Pave Transforms Equity Compensation Benchmarks Using Machine Learning

On behalf of the entire team at Pave, we are incredibly excited to announce the launch of our new Calculated Benchmarks feature for both free and premium Market Data customers! This release marks another significant step forward in our mission to bring the power of data science and machine learning to compensation and total rewards professionals.

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With Calculated Benchmarks, Pave’s Market Data platform will now automatically apply machine learning algorithms to our extensive real-time equity grant database—collected directly from HRIS, ATS, and equity management systems—to provide customers with market-leading equity compensation benchmarks by filling in gaps and normalizing results in areas where traditional survey providers struggle to give their customers reliable equity insights.

What is Pave’s Calculated Benchmarks feature?

Pave’s Calculated Benchmarks feature uses machine learning to identify patterns across our dataset that can be used to provide customers with more relevant, accurate, and timely equity compensation benchmarks. Using these patterns, we then apply a series of regression models to generate reliable results in places where robust data is often lacking (e.g., job families with low incumbent counts or markets with smaller concentrations of talent). 

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Prior to launch, our algorithm was tested extensively against real market data and by industry experts at multiple compensation consulting firms to validate outputs. In many ways, our approach emulates the manual data “smoothing” (or normalization) process already used by most compensation professionals.

How do Calculated Benchmarks work in Pave’s Market Data product?

Pave’s Calculated Benchmarks feature is currently deployed across both our free and premium Market Data offerings for the U.S. market only. In future releases, Calculated Benchmarks will be rolled out to more locations. 

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As of today, all U.S. market results in our Market Data product will automatically include a mixture of real data points, where raw data coverage and quality are high, and calculated benchmarks, where raw data coverage is sparse. Previously, areas with sparse data coverage showed no results.

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All data points generated by our machine learning algorithm are clearly visible in the product via a light gray dashed line underneath the equity benchmark value shown. Customers can click on all underlined values at any time to learn more about our methodology.

Why does Pave provide Calculated Benchmarks?

Companies, especially in the technology sector, need reliable equity compensation data to make informed pay decisions. Unfortunately, even when companies use traditional compensation surveys that are robust for cash pay, getting statistically viable information on equity award values and equity grant practices is difficult. Indeed, our data science team finds that generating reliable and stable equity compensation benchmarks requires 10 times the sample size relative to base salaries.

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Additionally, survey data is a point-in-time snapshot of the market that ages quickly and survey inputs often reflect equity grant guidelines as opposed to actual grant practices. Pave’s real-time equity grant database—collected directly from HRIS, ATS and equity admin systems—combined with our intelligent machine learning algorithms, gives customers an unmatched view of current market behaviors.

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Ready to learn more about how your company can sign up for Pave’s free or premium Market Data offering? Explore the data now, or get started for free. 

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