We now partner with PayScale to help ensure you are making the most favorable offer to attract and retain the best candidates. Help level the playing field by knowing the stats and making offers that are win win for both you and the candidate. The reports integrate directly into our onboarding and applicant tracking system.
PayScale has the world’s largest salary database!
By the numbers:
- 54 million salary profiles
- 15 thousand job titles
- 10 million monthly visitors
- 300 thousand completed surveys
Benchmarking Made Easy
Jobs are unique and constantly changing. PayScale prices positions based on the compensable factors that truly reflect the job, not generalizations made by traditional surveys or relying on old-school, less specific, employer-submitted data. PayScale uses Market-Matchtm algorithm and it looks at the potential compensable factors and the relationships between those factors when finding the ideal matches for positions, yielding the most highly accurate compensation predictions possible across a huge range of circumstances. PayScale then provides a detailed “PayScale Market Report,” containing a breakdown of total compensation, benefits, trends in the market over time, and how pay changes with different compensable factors(such as years of experience, education, etc.).
PayScale’s approach is led by a team of data scientists and a combination of machine learning and human validation process. 25 hard coded validation points that could trigger criteria for review, ensuring only the highest quality data makes it through.
FAQ – Frequently Asked Questions
Where Does PayScale Get Their Data?
PayScale crowdsources data from visitors who come to it’s site looking for real answers about what they should be getting paid. Whether they are prepping to ask for a raise, evaluating a job offer, or just want to know how they stack up against others, they have an intrinsic motivation to not only complete PayScale’s online salary survey but also to give honest answers.
How Does PayScale Make Sure the Data is Good?
After the data is crowdsourced, it is then run through verification filters to confirm its validity:
Weed Out the Outliers – Data is discarded that is too far outside of expectations.
Defend Against Attempts to “Stuff the Ballot Box” – Too much data coming from any one person is automatically detected and rejected.
Standardize the Data – You say “computer programmer,” I say “software developer.” PayScale has the technology to realize a user is talking abou the same job.
Augment the Data – PayScale knows a lot more than just what it receives from their surveys. For example, if a user tells them they’re in Kuna, Idaho, it knows that is in the Boise, Idaho metro area. In addition, it knows that the last census placed 606,376 people in the Boise area, and how being in a city of that size affects compensation.
Once the data goes throughthe above filters, PayScale still has more than 54 million profiles and counting of statistically relevant data.
Can PayScale Address “Very Unique Jobs” or “Very Obscure Locations”?
Yes. Even with as much data as PayScale has, there can still be gaps and in those cases, MarketMatch (their matching algorithm), makes sophisticated mathematical predictions to get the answers a user needs. For example you are inquiring what a Java programmer with 7 years of experience in Harlan, KY, should be paid and there’s not an exact match in the data. PayScale can determine how software developers are paid, in general and then adjust the prediction to account for having seven years of experience, for being in a small town in the upper south and for programming in Java rather than C++.