Job description best practices based on hiring data
Mar 07, 2017
You’ve made it to the end of the series! For the last 5 weeks I’ve been sharing data to help you be the best job post writer you can possibly be. Doesn’t time fly when you’re having fun? Check out these links to catch up on parts 1–4: How to attract, engage and convince great candidates to apply, and why you should even care.
You’ve probably noticed a common theme throughout the series — data, data, and more data.
Last week we talked about how it’s possible to take the subjectivity out of writing with enough data (like at Textio, where we get 10 million new job listings each month to measure how hiring language is performing). Having a huge database of job posts and their results allows Textio to detect patterns and tell you how to write better — like which words bring in the most qualified candidates.
Today we’re going to talk about one of my favorite topics, which is tracking how language changes over time. One need only look at the recently used emojis of a passing pre-teen to know that the way people communicate is constantly shifting. What if you could track and harness that linguistic change to help you write the best job listing ever?
Well, you can! Today we’ll cover three linguistic factors that Textio found should change the way you approach writing job posts. We’ll also share best practices for dealing with each, all backed by Textio’s predictive engine:
- What language works right now?
- What kind of job am I hiring for?
- Where is the job?
Trends shift, so should you
For the past two years, Textio has measured the biggest “winners” and “losers” in the world of hiring. Specifically, what words cause roles to fill quickly? What words are so uncool that roles take forever to fill?
We’ve specifically done this in tech, where the hottest trends can become cliches faster than snapping your fingers. In 2015, the biggest winner was Artificial Intelligence. *pats Textio on back* Heading into 2017, the biggest winners are inclusive language, like what you might see in an equal opportunity statement.
Staying in tune with how applicants react to your listings in real time gives you a leg up on the competition.
Don’t talk to engineers like they’re in sales
The language you would use to recruit engineers is not the same as the language you’d use to recruit for a sales or marketing role.
That may seem obvious, but where do you begin?
It might surprise you just how nuanced the differences are when Textio measures what works over tens of millions of job listings. Compare these phrases for retail and IT roles, for example:
If you’re looking for other type-specific help, there’s some great content already on Textio’s blog. For example:
- Teachers are dreamers
- Prioritize people over passion to hire better chefs
- The best bankers also have a sunny disposition
Synergy: only the worst thing ever depending on where you are
The best (or worst) thing about data is that it often defies our expectations. Take the word synergy, for instance. It’s achieved a level of corporate jargon-ism that’s almost laughable. But — wait… what’s that? Synergy sometimes performs well?
It turns out that some cities actually show positive affects when job listings include the word synergy. We have literally no explanation (unless they watched Barney Stinson’s CV video) but the data speaks.
Here are some other regional differences that Textio has detected:
- Which cities are “super”?
- Intensity not required
- Everything is awesome! Unless, you know… it isn’t
Thanks again for checking out the series! If you take anything away from these articles, remember this:
- The words you use matter.
- The more real-time data you have, the better.
If you have any more questions on Textio, the data, or how to write your best job post ever, don’t hesitate to get in contact with me!
Interested in hearing even more? I’ll be giving a talk at General Assembly in Seattle on April 4 (sign up details to come). Come ask me all of your juicy data questions in person!
For more tips on how to write your best job post ever:
