Python / Campaign operations
Campaign operations, designed.
A Python workflow turns prepared campaign data into Google Ads Editor changes and a written explanation of each action.
Recurring campaign decisions need a repeatable path from account data to reviewable changes.
Python automation design and development
A Google Ads Editor change sheet and an action summary with reasons
This story describes Shane's own Python script work. The diagram is explanatory; it does not show private code, account data, or measured time savings.
Make recurring decisions easier to review.
Campaign management involves returning to the same types of account questions again and again. The source data sits in a Grafana table built from base SQL data. Rebuilding the same changes by hand each time makes the work harder to repeat and explain.
I built Python scripts that fetch that table automatically, apply my campaign management rules, and turn the result into two connected outputs: a formatted changes spreadsheet for Google Ads Editor and a summary of the actions with the reasons behind them.
- 01Fetch
Pull a prepared Grafana data table automatically.
- 02Prepare
Apply campaign rules and format a change sheet for Google Ads Editor.
- 03Review
Read the action summary and reasons before pasting approved changes into Editor.
This diagram explains the method. It is not a live account screenshot.
A tool should show its work.
The spreadsheet is formatted so the proposed changes can be copied into Google Ads Editor. The paired summary explains what the script recommends and why. That gives a manager a clear review point before applying changes in the account.
The public diagram shows the operating pattern. Table names, authentication details, client data, account identifiers, and script source stay private.
From data to an accountable next step.
The delivered artifact is a change sheet plus its rationale. This page does not claim a quantified time saving or a client performance lift from the automation alone. What it demonstrates is a practical handoff: data is fetched, rules are applied, and a person can review the recommendation before it reaches Google Ads Editor.