Matthew Kassel
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Why Every Amazon Seller Has a Data Problem (And Most Don't Know It)

Bad data kills margins quietly. Here's what messy product data actually costs Amazon sellers — and how to fix it before it gets worse.

Let me tell you something I’ve seen over and over working in Amazon ecommerce: sellers obsess over PPC bids, review velocity, and keyword rankings — all legitimate things — while their product data quietly rots in the background.

Duplicate SKUs. Mismatched dimensions. Titles that haven’t been updated since 2021. Inventory counts that don’t match what’s actually in the warehouse.

It’s not dramatic. It doesn’t throw an error. It just… costs you. Slowly. Every day.

What “Bad Data” Actually Looks Like

Bad data on Amazon rarely looks like a catastrophic failure. It looks like:

  • A product listing with the wrong weight, so you’re eating unexpected FBA fees
  • Two SKUs that are actually the same product — split between two listings, cannibalizing each other
  • A supplier spreadsheet that hasn’t been reconciled with your actual catalog in six months
  • COGS that are wrong because someone updated the landed cost in one system but not the other

None of these are fatal on their own. Together, they create a margin leak that’s almost impossible to diagnose without clean data.

The Root Cause Is Usually a Process Problem

Here’s the thing: bad Amazon data is almost never the result of laziness. It’s the result of growth without process.

You started with 20 SKUs. You managed them in a spreadsheet. That was fine. Then you had 200. Then 2,000. The spreadsheet grew, got shared with more people, got exported and reimported and edited by three different people on three different tools.

By the time you realize the data is messy, the mess is everywhere.

What a Basic ETL Fix Looks Like

You don’t need a warehouse and a data engineering team. You need:

  1. One source of truth — usually your ERP or a master product catalog, not a Google Sheet
  2. A reconciliation routine — a script that runs weekly, flags discrepancies between your catalog and what’s live on Amazon
  3. Enrichment logic — standardize units, normalize titles, fill in missing fields from known data sources

I’ve built these pipelines in Python with Airflow. They’re not glamorous. They’re not AI. They’re just ETL — Extract, Transform, Load — and they work.

The Numbers That Make It Worth It

One reconciliation pass on a mid-size Amazon catalog (1,500 SKUs) will typically find:

  • 3-8% duplicate or near-duplicate listings that can be consolidated
  • 10-15% of SKUs with at least one incorrect attribute (weight, dimensions, category)
  • Significant FBA fee exposure from incorrectly classified products

Clean it up once, automate the checks, and you stop the leak permanently.

Start Small

You don’t need to boil the ocean. Pick one dataset — your product dimensions, your COGS table, your supplier SKU mapping — and clean it. Build a script to validate it on a schedule. See what you find.

Nine times out of ten, what you find will motivate you to do the next one.

Bad data is a solvable problem. Most sellers just haven’t looked at it long enough to know it’s there.

Nu, let’s fix it.

— Matthew