Imagine you were looking to buy a house in a specific neighborhood. The problem, however, is that you have no idea how many houses are in the neighborhood, how many are for sale, the going prices and the demand for these houses. You could post an ad online and ask people to call you. But this strategy is unlikely to generate the response you are looking for. You could retain a realtor but realtors have no information which people are considering listing their house.You could post an ad online and ask people to call you. But this strategy is unlikely to generate the response you are looking for. You could retain a realtor but realtors have no information which people are considering listing their house.
I am giving you this analogy because the challenges in recruitment are not very different. 95% of the people that companies look to hire for critical jobs are not looking for a job.
Traditional recruitment doesn’t work because it’s like roaming around the neighborhood knocking on doors, seeing if the owners are interested in selling. Recruiters typically search for talent on LinkedIn and contact people that seem like a good fit. They contact them one by one, hoping one of them will be interested in their offer.
It is sad to acknowledge that after almost 60 years of traditional recruiting (since employers started to pay placement fees) and more than 15 years since LinkedIn was founded, nothing dramatic has changed in the traditional recruitment industry. Recruiters – external and internal – are still roaming around the neighborhood, knocking on doors.
I believe that change is around the corner. New technologies based on AI and machine learning are changing the recruitment landscape. With all information available online, especially on social networks, technology can provide a picture of the talent marketplace for jobs, particularly hard-to-fill jobs.
To start a search, a company should know the available talent pool for its jobs. It should be able to tell how that talent pool is likely to grow or shrink if some of the requirements for the job were to change. For example, how many more candidates would be added to the potential pool if years of experience were reduced from 7 to 5. What would happen if they accepted a candidate who is lacking one skill that can be learned on the job?
Companies should not start a blind search with the hope of striking gold. They should be able to identify the talent that is more likely to change a job and pursue them first. Technology today can help do that. In my company we often turn down recruitment projects because the data shows us that there are not enough candidates in the market for a particular job that justify our efforts and the companies are unwilling to compromise on their requirements. Sometimes it takes them 6 months for such companies to face reality.
Having the data to pursue an intelligent search is a must but not enough. At 4.1% unemployment rate, there are not enough candidates to fill critical jobs, especially in the tech sector. Still, many companies have not developed recruitment strategies and processes necessary in this reality. Many of them behave as if we are still in 2010 when talent was abundant.
Unfortunately, it is not. The war for talent is over and the talent has won. They dictate the terms of engagement and expect to be treated like celebrities. I have found that the many recruitment processed are flawed, starting with the way companies qualify talent. Often, companies bring candidates to an interview to form an opinion on what kind of candidate they want to hire. There may be several opinions in the organization. HR may have one opinion and the hiring manager another. Rather than developing a dialogue between HR and hiring managers, many companies write job descriptions that are often not in alignment with the current talent pool. One of our clients disqualified a candidate that we submitted just because they were missing one out the 10 skills that the company required. We found this candidate another job in a week and the client had the job unfilled for another 6 months.
This is a strategy that is likely to lengthen the process and frustrate all stakeholders, including, hiring managers, candidates, and recruiters who work hard to find candidates and bring them to an interview.
I believe the recruitment market is ripe for disruption. With AI and machine learning, internal recruiters and hiring managers no longer need to be experts in search. Instead, they can focus on other mission-critical tasks.
But technology can’t do it alone. Recruitment still needs the human touch. After all, we are dealing with people. We still need human expertise to pre-qualify candidates or even help them put their best foot forward by refining resumes, salary negotiation or interview techniques.
In a market with a serious talent scarcity like we have today, we need to start thinking about how to re-invent recruitment by combining big data tools with human expertise. The time is now to win the war for talent so companies can grow, compete and be successful.
Gal Almog is the CEO and co-founder of Talenya, a technology company that disrupts the recruitment industry using big data, machine learning, and a revolutionary, new business model. www.talenya.com.
