How I Automated Client Reporting for a Digital Marketing Agency Using n8n and Claude

Vivekanand Malakar — Tue, 09 Jun 2026 05:40:22 +0000

Most digital marketing agencies I talk to spend 3 to 4 hours per client every week doing the same thing. Pull data from Google Analytics. Export from Search Console. Screenshot the Meta Ads dashboard. Open a Google Doc. Paste everything in. Format it. Send it.

Every week. For every client. Manually.

Here is how I automated that entire process using n8n and the Claude API, bringing it down to under 20 minutes with zero manual work.

The workflow in plain English

A scheduled trigger fires every Friday morning
n8n pulls data from Google Analytics 4 via the API
It pulls Search Console data for keyword rankings
It fetches campaign performance from Meta Ads API
All of that raw data gets passed to Claude with a structured prompt
Claude generates a plain English summary of the week, highlights wins, flags anything that needs attention
The output gets formatted into a Google Doc automatically
An email with the report link goes to the client
The whole thing runs while the agency team is asleep.

What Claude is actually doing here

The raw numbers on their own are not useful to a client. A client does not want to read that their CTR dropped from 3.2% to 2.8%. They want to know what that means and what to do about it.

Claude takes the raw data and turns it into a narrative. It compares this week to last week, flags anomalies, and writes in plain English that a non-technical client can understand. The prompt includes the agency's tone of voice guidelines so every report sounds consistent.

The part that surprised me

The biggest time saving was not the report generation itself. It was the back and forth that stopped happening. When clients get a clear narrative report every Friday without chasing anyone for it, the number of "quick update" emails during the week drops significantly. The agency gets their time back twice over.

Stack used

n8n for workflow orchestration, Claude API (claude-sonnet-4-6) for report generation, Google Analytics 4 API, Meta Graph API, Google Docs API for output.

If you are building something similar or have questions about the prompt structure I used for the Claude integration, drop a comment below.

You can see more of what I build at flowvanta.dev

I built a 3-agent Python + Claude pipeline: RSS ingestion AI scoring Gmail MCP email drafts. Repo inside.

Vivekanand Malakar — Tue, 19 May 2026 19:51:37 +0000

Multi-Agent Job Outreach Pipeline — Supervisor/Worker Pattern in Python + Claude

Wanted to share a multi-agent pattern I've been running — it's a supervisor/worker architecture that maps cleanly to n8n if anyone wants to port it.

The Pipeline

Agent 1 — Ingestion + Scoring

Pulls from Remotive API, WeWorkRemotely RSS, and Upwork RSS (all free, no keys needed)
Each item goes to Claude with a structured scoring prompt (1–10 against defined criteria)
Only items scoring ≥ 6 pass through to the next agent
Output: items_YYYY-MM-DD.json with score, reason, pain point, and pitch angle per item

Agent 2 — Context-aware Email Drafter

Reads the scored items JSON
For each item: Claude reads the full source text and writes a personalised email that references specific details — stack, stated problems, their own wording
No templates. Every draft is generated fresh against the actual source content
Output: drafts_YYYY-MM-DD.json — human reviews before anything is sent

Agent 3 — Gmail MCP Send

Approved drafts sent via Gmail MCP directly from inbox
Full send log maintained in JSON

Why This Pattern Is Interesting Technically

The scoring step is the key. Without it, you're passing 60+ low-signal items to an LLM and burning tokens on noise. With a Claude scoring gate, only ~10–15% of raw items make it through — and those are the ones worth spending a full generation on.

The other thing worth noting: keeping state in flat JSON files rather than a DB is underrated for single-user pipelines at this scale. Simple to inspect, easy to debug, no infra overhead.

n8n Equivalent of Each Agent

Agent	n8n Implementation
Agent 1	Schedule trigger + HTTP Request nodes + AI Agent node (scoring prompt) + Filter node
Agent 2	Manual trigger + Loop over items + AI Agent node (drafting prompt) + Write file
Agent 3	Manual trigger + Gmail node with approval gate

Stack

Claude claude-sonnet-4-6
Python 3.12 + httpx
Gmail MCP
JSON for state

Repo

https://github.com/vivekanandtech/ai-outreach-agent

Happy to share the scoring prompt or the drafter prompt in the comments — took ~20 iterations to get both right.

Anyone built something similar with native n8n nodes?

I build systems like this for agencies and ops-heavy businesses. Portfolio at flowvanta.dev

DEV Community: Vivekanand Malakar