Step 1
Meta Ads Manager
Account, campaign, ad set, ad, creative, delivery, spend, click, and conversion data.
Representative implementation case study
Give marketers their time back with automated Meta Ads reporting.
A mid-sized agency managing paid social for many client brands should not depend on marketers exporting, filtering, and rebuilding reports from Meta Ads Manager. This case study shows how Moss Agentic would turn that work into an automated reporting pipeline, dynamic Looker dashboards, and AI-assisted campaign-health insight.
Meta Ads source
Looker dashboards
AI-assisted review
Looker-style paid media reporting demo
Marketing Performance Dashboard
Synthetic live demo
Active view
Agency overview is now focused on the full paid-media reporting workspace. 3 filtered records in focus.
Agency overview score
80%
3 focused rows
Needs review80%
Agency overview actions
11
Review queue active
Risk rising100%
Agency overview trend
+2%
Improving signal
Needs review58%
Agency performance movement
All synthetic campaign records remain in view. Updates as filters and tabs change.
3 filtered rowsRisk strip: green / amber / red
AI decision layer
Creative fatigue
Risk risingAI summary: agency overview balances account health, review items, and trend direction. Current focus: creative set a is marked "creative fatigue". 3 filtered records in focus.
The operating problem
Manual exports were stealing the hours marketers needed for actual marketing.
The agency had the data. It lived inside Meta Ads Manager. The problem was the repeated motion required to turn that data into useful campaign-health reporting across many client brands.
Before automation
Open each client account in Meta Ads Manager
Export campaign and ad set data
Filter spreadsheets for status and pacing
Rebuild client updates from copied numbers
Spend strategy time checking data freshness
After automation
Scheduled pulls from the Meta Marketing API
Clean reporting tables by client and campaign
Looker dashboards that refresh automatically
AI-assisted notes for campaign-health review
Marketers start with the decisions that matter
System architecture
From Meta Ads Manager to decision-ready dashboards.
The pipeline is designed to be trusted before it is made beautiful. Extraction, validation, cleaning, and dashboard logic are separated so the agency can scale reporting without making every marketer responsible for the data operation.
Step 2
Scheduled extraction
API pulls replace recurring exports and preserve a raw source history for auditability.
Step 3
Validation layer
Expected accounts, dates, required fields, and refresh completeness are checked before reporting.
Step 4
Reporting model
Client brands, campaign names, owners, objectives, and core metrics are normalized.
Step 5
Looker dashboards
Agency, client, campaign, creative, and pacing views update without manual rebuilds.
Step 6
AI insight layer
Summaries, anomaly notes, and review prompts help marketers find the next decision faster.
Looker-style paid media reporting demo
Marketing Performance Dashboard
Synthetic live demo
Active view
Agency overview is now focused on the full paid-media reporting workspace. 3 filtered records in focus.
Agency overview score
80%
3 focused rows
Needs review80%
Agency overview actions
11
Review queue active
Risk rising100%
Agency overview trend
+2%
Improving signal
Needs review58%
Agency performance movement
All synthetic campaign records remain in view. Updates as filters and tabs change.
3 filtered rowsRisk strip: green / amber / red
AI decision layer
Creative fatigue
Risk risingAI summary: agency overview balances account health, review items, and trend direction. Current focus: creative set a is marked "creative fatigue". 3 filtered records in focus.
Dashboard experience
A campaign-health command center for account teams and leadership.
The Looker layer gives each team the right altitude. Marketers can inspect client and campaign detail. Account leads can prepare updates without rebuilding reports. Leadership can see which accounts need attention before the weekly meeting.
Client-brand filters keep views useful for account owners.
Campaign and creative tabs reveal where performance movement is coming from.
Internal QA views show missing data, stale refreshes, and mapping issues.
Client-facing views stay polished, focused, and easy to explain.
AI-assisted decisions
AI helps marketers find the story in the data faster.
The system does not pretend to run campaigns on autopilot. It prepares reviewable signals, summaries, and investigation prompts so experienced marketers can make better decisions with less manual searching.
Budget pacing risk
Several client campaigns are projected to miss planned spend unless the team reviews caps, delivery, or audience constraints.
Creative fatigue signal
A creative group is showing weaker engagement trend quality and should be reviewed before the next client update.
Campaigns needing review
The dashboard groups active campaigns by owner, severity, and likely reason so marketers can prioritize the day.
Moss Agentic delivery
How we would build the reporting operating system.
The build starts narrow enough to ship and test, then expands once the agency trusts the data. The goal is not another dashboard. It is a reporting rhythm that lets the team manage more client brands without turning marketers into data operators.
01
Map the agency reporting motion
Document how marketers currently move from Meta Ads Manager to spreadsheets, client updates, internal reviews, and leadership reporting.
02
Design the campaign-health model
Define the fields, naming rules, client-brand mapping, campaign objective groups, refresh schedule, and QA checks that make the dashboard trustworthy.
03
Build the dashboard system
Create Looker views for portfolio health, client performance, campaign status, creative movement, budget pacing, and account-owner follow-up.
04
Add AI-assisted decision support
Generate reviewable summaries that highlight what changed, which campaigns need attention, and which questions marketers should investigate.
Build the same operating advantage
If your marketers are still exporting Meta Ads data, the reporting system is doing too much of its work by hand.
Moss Agentic can map the reporting workflow, design the data model, build the dashboards, and add AI-assisted review so your team spends more time improving campaigns and less time assembling status reports.