Compare/Spreadsheets
DataGlass vs spreadsheets for marketplace sellers
Spreadsheets are how every marketplace seller starts. They stop scaling around the third platform, the hundredth SKU, or the first campaign week where the report is finished after the campaign is over.
01/Sufficient
When spreadsheets are enough
A single shop, a manageable catalog, a stable cost structure — spreadsheets handle the basics fine, and there is no reason to over-engineer.
- One marketplace, one shop, one ad account
- A handful of SKUs with stable supplier prices
- A weekly review cadence — not daily, not real-time
- No campaign-day pressure that requires acting before the report is built
02/Breakpoints
Where spreadsheets break
Multi-shop, multi-platform, ad-funded marketplace commerce stops fitting in a sheet around the same set of milestones for almost every operator.
- Shopee, Lazada, and TikTok Shop each export different data shapes — joining them by hand is a half-day every week
- Per-SKU contribution margin needs COGS, marketplace fees, vouchers, ads, and fulfillment in one place; pivot tables can do it but rarely stay current
- Campaign-day pricing decisions need the spreadsheet to be live, not from last weekend
- A stockout fires while the inventory tab is still being copied from the WMS
- The seller knows the answer is in the data and runs out of time before they get it out
03/Why
Why it breaks at scale
Spreadsheets are general-purpose, which is also the reason they break. Every join, every formula, and every cell of new data is a small piece of work the operator has to do — and the work scales with the number of shops × number of SKUs × number of platforms × number of campaigns. By the time those four dimensions multiply past trivial, the seller is spending more time maintaining the spreadsheet than acting on it. Reporting delay becomes decision delay, and decision delay is expensive on a marketplace that moves daily.
04/DataGlass
How DataGlass differs
DataGlass replaces the manual joining and formula maintenance with a system that ingests platform data, normalizes SKU and campaign structures, and computes the decision-grade metrics — true ROAS, contribution margin, stockout risk — directly. The seller stops being a data-prep operator and goes back to running the shop.
- Connect once — orders, ads, COGS, inventory all reconciled per SKU
- True ROAS and break-even ROAS computed automatically per campaign and per SKU
- Stockout-day forecasts that account for campaign demand spikes
- Decisions surfaced in a ranked queue, not pivot-tabled out of a spreadsheet
05/FAQ
Frequently asked
No. Most teams keep a planning spreadsheet for things DataGlass does not yet do. The bulk of the per-day reporting and decision work is what moves into DataGlass.
Yes. Every view supports CSV/XLSX export so the work that still belongs in a spreadsheet stays one click away.
The cleanest comparison is hours per week. A seller running three shops typically spends 4–8 hours a week joining and reconciling marketplace data. DataGlass takes that to under one. The remaining hours go into decisions, not data prep.