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Measurement Strategy & Analytics Audits

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Before you build anything, you need to agree what matters and confirm what is broken. We run end-to-end measurement audits, define KPI frameworks that connect to real business outcomes, and produce the measurement and tagging plans that engineering, analytics and marketing can all work from. AI-assisted tooling lets us cover far more ground than a manual audit, without losing the judgement that makes the findings useful.

Challenges we solve

Measurement debt accumulates quietly. A KPI gets defined in a deck and never in a query. A tagging plan is written once and never updated. Three teams build three definitions of "active user". By the time someone asks why the numbers moved, there is no document that says what they were supposed to be in the first place. An audit is not a list of complaints. Done properly it produces three things: an honest picture of the current state, an agreed definition of what you are trying to measure and why, and a prioritised, costed plan that says what to fix first. You should be able to act on it whether or not we do the implementation.

What we deliver

End-to-end measurement audits

Full-estate review across collection, tagging, consent, identity, data quality, reporting and activation. We crawl the site or app, replay key journeys, compare what fires against what should fire, and reconcile analytics figures against back-end sources so discrepancies are quantified rather than argued about.

AI-assisted audit tooling

We use LLM-driven tooling to parse tag containers, data layer payloads, network requests and documentation at a scale that manual review cannot reach — then apply human judgement to the findings. Coverage goes up, turnaround comes down, and the analyst time goes into interpretation instead of transcription.

KPI identification and metric frameworks

Workshops that connect commercial objectives to a small set of primary KPIs, supporting diagnostic metrics and guardrail metrics. Each metric gets an owner, a definition, a calculation, a source of truth and an acceptable variance — written down, in one place.

Measurement and tagging plans

The document engineering actually builds from: events, parameters, triggers, data layer structure, naming conventions, user and session properties, and destination mapping across analytics, media and warehouse. Versioned, reviewable, and tied to your release process.

SDK and data layer design

Specification and design of custom data layers and tracking SDKs for web and native apps, including reusable event helpers, type safety, validation at the point of dispatch, and a lightweight schema registry so bad events fail loudly in development rather than silently in production.

Analytics maturity and readiness assessment

Scored assessment across data collection, governance, tooling, skills, activation and AI readiness, benchmarked against comparable organisations, with a phased roadmap that sequences work by dependency rather than by enthusiasm.

Data quality monitoring design

Automated checks on volume, cardinality, null rates, referential integrity and unexpected schema changes, with alert thresholds and named owners, so problems surface within hours instead of at quarter end.

Governance model and operating rhythm

A practical model for who can change what: request intake, change approval, QA gates, release notes, and a standing measurement review that keeps the plan alive after the project ends.

Platforms & Tooling

  • Google Analytics 4
  • Adobe Analytics
  • Mixpanel
  • Amplitude
  • Server-Side GTM
  • Adobe Web SDK / Launch
  • Tealium
  • BigQuery
  • Piwik PRO
  • Google Tag Manager
  • Google Tag Gateway
  • Firebase

Process

Week 1

Discover

Stakeholder interviews, objective mapping, access and inventory.

Week 2

Assess

Automated crawl, journey replay, reconciliation, quality testing.

Week 3–6

Define

KPI framework and measurement plan workshops.

Week 7–8

Specify

Tagging plan, data layer spec, taxonomy and governance model.

Ongoing

Roadmap

Prioritisation, costing, sequencing and executive readout.

FAQs

The audit tells you what is true today. The strategy tells you what should be true and in what order to get there. We normally do both together, because an audit without a roadmap tends to sit in a folder and a strategy without an audit tends to be built on assumptions.

The coverage. Tooling can parse every tag, every data layer push and every network request across hundreds of pages and journeys, which a person cannot do inside a two-week engagement. The judgement about what matters, what is a real risk and what to fix first is still ours — the AI expands the surface we can inspect, it does not write the recommendations.

Yes, and many clients do. The tagging plan, data layer spec and KPI framework are written for an implementation team, not as sales collateral. You keep them either way.

We surface it early and force a decision. Most definition conflicts are not technical — they are two teams with different incentives. The framework names an owner per metric and records the decision, so the argument happens once rather than every quarter.

Yes. Native iOS and Android, React Native and Flutter, including SDK configuration, event parity with web, identity handling and store-level attribution.

Each item has a description, the business impact, the effort estimate, the dependencies, the owner and a suggested sequence position. It is deliberately shaped so you can drop it into your own backlog rather than treating it as a proposal.

Ready To Make Your Data Work Harder?

Let’s build a trusted measurement foundation that drives smarter decisions and measurable growth.

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