Move Stripe customer data into BigQuery every weekday before 8am.
Stripe
Map campaign sources. Pull from Meta, Google, TikTok, Microsoft, budgets, naming rules, outcome tables, and finance metadata.
Campaign optimization and pacing agent
Campaign optimization breaks when spend, taxonomy, budgets, outcomes, finance metadata, and approvals live in different systems.
Mage
I mapped this marketing use case as a Mage workflow.
- pull Meta, Google, TikTok, Microsoft, budget, and outcome data
- validate campaign taxonomy, naming rules, finance metadata, and pacing
- route exceptions and recommendations to the right reviewers
- preserve approved actions as reusable campaign context
You can inspect every platform pull, taxonomy check, pacing exception, recommendation, approval, and delivery target before campaign changes are activated.
Use Mage to run campaign optimization and pacing workflows from source data to approved action.
Campaign optimization breaks when spend, taxonomy, budgets, outcomes, finance metadata, and approvals live in different systems.
Meta, Google, TikTok, Microsoft, project budgets, naming rules, outcomes, and finance metadata feed Mage QA, pacing, exception, and recommendation workflows, with human approval before action.
From marketing source chaos to governed action
Use Mage as the execution layer between the specific sources, validation rules, governed delivery targets, and reusable context this marketing workflow needs.
Why did revenue freshness drop?
The Stripe ingest arrived 42 minutes late, delaying the customer revenue model.
Stripe ingest delayed 42 min
Validate every recommendation. Check taxonomy, spend freshness, pacing, budget rules, outcome joins, approval state, and downstream readiness.
Customer syncRun delayed
Inspect run
Late source foundRetry availableOpen logs
Register campaign context. Capture recommendation reasons, approval decisions, taxonomy rules, spend context, and run history as reusable campaign context.
Pipeline
Table
Chart
File
AnswerRevenue freshness dropped after the Stripe ingest arrived late.
Deliver approved actions. Deliver QA exceptions, pacing alerts, planner summaries, approved action files, dashboards, and campaign-agent context.
How Mage runs campaign optimization
Mage turns campaign optimization into governed execution from platform data through QA, pacing, recommendation, and approval.
Run our campaign optimization workflow. Pull ad platform data, check taxonomy and budgets, identify pacing exceptions, recommend actions, and require approval before activation.
The campaign optimization workflow is ready to review. I connected platform inputs, checked taxonomy and pacing, routed exceptions, prepared recommendations, and kept approval context.
Customer activity
3open support tickets34 daysto renewal
Start with campaign work. Start with the campaign, pacing report, budget check, or optimization workflow that still depends on manual pulls.
Map campaign inputs. Connect ad platforms, project budgets, naming rules, finance metadata, and outcome tables in one workflow.
Validate campaign quality. Check spend freshness, taxonomy rules, budget pacing, finance metadata, outcome joins, and downstream readiness.
Model campaign rules. Turn naming conventions, budget rules, campaign taxonomy, and optimization logic into reviewable workflow blocks.
Deliver action paths. Send QA exceptions, pacing alerts, planner summaries, approved actions, and campaign-agent context to the right teams.
Register campaign context. Keep the taxonomy rules, approval decisions, recommendation reasons, spend context, and run history attached.
What the workflow includes
Assemble campaign inputs Mage pulls spend, campaign taxonomy, naming rules, budgets, finance metadata, and outcome data from the platforms and systems media teams already use.
Validate before action QA, pacing, and exception checks run before recommendations reach planners, operators, or activation surfaces.
Preserve optimization context Approved recommendations, run history, and campaign context become reusable input for analysts and campaign agents.
Use case details
The workflow view shows sources on the left, Mage execution in the middle, and governed outputs plus AI context on the right.
Business problem
Campaign optimization breaks when spend, taxonomy, budgets, outcomes, finance metadata, and approvals live in different systems.
Mage workflow story
Run ad platform ingestion, campaign QA, budget pacing, exception routing, recommendations, and human approvals in one visible workflow.
Workflow diagram
Meta, Google, TikTok, Microsoft, project budgets, naming rules, outcomes, and finance metadata feed Mage QA, pacing, exception, and recommendation workflows, with human approval before action.
Campaign signals
MetaGoogleTikTokMicrosoft
Budgets · naming rules · outcomes · financeTikTok124%
Google61%
Meta97%
RecommendationShift $42k to GooglePolicy and finance checks passed
Human approval
Buyer reviewApprove move
Action staged
Bring Mage the platforms, budgets, naming rules, outcomes, and approval steps behind campaign optimization. Mage will map the ingestion, QA, pacing, exception, and recommendation workflow with you.