No more spreadsheet chaos – automated marketing reporting.
Campaign data flows together automatically – the team focuses on interpretation and recommendations.
Illustrative example: This scenario shows a possible implementation. It does not describe a specific client project.
Starting point
An agency runs campaigns for several clients on Google Ads, Meta, LinkedIn and email. At month-end the team exports data from every platform, copies it into spreadsheet templates, builds charts and writes comments.
Each client has slightly different metrics and formats. Reporting takes a lot of time that would be better spent on analysis and optimisation.
The challenges so far
Many data sources
Each platform delivers data in its own format.
Copy and paste
Exports are merged by hand – error-prone and time-consuming.
Inconsistent reports
Every report looks a little different; comparisons are hard.
Little time for insights
Real interpretation gets squeezed.
The possible AI and automation solution
Ad and analytics platforms are connected via APIs. A workflow pulls data regularly, standardises metrics and writes them to a central data store. From there, each client gets a dashboard or report. AI drafts a summary of notable developments, which the team reviews and completes.
- Automatic data retrieval from ad, analytics and email tools
- Consistent metric definitions per client
- Central data store as the basis for dashboards
- AI draft for summary and notable changes
- Automatic delivery after team approval
// What
What the automated workflow could look like
Step by step – from input to result.
- 01
Fetch data
Google Ads, Meta, LinkedIn and Analytics are queried automatically.
- 02
Standardise
Metrics are cleaned and named consistently.
- 03
Merge
All data lands in one central store per client.
- 04
Visualise
Dashboard and report template are filled automatically.
- 05
AI summary
A text draft highlights trends and anomalies.
- 06
Review & send
The team adds recommendations and approves the report.
Before/after comparison
Before
- Manual exports from every platform
- Spreadsheet template per client
- Charts built by hand
- Inconsistent metrics
- Little time for analysis
After
- Automatic data retrieval
- Central, clean data store
- Self-updating dashboards
- Consistent definitions
- More time for recommendations
Potential benefits
Qualitative assessment – the actual value depends on your workflows.
Less routine work
Exports and copy-paste largely disappear.
Reliable numbers
Consistent definitions and fewer transfer errors.
More current insights
Data isn't only available at month-end.
Better consulting
The team spends time on interpretation and optimisation.
Where people still decide
The AI only provides a draft. Evaluating results, strategic recommendations and client communication remain with the agency team. No report goes out without review and approval.
Possible technical integrations
Example systems – the selection depends on your existing software.
- Google Ads
- Meta Ads
- LinkedIn Ads
- Google Analytics 4
- Looker Studio
- Google Sheets / BigQuery
- Brevo / Mailchimp
- n8n / Make
Related services
FAQ on this scenario
Do we need an expensive BI tool?
Not necessarily. A central data store such as Google Sheets or BigQuery plus a dashboard tool like Looker Studio is often enough. The right setup depends on data volume and requirements.
Can reports still be tailored per client?
Yes. The data foundation is consistent; layout and metric selection can be customised per client.
How reliable is the AI summary?
It is based solely on the supplied numbers and serves as a draft. The team checks statements and adds context the data alone can't show.
Your reporting could get simpler.
Find out in the AI potential check how much reporting effort could be automated in your business.
