You rely on your gut daily to make recruitment and recruiting strategy decisions. It’s human nature. But a better and more comprehensive method for decision making, whether it’s who to hire or how to focus your candidate sourcing efforts, is a blend of data with technology and a bit of your instincts added in. That’s where social recruitment and engineering merge, says ERE Media.
Neither method is perfect and neither is wrong. There’s no accounting for experience in the recruiting industry, and no amount of data can duplicate it. But likewise, your instincts are also prone to mistakes. It’s in the combining of both approaches that recruiters can arrive at a more reasonably sound plan.
Good Decisions Stem From a Wealth of Information
It’s hardly a decision if you arrive at it blindly or only entertain one choice. But that’s what often happens when a recruiter focuses too tightly without considering the many alternatives that are available. Problem is, without data those alternatives are invisible.
Using an intuitive approach, even if it’s rooted in years of experience, recruiters might not assess the pros and cons of any approach with enough information. And that can lead to missed opportunities.
ERE Media uses the example of whether to invest in inbound or outbound recruiting methods. Outbound can be expensive, leaving the recruiter to think that the cons outweigh the pros. But using a recruitment engineering model, the recruiter might predict more accurately that the quality of the talent pool, and consequently the quality of hire, will outweigh the additional expense of outbound efforts. Data makes this alternative to the instinctive approach possible. Without it, all you’ve got are your wits.
You’ll Probably Need Different Models
There’s no single correct social recruitment engineering model. Further, you’ll probably need more than one to assess your options for different decision-making scenarios more accurately. The whole purpose is to make good decisions easier, not harder. So while developing models for your strategies does take an investment of time and resources, plus more than a little experimentation, the payoff is better hires with fewer bad choices and missed opportunities.
According to ERE Media, you’ll need four elements in each model. First is model specification. This portion determines which variables are needed to represent the situation where the decision is being made.
Next is model calibration. A good model should be adaptable and allow calibration based on your experiments with it. Then comes model validity and value. This answers the question of whether the model gives a realistic representation of the talent environment. And finally there’s model usability. Does the model work, and does it help recruiters make decisions? When all of the elements align for the situation, you’ve got a model that works.
The cost of bad hires and missed opportunities is high. But if you aren’t aware of valid, reasonable options, it’s only natural to rely on experience. That can get you into trouble.
Social recruitment engineering doesn’t rely solely on data, either. Your talent decision-making models help you focus on the issue at hand and use a more realistic impression of candidate behavior to complement, not replace, your intuition. Using decision-making models, you can help eliminate instincts that aren’t helpful and make better decisions for every part of your recruitment strategy.
Recruiters make critical decisions every day, and many of them can have a dramatic effect on the pool of attainable candidates.
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