Every Monday morning for three years, the operations director of a $4.1M e-commerce brand did the same thing: she opened four browser tabs — Shopify, Google Ads, Facebook Ads Manager, and their 3PL's fulfillment portal — and spent the next 3 hours pulling numbers into a master spreadsheet. Then she built the internal performance summary. Then she sent it.
Every Sunday afternoon before a big sale weekend, she did a version of the same thing. Every month end, she built the monthly summary. Every quarter end, she assembled the board deck data.
In total, between weekly, monthly, and quarterly reporting, she was spending 12–14 hours per week on reports. At her fully-loaded labor cost of $52/hour, that was $33,000–$36,000 per year. Spent moving numbers between screens.
The Situation Before Automation
The ops director knew it was a problem. She'd tried twice to fix it herself — once with a custom Google Sheets formula system that worked for 6 weeks before the data structure changed, once with a Supermetrics subscription that gave her better data but didn't eliminate the formatting and narrative work.
The core issue: her reports weren't just data exports. They were curated summaries — specific metrics arranged in a specific format for specific audiences (internal leadership, the founders, the ad agency). Each audience got a different view. Each required manual assembly from the same raw sources.
The secondary issue: data quality. Every manual step introduced error potential. A transposed ROAS figure. A wrong date range on a Facebook export. A fulfillment rate calculated on gross vs. net orders. She trusted her own work, but she couldn't trust it 100% — and the leadership team knew it too, which meant every report arrived with a quiet asterisk.
The Solution: A Fully Automated Reporting Stack
The build took 4 days of implementation work and 3 days of testing and stabilization. Here's exactly what we built:
Data pipeline via Make. Daily scheduled scenarios pulled data from four sources via their APIs: Shopify (revenue, orders, AOV, returns, best-sellers), Google Ads (spend, clicks, conversions, ROAS by campaign), Facebook Ads Manager (same structure as Google), and their ShipStation 3PL account (fulfillment rate, average shipping cost, order delay rate). All data wrote into a structured Google Sheet — one tab per source, one master data tab with standardized date-keyed rows.
Looker Studio dashboards. Three separate Looker Studio dashboards built on the master data tab: one for leadership (revenue, margin, channel performance), one for the ad agency (channel-specific spend and ROAS), one for the founders (high-level KPIs with period-over-period comparison). All three update live as the data pipeline runs. No one builds them. They just exist and stay current.
Monday 8am automated summary. A Make scenario fires every Monday at 7:45am. It reads the previous week's data from the Google Sheet, constructs a week-over-week comparison for each key metric, passes it to the OpenAI API with a prompt that generates a 5-sentence performance summary highlighting the biggest changes and flagging anything outside normal range. The summary plus the dashboard links are emailed to the distribution list at 8:00am. The ops director's Monday morning looks completely different now.
Anomaly alerting. A separate daily Make scenario checks four thresholds: Google ROAS below 2.0x, Facebook ROAS below 1.8x, fulfillment delay rate above 3%, and daily revenue more than 25% below the 7-day trailing average. If any threshold is breached, a Slack alert fires to the ops director and the relevant platform manager. They know about problems before they have to find them.
The Tech Stack
- Automation: Make (formerly Integromat) — 4 active scenarios, ~2,400 operations per month
- Data APIs: Shopify Admin API, Google Ads API, Meta Marketing API, ShipStation API
- Data storage: Google Sheets (free)
- Dashboards: Looker Studio (free)
- AI summaries: OpenAI API — GPT-4o-mini (approximately $8/month in API costs)
- Delivery: Gmail via Make (free)
- Alerting: Slack (existing subscription)
Total monthly tool cost added: $49/month (Make Core plan) + $8/month (OpenAI API) = $57/month. Everything else was already in their stack.
What Changed Beyond the Hours Saved
Data quality improved significantly. Manual step errors — wrong date ranges, transposed figures, formula mistakes — dropped to near-zero. The leadership team stopped second-guessing the numbers. Decisions got made faster because no one was waiting to verify the data or asking follow-up questions about whether the figures were right.
The ops director's role changed. With 12 hours per week returned, she shifted toward the work she'd been hired for — supplier relationships, inventory planning, process improvement. Within 60 days of the automation going live, she'd initiated two supplier renegotiations and identified a $19,000 annual saving on fulfillment packaging costs — work she'd never had bandwidth to do before.
The anomaly alerts caught a real problem in week 3. A Facebook campaign had its pixel tracking misconfigured after a website update, causing conversions to stop reporting. The ROAS alert fired on Tuesday morning. The ad agency identified and fixed the issue by Tuesday afternoon. Previously, the misconfiguration would have gone unnoticed until the Friday report — costing 3.5 days of unoptimized ad spend.
If you have a reporting process in your business that costs someone 5+ hours per week — and almost every business does — this is a solved problem. Book the free breakdown and we'll show you exactly what the automated version of your reporting stack looks like and what it would cost to build. See the full step-by-step guide at How to Eliminate Weekly Reporting.