Big data has permeated almost all aspects of the enterprise today—from sales and marketing to product management to back-end IT. Now, the smartest companies are using big data to help fuel next-generation HR operations.
Traditional tools like LinkedIn, outside recruiting firms and even the traditional Rolodex can still play a big role in snagging new employees. But in today’s ultra-competitive job markets—where it’s difficult to hire and retain sought-after employees like engineers, data scientists and designers, particularly in the high-tech industry—companies need to go deeper and use advanced, data-driven hiring tactics to stay ahead of the curve.
Have potential candidates recently updated their profiles on an online jobs site? Did they just hit a one-year work anniversary—at a company that was recently acquired? How close does a particular candidate live to your office? All these bits of information, carefully analyzed, could help you hire the right people or keep them from leaving if they’re already employees.
But big data in HR is still a relatively new practice. A 2014 study from Deloitte found that only 14% of companies use analytics for HR at all, and of those, 67% are ‘weak’ at doing so.
Here are four ways companies can apply big-data techniques to their hiring process, and stay ahead of your competition:
Define your dream candidate. Specifically defining what qualities you need in a new hire will help you find the best one. Start by taking a good look at your current star employees and managers. What traits do they share? Why, generally, are they so successful? You can use internal surveys to gather this type of data. Google, a leader in using analytics for hiring, regularly surveys employees, asking them about their personalities and attitudes toward company culture, projects and co-workers. The company then combines the survey data with hard statistics such as role, tenure, location and performance metrics to find out what makes employees great, and feeds that data to recruiters.
Make sure you reach out at the right time. Once you’ve identified great potential candidates through a data-driven search, you must reach out to them at the right time. (You’ve only got one chance to make a good impression, after all.) Using data analytics to find the best time is a good move, since conventional wisdom about when people leave their jobs (such as, after four years, or when their stock options are vested) is not accurate.
Hiring-intelligence platforms actually score potential candidates to find out if they are likely looking for work. These tools allow you to run queries to get an accurate read of a person’s ‘propensity to churn’ at any given time. Key data points include how long someone has been in his or her job — data shows that people are most likely to leave at the one-year mark. (The two-year mark is the second most likely time for departures.) Another important factor is how well a candidate’s company has been performing, since layoffs, sinking stock prices or other bad news/corporate scandals can make employees more likely to jump ship. It’s also important to remember that many people start looking for new jobs after their companies are acquired. And, obviously, people who are frantically updating their social-networking profiles may be trying to network and look for a new gig.
Don’t ignore the smaller bits of data. Every little data point counts when you’re attempting to get a read on a candidate. Even details such as which type of Web browser a candidate uses to submit his or her application can be important. Incredibly, data shows that applicants who apply using a non-default browser (e.g., Chrome or Firefox) stay in their posts 15% longer than those who use a default browser, like Internet Explorer or Safari. Another important data point is potential commute time to your office. According to Evolv, those who live closer to your office are far more likely to stick around.
Keep meticulous notes during the interview process. Data collection shouldn’t stop once you get a candidate started with the interview process. In fact, interviews provide a great opportunity to collect valuable data you can use later to identify the highest-value candidates. Have interviewers take notes, ask questions and score each candidate on a series of pre-defined metrics.
The metrics you choose to measure will depend on what type of employees you’re trying to hire and your own business situation. For example, online marketing firm HubSpot started off by writing down a set of attributes the company thought great salespeople would need to succeed there, then evaluated candidates against these attributes during interviews. Over time, as hiring managers collected more data, they were able to measure which of these attributes actually best correlated with long-term sales success, and then scored candidates with these attributes the highest.
Hiring is never going to be easy. But by marrying big data with old-fashioned networking and intuition, companies have a better shot at finding and retaining top talent—and boosting ROI.
Roger Lee is a general partner at Battery Ventures in Menlo Park, Calif.; this is his seventh “Software Entrepreneur’s Playbook” blog post about building a billion-dollar software company. This post originally appeared on USAToday.com.
The information contained herein is based solely on the opinions of Roger Lee and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as, legal, tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity.
This information covers investment and market activity, industry or sector trends, or other broad-based economic or market conditions and is for educational purposes. The anecdotal examples throughout are intended for an audience of entrepreneurs in their attempt to build their businesses and not recommendations or endorsements of any particular business.