There are not just the headline costs of advertising and agencies, but also the hidden costs of time to hire, the management effort needed to train new recruits, attrition costs for early leavers, time taken to be productive. An Oxford Economics report put the cost at $45,000 per employee.
Analytics holds the key to not only stripping out some of these costs, but also adding value. It can help firms spot the people who are likely to stay with a company rather than job-hop and spot those high flyers who are likely to make the greatest contribution.
Resumes, interviews and gut feel are no longer the only tools in the recruiter’s box-of-tricks – HR professionals can now apply science to the art of recruitment.
According to Josh Bersin, principal and founder of talent analysts Bersin by Deloitte, companies that have embraced analytics are doubling their improvements in recruiting.
The power of analytics comes in uncovering patterns in data that are quite unexpected. Bersin found that most firms still hire using the basic formula of good school and good grades make good performers. Yet, one of Bersin’s clients, a financial services firm, used analytics to look at the relationship between sales performance of new recruits for their first two years and turnover.
They found that rather than grades and references, factors such as an accurate and grammatically correct resume, ability to work under unstructured conditions and sales experience in high priced items were the key markers for success. Feeding that information back into the recruitment business saved that company a whopping $4 million in the first fiscal period.
Similarly, by analysing hires, Google and AT&T have found that it’s a demonstrated ability to take initiative that is a far better predictor of high performance than academic record. Google is one of the few firms globally that takes a scientific approach to recruiting (and everything else it does in HR too). It has developed an algorithm that predicts which employees are likely to succeed.
The information to make these judgements already exists in HR systems – for example, education, performance, even their location and other information that can go a long way to help the process of predicting top performers or leaders. Talent analytics is a matter of combining HR data with business data to create a picture of business performance.
So, it’s just a case of analysing that data, then. Easy to say, not so easy to do, particularly when that data may not be housed in the same place or format. Even with all the data in one place, you still need to know which questions to ask and which data is relevant.
So there are few key pointers to remember:
Better to focus on a few key metrics than trying to ‘boil the ocean’ and go metrics mad, gathering data from all quarters, but failing to act on that data. It’s vital to keep an eye on business goals, and make sure the metrics you capture are tightly aligned with those goals.
Track the success of each recruitment channel. While you will probably already know how many employees are hired through each channel and the cost, you can then also begin to analyse which staff stay the longest and how long it takes to recruit someone.
Analytics can help you identify the qualities and competencies of your high-performing workers and how long are they staying in your company or department. This information can then be correlated with where they were hired from.
Google, for example, used to have an incredibly long recruitment process – in one case up to 30 interviews. Through analysis it established that there was no benefit to having more than five interviews and that interviewers were ready to make a decision 75% of the time after one interview, gradually rising to 85% after four interviews and then plateauing.
You should also compare attrition rates with hiring rates to see whether there’s a problem with succession planning or engagement.
Not everything you try is going to work. And that’s absolutely fine. What’s great about using analytics is that you can quickly spot when something isn’t working before you’ve wasted too much time and money going down the wrong track.
Don’t trust blindly to the science. You need insight too. According to an InfoChimps survey, the key two reason analytics projects bomb are because managers fail to interpret the data to create appropriate insight, and because the projects failed to put that data into a business context.
Even that most data-driven of organizations, Google, does not slavishly follow the data without questioning. The analytics team act as consultants: they present the data to recruitment staff, together with the context and then people are trusted to act on that information.
Recruitment is the biggest cost to your business. Is this really something that you can leave to gut feel alone?