Services
Analytics & Growth
Know what's working. Prove it. Do more of it.
Most marketing teams have plenty of data and very little intelligence: a dozen dashboards, numbers that disagree with each other, and no confident answer to the only question that matters — what should we do more of? Analytics done properly closes that gap between what you can see and what you can decide.
We build the measurement layer first — clean tracking, sensible attribution, one source of truth — then the operating rhythm on top of it: reporting that leads with decisions, experiments with real hypotheses, and a growth loop where every campaign makes the next one smarter.
What's included
Measurement architecture
Tracking, tagging, and data flows set up correctly — so every number downstream can actually be trusted.
Attribution modelling
A model matched to your sales cycle that shows which channels genuinely influence revenue — not just which touched last.
Decision-first reporting
Dashboards and monthly reads that lead with what to change, in language leadership can act on.
Experimentation programme
A prioritised testing roadmap with proper hypotheses and sample discipline — so wins are real and repeatable.
Growth modelling
Funnel and scenario models that show where the constraint is and what removing it is worth — before you spend.
Who this is for
- Leaders who can't confidently say which marketing spend is paying back
- Teams with dashboards everywhere and decisions nowhere
- Businesses preparing to scale spend and needing to know where the return is before they do
Frequently asked questions
What does marketing attribution actually tell you?
Which channels and campaigns genuinely influence revenue, in what combination, over what time lag. Done well, it reallocates budget from what merely touches buyers to what moves them — often the single highest-ROI fix available to a scaling team.
Do we need expensive tools for good marketing analytics?
Usually not. Most businesses can get a trustworthy measurement layer from well-configured standard tools — the failure mode is configuration and discipline, not software. Where a paid tool genuinely earns its cost, we'll say so and why.
What is the first step to fixing messy marketing data?
A measurement audit: what's tracked, what's double-counted, what's missing, and which numbers disagree and why. Fixing the foundations comes before any dashboard — reporting on bad data just makes wrong decisions faster.
How does AI change marketing analytics?
It compresses the distance from data to decision: automated anomaly detection, natural-language querying of your numbers, and always-on reporting instead of month-end scrambles. The prerequisite is still clean data — AI on a broken measurement layer amplifies the noise.
Related insights
Ready to talk about analytics and growth?
Available as a defined-scope project, part of a monthly retainer, or a one-off strategy consultation — whichever fits where you are.