How to Automate a Weekly Marketing Briefing
A One-Page Structure, What AI Should and Should Not Write, and Three Ways to Put It on a Schedule
Short answer
Automate a weekly marketing briefing by pulling numbers from source tools and letting AI draft only the narrative. Send one page at the same time every week, with five sections: a headline, numbers against last week, market moves, what shipped, and decisions needed. The numbers must never come from the model.
1. What is a weekly marketing briefing for?
To get a small team to the same understanding of last week and the same short list of decisions for this one, in the time it takes to read one page. It is a decision document, not a performance archive.
A report records; a briefing directs. The monthly report can hold every chart. The weekly briefing exists so that the founder, the marketer and whoever runs sales all start the week knowing the two or three things that changed and what, if anything, to do about them.
That purpose is also the test for every line. If a number is in the briefing, someone should be able to say what they would do differently if it had gone the other way. If nobody can, it belongs in the monthly report or nowhere.
2. What goes into a weekly marketing briefing?
Five sections: a one-line headline, a small set of numbers against last week and the trend, what competitors and the market did, what your team shipped, and the decisions needed. One page, same order every week.
| Section | Question it answers | Source | Written by |
|---|---|---|---|
| Headline | If you read one line, what should you know? | The sections below | AI drafts, a person confirms |
| Numbers | Did the few metrics we steer by move? | GA4, Search Console, ad platforms, CRM | Pulled automatically, never generated |
| Market moves | What did competitors or the market do that matters? | Competitor tracking, alerts, reviews | AI summarizes from tracked sources |
| Shipped | What did we publish or launch? | Content calendar, campaign list | Pulled automatically |
| Decisions | What should we decide or change this week? | All of the above | AI suggests, a person owns |
A filled-in example for a team competing with Veltrix and Norvane:
- Headline: Demo requests held steady while Veltrix started leading its ads with a lower price.
- Numbers: Demo requests flat against last week, organic sessions up slightly, paid cost per lead up, email click rate flat. Each shown with last week and the four-week trend.
- Market moves: Veltrix launched two Meta ads leading with its entry plan cut from $39 to $33 a month. Norvane rewrote its landing page headline to target clinics.
- Shipped: Two LinkedIn posts on onboarding speed, one customer story, one email.
- Decisions: Do we answer the price angle this week? Owner: Head of marketing, by Wednesday.
3. Which parts should AI write, and which should it not?
Numbers come straight from the source systems, calculated by formulas or the tool itself. AI writes the words around them: the headline draft, the market summary and suggested decisions. A model should never produce or recompute a number.
The most damaging briefing error is a wrong number stated confidently. Language models are good at describing a table and unreliable at doing arithmetic across one, and they will fill a gap with a plausible figure rather than leave it empty. So split the work: a system pulls and calculates, the model narrates only what it is given.
Rules that keep the narrative honest:
- Give the model the finished numbers, with last week and the trend already calculated.
- Tell it to quote only numbers present in the input, and to write data missing when a source failed.
- Ask it to label anything that is interpretation, such as likely caused by, as a hypothesis.
- Keep causal claims out of the headline unless a person confirms them.
4. How do I automate it?
Three routes, from least to most work: a scheduled report inside a tool you already use, an assistant with connectors that you run on a schedule, or a script that assembles everything and sends it. Start with the first that covers your sources.
| Route | How it works | Good when | Watch out for |
|---|---|---|---|
| Scheduled report in a tool | The tool writes and emails a report from a brief on a fixed day and time | Most of your sources already live in that tool | It only sees the data that tool holds |
| Assistant with connectors | An assistant reads your sources through connectors or MCP and drafts the briefing on request | You want flexible questions and already use Claude or ChatGPT daily | Someone has to run it, and numbers still need checking |
| Script plus scheduler | A script pulls each source, calculates, asks a model for the narrative and sends the result | Sources are spread across several tools and you want full control | It needs maintenance when any source changes |
A mixed approach is common and fine: the numbers come from a dashboard or spreadsheet that refreshes itself, the market section comes from the scheduled report in a competitor tracking tool, and a person spends ten minutes combining them and writing the decisions. Automation does not have to mean zero human minutes; it means the minutes go to judgment.
5. How do I set it up, step by step?
Choose the metrics, fix the template, connect each section to its source, pick the send time, run it manually for two weeks, then switch on the schedule and add a failure alert.
- Choose the steering metrics. Pick up to five numbers the team makes decisions from, and write down the definition and source of each. If two tools disagree on a metric, choose one and say which.
- Fix the template. Use the five sections above in the same order every week. A stable layout is what lets people read it in two minutes.
- Connect each section to a source. Numbers to your analytics, ad and CRM exports; market moves to your competitor tracking; shipped to your content calendar. Note which parts are automatic and which need a person.
- Pick a send time and keep it. Monday early morning in the team's time zone works for most. The same time every week is part of what makes people read it.
- Run it by hand for two weeks. Assemble the first two briefings manually from the template. You will find the sections nobody reads and the number that is always wrong before automating either.
- Schedule it and alert on failure. Switch on the schedule, and make sure a failed source produces a visible data missing line or an alert, not a silent gap.
In Oppira, the market section can come from the weekly briefing and Monday insight cards, and the Playbook can send scheduled plain-language email reports, weekly or monthly, written from a brief you describe. On the Pro plan the same report can be created from the CLI, for example oppira reports create --prompt "weekly rival ad moves" --frequency WEEKLY --day 1 --hour 8. Your own site and CRM numbers still come from those tools.
6. How do I keep people reading it?
Keep it to one page, put a real decision in every issue, say so plainly when a week was quiet, and cut any section nobody has referred to in a month. A briefing that is always long is soon skimmed and then ignored.
Readership decays when the briefing stops changing anything. The fix is the Decisions section: if a week genuinely has none, write no decisions needed this week in one line. If several weeks in a row have none, the metrics are wrong or the market section is too shallow.
Review the template every quarter. Ask each reader which section they last used to make a choice. Cut the ones that nobody names, and replace them only if someone asks. The briefing should get shorter over time, not longer.
Key Takeaways
A decision document, not an archive
Every line should change what someone does. Everything else belongs in the monthly report.
Five sections, one page
Headline, numbers against last week, market moves, what shipped, and decisions needed.
Systems pull numbers, AI writes words
Numbers come from source tools and formulas. The model only narrates figures it was given.
Start with the simplest route
A scheduled report in an existing tool, then an assistant with connectors, then a script.
Run it by hand first
Two manual weeks reveal the unread sections and the unreliable number before they get automated.
Make failure visible
A missing source should read as data missing, never as a zero or a plausible guess.
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