Daily
Get a daily read on which Meta ads to scale or pause
Every morning yesterday's ad results are logged in your sheet, and you get an email saying which ads to scale, pause or fix, and why.
- Runs
- Daily
- Apps
- Facebook Pages, Google Sheets
- AI
- Yes, uses the built-in AI allowance
- Access
- ads_read; Sheet edit
Send this to your assistant
Create a ClickRuntime automation: every morning at 7, log yesterday's Meta Ads results per ad in my Ads sheet, then email me a short analysis of which ads to scale, pause or fix.
First time? Adding ClickRuntime to your assistant takes about two minutes.
Why use it
Why teams use this automation
A founder spends a few hundred a day on Meta ads and knows some ads are wasting money, but reading the numbers each morning and deciding what to change takes time they don't have. Bad ads run for a week before anyone notices.
Every morning the numbers are logged in a sheet and read for you. The email names the ads worth more budget, the ones to pause and the ones with a fixable problem, such as a good click rate but poor conversion, with the figures behind each call.
What you get out of it
Stop wasting budget sooner
Poor performers are flagged the next morning, not at the end of the month.
Know where to put more money
Ads that are working are called out so you can scale them while they last.
Reasons, not just numbers
Each suggestion explains which figures led to it, so you can judge it yourself.
History in the sheet
Daily rows let you check the trend behind any suggestion.
How it works
What happens, step by step
Every morning, ClickRuntime gets yesterday's results for each of your ads from Meta.
It adds a row per ad to your sheet.
AI compares each ad with your target cost and account averages and decides whether to scale, pause or fix it.
You get one email with the suggestions and the numbers behind them.
Before it runs live
- Connect Facebook Pages
- Connect your Google Sheets account and pick the spreadsheets it may use
What you get
Build your own
Need it to work differently?
Your own rules
Conditions, thresholds, filters and exceptions, written the way you'd explain them to a colleague.
Any number of apps
One automation can read from one app and update several others. Over 248 apps are ready, and anything with an API works too.
AI where it helps
Summaries, classification, extraction and drafts use ClickRuntime's built-in AI, with no extra key.
On a schedule or on each event
Runs as often as every 5 minutes, or the moment an app sends an event, and keeps track of what it already handled.
Changes you can ask for
- Run it at a different time, more or less often, or only on weekdays
- Change what the AI looks for, or how it writes the result
- Add another app, for example also send it to Slack
- Combine it with other Facebook Pages and Google Sheets steps into one automation
Under the hood
The code that runs this automation
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from clickruntime import on_trigger, credentials, http, ai, notify
AD_ACCOUNT, SHEET_ID, TARGET_CPA = "act_1234567890", "your-spreadsheet-id", 25
@on_trigger
def run(event):
data = http.get(f"https://graph.facebook.com/v21.0/{AD_ACCOUNT}/insights",
params={"level": "ad", "date_preset": "yesterday", "access_token": credentials.connection("facebook", "work"),
"fields": "date_start,ad_name,spend,clicks,ctr,actions"}).json()["data"]
rows = [[d["date_start"], d["ad_name"], float(d["spend"]), int(d["clicks"]), float(d["ctr"]),
sum(int(a["value"]) for a in d.get("actions", []) if a["action_type"] == "purchase")] for d in data]
if not rows:
return {"ads": 0}
sheets = build("sheets", "v4", credentials=Credentials(credentials.connection("google", "work")), cache_discovery=False)
sheets.spreadsheets().values().append(spreadsheetId=SHEET_ID, range="Ads!A:F", valueInputOption="USER_ENTERED",
body={"values": rows}).execute()
table = "\n".join(f"{r[1]}: spend {r[2]}, clicks {r[3]}, CTR {r[4]}%, purchases {r[5]}" for r in rows)
advice = ai.generate(f"Target cost per purchase: {TARGET_CPA}. For each ad say scale, pause, fix or too early, with a reason:\n{table}",
max_output_tokens=700).text
notify.email_me("Meta Ads: what to change today", advice)
return {"ads": len(rows)}FAQ
Common questions
Does it change my ads?
What does it base its suggestions on?
Does it need a lot of data?
What permissions does it need?
Does it need AI?
How many runs does it use?
Can I change how it works?
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