Digital Analytics
Analytics implemented so the numbers can be trusted: events designed deliberately, consent handled correctly, and reporting that answers a question someone actually asked.
Overview
Most analytics setups were configured once by whoever had access, extended a few times under pressure, and have not been audited since. The result is data that looks complete and is quietly wrong: duplicate events, self-referring traffic, internal visits counted, and conversions firing on page load.
Fixing it is mostly discipline rather than cleverness. Design the events around the questions you ask, implement them consistently, validate against something you already know is true, and then remove the reports nobody opens.
Who this is for
- Companies whose analytics disagrees with their own sales figures
- Teams who migrated to GA4 hastily and never revisited it
- Businesses with several dashboards and no trusted number
How we approach digital analytics
The specific pieces of work a typical engagement covers. Scope is agreed up front — nothing here is a surprise line item later.
Measurement plan
The events and properties you need, derived from the decisions you make, written down before anything is implemented.
Implementation
Consistent event naming and parameters through a tag manager or directly in code, with a data layer that is designed rather than accumulated.
Validation
Checking reported numbers against a source you trust, such as order records. Analytics that has never been reconciled is not evidence.
Consent integration
Consent mode and correct behaviour before permission is given, which is both a legal requirement and a large influence on what data you have.
Filters and hygiene
Internal traffic, bots and referral spam excluded, which is a small job that removes a surprising amount of noise.
Reporting
A small set of reports tied to real decisions, and the removal of the ones nobody opens.
From first call to measured result
The same sequence every time, so you always know what happens next.
Audit
Review the current setup for accuracy, duplication and gaps, and reconcile against a known source.
Plan
Design the measurement plan against the questions the business actually asks.
Implement
Build and test the tracking, verifying each event rather than assuming deployment means working.
Report and monitor
Build the reporting layer and add monitoring so a broken tag is noticed within days.
Outcomes, not deliverables
A pile of artefacts isn't progress. These are the changes the work is meant to produce — and what we report against.
Numbers people believe
Reconciliation against a known source is what ends the meetings that are really arguments about data quality.
Fewer reports, more use
Cutting unused dashboards makes the remaining ones visible and maintained.
Data that is lawful
Correct consent handling protects you and improves data quality at the same time.
Breakage caught early
Monitoring means a failed tag is a small problem rather than a quarter of missing data.
Common questions about Digital Analytics
The things people ask before they get in touch. If yours is not here, ask us directly.
Why does GA4 not match our sales figures?
Usually a combination of consent rejection, ad blockers, cross-device journeys and attribution windows, plus implementation faults such as duplicate events. A gap is normal; a large or unstable one indicates something fixable. We quantify each cause rather than accepting the gap as unavoidable.
Should we use something other than GA4?
Alternatives exist that are simpler and more privacy-friendly, and for some businesses they are a better fit. GA4's advantage is its integration with advertising platforms. If you spend heavily on Google advertising, moving away has real costs beyond the tool itself.
How is this different from your data strategy service?
This is implementation: collecting data correctly and reporting it. Data strategy and measurement decides what is worth measuring at all and defines the metrics. If your problem is that two teams report different numbers for the same thing, start there.
How long does an analytics rebuild take?
Audit and plan in two to three weeks, implementation another two to four depending on how many systems are involved. Validation is what takes the time and is the part worth protecting.
Thinking about Digital Analytics?
Tell us what you are trying to change. If we are not the right fit we will say so, and point you somewhere better.
Looking at the wider picture?
Digital Analytics usually sits alongside other work in Data, Analytics & Measurement. Browse the full area to see what it connects to.