measurement optimisation
Tracking and Analytics
Conversion tracking, GA4, Tag Manager and dashboards implemented properly, so every marketing decision is made on data you can trust.
01 / Friction
The problem this solves
Marketing runs on guesswork because conversions, calls and pipeline are not tracked back to their source.
Typical warning signs
- Ad platforms, GA4 and the CRM report three different numbers for the same month.
- Conversion events fire twice, or not at all, and nobody is sure which.
- Attribution is a spreadsheet argument instead of a data model.
- Consent banners were added but tracking was never adapted to them.
- Reports take days to assemble and are out of date on arrival.
02 / Change
Outcomes it is designed to create
- A documented event taxonomy implemented consistently across the stack.
- Conversion tracking verified end to end, including offline and CRM outcomes.
- Consent-aware analytics that respect visitors and still inform decisions.
- Dashboards that answer the questions leadership actually asks.
If the data is wrong, everything downstream is wrong
Budget decisions, channel decisions, agency evaluations: all of them inherit the quality of your measurement. Most stacks I audit have double-firing conversion events, GA4 half-configured from a rushed Universal Analytics migration, and a consent banner that either blocks nothing or breaks everything.
Fixing measurement is unglamorous and it pays for itself faster than any campaign.
What the engagement covers
Data quality audit. A systematic pass through what is actually firing.
- Reviews tags, triggers, events and platform configurations.
- Produces a list of what is broken, duplicated or missing.
- Ranked by decision impact, not alphabetically.
Measurement plan. The questions the business needs answered, made concrete.
- Translated into an event taxonomy with naming conventions and parameters.
- Every event has an owner.
- Documented so the next hire can follow it.
Implementation. One control point, wired end to end.
- Google Tag Manager as the single control point.
- GA4 configured deliberately, conversion tracking wired to ad platforms.
- CRM outcomes connected, so pipeline and revenue sit next to campaign spend.
Consent-aware analytics. Tracking that respects consent instead of ignoring it.
- Behaviour adapts to consent state.
- Privacy-conscious options where they fit the audience.
- Matched to the risk profile, not a one-size template.
Dashboards and reporting. Built around decisions, not decoration.
- Budget allocation, channel performance, funnel health.
- Made in Looker Studio, delivered automatically.
- So reporting stops consuming analyst time.
Deliverables
What you receive, concretely.
- Tracking audit and data quality review
- Measurement plan and event taxonomy
- Google Tag Manager implementation
- GA4 configuration and conversion setup
- Server-side tagging where justified
- Consent mode and privacy-aware configuration
- Campaign attribution setup
- Dashboard design in Looker Studio
- Marketing reporting automation
How the engagement runs
Audit
What fires, what breaks, what double-counts. A blunt data-quality report.
Plan
Event taxonomy and measurement plan tied to business questions, not tool defaults.
Implement
GTM, GA4, conversion APIs and consent wiring, tested with debug tooling.
Report
Dashboards and automated reporting with definitions everyone agrees on.
Technology used
- Google Tag Manager
- GA4
- Looker Studio
- Microsoft Clarity
- Hotjar
Relevant articles
crm
Automating HubSpot with Python: deduplication and data cleaning
A practical guide to keeping a HubSpot CRM clean with the v3 API and Python, covering batch reads, duplicate detection, safe merges, property normalisation, a data-quality score and a repeatable maintenance job.
Read insightanalytics
Calling the GA4 Data API and Search Console API in Python
A practical guide to pulling live data directly from Google Analytics 4 and Search Console with Python, covering authentication, report requests, pagination, joining the two datasets and building a reusable extraction layer.
Read insightcrm
Lead capture and conversion tracking with the HubSpot API
A practical Python guide to capturing leads reliably and tracking their conversions in HubSpot, covering form submissions, the Forms API, contact upserts, custom behavioural events and closed-loop attribution.
Read insightFrequently asked questions
Do we need server-side tracking?
Only sometimes. Server-side tagging adds cost and complexity, and it is justified when ad-blocking losses, data residency or conversion API requirements are material. The audit tells you whether that is your situation before anything is built.
Can you fix attribution?
I can make attribution honest. No model recovers perfect truth, but a consistent taxonomy, deduplicated conversions and CRM-connected outcomes turn attribution from an argument into a usable decision input.
Related services
Most engagements combine two or three of these. The project brief helps you choose.
Paid media
Paid campaigns measured to pipeline, optimised on lead quality, and governed by explicit budget rules.
Google and LinkedIn campaigns run with tracking readiness, honest reporting and lead quality analysis, not vanity metrics.
- Google Ads
- LinkedIn Ads
- Meta Ads
- TikTok Ads
CRM implementation
A CRM the team actually uses, with clean data, clear lifecycle stages and reporting leadership believes.
HubSpot and CRM implementations designed around your sales process, adopted by your team, and connected to marketing and reporting from day one.
- HubSpot
- Salesforce
- Brevo
- Zoho
Web development
A fast, accessible website that converts, ranks and can be maintained without a developer on retainer.
Fast, maintainable marketing websites built on modern architecture, migrated cleanly off heavy CMS setups and measured on Core Web Vitals.
- Astro
- React
- TypeScript
- WordPress
Next move
Ready to fix tracking and analytics?
Describe your situation in the project brief and get an honest assessment of what I would build and why.