How PayFit closed its data gaps in emerging tech roles and markets with Ravio

PayFit builds payroll and HR software for more than 20,000 small and medium-sized businesses across Europe. 

For its own 650-person workforce, the People team faces the same challenge its customers do: making compensation decisions with confidence, in markets that don't always behave predictably.

Clara Nicolas is Senior Compensation and Benefits Specialist at PayFit. Her team of four manages the full annual People cycle (performance and salary reviews, calibrations, bonus payouts) for an organisation that went through restructuring between 2023 and 2024, where headcount fell from 1,000 to 650 – including compensation team members. At the same time, sixty different bonus schemes were rationalised into a single, company-wide framework. The compensation function got leaner at exactly the point it needed to be sharper.

Part of that was getting the data infrastructure right, transitioning the team from running on a mix of spreadsheets and a homemade HRIS that nobody filled out consistently to clean, trusted internal data processes that made everything that followed, including compensation benchmarking, easier to do well – here’s how Ravio helped them get there.

The problem with their previous benchmarking providers

Before Ravio, PayFit used two external providers: a local market specialist and a traditional survey provider built for larger enterprises. 

The survey provider's 15-plus level structure never mapped well onto PayFit's own architecture. Then PayFit cleaned up and standardised its job levels – and the mismatch got worse, not better.

With the local benchmarking provider, Clara needed to extract benchmark data and reformat it for managers, but the provider did not offer data export. 

Beyond the time cost, there was a coverage gap. Her local provider had really strong data for France. For the UK, and particularly Spain, Clara found it thin.

"Spain is really Barcelona or Madrid in terms of work location, and we receive really mixed signals from that market," she says. "Salaries are low in some places, but at the same time more and more tech companies are settling there with higher salary expectations. It's a really weird mix."

When unexpected turnover happens in Spain, Clara needs to know quickly whether PayFit's offers are competitive or whether the market has moved. A single provider with limited local data wasn't enough to answer that with confidence.

"We don't want to treat Spain and the UK as second locations we'll consider later when we have time," she says. "We want to make sure it's still accurate over there."

How introducing Ravio changed things

When PayFit started evaluating new compensation data providers, Clara wasn't just looking for an upgrade. She was looking for something that would work alongside what they already had – to fill those data gaps in Europe, and reduce manual work.

Ravio came in not as a replacement for her local provider, but as a complement to it.

The decision came down to a few things: the ability to export data directly to Excel, flexibility to select specific percentiles, stronger data coverage for the UK and Spanish markets, and a market insights layer that provides context on hiring trends and salary movements by sector.

The time saving on data extraction was immediate – three to four hours became 30 minutes with Ravio.

Having two providers has also changed how the team approaches benchmarking decisions internally.

"When you have just one provider, you're a bit blinded," says Clara. "There's no way to know if there are gaps in the data or if something local is affecting it. With two sources, you can cross-check. You have a stronger base for the conversation."

She rates her confidence in PayFit's compensation data at 9 out of 10 today, up from 7 when the team was working from a single source.

Benchmarking emerging roles that don't fit neatly

One use case that's grown over time is benchmarking emerging roles that sit outside standard job architectures. Ravio's tech-focused job taxonomy runs deeper than most benchmarking providers, which has become more relevant as PayFit, like most companies, finds itself hiring for roles that didn't exist a few years ago.

AI-adjacent roles have been a particular example. As PayFit looks at AI Ops specialists, AI generalists, and other emerging functions, standard benchmarks struggle. The question isn't just what they earn, it's how they sit relative to other roles in the market.

"Should they be paid as a business operations specialist, or more like a software engineer with different skill sets? Ravio is useful because I know you're close to those market trends."

Preparing for pay transparency

PayFit is also in the process of building out its approach to the EU Pay Transparency Directive (EUPTD). The framework is taking shape: salary grids with a defined range between minimum and maximum pay at each level, with the 65th percentile as the midpoint reference. 

The longer-term goal is a full internal catalogue (every job, every level, with clear definitions), so that any employee can understand their own position and how it compares to others in the organisation.

"Our vision is that everybody has access to all the salary grids for all jobs and all levels," says Clara. "So as a Compensation and Benefits specialist, I would know what the grid is for level one, but also for levels three, four, five, six, and seven – and for every job in the company."

The confidence that underpins that work comes, in part, from having external data that can be challenged and cross-referenced before it goes in front of employees and leadership.

What’s next for PayFit?

Asked about where compensation data is heading, Clara is direct about one thing: AI can only do so much if the tools around it aren't connected.

She's equally clear that human judgment in compensation isn't going away.

"It always matters that a company has an internal fairness consideration and makes its own decisions, even if it's not 100% at par with the market. That's their decision to preserve internal fairness or competitiveness. The human decision is still super important."

For Clara, the rigour is worth it. "I love my job," she says. "It's super interesting."

It's a reflection of what good compensation work actually looks like – getting the data right, making the case internally, and building a framework that holds up when employees ask hard questions. The tools that support that work matter.

The impact: Before and after Ravio