CONTENTS
The challenge: estimated data, zero defensibility
About:Energy is a battery software company that helps automotive and aerospace manufacturers get products to market faster. With a team of around 20 - predominantly scientists and engineers - the company operates in a deeply technical world where evidence matters.
Emily joined as the sole people ops person in a company that had grown from an academic spin-out. Her role is part people operations, part internal consultant - translating best practice into something that actually fits a 20-person deeptech company. Salary benchmarking, in that context, isn’t a process that happens in a vacuum. It has to be backed by robust data.
Before Compensation IQ, About:Energy was using an alternative platform that pulls from live company-reported pay data. For many usual roles in software companies, it works well. For a deeptech battery technology company hiring electrochemists, lab technicians, and heads of battery science in London, it surfaced a different story.
Vague job titles, estimated data, unreliable ranges
The core problem was granularity - or the lack of it. Their original platform mapped highly specialist roles into catch-all categories. A lab technician, a head of electrochemistry, and an R&D engineer could all land in the same bucket: “R&D Technician.” The salary ranges attached to that bucket were, in many cases, AI-estimated rather than real.
A battery scientist would be registered as an R&D technician. But a lab technician would also be an R&D technician. And a head of electrochemistry would also be an R&D technician. You had all these very different roles shoved into a vague job title based on estimated data.
The downstream effects were challenging. For generalist roles - head of finance, HR, software engineers - the data felt reliable. But for the specialist science and engineering roles that make up the majority of About:Energy’s headcount, confidence in the data was low.
A pay process that described itself as market-data-driven, but was in practice a patchwork of estimated figures, arbitrary percentage uplifts, and informal sense-checks. For a company of scientists, that’s a problem waiting to happen.
Ranges that didn’t make sense to real people
There was another layer to the issue. About:Energy had tried to make the data work by applying it at a functional level - one range for tech, one for ops, one for commercial. The resulting bands ended up being wide and disconnected from people’s job titles, and employees struggled to locate themselves within them.
We had these huge ranges that people were very confused about. They couldn’t relate to the title of ‘R&D Technician’ because we don’t have an R&D Technician here.
With annual pay and performance reviews approaching in January 2026, Emily knew the existing data wasn’t up to standard.
Finding Compensation IQ: a conference, a lightbulb moment
Emily first came across Compensation IQ at the Operations Nation conference in October 2025. Hearing the presentation on salary benchmarking from Josephine, CEO and Co-Founder, set off alarm bells about how unprepared About:Energy was heading into January.
She also evaluated Ravio alongside Figures, but felt they had limited coverage for niche roles, opaque data sourcing, and a user experience that didn’t suit a time-poor ops team. Figures was also more expensive relative to the value it delivered.
Ultimately, the decision came down to three things:
Data breadth and transparency
Unlike platforms relying on a single data source, Compensation IQ’s multi-source approach - pulling from salary survey data, job postings data, self-reported pay and live real-time benchmarks - meant there was genuine coverage for specialist roles. Critically, you could see exactly where each data point had come from, and what had been excluded and why.
Data confidence and precision
“What I liked about Compensation IQ is it’s really clear when you don’t have the data for something,” Emily explains. That honesty - flagging gaps rather than filling them with AI estimation - was more valuable to a scientist than a confident-looking number that couldn’t be explained.
Strategic Support, not just a platform demo
As the only people ops person in the company, Emily needed more than software access. The conversations with Compensation IQ went beyond demos into how to develop a robust approach to salary frameworks. “It was really nice to have conversations that were strategic, rather than just platform-based.”
Making the switch: getting internal buy-in
The Head of Finance was an easy win because the subscription cost was significantly lower than their current provider. The CEO required more persuasion. His concern was that their existing benchmarking platform had been tightly integrated with the company’s progression framework. Emily’s response was direct: the framework might look polished, but the numbers inside it were unreliable.
She reframed the goal: instead of a platform that pretended to own your pay philosophy, the team would use Compensation IQ to own it themselves - defining what years of experience means, which industries are relevant comparators, and why certain job titles were included or excluded. That ownership, she argued, was what would actually hold up under scrutiny.
The CEO was convinced. The switch was made.
The solution: multi-source salary benchmarking with full transparency
Onboarding
Emily describes onboarding as unexpectedly straightforward. Rather than a generic product walkthrough, the process began with strategic conversations about About:Energy’s compensation challenges - their pay philosophy, their specialist roles, and how they needed the data to be used in practice.
It went beyond demos. It went into what was fundamentally not working for us beyond just the data. Onboarding actually filled in a lot of gaps.
After a 1:1 onboarding session and a follow-up where she walked through her first benchmarking attempts, Emily documented her search methodology so it could be handed to colleagues - for example, the Head of Finance - if needed.
Salary benchmarking: precision over generic pay data
Using Compensation IQ’s multi-source approach, Emily benchmarked all roles within a few days. For each role, she pulled data primarily from job postings - which offered the widest variety of comparable job titles - and cross-referenced with self-reported pay data as a sense check.
For a role like lab technician, Emily didn’t just pick the closest job title. She worked with the CEO to interrogate what About:Energy’s lab technicians actually do - battery chemistry, not biology; electrochemical engineering, not mechanical. That meant ruling out “mechanical engineering technician,” and keeping only the titles that genuinely mapped to the role, such as “electronic technician”.
I wanted to make sure the data I collected was accurate to what we were doing in our lab - because it’s a battery lab. It’s not a biology lab. It’s a mix of chemistry and engineering.
The platform’s ability to add notes directly to each benchmark - recording which titles were included, which were excluded, and the reasoning behind each decision - turned the benchmarking process into living documentation. That proved invaluable in pay review conversations later.
The impact: real numbers, less time, more trust
Dramatic reduction in post-review friction
Perhaps the most striking operational change was in what didn’t happen.
Under the old system with unreliable data, unresolved pay queries would cascade through multiple stakeholders: Emily, the CEO, the Head of Finance, and sometimes external contacts - each spending hours trying to patch together a defensible answer.
With Compensation IQ, Emily handled the entire post-review process alone in two hours, because the evidence was already built into the platform.
With the defence cards, it felt collaborative. You can explain things, people ask questions, and they can see data they’ve never had access to before.
Emily was able to resolve follow-up queries in a single meeting using the notes and data already documented in the platform. Total time spent on post-review queries: approximately two hours.
For a team of scientists who expect rigour in everything they do, that transparency wasn’t just nice to have. It was the minimum bar. And for the first time, About:Energy’s salary process cleared it.
Data confidence nearly doubled
Emily estimates her confidence in the pay data moved from roughly 50% under the previous system to approximately 90% with Compensation IQ. The remaining 10% reflects the inherent challenge of benchmarking truly niche roles - but even here, the platform’s ability to accept custom data means accuracy will only improve over time.
The confidence bit is what saves you time. You go to people, you show them where the data comes from, and they trust it. Before, it took multiple people a lot more time to get less clear answers.
Advice for HR and ops leaders looking for better benchmarking
Emily’s perspective on salary benchmarking has shifted considerably through this process - from something you outsource to a platform, to something you actively own.
A framework built for ongoing use
Beyond the annual cycle, Emily now uses Compensation IQ for ad-hoc benchmarking whenever the CEO is considering a new hire. The consistent methodology means every role - whether an existing position or a future one - is evaluated against the same real-time, multi-source salary data.
In every company I’ve worked at, salary benchmarking has been a headache. What I like about Compensation IQ is you can just go in and apply the same philosophy to any role. When my CEO asks for a rough benchmark on a new hire, I can do it in minutes.
So what’s the NPS?
When asked how likely they are to recommend Compensation IQ on a scale of 1 to 10, Emily was a 9 out of 10.
The one improvement she’d love: a ‘search again’ button to rerun benchmarks more quickly as data updates. (Not “insufficient search functionality,” as one AI review tool apparently misread in her G2 feedback - a detail that made both Emily and the Compensation IQ team laugh.)
As About:Energy continues to grow, having a salary benchmarking process that is transparent, defensible, and built on real evidence will be critical. For a company of scientists and engineers, the data has to hold up to peer review. Now, it does.