A monthly content performance report, ready on the first day of the month

Implementation example. This scenario describes a request we receive in this form. It is not a project delivered to a named client, and no results are claimed.

The context

A marketing team publishes across several channels: website and blog, social media, newsletter, paid campaigns, sometimes an online store. It reports to management every month, and sometimes to brands or subsidiaries.

The data sits in different tools: Google Analytics 4, Search Console, social media analytics, the email marketing tool, ad platforms, the CRM. Each tool has its own definitions and exports.

The day-to-day problem

The first few days of every month are spent on reporting: exporting, copying into a spreadsheet, redoing the charts, writing a commentary. The figures change depending on who pulled them. Definitions drift (“visitors,” “conversions,” “engagement”).

The report arrives mid-month, too late to correct anything. Management reads the key figures, rarely the rest. No one has time to analyze why a piece of content worked.

What the team wants

“I want the figures ready on the first day of the month, always calculated the same way, with a commentary that says what worked, what didn't and what we're changing.”

The solution, step by step

1. Choose the metrics. With the team and management, we set a short list of metrics per channel and their exact definitions: sessions, conversions, open and click rates, engagement, cost per result, leads generated. A definitions sheet accompanies the dashboard.

2. Connect the sources. Data is pulled automatically from each tool (Google Analytics 4, Search Console, social media, email marketing, ad platforms, CRM) and brought together in a single database, updated daily.

3. Build the dashboard. A dashboard shows the metrics by channel and by piece of content, compared with the previous month and the same period the previous year. It is available at any time, not just at the end of the month.

4. Have AI write the commentary. On the first day of the month, AI writes a commentary based on the figures: main changes, best- and worst-performing content, anomalies to check. Every statement links to the figure that supports it. AI only comments on what is in the data.

5. Review and add to it. The team reviews the commentary, corrects it, and adds the context only it knows (a delayed campaign, a technical incident) and its recommendations for the following month.

6. Distribute. A one-page summary goes to management, with a link to the full dashboard.

What AI does, what the team does

The tool collects and calculates. AI spots changes and writes a first sourced commentary. The team interprets, adds context, decides on actions and signs off on the report.

Data and access required

• read access to Google Analytics 4, Search Console and the other tools;
• reports from recent months, to carry over the metrics being tracked;
• the year's objectives, if any;
• the list of report recipients.

Project timeline

• Weeks 1 and 2: assessment. Review of current reports, sources and definitions. This is the format of our audit (8 to 10 days).
• Weeks 3 and 4: connections. Connecting the sources, database, definitions sheet.
• Weeks 5 and 6: dashboard and commentary. Building the dashboard, first monthly report with commentary, adjustments.
• After that: follow-up. Adding sources or metrics as needed.

Deliverables

• a metrics definitions sheet;
• automated connections to the data sources;
• a dashboard updated daily;
• a written, sourced monthly commentary, ready for review;
• a one-page summary for management;
• a short training session for the team.

Metrics to track

• the time spent on reporting each month;
• the report's distribution date;
• the number of figure discrepancies reported;
• the number of decisions made based on the report.

These metrics are recorded at the start, then tracked every month. No results are promised in advance.

Limitations and prerequisites

• If conversion tracking is poorly configured in the tools, it must be fixed first: the dashboard can be no more accurate than its sources.
• Some platforms restrict access to their data; some figures are still entered manually.
• The AI commentary describes the figures; interpretation and decisions remain human.

Indicative budget

The assessment corresponds to our audit, from €1,000 excl. VAT. Implementation is quoted on request, depending on the number of sources and metrics.

Frequently asked questions

Do we need a paid dashboard tool? Not necessarily. The choice depends on the tools already in use and the number of readers; we discuss it during the audit.

Can AI get its commentary wrong? It can misinterpret a change. That is why every sentence links to its figure and the team reviews before distribution.

Can we have a report by brand or by country? Yes, with filters on the same dashboard.

Can we ask questions about the figures? Yes, the dashboard can be queried in natural language, with links back to the data.

Further reading

• Process automation expertise
• Client case: automated weekly marketing report
• Watch Your Stats, Your Audience Will Grow
• Never entrust to AI what a calculation can do

What next?

If your monthly reporting takes several days and arrives too late, the audit lets us see in 8 to 10 days what can be automated and how to make your figures more reliable.