Marketing & Growth

How to send a weekly marketing report with AI

Marketing & Growth3 AI tools7 steps6 friction points

Want an agent to handle this on your live data? See the Starch version →

A weekly marketing report is the ritual most marketing leads and operator founders quietly dread. You need to pull channel performance data — paid ads, organic traffic, email metrics, maybe social — summarize what moved, explain why, and ship something coherent to your team or leadership before Monday morning. It's not intellectually hard. It's just tedious, repetitive, and dependent on data living in four different places.

The task looks like a perfect AI job: synthesize numbers, write a narrative, format an output. There's no creative leap required, just structured analysis and clear writing — exactly what large language models are trained to do well. The appeal is obvious: paste in your data, get a draft report back in seconds, spend your time editing instead of assembling.

ChatGPT, Claude, and Gemini can genuinely help here. Give them a CSV export from Google Analytics, your ad spend numbers from Meta or Google Ads, and your email open rates from Mailchimp, and they'll produce a solid narrative summary — with channel comparisons, trend callouts, and even suggested action items. The quality of the writing is real. The limitations show up before and after the generation step, in the data wrangling and the delivery.

Skip the manual work → See the Starch version

Marketing & Growth3 AI tools7 steps6 friction points

AI walkthrough

How to do it with AI today

A practical walkthrough using ChatGPT, Claude, and other off-the-shelf LLMs — what they're good at, what you'll have to do by hand.

Tools that work for this

ChatGPTClaudeGemini

Step-by-step

1 Export last week's data manually from each platform: download a CSV from Google Analytics 4 for traffic and conversions, pull a performance summary from Google Ads and Meta Ads Manager, and export your email stats from Mailchimp or whatever ESP you're using.

2 Open ChatGPT (GPT-4o) or Claude and paste your exported data directly into the chat window. Combine sources into a single message with clear labels — 'GA4 data:', 'Meta Ads:', 'Email:' — so the model can distinguish channels.

3 Use a structured prompt asking the model to act as a marketing analyst: specify the audience (your CEO, your team), the time period, the KPIs that matter most to you (ROAS, CAC, conversion rate, traffic by source), and the format you want the output in.

4 Review the draft and paste in any missing context — a product launch that happened mid-week, a campaign that was intentionally paused, a budget change. Ask the model to revise its interpretation accordingly.

5 Prompt the model separately to generate 2-3 recommended actions for the coming week based on what the data shows. Keep this in a second message so the analysis and the recommendations stay distinct and easier to edit.

6 Copy the output into your report template — a Google Doc, a Notion page, or a slide deck — and format it manually. If you need slides, paste the text into Gemini and ask it to restructure it as talking points with headers.

7 Send the finished report via email or Slack manually, or paste it into your team's standing update thread.

Prompts you can copy

You're a marketing analyst. Here's last week's data across three channels: [GA4 export], [Meta Ads summary], [email stats]. Write a 300-word weekly marketing report for a non-technical founder. Lead with the most important change, then cover each channel briefly, and close with two specific recommendations.

Based on these ad performance numbers for the week of [date], identify which campaigns had the best ROAS, which had the worst, and explain in plain English what likely drove the difference. Format as bullet points.

Compare week-over-week traffic from this GA4 export. Highlight any channels where sessions increased or dropped by more than 15%. For each notable change, suggest one likely cause and one thing worth testing next week.

Turn this weekly marketing data into 5 slide titles and bullet points for a team standup presentation. Keep each slide to 3 bullets max. Audience is a small startup team, not investors.

Here are my email campaign stats for this week: [open rate, CTR, unsubscribes, revenue attributed]. Write a two-paragraph summary of how the email channel performed and whether we should adjust send frequency or subject line strategy.

Reality check

Where this gets hard

The walkthrough above works — until your numbers change, the LLM hallucinates, or you have to re-paste everything next month.

Every run starts with a manual data pull — no live connection to Google Analytics, Meta Ads, or your ESP means you're downloading and pasting CSVs every single week.

Context window limits force tradeoffs: if you're running multiple campaigns across multiple channels, you can't paste everything in at once, so you either truncate the data or make multiple calls and stitch the output yourself.

Output format drifts week to week — the structure you got last Monday isn't guaranteed to match this Monday, which makes version-to-version comparison harder and light editing into heavier reformatting.

Nothing is delivered automatically — the model produces a draft, but you still manually copy it into a doc, format it, and send it. The 'automation' stops at generation.

No institutional memory — the model doesn't know that last week's traffic dip was a planned site migration, or that you paused a campaign intentionally. You re-explain context from scratch every time.

Data from different tools rarely aligns cleanly — date ranges, attribution windows, and metric definitions vary by platform, and the model can't resolve those discrepancies without you flagging them explicitly.

Tired of the friction?

Starch runs the whole workflow on live data — no copy-paste, no hallucinated numbers, no re-prompting next month.

See the Starch version →

Starch alternative

The same workflow on Starch

Starch is an agentic operating system — for this workflow, that means an agent builds the persistent app that pulls your live marketing data on a schedule, drafts the weekly report, and delivers it automatically, without you touching a CSV.

Connect Google Analytics, Mailchimp, and your ad accounts from Starch's integration catalog — the agent queries them live each week so your report reflects actual numbers, not a snapshot you manually exported three days ago.

The Growth Analyst starter app connects to PostHog, surfaces signup trends, top referrers, and conversion changes by channel, and emails you a weekly digest automatically — no dashboard to remember to check, no prompt to re-run.

Describe the report format you want in plain English — 'every Monday at 8am, pull last week's paid and organic performance, write a 300-word summary, and post it to our #marketing Slack channel' — and Starch builds that automation and runs it continuously.

Starch connects directly to Slack on a scheduled sync, so report delivery is part of the same automation — the agent drafts, formats, and posts without a manual copy-paste step in your workflow.

Ads Agent — currently in development, request beta access — will handle cross-channel ad performance reporting across Google, Meta, and TikTok in one place, so the paid portion of your weekly report assembles itself rather than requiring three separate exports.

The report structure you define persists — same format, same delivery time, same channel breakdowns every week — so you're editing a standing output instead of regenerating from scratch every Monday morning.

Get closed-beta access →

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