Taking an hour of copy-paste out of a $500K-a-day finance function
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1–2 hrs
Of daily exports and spend tracking, gone
5–10 min
Target daily review in place of the manual pull
7am ET
Daily run lands sales and ad spend in one sheet
TL;DR
Mellow Sleep spends $300,000 to $500,000 a day on marketing, and the numbers behind it were assembled by copy-paste every morning.
A scheduled n8n job now pulls the Shopify and Triple Whale APIs into one Google Sheet at 7am ET, with ROAS and actual against forecast spend.
It was designed to turn one to two hours of exports into five to ten minutes of review; the realised saving is not yet confirmed in production.
Mellow Sleep spends between $300,000 and $500,000 a day on marketing. Every morning meant a Shopify sales report pasted in one day at a time, spend tracked by hand across Meta, Google, TikTok, Snapchat and the rest, then working out card reload timing. One to two hours before a single decision. We took the data collection out of it.
The challenges
Finance is the business function that involves managing cash flow, spend and timing — and at Mellow Sleep it had just changed hands, with the cash-flow logic now living in a manual daily forecast in Excel. It worked. It cost the front half of every day.
Sales came out one day at a time
A Shopify sales-by-channel report was pasted in for the previous day, three times on Mondays for the weekend.
Spend was tracked by hand across five platforms
On $300,000 to $500,000 of daily spend, a copy-paste error is not a rounding difference.
Timing mattered more than usual
Inventory was being front-loaded for Q4 and a 20 to 30% dip landed mid-engagement, so cash-flow precision decided whether spend, supplier wires and payroll were met on time.
What we built
One scheduled automation, running daily at 7am Eastern, with two flows into a single Google Sheet. The Shopify API writes gross and net sales, returns, discounts, shipping and taxes by channel to a Channel Sales tab. The Triple Whale API writes actual spend across AppLovin, Facebook, Snapchat, Google and TikTok to a Marketing Trends tab. ROAS, actual against forecast and cumulative spend sit on top, with a Slack notification on every run.
Google Sheets over Excel, on purpose
Deliberately scoped as a data problem
Three weeks blocked on access, five minutes to fix
Conclusion
What used to be an hour or two of exports is now a run that has finished by the time the sheet is opened, designed to bring the morning down to five to ten minutes of review. As of mid-April the new model was not yet fully adopted, so the saving is built and delivered rather than confirmed in production.
Want your finance data arriving before you do? Get in touch.
1–2 hrs
Of daily exports and spend tracking, gone
5–10 min
Target daily review in place of the manual pull
7am ET
Daily run lands sales and ad spend in one sheet
Orchestration: n8n, scheduled daily at 7am ET
Sources: Shopify API for sales by channel, Triple Whale API for actual ad spend by channel
Model surface: Google Sheets, with native version history as the audit and rollback trail
Derived: ROAS, actual against forecast marketing spend, cumulative spend
Deferred by design: Plaid bank integration, payment-prioritisation agent, inventory and ERP feed, dashboard
FAQ
How do you automate daily e-commerce finance reporting from Shopify and Triple Whale?
One scheduled n8n job with two flows: the Shopify API writes sales by channel to one tab, the Triple Whale API writes actual ad spend by channel to another, and ROAS, actual against forecast and cumulative spend sit on top.
Why Google Sheets instead of Excel?
The n8n Excel node writes a single sheet and drops formatting. Google Sheets has stronger API support and native version history for audit and rollback.
What slowed the build down?
Platform access. Shopify’s January 2026 app-permissions change blocked app creation for roughly three weeks, fixed in about five minutes once the founder created the app from his own account.
Is the time saving confirmed?
Not yet. It is built and delivered, and the realised figure should come from the client. Related: why finance teams get stuck in pilot purgatory.
How does the finance function contribute to the business here?
On $300,000 to $500,000 of daily marketing spend, cash-flow precision decides whether spend, supplier wires and payroll are met on time. Getting the numbers assembled before the morning starts frees that time for the decisions themselves.
How does automation change the organization of finance function work?
It moves the daily finance operation from assembling numbers by hand to reviewing them. The scheduled run collects sales and spend before the team logs on, so the morning goes to decisions rather than data entry.
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You’re all set!
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