Web & App Analytics
We implement, migrate and govern web and app analytics so the numbers hold up under scrutiny. GA4, Adobe Analytics, Amplitude and Mixpanel; client-side and server-side tagging; Conversions API and Enhanced Conversions; raw event data in BigQuery. Every implementation ships with a tagging plan, test evidence and documentation your team actually owns.
Challenges we solve
Most enterprise analytics problems are not analysis problems. They are collection problems: a data layer that drifted from the spec three releases ago, events named four different ways across three properties, ad blockers and ITP quietly removing a third of the signal, and a consent banner that fires tags in an order nobody has checked since it was installed. The symptom is always the same. Marketing quotes one conversion number, finance quotes another, and the meeting turns into a debate about the data instead of a decision about the business. We fix the collection layer first, prove it with test evidence, and then hand you the documentation that keeps it from drifting again.
What we deliver
GA4 implementation, migration and remediation
Full GA4 builds and rescues: event and parameter design, custom dimensions and metrics, conversions and key events, cross-domain and subdomain tracking, internal traffic and bot filtering, data retention, and a property structure that survives an org change. Where a previous implementation is salvageable we remediate rather than rebuild.
Adobe Analytics and Web SDK migration
Migration from AppMeasurement and legacy Launch extensions to the Adobe Web SDK and Edge Network, including XDM schema design, datastream configuration, identity map, consent integration and parallel-run validation so you can prove the new implementation matches the old one before you switch off the old one.
Product analytics: Amplitude and Mixpanel
Event taxonomy design, user and account identity resolution, cohort and funnel configuration, and SDK instrumentation for web, iOS, Android and React Native. We align product analytics events with the marketing measurement plan so product and growth stop counting differently.
Server-side GTM and Google Tag Gateway
Server-side tagging in GCP or your own cloud: container architecture, custom clients and tags, first-party cookie handling, request transformation, PII stripping, cost modelling and load testing. Google Tag Gateway configuration where a first-party serving path is preferable to a full server container.
Conversions API and Enhanced Conversions
Meta CAPI, Google Enhanced Conversions for web and leads, LinkedIn CAPI, TikTok Events API and Pinterest API for Conversions — deduplicated against browser events, hashed correctly, and match-rate monitored after launch rather than assumed. This is usually where lost conversion signal is recovered.
BigQuery integration and event-level analysis
GA4 and Firebase BigQuery exports, session and attribution reconstruction, scheduled queries, materialised reporting tables, and analysis that is simply not possible in the GA4 interface: true user paths, cohort retention, cross-device stitching and custom attribution.
Mobile and app measurement
Firebase and GA4 for Android and iOS, SDK implementation and review, app-to-web journey stitching, deep-link and campaign attribution, SKAdNetwork and Privacy Manifest considerations, and consistent event naming between app and web.
Data strength and Data Manager programmes
Improving match quality and durability of first-party signal: Google Data Manager onboarding, customer data ingestion, identity strategy, hashed identifier coverage, and a measurable plan for raising match rates rather than a one-off upload.
Platforms & Tooling
- Google Analytics 4
- Adobe Analytics
- Adobe Web SDK / Launch
- Amplitude
- Mixpanel
- Piwik PRO
- Google Tag Manager
- Server-Side GTM
- Google Tag Gateway
- Tealium
- BigQuery
- Firebase
Process
Audit
Crawl the current implementation, map what fires, quantify what is missing.
Plan
Measurement plan, event taxonomy and data layer spec agreed with engineering.
Implement
Containers, SDKs, server-side infrastructure and API integrations.
Validate
Parallel run, deduplication checks, match-rate baselines, sign-off.
Govern
Release-gate QA, monitoring alerts and a quarterly tracking review.
FAQs
It depends on how much signal you are losing and what you do with it. If a meaningful share of your paid media budget is optimised against conversions that browsers are dropping, server-side collection usually pays for itself. If you are a low-volume B2B site with long sales cycles, the case is weaker. We quantify the loss during the audit so the decision is made on your numbers rather than on principle.
We fix it wherever fixing is cheaper and safer, which is most of the time. A rebuild is only warranted when the property structure itself is wrong — for example when three brands share one property with no clean way to separate them, or when historical data is already unusable. The audit tells us which situation you are in.
For a single-brand site with a reasonably clean Launch implementation, six to ten weeks including parallel-run validation. Multi-brand estates, heavy use of processing rules, or complex identity requirements push it further. We always parallel-run before decommissioning AppMeasurement so you can prove the numbers match.
Not if the data layer is specified and owned. We give engineering a data layer contract, add tracking checks to your release process, and set up monitoring that alerts when a key event stops firing. Drift is a process problem more than a technical one.
That is the normal arrangement. Most of our work is alongside an internal team — we take the implementation and architecture load, and hand over documentation and enablement so the team can run it afterwards.
Yes, within what consent allows. We implement Consent Mode v2 and CMP-aware firing rules so tags respect user choice, and we use consent-appropriate modelling and server-side collection to preserve as much legitimate signal as possible. That work is covered in detail on our Consent, Privacy & Data Governance page.
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