When focusing on human capital management (HCM), cost per hire is an important figure keep in mind. The Society for Human Resource Management (SHRM) 2017 Talent Acquisition Benchmarking Report found that in the U.S., the average cost per hire is $4,425. In addition, companies noted that 17 percent of early separations were from employees leaving within their first six months and 26 percent during their first year, making recruitment and retention that much more costly. Now, consider how many positions a small-to-medium business (SMB) fills in an average year. Quite a bit of money goes into recruiting quality talent!
However, there is something a small-to-medium business (SMB) can implement to reduce the cost per hire and also minimize the chance of onboarding a non-quality hire — machine learning technology.
Machine learning makes recruitment a more efficient process. During the application and résumé review phase, it helps screen for keywords, leverages social data for identification, and utilizes responses from online questionnaires. Machine learning also deploys artificial intelligence (AI) assistants and chatbots to respond to candidates or schedule interviews during pre-engagement. Let's not forget about talent sourcing, either. Machine learning narrows a large pool using key attributes to find the prime candidates.
So, how is it doing all of this?
Machine learning iteratively applies algorithmic analytical models to preprocessed data with the attempt to uncover hidden patterns or trends which can be used to do things such as:
While these functions significantly help to reduce time and money spent to fill positions in a SMB, there is another focus where machine learning can make the biggest impact in ensuring you onboard the right person — talent sourcing.
Will machines have better success in finding the ideal candidates for your vacant positions than human recruiters do? Technology can more quickly find correlations and patterns that a human could potentially overlook, and this may lead to increasingly higher-quality candidates. If you're thinking about using machine learning to help with talent sourcing, here are few things to consider first.
To leverage machine learning, first define the variables on which to "train" the system, and this will depend on your approach.
For example, are you sourcing passive candidates — individuals who aren't actively searching for a new job — or are you looking to find the top candidates from a large pool of applicants? If it's the former, consider attributes such as how recently (or frequently) they updated their LinkedIn profile, as this could indicate their potential interest in seeking new employment. Current employer stability is another factor to consider, such as mergers and acquisitions, layoffs, and stock fluctuations. Other options include looking at market indicators to assist in predicting a downturn in a particular industry or company, which may create a plethora of available candidates, giving you an early advantage.
Imagine receiving hundreds, if not thousands, of applications for open positions. Machine learning technology can help identify the top candidates, depending on the trainability of your data, so long you have enough historical and relevant data on successful candidates or employees to "train" your system.
Think of it as if you're looking for a "mini-me" based on the profile of how you've defined the ideal employee. The attributes here will depend on the role, but one approach could be to reverse-engineer the fit by identifying the attributes of successful employees in that role, such as their work experience, industry, and work product.
Additional attributes to consider are the number of jobs they've had in the last five years, their tenure in each job, hobbies, college major, and extracurricular activities for a recent-college-graduate hire, such as competitive sports potentially being a good indicator for a sales position. Machine learning also helps to target candidates who have a higher probability of success based on prior recruiting strategies.
Machine learning can help a SMB who is looking to fulfill a certain Equal Employment Opportunity (EEO) ratio. One company used machine learning technology to increase female hires from 40 percent to 47 percent and minority technical hires from 1.5 percent to 11 percent. However, in some cases, you don't want to bias the talent pool based on gender or ethnicity. So how do you manage these situations? It's critical to eliminate the bias that could become inherent in machine learning. Data anonymity, clustering, and data aggregation are some ways to avoid the inherent biases. For example, ensuring that protected classes, gender, or age do not become factors in the algorithms.
Machine learning technology helps to reduce recruiting cycle time, cost, and number of bad hires, but human intervention is still needed to manage the candidate experience. This can be accomplished through actions such as frequent personal communication and high-quality and consistent interviewing techniques, regardless of the outcome for the candidate.
Technology is rapidly transforming many areas of human capital management for the small-to-medium business, and machine learning is one tool that will revolutionize recruitment. The question is – are you ready? And if so, how will your organization embrace technology in the effort to reduce hiring costs and increase your chances of hiring the perfect person the first time?
By Susan Poser, Senior Director, Strategy & Operations, Oracle