Meta Ads Audiences: Broad vs Narrow Targeting

For the surrounding account decisions, compare Meta Ads Custom Audiences: Data Governance and Activation and use Meta Ads Formats and Placements: A Readiness Test as the next diagnostic.

In short

This guide owns one decision artifact: the filled, auditable structure below. Reader-supplied thresholds stay explicit; missing evidence stays missing.

Broad-versus-narrow eligible-population test

• Cell A: Broad cell: selected geography/age eligibility with purchaser, employee, test, and narrow-cell suppression IDs excluded where feasible.
• Cell B: Narrow cell: the declared interest/list/lookalike rule inside the same geography/age, with the same purchaser, employee, and test exclusions.
• Falsifiable expectation: Broad produces different matured contribution per assigned dollar or CAC than narrow; equality or reversal falsifies the directional claim.
• Held invariant: Offer, creative IDs, destination, optimization event, bid, placements, schedule, attribution, and order-cost treatment.
• Budget allocation: Declare B and reserve B ÷ 2 per cell; freeze reallocation. Track spend imbalance against a reader-set tolerance.
• Maturity window: Concurrent delivery plus observed conversion/cancellation/return cutoff.
• One primary metric: Matured contribution per assigned dollar = (recognized revenue − variable order costs − media spend) ÷ (assigned test dollars); secondary CAC = acquisition spend ÷ first-party-verified new customers. Sources: Meta cell spend and commerce/customer ledgers; attribution is not incrementality.
• Stop rule: Stop for consent, policy, inventory, cash, claim, or tracking-integrity risk.
• Inconclusive rule: Inconclusive if delivery imbalance exceeds tolerance, shared-membership or cross-cell delivery contamination exceeds the preregistered limit, exclusions fail, or outcomes remain immature.

Interpretation boundary

Use the broad-versus-narrow eligible populations only for its stated decision. Define both populations and identical exclusions before allocating B equally; measure shared membership and cross-cell delivery leakage. Delivery imbalance, failed exclusions, excess contamination, or immature outcomes makes the comparative contribution/CAC read inconclusive. Reader-supplied thresholds remain inputs, not universal standards.

Can software help?

Bach.ai audits your connected Meta account against 100+ checks, ranks what it finds by estimated impact, and proposes specific fixes. It stays read-only until you approve a change, then executes the approved change on Meta; connected Google Ads data is used for intelligence only. Think of it as an automated audit layer that surfaces issues and proposed fixes for your review — not a replacement for your team’s judgment, and it does not generate your creative.

FAQ

How do you design a fair broad-versus-narrow audience test?

Define both populations and identical exclusions before allocating B equally; measure shared membership and cross-cell delivery leakage.

What invalidates a broad-versus-narrow audience comparison?

Delivery imbalance, failed exclusions, excess contamination, or immature outcomes makes the comparative contribution/CAC read inconclusive.

Can this audience test establish which targeting approach caused the difference?

It compares the declared cells in the broad-versus-narrow eligible populations. A causal interpretation additionally depends on valid assignment, stable invariants, adequate power, and contamination within the preregistered limit.