We’ve all faced the challenge of determining “the right number” for an offer:
Our former reality was a best effort attempt to parse incomplete datasets, scour the internet for self-reported numbers, and check in with peers and unreliable surveys. All we could do was make an educated guess with the right intentions.
It’s time for us to stand up and acknowledge how broken the current state of compensation data really is.
Datasets are hidden behind multi-thousand dollar paywalls. We contribute to surveys by spending hours (or days) getting information into spreadsheets only to be sent something back three months later when the data is stale. We see snapshots in time without context of the movement in the market.
The status quo is broken but fortunately there is a way out.
We’re a group of ambitious compensation experts, data scientists, and engineers seeking to bring transparency and fairness to the world of compensation.
To that end, we’ll be publishing insights on this blog which answer frequently asked hiring, retention, and compensation questions using our own benchmarking product.
We’re doing this because determining compensation is hard. So let us work with you and build towards the future of transparent compensation.
We want to transform the compensation world away from a closed system where insights are determined behind closed doors without reference to a broad base of data.
No more self-reported numbers, small sample sizes, or year old data.
How are we doing this?
We are proud to be in the middle of a vibrant ecosystem of HRIS, Payroll, ATS, and Cap Table systems. We have thousands of real-time integrations with companies around the United States and want to support all systems to bring the world of compensation data online.
With Pave you’ll know exactly where the market’s going based on who’s getting paid what, today.
And every one of these insights is taken from Pave’s free real-time benchmarking tool. If you like what you see in the Lab you can take 5 minutes to contribute to the network and get access to the data here.
Have your own data request that you want the Lab to dig into? Send an email to PDL@pave.com and we’ll get to work!