Scheduled Monthly Performance Email: A Quick Client Touch Between Full Reports

What This Builds

Between full monthly reports, clients often go quiet for weeks with no sense of how their campaign is doing, especially when delivery work crowds out the check-in emails that used to happen naturally. This build runs on a monthly schedule, pulls each client's key numbers, writes a short AI-generated summary, and emails it automatically a week or two ahead of the formal report. It's a lighter touch than the full reporting pipeline, meant to keep clients feeling informed without adding to your workload.

Prerequisites

• A Make account on a paid plan (the Core tier is Make's typical entry point for scheduled multi-step scenarios with tighter timing than the free plan allows; check make.com/pricing for current tiers and rates before signing up)
• A ChatGPT account ($20/month for Plus, if you connect it directly rather than using a native AI module inside Make)
• A spreadsheet or CRM view with each client's key monthly numbers already being tracked somewhere Make can read
• An email account Make can send from, either a connected Gmail account or your existing email platform
• 1-2 hours to build and test with one client's data before scheduling it for the full roster

This build's ongoing cost is whatever paid Make plan you choose, on top of your existing chatbot subscription. Check make.com/pricing directly since Make's tier names and rates change; do not assume last year's figures still apply.

The Concept

Think of this as a short check-in postcard between two full letters. The formal monthly report is the letter, detailed and complete. This scheduled email is the postcard sent a week or two before it: a quick "here's how things are going" that keeps the client from feeling like they only hear from you once a month, without you having to write a fresh note by hand every time.

Build It Step by Step

Part 1: Set the schedule

1. In Make, create a new scenario and choose a scheduled trigger, set to run monthly on a date ahead of your usual full-report delivery, for example the 20th of each month if full reports go out on the 1st.
2. Confirm which client data source Make will read from: a shared spreadsheet, a CRM field, or an export from your reporting tool.

What you should see: A trigger that fires once, on the date you chose, when you run a manual test.

Part 2: Pull each client's key numbers

1. Add a step that reads the relevant row or record for each client: month-to-date spend, leads or conversions, and a comparison figure from the prior month if available.
2. If you have multiple clients, use an iterator module so the scenario runs once per client rather than needing a separate scenario for each.

Part 3: Generate the summary

1. Add an AI module, either a native module for your chosen provider or a connected ChatGPT step.
2. Use a prompt along these lines:

Write a short, friendly check-in email (three to four sentences) for a marketing agency client, based on the month-to-date numbers below. This is not the full monthly report, just a quick update ahead of it. Mention one specific number and keep the tone conversational, not formal. Do not invent numbers not provided below.

Client name: {name}
Month-to-date spend: {spend}
Month-to-date conversions: {conversions}
Comparison to prior month: {comparison}

1. Route the output into an email-send step, addressed to the client's contact, with a subject line like "Quick mid-month update."

Part 4: Test and refine

Run the scenario manually against one client's real data before scheduling it for the full roster. Read the email as if you were the client receiving it. If it reads stiff or repeats the same sentence structure every month, adjust the prompt to ask for more variation.

Real Example: A Mid-Month Check-In

Setup: You've built the scenario to run on the 20th of each month, pulling data from the same spreadsheet the full monthly report draws from.

Input: A social media retainer client is at 2,400 dollars spent for the month against a 3,500-dollar monthly budget, with 24 leads so far compared to 19 by the same point last month.

Output: The AI drafts: "Quick mid-month update: you're at 2,400 dollars spent so far this month, on pace with budget, and we've already generated 24 leads compared to 19 at this point last month. Full report with the complete breakdown coming your way in a couple of weeks."

Result: The client gets a low-effort, specific touchpoint that shows momentum without requiring you to write it by hand.

Time saved: Turns what would otherwise be a skipped or rushed check-in into a few minutes of review time per client instead of a written-from-scratch email.

What to Do When It Breaks

• The email goes out with stale or missing numbers → Check the data source first. A common cause is the spreadsheet or CRM field not being updated yet when the scenario runs; consider moving the schedule a day or two later in the month.
• The scenario silently stops running and nobody notices clients stopped getting the check-in → Make has scenario-level notifications for errors; turn those on, and separately do a rough monthly gut check that the emails actually went out, since a client going quiet is easy to mistake for a client who is simply busy.
• The tone feels off for one particular client relationship → Add a client-specific note field to the data source (for example, "prefers brief, no exclamation points") and reference it in the prompt so the tone can vary by account.
• A client replies to the automated email with a question → Route replies to an inbox your team monitors, and treat any reply as a signal to check in personally rather than letting the automation try to respond.

Variations

• Simpler version: Send the same check-in manually from a saved template, using AI only to fill in the specific numbers each month rather than automating the whole send.
• Extended version: Add a conditional branch that flags any client whose month-to-date numbers are meaningfully behind pace, so those get a personal note from you instead of the automated version.

What to Do Next

• This week: Build the scenario for one client and compare the AI draft against what you'd write yourself.
• This month: Roll it out to your retainer clients, adjusting the prompt for accounts with different goals (leads versus revenue versus brand metrics).
• Advanced: Pair this with the full weekly reporting pipeline so clients get a light touch mid-month and the full detailed report on schedule, without either one competing for your time.

Advanced guide for marketing agency owner professionals. These techniques use more sophisticated AI features that may require paid subscriptions. Client performance data flows through Make and your AI tool as part of this build. Review your client contracts' data-handling clauses before connecting live accounts, and read each month's AI-drafted email before the first sends go out, since a wrong number in a client-facing message is harder to walk back than a missed email.