Results
Case Studies
How we found $48K in annual waste. How we eliminated 11 manual exports. How we turned spreadsheet chaos into systems that run themselves.
From 6 spreadsheets to one self-updating dashboard
A 7-figure Shopify brand was spending 4+ hours every Monday manually rebuilding reports from disconnected spreadsheets. Their operations team couldn't keep up.
The challenge
Sales, inventory, and marketing data lived in 6 separate spreadsheets maintained by 3 different people. Reports were rebuilt from scratch every week. Numbers rarely matched between sources, leading to constant debates in team meetings instead of decisions. The ops director estimated the team spent 15–20 hours per week on reporting alone.
What we did
Our team ran a Workflow & Efficiency Audit to map every data source and manual touchpoint. We consolidated all 6 spreadsheets into a single BigQuery data warehouse, built automated pipelines to pull data from Shopify, ad platforms, and inventory systems, then designed a Looker dashboard tailored to the team's actual daily decisions.
The results
Monday reporting went from 4 hours to 15 minutes. The team reclaimed ~16 hours per week previously spent on manual data work. Every team member now sees the same numbers, updated daily, with no human intervention required. The system survived two team member departures without breaking — because it doesn't depend on any one person.
Key metrics
11 manual CSV exports eliminated across 5 marketplaces
A multi-marketplace e-commerce retailer was manually exporting and reconciling data across 5 international marketplaces — every single week.
The challenge
The team managed listings across Amazon (3 regions), eBay, and their own Shopify store. Every week, someone spent an entire day downloading 11 CSV exports, reformatting them into a master spreadsheet, and reconciling inventory and sales figures. The process was fragile — one missed export or formatting change broke the whole chain. Inventory decisions were always based on data that was at least a week old.
What we did
We built automated data pipelines from each marketplace into a centralized BigQuery warehouse using API integrations (including Amazon SP-API). Inventory, sales, and order data now flow in on a daily schedule with no human involvement. We added a consolidated dashboard showing cross-marketplace performance and inventory levels in real time.
The results
11 weekly manual exports eliminated entirely. The team now makes inventory decisions based on data that's less than 24 hours old instead of a week. The full-day weekly reconciliation process no longer exists. The ops team redirected that time to strategic inventory planning and marketplace expansion.
Key metrics
Operations audit reveals $48K/year in hidden labor costs
A growing distribution company knew their reporting was inefficient but couldn't quantify the problem — or figure out where to start fixing it.
The challenge
The company had grown from $2M to $8M in three years, but their data infrastructure hadn't changed since day one. Reports were still built in Excel by the ops manager and emailed as attachments. The leadership team had no real-time visibility into margins, inventory turnover, or fulfillment performance. They knew they needed to modernize but didn't know which tools to pick or what to prioritize.
What we did
Our team ran a comprehensive Workflow & Efficiency Audit over two weeks. We mapped every reporting workflow, interviewed key stakeholders, documented every manual touchpoint, and quantified the labor cost of each. We then delivered a prioritized roadmap including tool recommendations (BigQuery + Omni), a phased migration plan, and a list of quick wins they could implement immediately.
The results
The audit identified $48K/year in labor costs spent on manual reporting that could be automated. Three quick-win automations were implemented in the first week after the audit, saving the ops manager 6 hours/week immediately. The company used the roadmap to plan a phased infrastructure upgrade over the following quarter.
Key metrics