Campaign ROI Intelligence for Multi-Location Operators
A large franchise operator replaced weekly manual campaign reporting with a live cross-source dashboard — from raw sources to executive brief in 21 days.
City-wide campaign, same-day visibility, zero engineering after setup.
At a Glance
Key Metrics
Campaign Visibility
7 days → same-day
Time from a store underperforming to someone actually seeing it.
Time to Live
21 days
Read-only connection to a live campaign dashboard.
Engineering Dependency
Zero
Engineering hours to keep the pipeline running week over week.
The Situation
A franchise operator running a city-wide promotional campaign had spend data in Google Ads and Meta Ads, payment gateway splits in their operational database, and store-level sales data in a data warehouse. The exec team wanted live visibility on coupon redemption by store, campaign ROAS by channel, and underperforming store identification — updated as the campaign ran. What they had was a weekly spreadsheet assembled manually by two analysts, usually ready by Tuesday for the previous week. By the time an underperforming store was identified, the campaign window was nearly closed.
Data sources
Ad Platform Exports
Google Ads & Meta spend data
Payment Gateway DB
Coupon redemption & transaction splits
Store Sales DWH
Location-level sales & revenue
Failure events
What Was Breaking
Why manual operations couldn't scale
Spend, redemption, and sales lived in three places.
Google Ads and Meta Ads held spend, the payment gateway held redemption and transaction splits, the warehouse held store-level sales — none of it built to be queried together.
The only view was a weekly spreadsheet.
Two analysts assembled it by hand, usually ready the following Tuesday — for the previous week's numbers.
Underperformance surfaced too late to act on.
A store trending 20% below the regional average was typically found after the campaign window that could have saved it had already closed.
Every ad-hoc question meant another manual pull.
The CRO asking why a specific store dropped last week meant a new spreadsheet, not an answer.
The Approach
Connect your sources
Read-only connection to ad platforms, payment gateway, and data warehouse — no engineering sprint.
Configure ROAS rules in plain language
Campaign definitions, underperformance thresholds, region mappings — set by ops, not engineers.
Autonmis runs the pipeline continuously
Ingestion → transformation → dashboard refresh runs as a governed DAG. Alerts fire when thresholds are crossed.
After
Ad Platform Exports
Google Ads & Meta spend data
Payment Gateway DB
Coupon redemption & transaction splits
Store Sales DWH
Location-level sales & revenue
Autonmis
Governed Intelligence Layer
Knowledge Base
rules · thresholds · logic
Connected to all three sources. The Knowledge Base was configured with campaign definitions, ROAS calculation rules, store region mappings, and underperformance thresholds. Autonmis built a multi-step pipeline — source ingestion, transformation, mart build, dashboard refresh — governed under a DAG where each step only executed when its upstream dependencies completed successfully. The campaign dashboard updated continuously. When a store's redemption rate dropped 20% below the regional average, a Slack alert fired to the ops lead. The CRO could ask "why did Noida drop last Thursday?" and receive a structured, data-grounded answer without opening a notebook.
The Workflow
Results
7 days → same-day
Campaign visibility lag
Dashboard updates continuously as campaign runs
Audited against the prior weekly-spreadsheet cadence, same campaign type, before and after connection.
End of week → within hours
Underperforming store detection
Fires when store drops 20% below regional average
Eliminated
Weekly analyst reporting hours
Two analysts freed from manual spreadsheet assembly
Audited against the two analysts' manual assembly time prior to connection.
Without raising a ticket
Executive self-serve questions answered
CRO queries answered with grounded SQL-executed data
21 days
Time from sources connected to live dashboard
From raw exports to production campaign intelligence
Governance Note
Every ROAS and underperformance number here runs against the campaign and region definitions the ops team set in the Knowledge Base — the CRO's ad-hoc questions get the same grounded answer a scheduled dashboard does, with the SQL visible on request.
Implementation
Time to live
3 weeks to live dashboard
Sources connected
3 (DWH, operational DB, ad platform exports)
Engineering dependency
Zero after governance sign-off
Ready to see it in your stack?
We can scope your use case to a live workflow
in the first session.
Three sources. No engineering dependency. First automation in under three weeks.
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