Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com (http://Make.com) a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
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I designed and built Distill IQ, a full-stack retail pricing intelligence platform for a US-based liquor business. The application processes and presents large-scale pricing data through interactive product surveys, competitor comparison analytics, raw data exploration, and user/access management. I worked across the React frontend, Python backend, APIs, BigQuery data layer, business logic, and UI components to build the platform from the ground up.
Key features:
Pricing Survey: 5,200+ SKUs, 110+ brands, search and advanced filtering
Competitor Analytics: Compare products across markets with average, median, mode, price ranges, and price gaps
Market Analytics: Interactive price distribution, historical trends, and competitor comparison
Raw Data Explorer: BigQuery-backed data exploration with filtering, pagination, and export
User Management: User accounts, roles, permissions, market access, and catalog management
Reusable UI: Tables, filters, cards, charts, forms, and responsive components
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I managed Google Ads for W.A.H., a veterinary practice with a reasonable advertising budget but an early challenge: reaching the audience that would engage with the practice and generate more calls and form leads.
Rather than rely on additional spend, I worked on keyword coverage, performance-based pruning, and audience and location targeting. I reviewed search trends to identify promising keywords that were missing from the campaign, paused underperformers against monthly ad-group benchmarks, and refined the search radius.
For February 19, 2025–August 17, 2026, Google Ads recorded 253 conversions from CA$9,354.96 in advertising spend, equivalent to CA$36.98 per recorded conversion. The clearest pattern in my analysis was the contribution from keywords that explicitly named the local area: they accounted for 52.4% of conversion credits while using 22.3% of keyword spend.
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