Employee compensation benchmarking startup Pave raises $46M

Employee compensation benchmarking startup Pave raises $46M

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Pave, a startup leveraging HR data to help companies develop compensation strategies, today announced that it raised $46 million in series B funding at a $400 million post-money valuation led by YC Continuity. Effective this morning, Pave also made its benchmarking info available, allowing companies to view data based on anonymized employee records across over 900 brands from 25 participating partners including Y Combinator, Andreessen Horowitz, and others.

The pandemic has accelerated equity shifts in the workplace, creating an urgency for insights amid a need for greater transparency and pay parity. According to Pave CEO Matt Schulman, it’s concretely led to a desire for compensation data and tools powered by an ecosystem of APIs that connect to HR systems.

Schulman founded Pave in 2019, aiming to build a platform that could draw on HR systems to replace spreadsheet-based processes reliant on outdated information. By incorporating HR and equity systems directly, Pave makes the case that it’s able to offer up-to-date market data that companies can use to communicate and compare compensation in real time.

“I have always been a personal finance nerd. So naturally, after quitting my job as a software engineer at Facebook in June of 2019, I talked with as many VPs of people and chief financial officers as I could and asked them about the biggest pain points they had related to personal finance,” Schulman told VentureBeat via email. “Countless times, I heard the same exact complaint: stock options are confusing and stressful … We’ve seen an emergence of modern human resources tech systems in recent years, but compensation has been firmly trapped in an offline, spreadsheet-driven era. We have lacked compensation transparency and fairness for all constituents in the ecosystem.”

Data-driven compensation

Pave’s platform allows companies to administer bonuses and equity refreshers during merit cycles with recommendation and approval workflows. They can access benchmarks to determine how much to pay an employee based on their role or extend offers to new candidates, enabling talent to model out total compensation packages, exits, and more.

Schulman says that Pave’s benchmarks are powered by a “robust” machine learning engine that classifies over 65,000 employee records into a normalized hierarchy of job families and job levels. For example, if a customer asks, “What should I pay a senior software engineer in San Francisco, California,” Pave can highlight anonymized people who are consistently classified as senior software engineers or variations of the title, like “principal software engineer” and “staff software engineer.”

“A big challenge of compensation benchmarking is consistently classifying employee records to a normalized framework of job families and levels … Human resources leaders waste days, if not weeks, every single year classifying their employees, [and] every single HR leader has a nuanced mental model for the distinctions between different job levels and families and how to map to the job leveling schemas used by incumbent compensation surveys,” Schulman said. “[We accomplish this with] models that take into account years of relevant industry experience, title, department, existing job leveling mappings to incumbent frameworks, number of direct and indirect reports, and other variables.”

Above: Pave’s compensation analytics dashboard.

In the future, Pave plans to offer more AI-powered products for enterprise customers, including one that will attempt to identify the “right” compensation package by considering both an organization’s needs and what candidates are willing to accept. Another capability will alert for employees at risk of churning, tapping Pave’s existing data on market patterns and outcomes.

Beyond these additions, Pave recently launched the Pave Data Lab, showcasing compensation trends in the market backed by Pave’s datasets. And Schulman says that the company will roll out a planning dashboard for cash and equity burn metrics, as well as diversity and inclusion tools that allow companies to have a pulse of potential inequities within their companies. Also on the horizon are offer letter acceptance intelligence and international expansion to “adjacent markets.”

“Companies are increasingly hiring outside of their HQ and have an urgent need for international compensation benchmarking data. We plan to target expansion into Canada, the UK, Europe, Israel, and more frequently requested locations in 2022,” Schulman said. “We strongly believe that all customers should have free access to Pave’s real-time compensation benchmarking dataset … We are relentless in pursuit of building the most comprehensive marketplace of real-time compensation benchmarks for the tech industry and eventually for all industries worldwide.”

Pave, which has a workforce of more than 50 employees, competes with incumbents like Radford, Option Impact, Redcat, Workday‘s Advanced Compensation module, and new startups including Pequity, Welcome, and Figure. But Schulman says that the company has experienced “tremendous growth” across all indicators, including 25 times growth in revenue since its series A funding round in late 2020.

Pave now has over 900 customers with over 65,000 users.

“We have witnessed an unprecedented flux in the labor markets. First came the abrupt global Covid lockdown of March — Sequoia called it The Black Swan of 2020. Shortly thereafter, the public markets crashed and we endured the largest rounds of concentrated layoffs since The Great Depression. Unemployment rates peaked at 14.8% in April,” Schulman noted. “Remote and hybrid work is now a norm rather than an exception to the rule … Now, compensation is a topic of discussion at Board meetings for every company in the world.”

The latest capital infusion brings Pave’s total raised to $63 million to date. In addition to YC Continuity, the startup’s series B had participation from return backers Andreessen Horowitz, Bessemer Venture Partners, and others.

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