About Measurelab
It all depends on data
Which is why it's all we do. Measurelab is an independent consultancy that has been solving marketing data problems since 2013.
Who we are
We help marketing teams trust the data their marketing runs on. That's the whole job - some call it digital analytics - and it's all we've done since 2013, even though it's evolved somewhat over the years!
Dara and Mark met at the Google Analytics Summit in Mountain View in 2010 and started the company three years later, founded on principles of doing good work, for good people - powered by good coffee. In 2020 a little global pandemic came along and we all started working remotely. We still do.
On paper we're a marketing data infrastructure, analytics and AI consultancy. In practice we're the people you ring when the numbers don't add up.
In 2023 The Sunday Times included us in its Best Places to Work, based on anonymous feedback from our own team.
In April 2025 the IPA named us one of 25 agencies on its first Beacon List, which celebrates growth stories from across the UK agency sector.
What we believe
What we've come to think about the work, and what we'll push back on.
Nobody should mark their own homework
We don't buy or sell media and take no cut of anyone's ad spend. So when we tell you a channel isn't performing, we have nothing riding on the answer. We have technology partnerships, and we say why we recommend a tool for your situation. That's the point of having someone independent look.
Data deserves specialists
Consultants, analytics engineers, analysts and delivery specialists. We bring those skills together for problems that start in a tag and end in a warehouse. You can talk the details through with the people doing the work.
You should own what we build
The data, the definitions, the code and the documentation are yours. We'd rather you could stop working with us tomorrow and carry on than depend on us to keep the lights on.
Progress beats perfection
We have found that over-planned data initiatives fail. That's why we believe in agile analytics. So we start with the question worth answering now, build something that works, then get on with the next one!
Meet the team
Consultants, engineers, analysts and delivery specialists working together. You have a named lead and direct access to the people doing the work.
Claire Wilder
Delivery Manager
Dara Fitzgerald
Consulting Director (Co-founder)
Elisa Liu
Lead Digital Analyst
Graham Marsh
Analytics Consultant
John Barnes
Analytics Consultant
Katie Kaczmarek
Lead Analytics Engineer
Lizzie Boyd
People Lead
Lowri Lewis
Data & Insights Analyst
Magdalena Pajak
Analysis Lead
Mark Rochefort
Product Director (Co-founder)
Matthew Hooson
Director of Engineering & Technology
Nasima Khatun
Lead Analytics Consultant
Steven Elliott
Managing Director
Victor Sarker
Senior Analytics Engineer
Where we are
We work from wherever we live and come together in Manchester, London and Brighton.
Addresses and directionsOur values
Our guiding principles for how we hire, and what's defined our culture.
Initiative
We spot what needs doing and do it, without hiding behind process or waiting for perfect information.
Adaptability
Plans change and requirements shift. We stay focused on what matters now, not what mattered last quarter.
Support
We share what we know, and we make our clients better at what they do - even if that means they stop needing us.
How we actually use AI
We use agents on our own work - connected to our own systems, doing documentation, QA and the parts of analysis that are more tedious than difficult. We do it to find out where they hold up and where they quietly make things up.
The lesson keeps being the same one. Ask three systems for the same number and you get three answers, and an agent asked the same question just guesses with more confidence. So we write the definitions down first: what a customer is, what counts as revenue and which source wins when two disagree. Those definitions should be visible, owned and maintainable.
We do this for ourselves with SEAM, our working approach to giving agents shared context. Our own metrics, such as billable hours and utilisation, and the way a client is identified across our CRM, timesheets, project tracker and warehouse are written down as plain files that our agents read before they answer.
None of that is a reason to put an agent in front of your data yet. It is a reason to be specific about what it can reach, how its answers get checked and who looks when something drifts.
We share what we are learning, mistakes included, at Swarm, the meetups we run for data people building with AI agents. When we use AI in what we publish, our editorial policy says how: a practitioner reviews and edits every piece before it goes out.
Come and talk to us
Whether you've got a problem you'd like an independent view on, or you fancy working here.