation in job searching. And they don't need to. AI technology can help find the best sources for quality candidates across multiple channels based on your hiring need. Essentially, algorithms can sleuth out your best candidates for you based on various forms of data streaming in, helping save time and money for a more effective sourcing strategy and forging the best pathway to the candidates you want.
Some new solutions, like pandoIQ, use AI-enabled algorithms to use analytics to predict the performance of your job ads. From the start of the campaign, you know what sources will perform the best and can target the right candidates while maximizing your ROI. You avoid blindly spending your recruitment budget on sites that will not deliver results. Your ad is placed on the right sites, in front of the right people, at the right time delivering the candidates you want. The sleuthing is done for you and you have effectively saved time and money in the process.
To reiterate, AI tools like this help you to determine the best sourcing strategy for your specific job postings based on predictive data, machine learning, and automation. The Pando algorithm ''reads'' your job description through natural language processing, indexes key requirements for the job like education and skills, and then assigns it a classification in a ''recruitment taxonomy.'' AI determines, in a complex and thorough way, what type of job you are posting and matches it against historical data for the performance of ad campaigns for similar jobs. Then it provides strategic recommendations to make your job ad campaign the most effective, allowing you to optimize job ad spend.
How this all affects your sourcing
So is this intuitive? It's not a gut-check; the knowledge is in the numbers. And analyzing the historical numbers before any dollar is spent will keep those all-important numbers (in cost) low. Learning the best sources for talent can have many benefits in other categories across recruitment metrics. It can reduce cost-per-hire, time-to-hire, lead to greater quality of hire, and retention rate-all which contributes to the health of the company, ensuring that each new hire leads to a better ROI.