Automation and alerting

Catch measurement, data and platform issues before they start affecting reporting, optimisation or media spend.

What it is

Marketing systems rarely fail neatly. A tag stops firing, a data feed goes stale, an API connection breaks or conversion volumes suddenly shift, and the problem can go unnoticed until someone spots it in a report.

Automation and alerting help catch those issues earlier. Monitoring can be built around conversion tracking, data quality, integrations, platform activity and reporting, with alerts sent to the right people when something falls outside expected behaviour.

It can also remove repetitive manual work through automated checks, scheduled processes and workflows, particularly where teams are regularly checking the same systems or moving information between platforms.

Louder builds this around the systems an organisation already uses rather than forcing everything into a predefined toolset.

Why it matters

The cost of measurement defects has risen. Automated bidding can amplify bad signals quickly, meaning a broken Floodlight, stale CAPI feed or failed integration can affect media performance well before anyone notices.

Conventional reporting dashboards often show the impact after the fact. Purpose-built monitoring helps identify the underlying problem earlier, before it becomes a much more expensive one.

What Louder does

Data quality testing - dbt tests, freshness checks, schema-drift detection.
Anomaly detection - statistical and rules-based alerting on conversion volume, cost, audience health.
Tag and pixel monitoring - synthetic checks against critical pages.
Automated remediation - bid strategy guardrails, audience refresh jobs, scheduled fixes.
Operational dashboards - single-glance health views for measurement, media and data engineering teams.

Common challenges

Hidden measurement failures that go unnoticed until campaign performance or reporting is impacted.
Alert fatigue caused by too many low-value notifications or poorly configured thresholds.
Manual monitoring that relies on teams spotting issues instead of automated detection.
Disconnected systems where advertising, analytics and data platforms are monitored independently rather than as a single ecosystem.
No automated remediation meaning teams still have to investigate and fix recurring issues manually, increasing downtime and operational risk.