Build Audience Targeting and Exclusion Lists

    You are a paid media strategist. On today's platforms, exclusions and signal quality matter more than the targeting options you pick.

The platform and campaign type: {{platform_and_type}}
Who I want to reach: {{target_audience}}
Customer data I hold and can upload: {{first_party_data}}
Who I definitely don't want to pay to reach: {{unwanted_audiences}}
What I sell and the typical buying cycle: {{context}}
Other campaigns running at the same time: {{other_campaigns}}

Produce:

**Set expectations about control.** On {{platform_and_type}}, say honestly how much targeting control I actually have. On the increasingly automated campaign types, detailed targeting is a suggestion rather than an instruction — the system will find who converts. That shifts the work to giving it good signal and stopping it from spending in places I don't want.

**Exclusions first**, because they save money immediately:
- **Existing customers**, unless I'm deliberately targeting them. Paying to acquire people who already bought is a common and invisible waste.
- **Recent converters** and anyone mid-purchase.
- **Current job applicants, employees, competitors, and my own team** — internal traffic distorts small campaigns badly.
- **Audiences from {{unwanted_audiences}}** — wrong geography, wrong company size, students, people outside my service area.
- **Placements that never convert** — low-quality app inventory and automatic placements that quietly absorb a share of budget on most accounts.
- **Branded search terms** on automated campaign types, so the campaign doesn't take credit for demand I already had.

For each: how to implement it on {{platform_and_type}}.

**Audience overlap.** Against {{other_campaigns}}: where my own campaigns are bidding against each other for the same people, raising my own costs. How to detect it and what to do — usually consolidate rather than add more exclusions.

**First-party data as signal.** From {{first_party_data}}: what to upload, what it's genuinely useful for — seed audiences for lookalikes, exclusion lists, and improving conversion signal quality — and the practical requirements. Note that list quality and size matter more than segmentation cleverness, and that platforms have lost a large share of the conversion signal they once had, so anything I can supply directly is disproportionately valuable now.

**Lookalike and similar audiences.** What to build them from — the highest-value customers, not all customers — and how narrow to make them. A lookalike built from a poor seed list reliably produces poor results.

**Signals that still work.** For {{platform_and_type}}, the targeting inputs that genuinely influence delivery versus the ones that are largely decorative now. Be specific rather than listing everything available.

**Privacy and compliance.** What's required to upload customer data, what consent it depends on, and the sensitive categories that can't be targeted or inferred. Flag anything in {{target_audience}} that touches a restricted category — health, finance, employment, housing, or anything inferring a protected characteristic — since these carry both platform restrictions and legal ones that vary by jurisdiction.

**A monthly exclusion review**, since these lists go stale and new waste appears.