There is strength in numbers. Not just small, magical numbers like 23, or 13, or 9. The real power lies in really big numbers, like 259 million. Because when you have a data set that large, data mining can start to uncover subtle but important patterns, revealing useful insights that go far beyond what you can humanly discern without the help of machine learning.
Right now, Textio’s predictive engine is analyzing text from more than a half billion job postings with real-world hiring results, like how long it took to fill the role, and how many applicants were qualified enough to interview. Every month, about 10 million new listings with outcomes are added. The more posts there are in the data set, the better the engine can predict what kinds of writing will attract the strongest candidate pool to that one job post that you are writing today.
Soon you’ll post that job listing, and you’ll receive applications from it, and eventually you’ll hire someone. If those recruiting and hiring stats from that one listing could somehow loop back into the original data set, then Textio could see how accurate its predictions were, making the guidance you get in the future even more effective. Now imagine everyone is doing that with their job posts. That’s when the power of data begins to multiply on itself, forming a true learning loop. And that’s where Data Exchange comes in: everybody wins.
It’s not by coincidence that Textio offers customers a significant credit for becoming a Data Exchange partner. Partners add their hiring stats to Textio’s data set. That creates better writing guidance, which in turn results in more effective hiring for everyone in the loop. All of those outcomes are then also added to the data set, and the cycle just keeps building and spiraling, upward and outward. Textio is literally fueled by hiring data, so the more data you contribute, the less you should have to pay for your subscription, because you’re making the results better for everyone.
Participation in Data Exchange is as simple as exporting the hiring statistics that you’re probably already keeping in your applicant tracking system:
Time to fill
This metric is a basic, minimum requirement for hiring data: how many days passed between the time you posted a new job listing to the time you filled the role?
Screening rates
This is the number of candidates who make it past each stage of screening for a job. For example, how many applications did you receive, and then how many were qualified enough for an initial phone screening? How many people made it past the phone screening to an interview? The more detail you track in your hiring funnel, the better.
Demographics
If you also provide anonymous demographic stats from your applicants, you’ll be able to see exactly how your company is executing its diversity objectives. Note that these metrics are anonymous and in aggregate—Textio follows strict guidelines that do not allow us to accept any personally identifiable information associated with any hiring data.
Every company could (and probably should) be tracking all of this hiring data themselves and analyzing it to see what works well in their job posts and what doesn’t. But the reality is that even very large companies simply don’t generate a wide enough range of job postings to detect the kind of data patterns that 10 million listings a month can reveal. With the help of our partners, Textio aggregates and analyzes massive volumes of anonymized data from a range of different industries around the world, so that everyone using Textio gets better hiring outcomes.
Because there is strength in numbers.