Ad analytics and BI built on a modeled warehouse: automated cross-platform reporting, dashboards, attribution, and measurement your clients can trust.
When reporting is the product, manual does not scale
For an agency, reporting is not an internal convenience. It is a deliverable the client sees every month, and often the main evidence that the retainer is working. Yet it is usually assembled by hand: exports from six platforms, a spreadsheet with formulas nobody dares touch, and a slide deck built the night before. It scales linearly with headcount, breaks when one person is out, and every disagreement about a number costs a meeting.
What the analytics layer covers
- A governed metric layer where ROAS, CAC, and contribution are defined once and reused everywhere
- Cross-platform reporting that assembles and delivers on a schedule with no manual step
- Client-facing dashboards with row-level access so each account sees only its own data
- Attribution and incrementality measurement appropriate to your data, not a default model
- Pacing and anomaly alerting so overspend is caught in-flight rather than in hindsight
- Enablement so your analysts extend the models themselves
How we work
Agree the metric definitions with the people who defend the numbers to clients
Model those metrics in dbt with tests so every surface reads from one place
Rebuild the highest-effort recurring report first and measure the hours it returns
Layer on dashboards, alerting, and deeper measurement once the base is trusted
Typical stack
Frequently asked questions
For anything beyond a couple of platforms, yes. Dashboards wired directly to platform APIs cannot join across sources, cannot hold history once a platform ages data out, and re-query on every page load. The warehouse is what makes the reporting layer both fast and consistent.
Usually the one your team already knows. Looker Studio is hard to beat for client-facing marketing reporting on cost. Power BI wins where the organization is already on Microsoft. The tool matters far less than whether the metrics underneath it are modeled and tested, which is where we focus.
The pattern we see most often is a multi-day monthly reporting cycle collapsing to a review-and-comment pass. The gain is not only hours: it is that analysts spend them on interpretation instead of assembly, which is what clients are paying for.
Yes. We implement row-level security so each client sees only their accounts, and we can white-label the surface so it reads as your platform rather than ours.