NathanParcells | , , , , , , , , , , , , , , , , ,| By
There has been a data-revolution taking place in the last couple of decades. I am not referring to the trendy conversation taking off about the Big Data industry, which companies like McKinsey are calling the next frontier in innovation, but instead I am referring to the increased focus by many companies on using data to test whether or not their assumptions about marketing tactics, sales process, and other strategies like hiring are effective and well-optimized.
A quick look at the rise of data-driven decision making.
While using data to track and test assumptions is nothing new, it is certainly on the rise. A lot of people credit this to the success of Google, a notoriously data focused company. Google is known for testing everything in their company from button colors to teams without managers, and using numbers to validate which approach is most successful.
Advocates of data-driven decision making point out that many companies who don’t use data end up relying on HiPPO decision-making–deferring to the Highest Paid Person’s Opinion. Companies like Amazon, Google, and more recently the Obama campaign, have found that routinely our highest paid decision maker’s might be great managers but their intuition is often wrong and testing assumptions is not only valuable, but also allows you as a company and team to invest more heavily in ideas you know work.
The data trend has been moved forward with the advent of new technologies that now make it incredibly easy to run tests online (but also increasingly offline) and discover that something as minor as choosing between a red and green button can lead to a 21% or greater likelihood that a desired action is taken.
(PS — What color is the apply button on your corporate site? If you are like Best Buy and many other companies who follow brand guidelines over optimization guidelines you may be missing out on candidates).
Why Hiring A Data Scientist Can Help Your Team Get More Money, More Traction and Better Candidates
There are a number of situations for which data-driven decision-making may not be a great fit. That being said there are a number more for which it will help your team make hard decisions and become an even more competitive and effective hiring organization. Here are a few:
Rallying internal support. I enjoyed Kathleen Missildine-Martin’s fantastic post in HR Examiner about the importance of creating a great HR data storyGoogle Analytics. I hear it time and again from our clients and from HR practitioners at conferences—when you have a small over-worked team, taking employees off projects to try new effective hiring tactics is difficult. It becomes even more difficult when trying something new also requires making a sales pitch to your VP of HR and the rest of your team in order to get buy-in. If you have someone on your team who can easily and accurately track any new campaign you try, a lot of these barriers go away. Campaigns stop being risks, and insteadbecome a well-tracked experiment. With simple, free tools like , and a data guru at the steering wheel, even tough to value social efforts are actually analyzable.
Getting more funding. Another challenge HR practitioners face is getting the funding necessary to try new tools and strategies and stay on the cutting edge of hiring the right people. I experienced the power of data on this front with my own company. At InternMatch we were working with a Fortune 500 company for a second year and our company contact wanted to move forward with a renewal, but was receiving pushback from their boss about lowering costs at the company overall (who isn’t?).
We sat down and reviewed the data and quickly saw that more intern hires had come through our product than any of their previous sources that they had relied upon for years. The result? Our contact returned to their boss with this information and not only did the funding come in, but they got double the budget to invest in our partnership. Data de-risks investments and helps funding flow more freely.
Benchmarking and hiring the right candidates. The last and important case for data scientists within HR is not just for looking at sources for making hires but also the success of sources over time. In the University Recruiters LinkedIn group Shawna Buchannan, CBS Interactive’s head of College Recruiting recently asked, “How does your org define Recent College Grads for metrics/tracking purposes? It looks like most orgs define RCG’s as b/w >6 m & >12 m out of college (BA or MA).” (Curious your thoughts?).
I am fascinated with how different views of these kinds of benchmarks can influence internal successrates. Are graduates hired 6 months after school more likely to succeed at your organization than those 12 months after? What does this mean for your hiring tactics? These long term success rates are more difficult to track but again are the bread and butter of a good data scientist and will dramatically increase your longterm hiring success.
Be Smart, Not Quick
If your HR team is lean and cash-strapped it might seem odd to bring on board a new hire who doesn’t immediately move the needle on your bottom line, but by allowing you to better analyze new ideas, better evaluate campaign costs and make smarter hires, this role will raise the success of your entire team.
What are some techniques you’ve used to hire smart when your HR team might be cash-strapped or low on funds?