Managed analytics

Ongoing measurement, optimisation and support to keep your analytics accurate, reliable and delivering business value.

What it is

Managed analytics is an ongoing service that maintains, improves and governs your measurement environment long after implementation is complete.

It combines technical support, platform administration, reporting, troubleshooting and strategic guidance into a single operational capability.

Whether fully managed, delivered alongside an internal analytics team or providing specialist support when needed, Managed analytics ensures your measurement stack continues to evolve as your business grows.

The goal isn’t simply to keep platforms running, it’s to ensure measurement continues to answer the questions your business needs answered.

Why it matters

Analytics platforms release new features, websites are updated, privacy requirements evolve and measurement strategies become more sophisticated.

Without ongoing management, small issues accumulate over time-broken tags, outdated reports, inconsistent definitions and undocumented changes that gradually reduce confidence in the data.

As organisations adopt server-side measurement, cloud infrastructure and AI-powered marketing, analytics has become an operational capability rather than a one-off project.

Continuous management helps organisations maintain reliable measurement while adapting confidently to ongoing change.

What Louder does

Signal audit - what proportion of conversions are observed, consented, modelled or lost, broken down by browser, device and platform, expressed as a number rather than a worry.
Durable collection - server-side tagging design and first-party context configuration.
Enhanced conversions and conversion APIs - getting consented signal back to the platforms that bid on it.
Consent Mode configuration and validation - including checking the modelling it produces against a period of known-good data.
Independent measurement - geo experiment design, incrementality testing, and readiness for marketing mix modelling.
A degradation plan - what is likely to break next, and what is already in place for when it does.

Common challenges

Consent Mode implemented, modelling never validated. Modelled conversions are accepted at face value with no comparison against a period where the observed data was complete.
Server-side tagging treated as a privacy solution. It improves durability, not lawful basis. Deploying it without consent enforcement increases exposure rather than reducing it.
Modelled and observed conversions blended into one number. The same figure then drives both bidding and board reporting, and neither audience knows what it contains.
No baseline captured before a change. Once consent enforcement or a platform shift lands, the effect cannot be measured retrospectively - the comparison period had to exist beforehand.