Lookalike Audiences
Lookalike seed labs without bloated reach
Design seed pools that mirror buyers without ballooning spend on broad placements.
Inside the module
Operators often overfill seed audiences and dilute signal. This course tightens seed construction, pairs it with negative guardrails, and shows how to document assumptions so future teammates can inherit the setup without guesswork.
What you will handle
- Seed pool scoring rubric with qualitative notes
- Negative audience guardrails for overlapping ASINs
- Documentation blocks for handoffs to agencies
- Scenario cards for seasonal catalog shifts
- Cross-org workflow map for creative + retail sync
- Lightweight QA script before each expansion
- Office-hours style review prompts for peer critique
Outcomes
- Ship a documented seed definition under three concise hypotheses
- Pair each expansion with a rollback trigger tied to delivery drift
- Present a one-page brief external reviewers can scan quickly
Lead steward
Sora Malik
Course director translating audience science into practical seller workflows.
FAQ
Yes, though several labs assume at least two hero ASINs so you can compare seed behavior meaningfully.
Examples stay inside the Amazon Ads console. DSP parallels are mentioned as footnotes only.
We stay away from automated bidding recipes; you bring your own bid comfort and test windows.
Operator notes
The scenario cards forced us to write down why each seed exists. That small habit trimmed overlapping audiences we did not realize we kept cloning.