--- title: "Automate Weekly Marketing Reports from GA4 with n8n + AI" description: "Build a scheduled n8n workflow that pulls GA4 metrics, writes an AI summary with insights, and emails your team — AI credits included, no API key setup." canonical: https://agentroost.app/en/blog/weekly-marketing-reports-ga4-n8n-ai date: 2026-06-02T04:00:00Z --- [Canonical URL](https://agentroost.app/en/blog/weekly-marketing-reports-ga4-n8n-ai) Every marketing team has a version of the same Monday ritual: someone opens GA4, exports last week's numbers, pastes them into a doc, writes two paragraphs of context, formats the table, and sends it to the distribution list. It takes 30–60 minutes and it happens 52 times a year. This post shows you how to build that workflow in n8n so it runs itself — and how an AI node writes the narrative paragraph so the output is actually readable, not just a raw data dump. ## What the finished workflow does 1. Fires every Monday at 08:00 (your time zone). 2. Fetches last week's GA4 data: sessions, new users, bounce/engagement rate, goal completions, and top channel groupings. 3. Optionally fetches spend data from Google Ads or Meta Ads (same pattern, different node). 4. Passes the metrics to an AI/LLM node that writes a 150–200 word plain-English summary with a "What worked", "What to watch", and "Suggested next steps" structure. 5. Sends the finished report as a formatted HTML email to your stakeholders. Total node count: 6–8 nodes. Build time: roughly 45 minutes the first time. --- ## Step 1 — Schedule Trigger Drop a **Schedule Trigger** node. Set it to **Cron** mode and use: ``` 0 8 * * 1 ``` That fires at 08:00 every Monday. Set the **Timezone** field to match your team (e.g. `Europe/Istanbul` or `America/New_York`) — n8n respects it exactly, no UTC math needed. --- ## Step 2 — Pull GA4 data with the Google Analytics node Add a **Google Analytics** node (search "Google Analytics" in the node panel — it's the first-party one, not a custom HTTP Request). **Authentication:** create a **Google Analytics OAuth2** credential. You need a Google Cloud project with the Analytics Data API enabled, plus an OAuth 2.0 Client ID scoped to `https://www.googleapis.com/auth/analytics.readonly`. Paste the client ID and secret into n8n's credential modal, click Connect, and authorize. n8n handles token refresh from that point on. **Node config:** | Field | Value | |---|---| | Property ID | Your GA4 numeric property ID (find it in GA4 → Admin → Property Settings) | | Date Range | `last7Days` (or use expressions: `{{ $today.minus({days: 7}).toFormat('yyyy-MM-dd') }}` for the start) | | Dimensions | `sessionDefaultChannelGroup`, `country` | | Metrics | `sessions`, `newUsers`, `bounceRate`, `conversions`, `engagedSessions` | | Limit | 20 | The node outputs one item per dimension combination. A **Summarize** or **Aggregate** node can roll them up by channel group before passing to the AI step. --- ## Step 3 — (Optional) Pull ad spend from Google Ads or Meta If you run paid campaigns, add a second data fetch in parallel. For **Google Ads**, use an HTTP Request node hitting the Google Ads API: ``` POST https://googleads.googleapis.com/v18/customers/{customer_id}/googleAds:search ``` Body (minimal, returns last 7 days of campaign cost + impressions + clicks): ```json { "query": "SELECT campaign.name, metrics.cost_micros, metrics.impressions, metrics.clicks FROM campaign WHERE segments.date DURING LAST_7_DAYS" } ``` Divide `cost_micros` by 1,000,000 for actual spend. A **Set** node can reshape the fields into clean labels before the merge step. Use a **Merge** node (mode: **Merge by Index** or **Append**) to combine GA4 output with the ads data into a single payload. --- ## Step 4 — Build the AI prompt with a Set node Before calling the LLM, use a **Set** node to construct a structured prompt string. This keeps your prompt readable and lets you inject live values cleanly: ``` You are a marketing analyst. Below are last week's website and paid media metrics. GA4 Summary: - Sessions: {{ $json.sessions }} - New Users: {{ $json.newUsers }} - Bounce Rate: {{ $json.bounceRate }}% - Conversions: {{ $json.conversions }} - Top Channel: {{ $json.topChannel }} Ad Spend: ${{ $json.totalSpend }} | Clicks: {{ $json.totalClicks }} | Impressions: {{ $json.impressions }} Write a concise 150-200 word marketing performance summary with three sections: 1. What worked this week 2. What to watch (anomalies or drops) 3. Two specific recommended next steps Tone: direct, data-backed, no filler. ``` Storing the prompt in a Set node (rather than inline in the AI node) also makes it easy to version and tweak without touching the node logic. --- ## Step 5 — AI/LLM node writes the narrative Add an **AI** node (labeled "Basic LLM Chain" or "Chat Model" depending on your n8n version). Point it at the prompt output from the previous Set node. - **Model:** any chat model in the list — GPT-4o, Claude 3.5 Sonnet, and Mistral Large all produce good analytical prose. Switch models without touching the rest of the workflow. - **System prompt:** "You are a concise marketing analyst. Return only the report text, no preamble." - **Max tokens:** 400 is enough for a 200-word summary. The node outputs `{{ $json.text }}` — the finished paragraph. --- ## Step 6 — Send the email Add a **Gmail** node (or **Send Email** for SMTP). Build the HTML body by combining a header, a metrics table, and the AI narrative: ```html

Weekly Marketing Report — {{ $today.minus({days:7}).toFormat('dd MMM') }} to {{ $today.minus({days:1}).toFormat('dd MMM yyyy') }}

Sessions{{ $('GA4 Fetch').item.json.sessions }}
New Users{{ $('GA4 Fetch').item.json.newUsers }}
Conversions{{ $('GA4 Fetch').item.json.conversions }}

AI Summary

{{ $('AI Node').item.json.text }}

``` Set **To** to a comma-separated list of stakeholder addresses, or pull them from a **Google Sheets** node if your list changes. --- ## Common pitfalls - **GA4 sampling on large date ranges.** The Data API applies sampling for properties on the free tier with high-cardinality queries. Keep dimensions to 2–3 and use date ranges ≤ 30 days to stay in unsampled territory. - **Merge node item count mismatch.** If GA4 returns 12 rows and the Ads node returns 5 campaigns, "Merge by Index" will truncate. Use **Append** mode and let the AI node receive all rows, or aggregate both sides first. - **OAuth token expiry.** n8n auto-refreshes the GA4 token, but if the credential is dormant for more than 6 months Google may revoke it. Add an **Error Trigger** node that sends you a Slack or email alert when the workflow fails — catches this before a Monday report silently skips. --- ## Running this on AgentRoost This workflow needs to run reliably every Monday whether your laptop is on or not. That means an always-on instance, not a local n8n process you manually restart. On AgentRoost you get **your own n8n instance** — your login, your data, your workflows — hosted on a public subdomain (`https://.agentroost.app`). You own it: self-hosting without the DevOps. The Schedule Trigger fires on time because the instance is always running. Webhook URLs work immediately with no reverse-proxy setup. The part that usually blocks teams: the AI node requires an OpenAI or Anthropic API key, another billing account, and BYOK configuration. **On AgentRoost that step doesn't exist.** LLM credits are included in the subscription. Drop the AI node, pick your model, write your prompt, and run — no API key, no second billing account. Getting started takes about two minutes: 1. [Sign up at AgentRoost](/en/agents/n8n) with email, Google, Microsoft, or Discord. 2. Pick the **n8n** framework, name your instance. 3. Your private n8n editor opens at `https://.agentroost.app`. 4. Build the workflow above — the AI node already has credits. Pricing starts at **$19.99/mo all-in** with a 14-day money-back guarantee. [Compare plans](/en/pricing) to see which tier fits your report volume and model preferences. --- A weekly marketing report that writes itself and lands in inboxes before the team's Monday standup is not a complex automation project — it's eight n8n nodes and one afternoon of setup. The only thing that usually makes it feel harder than it is: the AI writing step requires a separate API account everywhere except here.