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Case Study · Amazon Advertising

How we cut Amazon ad scripting from an afternoon to 2 minutes.

We built VideoScienceOptimizer for Amazon sellers running Sponsored Brands Video. Paste in your keywords and reviews, and it hands back ready-to-shoot, moderation-ready scripts, matched to how shoppers actually search.

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The challenge

Amazon sellers were shipping one generic video for every shopper, even though buyers search for the same product in wildly different ways. Sorting keywords, mining reviews, and writing scripts by hand ate an entire afternoon per listing, and a single unsubstantiated claim could get the whole video rejected.

🎯

One pitch, many intents

A single generic video can't speak to buyers arriving from wildly different searches, so it speaks to none of them well.

⌛

Hours of manual triage

Sorting keywords into themes and scripting each scene by hand is slow, subjective, and rarely gets done well.

🚫

Moderation roulette

Amazon rejects videos with unsubstantiated claims or the wrong structure, and sellers eat the rejection cycles.

The solution

VideoScienceOptimizer turns that afternoon of manual work into a guided, two-minute flow. No ad-spend minimum, no waiting on approvals, just paste in what you already have.

  1. Paste your keywords and reviews. No new data to collect, just what's already in Seller Central.
  2. It clusters shoppers by intent. Keywords get grouped into 2–3 ranked, named shopper segments automatically.
  3. It drafts a script per segment. Each 20-second script is built to Amazon's approval structure and written from customers' own words.
  4. Every claim is source-tagged. Nothing goes to moderation without a named source behind it.

The results

The build replaces a slow, subjective manual process with a repeatable pipeline, and hands the seller a better-targeted, moderation-ready deliverable at the end of it.

2–3
ranked shopper-intent groups generated per product
~2 min
from pasting keywords to an exportable script
20s
scene-by-scene script, built to Amazon's approval structure
100%
of on-screen claims tagged with a named source
The manual way
  • ✕ One generic video, for every shopper
  • ✕ Keywords sorted by eye, if at all
  • ✕ Scripts written from a blank page
  • ✕ Claims unverified, rejection risk
  • ✕ Hours of work per listing
After VideoScienceOptimizer
  • ✓ 2–3 intent-matched scripts per listing
  • ✓ Keywords auto-clustered by shopper intent
  • ✓ Scripts drafted from customers' own words
  • ✓ Every claim source-tagged for moderation
  • ✓ ~2 minutes from paste to export

See it in action

videoscienceoptimizer.app
VideoScienceOptimizer landing page
The pitch. Built for Amazon agencies and brands running Sponsored Brands Video.
Keyword clusters grouped by shopper intent
Intent clusters. Keywords auto-grouped into ranked shopper-intent segments.
Generated scene-by-scene video script
The script. A scene-by-scene breakdown, ready to hand an editor.
Claims table with sources tagged for moderation
Claims table. Every on-screen claim tagged with a named source before it reaches moderation.

"The hard part isn't generating a video. It's deciding which shopper to talk to, and proving every claim before Amazon ever sees it."

— Adam Harari, builder, Atom AI

Have a workflow like this?

If your team is doing this kind of work by hand, there's probably a build like VideoScienceOptimizer waiting for it.

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