CASE STUDY / DATA & ANALYTICS

Multi-platform delivery analytics for an independent restaurant

A Google Cloud pipeline and BI dashboard that unified UberEats, DoorDash, and Grubhub reporting into one operational view. Built end to end by ACTsavi.

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AT A GLANCE
CLIENT
Independent restaurant, delivery-heavy operation
ENGAGEMENT
Data pipeline build and BI dashboard
PLATFORM
Google Cloud (BigQuery, Cloud Functions, Looker Studio)
STATUS
Delivered, in production use by the owner
3
Delivery platforms unified into one operational view. No more switching between portals
~50%
Peak platform fee burden uncovered at maximum ad spend, up from a ~30% baseline
0
Manual report collection. The pipeline runs unattended and refreshes on schedule
BI
Real-time decision support replaces monthly hindsight and gut feel
01 / THE CHALLENGE

Three delivery platforms, three siloed reports, one overwhelmed owner

The restaurant operator was managing UberEats, DoorDash, and Grubhub at the same time while wearing every other hat in the business. Each platform reported in its own format, on its own schedule, with its own fee structures.

Delivery platforms were applying pressure to increase marketing spend. Sales numbers were declining. There was no time to actually compare performance across platforms, and no easy way to see whether the marketing dollars were earning anything back.

A
Consolidate reporting from three delivery platforms with incompatible schemas
B
Make the true cost of each platform's marketing spend visible against actual net revenue
C
Replace weekly manual report collection with an automated, unattended process
D
Give the owner real-time visibility instead of monthly hindsight
E
Keep the operating cost of the analytics platform itself low and predictable
02 / WHAT WE BUILT

A cloud-native pipeline, sized for an independent operator

No on-prem servers, no shadow IT, no spreadsheet ops. Everything runs on Google Cloud with usage-based pricing, so the analytics layer stays proportional to the business it supports.

01

Automated Data Ingestion

  • Centralized Google Cloud Storage bucket collects platform reports
  • Scheduled, unattended fetches replace manual downloads
  • Versioned raw archive preserves source-of-truth records
  • No interruption when one platform changes its export format
02

ETL Pipeline

  • Python ETL running on Google Cloud Functions
  • Automated data cleaning and standardization across platforms
  • Schema reconciliation across incompatible source formats
  • Idempotent, restartable, and observable
03

Cloud Data Warehouse

  • BigQuery for unified analytical storage
  • Custom SQL models for cross-platform comparison
  • Cost-controlled query patterns sized for SMB usage
  • Historical trend data preserved for year-over-year analysis
04

Owner-Facing BI Dashboard

  • Looker Studio dashboard for real-time monitoring
  • Cross-platform performance metrics in one view
  • Automated fee analysis and trend tracking
  • Visualized KPIs for sales, fees, marketing spend, and net
05

Analytical Findings That Drove Decisions

  • Increased marketing spend grew gross sales, but platform fees climbed from ~30% to over 50%, eroding net revenue
  • UberEats produced the highest volume but the most volatile fee structure
  • DoorDash maintained moderate volume with consistent fee levels
  • Grubhub showed lower but more stable performance metrics
METHOD

Numbers that survive scrutiny

Every metric on the dashboard traces back to a source record in the raw archive. The pipeline is observable and idempotent. When the owner questions a number, ACTsavi can show exactly where it came from, when it landed, and how it was transformed.

ACCOUNTABILITY

One point of contact, full accountability

The restaurant works directly with ACTsavi. No account manager layer, no handoff between sales and engineering. The team that designs and builds the pipeline is the team that maintains it.

03 / THE OUTCOME

Decisions backed by data the owner can actually see

✓

Unified View

One dashboard replaces three platform portals, three report formats, and a weekly consolidation chore.

✓

True Economics

Platform fees and marketing spend are visible against net revenue, not just gross sales.

✓

Optimized Allocation

Marketing dollars are directed at platforms where the math actually works, not where pressure is loudest.

✓

Time Returned

The owner gets hours back every week. The pipeline runs unattended, and the dashboard is always current.

Managing multiple delivery platforms and flying blind on the real numbers? Let's scope it.
Schedule a consultation →
CAPABILITIES DEMONSTRATED
Cloud data engineering (Google Cloud) ETL on Cloud Functions (Python) BigQuery data warehousing Custom SQL analytical models Looker Studio dashboard development Cross-platform schema reconciliation Marketing ROI analytics Owner-facing BI design Full-lifecycle delivery
WHO THIS IS FOR

Restaurant operators who deserve better than monthly hindsight

This engagement is the foundation for a repeatable offering. New restaurant clients get the same data pipeline approach and the same owner-facing dashboard model, adapted to the platforms they actually use.

✓Independent restaurants managing two or more delivery platforms
✓Multi-location operators that need consolidated reporting across stores
✓Hospitality businesses making marketing decisions without real ROI visibility
✓Owner-operators without an internal analytics team or BI tooling already in place
START HERE

Ready to see your real numbers?

Schedule a consultation to scope a delivery analytics build for your restaurant. You will talk to the engineer who builds it, not a salesperson.

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Performance figures reflect findings from one specific operator and are not predictive of results for other restaurants.