Compiling a weekly Google Ads client report takes 45 minutes to 2 hours. Multiply that by five clients and you burn 5 to 10 hours every week on mechanical work. Copying figures from Google Ads, pasting them into a spreadsheet, calculating week-over-week changes, formatting an email. None of these steps require strategic thinking.
Make lets you automate this entire chain. This guide walks through reproducible scenarios for Google Ads, GA4, and SEO (Search Console) reporting, with precise module configurations for each workflow.
The Architecture of an Automated Reporting System in Make
A solid reporting system rests on four layers. Each layer maps to a distinct set of Make modules.
| Layer | Role | Make Modules |
|---|---|---|
| Collection | Pull raw data from platforms | Google Ads, GA4, Search Console, Facebook Ads |
| Processing | Calculate KPIs, variances, averages | Math, JavaScript Code, Aggregator |
| Storage | Archive for historical trends | Google Sheets, Airtable, PostgreSQL |
| Distribution | Deliver the report in the right format, at the right time | Email, Slack, Google Slides, PDF |
This modular architecture lets you modify one layer without breaking the others. Adding a new data source (Meta Ads, LinkedIn Ads) simply means plugging a new collection module into the pipeline. The processing and distribution layers stay untouched.
According to a 2024 HubSpot survey, marketing teams spend an average of 3.55 hours per week on manual reporting tasks. Automation reclaims that time for analysis and strategy.
Scenario 1: Weekly Google Ads Report to Slack + Sheets
This scenario generates a summary of last week's Google Ads performance every Monday morning.
Detailed configuration:
- Scheduler: triggers Monday at 8:30 AM, your local timezone
- Google Ads - Get Campaign Performance:
- Period: last 7 days
- Metrics: impressions, clicks, cost, conversions, conversion rate, average CPC, ROAS
- Segmentation: by campaign
- Iterator: loops through each campaign to calculate week-over-week changes
- Google Sheets - Add Row:
- Sheet "Ads History" with columns: date, campaign, impressions, clicks, cost, conversions, CPC, ROAS
- Automatic feed to build a 12-month historical view
- Aggregator: totals all metrics across campaigns
- Text Aggregator: formats a Slack message with key figures:
- Total spend vs. budget
- Conversions and cost per conversion
- Top 3 campaigns by ROAS
- Alerts for any campaign running at a loss
- Slack - Send Message: posts in the #reporting channel
Result: a report delivered in 15 seconds every Monday. The Sheets history enables long-term trend detection. Built-in alerts flag underperforming campaigns without waiting for manual review.
Scenario 2: GA4 Dashboard in Google Sheets, Updated Daily
GA4 offers native reports, but extracting them into a shareable format remains tedious. This scenario keeps a Google Sheets dashboard current with your core metrics.
Detailed configuration:
- Scheduler: daily at 7:00 AM (previous day's data)
- HTTP Request - GA4 Data API:
- Endpoint: https://analyticsdata.googleapis.com/v1beta/properties/{ID}:runReport
- Dimensions: date, sessionSource, sessionMedium, landingPage
- Metrics: sessions, totalUsers, newUsers, bounceRate, averageSessionDuration, conversions
- Auth: OAuth 2.0 via connected Google account
- JSON Parser: extracts data rows from the API response
- Iterator: processes data row by row
- Google Sheets - Search Row: checks whether the date+source row already exists (avoids duplicates)
- Google Sheets - Add/Update Row: inserts or updates the row
Technical note: the GA4 Data API limits requests to 10,000 rows per call. For high-traffic sites, add a dimension filter (e.g., top 20 sources) or use paginated calls.
Key metrics to track by source:
| Source | Priority Metrics | Alert Threshold |
|---|---|---|
| Google / Organic | Sessions, average position, CTR | Drop > 15% week over week |
| Google / CPC | Cost, conversions, ROAS | ROAS below target |
| Direct | Sessions, bounce rate | Suspicious spike (bot traffic?) |
| Referral | New users, pages/session | Unknown source to investigate |
| Social | Traffic, session duration | - |
Scenario 3: SEO Reporting via Search Console
Google Search Console holds valuable ranking data, but the interface does not export automatically. This scenario extracts SEO metrics and archives them for trend analysis.
Detailed configuration:
- Scheduler: weekly, Wednesday at 9:00 AM (Search Console data has a 48-72 hour latency)
- HTTP Request - Search Console API:
- Endpoint: https://www.googleapis.com/webmasters/v3/sites/{siteUrl}/searchAnalytics/query
- Body: dimensions ["query", "page"], 7-day period, type "web"
- Returned metrics: clicks, impressions, CTR, average position
- Array Aggregator: groups data by page (sum of clicks and impressions per URL)
- Google Sheets - "SEO Weekly" tab: archives with columns for date, page, query, clicks, impressions, CTR, position
- Filter: isolates pages with average position between 4 and 15 (quick win opportunities)
- Slack: sends the quick wins list to #seo with impression count and current position
Why positions 4-15 are strategic: these pages appear on page one or two of Google but do not yet capture significant click volume. A gain of 2-3 positions can double the clicks. Research from Advanced Web Ranking shows that position 1 captures roughly 39% of clicks, while position 5 drops to around 5%. This scenario automatically identifies these weekly opportunities, feeding directly into your SEO strategy.
Scenario 4: Multi-Platform Anomaly Alerts
Detecting an anomaly (traffic drop, CPC spike, tracking error) on the same day rather than in the weekly report can save thousands in wasted ad spend or lost revenue.
Detailed configuration:
- Scheduler: every 6 hours (4 times daily)
- Route 1 - Google Ads: compare today's cost to the average of the previous 7 days. Alert if variance exceeds 30%
- Route 2 - GA4: compare today's sessions to the 7-day average for the same hours. Alert if drop exceeds 25%
- Route 3 - Search Console: check crawl errors via API. Alert on new 404 or 5xx errors
- Merger: consolidates all detected alerts
- Filter: only notifies if at least one alert exists
- Slack + Email: dual notification with details for each anomaly
Types of anomalies detected:
- Unusual Google Ads budget overspend
- Sudden organic traffic drop (potential deindexation or algorithm update)
- Average CPC increase (new competition on your keywords)
- Massive crawl errors (server issue, poorly managed migration)
- Conversion rate decline (technical issue on the site)
Scenario 5: Monthly Client Report in PDF
For agencies and consultants, delivering formatted client reports is a recurring deliverable. This scenario generates a complete report and sends it automatically.
Detailed configuration:
- Scheduler: 1st of each month at 10:00 AM
- Google Ads + GA4 + Search Console: collects the previous month's metrics
- Aggregator: calculates consolidated KPIs (total cost, total conversions, overall ROAS, organic traffic, average positions)
- Google Slides API: fills a report template with data (via {{metric_name}} placeholders)
- Google Slides to PDF: exports as PDF
- Google Drive: stores in the client folder
- Gmail: sends to the client with the PDF attached and a text summary of key takeaways
Typical report template (slide contents):
- Slide 1: Executive summary (3 key KPIs + trends)
- Slide 2: Google Ads performance (charts)
- Slide 3: SEO performance (organic traffic + positions)
- Slide 4: Conversions and attribution
- Slide 5: Recommendations and next month's action items
This workflow cuts the production time for a client report from 2-3 hours down to under 5 minutes. Quality stays consistent. Oversights disappear.
Reliable Reporting: Pitfalls to Avoid
Automating your reporting comes with traps. A few rules to keep things accurate and dependable:
- Always archive raw data before processing. If a calculation is wrong, you can correct it without re-running the collection step.
- Name your scenarios explicitly: "Weekly Ads Report - Client X - Monday 8:30" rather than "Scenario 12."
- Test each scenario for 2 weeks before putting it into production.
- Configure error notifications: Make can alert you when a scenario fails (API timeout, expired token, quota exceeded).
- Check API quotas: GA4 allows 10,000 requests per day per project. Google Ads: 15,000 requests per day. Size your execution frequencies accordingly. According to Google's own documentation, exceeding these quotas results in 429 errors that can silently break your pipeline.
For a guided setup, our marketing automation consultants design and deploy these scenarios and train your team to maintain them.
Frequently Asked Questions
Do I need a Google developer account to connect Make to GA4?
A Google Cloud project with the Analytics Data API enabled is sufficient. Creating one is free. Make handles OAuth 2.0 authentication through its interface. Allow about 15 minutes for the initial setup. Make sure your GA4 configuration is clean before automating your reporting.
Can Make connect to Google Ads without the official API?
Make offers a native Google Ads module that simplifies the connection. For advanced queries (custom segments, calculated metrics), a direct HTTP call to the Google Ads Reporting API is possible but requires more configuration.
How much does Make cost for multi-client reporting?
Each reporting scenario consumes between 50 and 200 operations per run (depending on the number of campaigns and dimensions). For 5 clients with weekly reports plus daily alerts, expect roughly 8,000-15,000 operations per month. The Make Pro plan at $16/month covers that volume.
Can these scenarios be replicated in n8n?
Yes, the logic is identical. n8n offers the same connectors (Google Ads, GA4, Search Console, Slack). The n8n advantage: no per-operation cost when self-hosted. The Make advantage: a more intuitive interface for data manipulation. Our n8n vs Make vs Zapier comparison details the trade-offs.
