Ecommerce Data Analyzer
Product research across Amazon and TikTok Shop, consolidated into one weighted ranking. Helium 10, Keepa and FastMoss exports become a single Excel report with explicit, repeatable scoring logic.
In production · built for marketplace research · Python + pandas + NumPy
Why it exists
Rankings were vibes
We were manually exporting product data from three platforms and dealing with mismatched column formats. Judging which product was better came down to guesswork, and people looking at the same data often reached different conclusions.
The analyzer replaces whichever spreadsheet happened to be open last with one scored ranking and a documented reason behind it.
What we built
One metric, defined in code
- Import and standardization of Helium 10, Keepa and FastMoss exports
- Configurable filters for price ranges, minimum ratings and sales-velocity cutoffs
- A weighted scoring system with adjustable weights per column
- Ranking across all platforms combined, not separately per platform
- One-click Excel report that documents the scoring logic
How it works
From raw exports to a decision
Load exports
Drop in the files; column names and formats are normalized first.
Set filters
Price bands, rating floors and velocity thresholds are made explicit.
Weight the score
A profitable-but-slow product can rank properly against a cheap-and-fast one.
Open the report
One ranked list, with the logic documented beside the result.
Key takeaway
Consolidate once. Define the metric in code.
Let one number settle the argument. If your team is making decisions across disconnected exports, we can map the same kind of system around your actual rules.
No obligation · No sales deck · We reply within one business day.