Automate Your Agency’s Client Reporting in One Week
Client reporting is the task agency teams dread most and value least — hours of manual data assembly that produces a document most clients barely read. This guide shows you how to build a fully automated client reporting system in one week that produces better reports with zero manual effort.
What Gets Built
| Report Element | Data Source | AI Role | Output |
|---|---|---|---|
| Performance headline | Primary KPI from the main platform | Claude generates the lead narrative sentence | The most important result, clearly stated |
| Channel performance | Individual platform APIs | AI compares vs prior period and target | Narrative section per channel |
| What worked | High-performing content/campaign data | Claude identifies and explains success patterns | Insights section |
| What to improve | Underperforming elements | Claude identifies gaps with suggested actions | Recommendations section |
| Next period plan | Upcoming campaigns and activity | Claude connects plan to performance context | Forward look section |
| Executive summary | All of the above | Claude synthesises into 3 key points | Opening summary for client |
Day by Day
Day 1: Connect your data sources
Identify the platforms your reports draw from: Google Analytics 4, Google Search Console, Meta Business Suite, Google Ads, LinkedIn Campaign Manager, email platform (Klaviyo, Mailchimp, GoHighLevel), and any other primary data sources. For each platform: authenticate the Make.com connection (most have native modules — authenticate via OAuth in Make.com’s Connections section). Test each connection by running the relevant module in a test scenario and verifying data is returned. All platforms connected and tested by end of Day 1 — the data foundation is the most important prerequisite.
Day 2: Build the data collection scenario
Create a new Make.com scenario: the trigger is a Schedule module (set to run on the reporting day — typically the first business day of each month or week depending on your reporting cadence). Add a module for each platform: retrieve the key metrics for the reporting period (traffic, conversions, revenue, reach, clicks, impressions — the specific metrics relevant to each client). Store the collected metrics in a Bubble.io ReportData record or pass directly to the next step. By end of Day 2: data flows automatically from all platforms on schedule.
Day 3: Build the AI narrative generation
Add the Claude API HTTP module to the scenario. The prompt: Generate a client performance report narrative for [client name] – [reporting period]. Performance data: [paste all collected metrics]. Prior period data: [prior period metrics for comparison]. Client context: [client’s goals and KPIs from a stored client profile]. Generate: (1) a 3-bullet executive summary – the three most important results, (2) a channel-by-channel narrative (2-3 sentences per active channel – what happened and why), (3) top 2 wins this period with the specific result, (4) top 2 improvement opportunities with specific recommended action, and (5) the forward look for next period in context of this period’s results. Tone: honest, professional, and specific. Never use vague language like good performance – always quantify. Test the prompt with one client’s real data by end of Day 3.
Day 4: Build the report formatting and delivery
Format the AI narrative into the report template. Options: Google Docs via API (Make.com Google Docs module fills a template with placeholders replaced by the AI narrative — cleanest for shared documents), HTML email via GoHighLevel (the narrative formatted as a professional HTML email — fastest delivery), or PDF via a PDF generation API (most professional format for premium clients). Configure delivery: report emailed to the client contact from the account manager’s address, with a copy to the account manager for review before sending (or sent directly if you have built sufficient confidence in the AI quality). Day 4 target: first automated report delivered to a test client.
Days 5-7: Refine, test with all clients, and activate
Day 5: review the first automated report with the account manager — what needs adjustment in the prompt, the data collection, or the formatting? Refine based on feedback. Day 6: run the scenario for all active clients and review each output — are there client-specific customisations needed (different KPI emphasis, different comparison period, different tone)? Build client-specific prompt adjustments where needed. Day 7: activate the scenario for all clients, set the schedule for the first real automated report delivery. The reporting system that consumed 30 to 50 hours of agency time per month now runs automatically.
📌 The highest-value refinement after launch: the comparison context. A report that says email open rate was 24% this month is informative. A report that says email open rate was 24% this month — above the industry average of 21% and up from 19% last month — is insightful. Build the comparison context into your prompt: the prior period figures, the industry benchmarks (stored in the client profile), and the client’s own targets. The AI narrative that contextualises every metric produces the reports clients actually read rather than file without opening.
What if a platform does not have a Make.com module?
Most major marketing platforms have Make.com native modules or support HTTP API calls that Make.com can make directly. For platforms without either: export the data manually as a CSV or Google Sheet and build a scenario that reads from the Sheet — semi-automated is dramatically better than fully manual. The manual step is downloading and uploading the export; Make.com and Claude handle everything else. Fully manual reports take 3 to 5 hours; the semi-automated version with one manual step takes 15 minutes.
How do clients respond to AI-generated reports?
Client response to automated AI reports is consistently positive when the reports are high quality and specific — which is typically better than manually produced reports that were rushed at month end. Clients do not know or care how the report was produced; they care whether it is accurate, clear, and useful. The agency should not proactively disclose that reports are AI-generated — the information is not material to the client’s assessment of the report’s value. If asked, be honest. But the quality of the report, delivered consistently on time, is what clients actually evaluate.
Want Your Agency Reporting Automated This Week?
SA Solutions builds agency client reporting systems — data connections, AI narrative generation, branded report templates, and scheduled delivery — in 5 to 10 working days.
