lookalike-audience-seeds

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---
name: lookalike-audience-seeds
description: "Select and size the seed customer list behind a lookalike, similar, or value-based audience - which customers to upload, how many, and whether the matched count (not the row count) clears the platform floor - with RFM and value-based selection, a negative-selection pass, a privacy and consent gate before any customer list upload, and a fallback ladder when the floor cannot be cleared. Use whenever the user mentions a lookalike or similar audience, a seed list, a customer list upload, match rate, an audience that is too small, or says their lookalike isn't working - even if they never say 'seed'. Covers B2B and B2C. Do NOT use to design the whole targeting tier structure - use mbfinotti/advertising-skills@ad-audience-targeting instead."
license: MIT
metadata:
  author: Maya-Beth Finotti
  version: "1.3.6"
---

# Lookalike Seeds

Select, size, and validate the seed customer list that a lookalike, similar, or value-based audience will be built from. Three principles govern every step below:

- **Match count, not row count** - every uploaded list shrinks by its match rate before it reaches the platform's minimum. A 40% match on 5,000 rows is a 2,000-person seed, and the platform floor applies to that matched number - upload 2-3x the target.
- **Homogeneity and value concentration beat size** - Meta's own guidance holds that a seed's homogeneity affects audience effectiveness more than its size. Practitioner consensus (Stackmatix, Grow With Sakib - directional, not audited) is that a few hundred high-value customers outperform thousands of undifferentiated subscribers.
- **Inclusion is a suggestion, exclusion is a rule** - on Meta (Advantage+) and Google (Demand Gen, where lookalike reach tiers now function as signals rather than hard segments), the delivery algorithm may override an inclusion audience. Only exclusion/suppression audiences remain hard rules.

The seed's job is feeding the algorithm the cleanest signal, not fencing an audience - which raises the quality bar rather than removing it.

This skill ends at a specified, validated seed definition plus its export/handoff spec. It never walks through creating or uploading the audience inside any ad platform's interface.

Where each adjacent concern lives:

- Overall targeting plan (interest, behavioural, lookalike, and retargeting tier layering) - seed mechanics live here, tier architecture lives in `mbfinotti/advertising-skills@ad-audience-targeting`.
- Retargeting sequence design, audience windows per funnel stage, and frequency caps - `mbfinotti/advertising-skills@retargeting-funnel`.
- B2B buying-committee and persona mapping - `mbfinotti/advertising-skills@ad-buyer-group-mapper`.
- Root-causing broad account underperformance - `mbfinotti/advertising-skills@ad-account-diagnostic`.
- Pixel/CAPI instrumentation feeding engagement-based seed sources - `mbfinotti/advertising-skills@ad-conversion-tracking`.
- Reconciling conflicting performance reads on a seed across attribution sources - `mbfinotti/advertising-skills@ad-attribution-gap`.

## Interview

Ask before selecting anything. One question per message; offer multiple-choice answers where possible; skip anything already answered or visible in supplied data.

- B2B, B2C/ecommerce, or both?
- Which platform(s) will consume the seed? (Meta / Google / LinkedIn / TikTok / other - floors and match mechanics differ)
- What customer data exists, and where does it live? (CRM / data warehouse / email platform / pixel-and-platform events only / almost none)
- Roughly how many total customers, and which identifiers exist per row? (email / phone / name / postal address / mobile ad ID - more identifier types per row lift match rate)
- Is there a usable value column - and what does it actually measure: cumulative lifetime revenue, margin, predicted LTV, or just order count? Provenance matters more than presence.
- How recent is the data - what share of the list purchased or converted wi