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PayScale Introduces Predictive Pricing Recommendations with Machine Learning

So many compensable factors, so little time! If only a recommendations engine could use machine learning to tell you exactly what an employee should be making.

Compensable factors are the attributes that affect how a job is priced and how someone gets paid. No matter what job it is, all jobs have unique compensable factors and skills that help determine a pay range.

For a manager, the number of people you supervise can be a compensable factor. For a Software Developer, the technologies and coding languages you use are compensable factors.

In PayScale’s Insight Lab product, a user selects and attaches these compensable factors to a job title. This allows you to define different responsibilities and levels for every job at your company.  Even more important, it allows you to accurately price jobs to attract and retain top talent.

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If you work in the HR space, you know that reviewing and identifying these factors are anything but easy. It takes consistent market research and conversations to know what skills and compensable factors you should use when pricing a role. Yet, in the emerging world of machine learning and automation, PayScale provides a new feature to help you keep your fingers on the pulse of the market and price your jobs quickly and confidently.

This new feature is called Predictive Pricing Recommendations.  With our compensation recommendations engine, data is analyzed from multiple sources to automate compensable factor recommendations for every position. This means you don’t have to guess how the market is pricing a role. PayScale automates and delivers market pricing information as a predictive recommendation using machine learning. This has two major payoffs:

  1. Predictive Pricing Recommendations delivers compensable factor recommendations you can trust based on how users just like you are pricing the same job.
  2. This results in less time spent pricing jobs and more time working on strategic initiatives at your organization.

So, where exactly are the Predicative Pricing Recommendations coming from and how good is the data?

PayScale has never been your average compensation SaaS. We have been innovators from the start by delivering skill-specific data. So, it’s not surprising that we look at a variety of sources when it comes to the data we analyze and use.

Predictive Pricing Recommendations delivers job and compensable factor recommendations from:

  • PayScale’s crowdsourced data, back by our patented MarketMatch Algorithm
  • HR and Comp Pro product users
  • PayScale data analysts’ research

Pulling together compensable factors from these various sources gives PayScale a unique advantage.

First, by sourcing compensable factors from our crowdsourced profiles, we are getting compensable factors directly from individuals who are in that position and know the exact skills they bring to work every day. This is where PayScale gets our skills data, and what allows us to add more emerging and specialty titles. All straight from the consumer on a daily basis.

Second, by looking at historical client usage data in our products we get a very different perspective: how does someone designing the compensation structure for their company define roles within their organization? Here we are leveraging the wisdom of thousands of HR managers and comp pros by looking at how they use PayScale products. By using the Predictive Pricing Recommendations, we allow our users to pull from this valuable pricing work that has already been done. No more starting from scratch.

Finally, we are pulling compensable factors recommendations from our own detailed survey research. In this research, factors are chosen based off research done by PayScale data analysts to determine what factors move the needle for certain positions in given industries. If you think of the crowdsourced data as the expert on the street, and client data as the expert peer in the HR sphere, the data analyst research is your in-house comp pro.

As the PayScale platform continues to validate and evolve its machine learning, we will improve each successive release by looking at trends in our user data. Where are our customers using our recommendations engine and where are they not?

Compensation data in an ever-changing compensation landscape is an ideal place to apply machine learning through a recommendations engine. It’s here we can reference large data sets to analyze client feedback and usage across the greater PayScale community — and, in the near future, make the task of confidently pricing every role effortless.


Chloe Huber
Data Analyst II at PayScale
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