Automated agency client reporting

The solution pulls data from ad accounts, analytics, and CMS, turning raw metrics into a client report with explanations and recommendations. This removes the repetitive work of consolidating spreadsheets, preparing screenshots, and writing comments on the numbers.

What automation does:

1. Pulls data from connected sources — ad platforms (Google Ads, Meta Ads, TikTok Ads), product analytics (GA4, Plausible, Mixpanel), CMS and content platforms (WordPress, Webflow).
2. Calculates key metrics for the period: cost per lead, CTR, ROAS, organic traffic, content views, conversions.
3. Compares metrics against the previous period and the goals set in the client brief.
4. Extracts insights from unstructured data: campaign comments, article headlines, content tags.
5. Generates a report draft: structured text with sections for 'results', 'what worked', 'what underperformed', 'recommendations for the next period'.
6. Assembles visualizations — charts, tables, comparison cards — into a PDF or web page with agency branding.
7. Sends to the client on a schedule (weekly, biweekly, monthly) and notifies the responsible manager in Slack.
8. Stores the report history in an archive to refer back to when planning the next quarter.

Automation reduces time per client — in the SaSame case from 13 to 7 hours — but leaves the manager with final control: editing phrasing, adding non-standard comments, aligning with the strategist.

What automation does NOT do:

• Does not replace strategic discussion with the client — meetings, zoom calls, and interpreting 'why' remain with people.
• Does not form hypotheses based on missing data: if there is no tracking or access to the source, the section will remain blank with a note.
• Does not guarantee data quality in sources — if UTM tags are set up incorrectly or the pixel is not working, the report will show this but will not fix it.

The workflow is built from three layers: data collector, narrative generator, delivery. Each layer is isolated, which simplifies debugging and component replacement.

Flow architecture

1. Data collector. The scheduler (low-code platform, Zapier, cron) calls the source APIs once a day: Google Ads, Meta Ads Business, GA4, Mixpanel, Plausible, WordPress REST, Webflow CMS. Data is stored in a single table (Airtable, BigQuery, or PostgreSQL) broken down by client, channel, campaign, and date.
2. Metrics aggregator. The script calculates derived metrics (CPL, ROAS, engagement rate, subscriber growth), compares them with the previous period and with the goals from the brief. The result is a structured JSON with facts, without interpretation.
3. Narrative generator. The AI agent based on an AI model receives the JSON and the agency's report template. It writes the sections “results,” “what worked,” “what did not work,” “recommendations,” referencing specific figures and events. The agent also extracts insights from unstructured sources — editor comments, publication headlines, UTM tags.
4. Document assembler. The template engine (Google Docs API, Notion API, custom HTML→PDF pipeline) inserts text and charts into the agency’s branded template.
5. Delivery. The finished report is sent to the client via e-mail, Slack, or the client portal. The manager receives a copy marked “ready for review” and makes edits before sending.

How to implement in 2–4 weeks

1. Compile a list of all sources from which report data is pulled, and verify that an API or export is available.
2. Select one client for the pilot — preferably one with a standard report format.
3. Set up connections: OAuth authorization in ad platforms, service account in GA4, API tokens in CMS.
4. Create a single data storage (Airtable for simple cases, BigQuery for 20+ clients).
5. Describe the report template in natural language — what should be in each section, which metrics are mandatory.
6. Connect the AI agent (a language model via Anthropic API) and test generation on 2–3 past reports.
7. Assemble the delivery pipeline — e-mail via SendGrid/Postmark or integration into the client portal.
8. Run the pilot for 2 reporting cycles and collect edits from managers.
9. Scale to the remaining clients in batches of 3–5.

Stack components

Important detail: the final review stays with the manager. The agent produces a draft rather than sending the report directly to the client — this reduces the risk of errors in figures or wording that directly affect trust.

Automation requires prepared access and a team. The more complete the input, the faster the pilot will proceed.

Data and access:

• API keys or OAuth tokens for all metrics sources: advertising platforms, analytics, CMS, CRM.
• A service account in GA4 or a similar tool with «viewer» permissions for all client projects.
• A list of clients with briefs and fixed goals — without goals, the narrative reduces to listing numbers.
• A standard agency report template in a format that can conveniently be turned into a programmatic template (Google Docs, Notion, Markdown).

Team and processes:

• An account manager available for 2–3 weeks to review drafts and record edits for template fine-tuning.
• A technical executor (in-house or contracted): knowledge of an orchestrator or Zapier and basic API work.
• The final automation owner — COO or head of operations, who makes decisions on edits and the production launch.

Organizational readiness:

• The report format for all clients is agreed upon or reduced to 2–3 standard types.
• Data ownership in sources is established: who monitors UTM tags, pixels, and campaign tagging.

Expected duration:

1. Pilot with 1 client — 2 weeks.
2. Scaling to 5–10 clients — another 1–2 weeks.
3. Full roll-out across a portfolio of 20+ clients — up to 4 weeks, accounting for edge case resolution.

Weekend complexity means that a working automation prototype can be assembled over a weekend by a single engineer, but a production launch requires a full validation cycle.