How We Automated Weekly Reporting for a Marketing Agency

The before-state

Analysts exported CSVs from Google Ads, Meta, LinkedIn, HubSpot, and Shopify, pasted into spreadsheets, fixed formulas, and built slides by hand. QA was ad-hoc, and Monday delivery sometimes slipped to Tuesday. Clients noticed inconsistencies; account managers burned time fixing typos instead of advising.

Goals we set with the agency partner

• Deliver reports by 9 a.m. every Monday without human prep.
• Standardize metrics and calculations; eliminate formula drift.
• Give AMs narrative-ready summaries and alert them to anomalies.
• Keep PII and ad platform tokens secure; log every run.

Architecture at a glance

Step-by-step build

1) Source pulls

We scheduled n8n to run at 5 a.m. Monday. Each connector fetches the last 14 days and the prior comparable period, writing to BigQuery staging. Failures retry with backoff; tokens live in n8n credentials.

2) Validation

Row counts and spend checks run after each load. If today’s spend is +/- 30% vs. 7-day average, we flag it. Schema drift (new columns) triggers a Slack alert to ops.

3) Transformations

We run SQL models for CAC, ROAS, MER, CTR, CVR, LTV:CAC, and pacing vs. budget. Calculations live in versioned SQL files; Looker Studio only reads the curated views.

4) Deck generation and delivery

Looker Studio renders per-client dashboards. n8n exports PDFs, attaches them to Slack channels and email, and posts a concise summary: spend, conversions, top movers, and any anomalies.

5) Alerts and exceptions

An anomaly lane pings AMs if metrics breach thresholds (e.g., CVR down >20% WoW). AMs get a button to acknowledge and add commentary before the client sees the report.

Results after four weeks

• 12 hours/week reclaimed across analysts and AMs.
• 90% reduction in reporting errors; no missed Monday deliveries.
• AMs spend more time on insights; client escalations about reports dropped sharply.
• Rollout to five clients completed in under four weeks; templates ready for the rest.

Playbook you can copy

1. Define a metrics contract: names, formulas, owners.
2. Stage raw data and validate before visualizing.
3. Parameterize dashboards; avoid one-off slides.
4. Add anomaly alerts with human acknowledgment.
5. Version SQL and templates; keep orchestration stateless.

Security and governance

Tokens stay in n8n secrets; PII is minimized and never sent to LLMs. Access to BigQuery is scoped per service account. Run logs and errors flow to ClickUp for auditability.

Timeline

• Days 1–3: Metric contract, access, and source pulls.
• Days 4–7: Staging schemas, transforms, first dashboard, pilot client.
• Days 8–14: QA, alerts, PDF delivery, rollout to five clients.

If you want this outcome

Start with one client, one dashboard, and a tight metric contract. Ship the run logs and alerts on day one. Expand once Monday is boring.