Unlocking the compensation secret vault: An interview with Chris Bolte of Paysa

Salary negotiation can be a balancing act unless you have good information.
Salary negotiation can be a balancing act unless you have good information.

One of the great secrets in HR for both companies and for candidates is “what is someone worth?” Why is that question asked? The answer is companies want to know what they need to pay someone, either as a new hire or in order to forestall having them leave. Employees want to know that information in order to better prepare for salary negotiation as they change jobs or ask for a raise. The bigger question becomes how do you do that?

Salary information

The obvious answer to that question is the INTERNET. You go search for sites that offer salary data to get some idea of what other people like you are paid. Salary expert Jack Chapman says:
These sites can help shape your opinion:

  • PayScale.com — collects ongoing salary data directly from visitors.
  • Salary.com — collects salary data from companies and customizes it to location, size of company, etc.
  • CareerJournal.com — has articles about salary trends.
  • Bureau of Labor Statistics — supplies surveys of corporate payroll data and employee questionnaires.

You won’t get one simple numeric answer, but with an hour or so of effort, search and printouts, you can get a range for the pay-level comparison.”
Unfortunately that is often more work than people really want to do. That is where Paysa comes in. In a recent conversation Paysa co-founder Chris Bolte said most of the year old salary data can be as much as 20-30% off. “Paysa”, say Bolte, “thrives on recency.”

Big data

Paysa is an example of the use of “big data.” Their “machine” constantly trolls the Department of Labor, multiple public websites, salary reports, recruiters and job hunters themselves. They have collected 35 million salary points, looked at 90 million people and millions of jobs. As their website says “Leveraging its massive data sets of jobs, employee profiles, and compensation data points, plus utilizing its proprietary machine learning algorithms, Paysa calculates an employee’s total Paysa Market Salary.”
This data has use to both employers and employees. It allows employers to build a realistic budget. CFO’s and HR find this to be very valuable. They can get some idea of contribution in relation to value.
It helps employees by letting them understand and establish their worth to better prepare them to talk to employers about salary. Bolte told me this was his inspiration for creating Paysa. According to the company website, their tool called Paysa Explore:
“gives users the ability to research the market to discover:

  • New skills that could increase their value
  • What their salary will likely be at other companies and the likelihood that they could get that job
  • What it would look like if they moved to another location – what jobs are available and what they would be paid and how that looks with the cost of living adjustment
  • What people like them are making at other companies”

And unlike the research suggested by Chapman, it can all be done in one place.
They have another tool as well, called Paysa Jobs. Based on a user’s profile,”Paysa Jobs scours the internet for all jobs posted that relate to their profile, and deliver a personalized list to the user’s Inbox, with their market salary at that job calculated for them.”
The good news is that it is free to the employee user. All you have to do is feed it data. The bad news is that right now it limited to professionals in software, data science, or IT, in the U.S. Bolte told me they have ambitions to extend their tools to an ever increasing pool of jobs.
I was interested to see this creative use to big data and if you are in HR in a software company or you hire IT professionals (and who doesn’t) then you may find this to be a very valuable tool. You can find them on Twitter @getpaysa.
By the way, this was not a paid blog post. I received no inducement to write this I just that it was a very creative HR way to use big data.

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