> For the complete documentation index, see [llms.txt](https://support.sayprimer.com/primer/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.sayprimer.com/primer/use-cases/run-meta-lookalikes-with-exclusions.md). # Run Meta Lookalikes with Exclusions Use Primer to build high-quality seed and exclusion audiences from your CRM, sync them to Meta, and A/B test **lookalike-only vs. lookalike+exclusions**—without disrupting your winning campaigns. ### What you’ll do 1. Create a **Champion (Best Customers)** seed in Primer 2. Create **Anti-Persona** exclusion lists in Primer 3. Sync both to **Meta** 4. Set up a **clean A/B test in Meta Experiments** (no reset to your main campaign’s learning) 5. Measure lift on lead quality and acquisition efficiency For additional details and real campaign outcomes, check out Primer's [Meta Lookalikes Use Case](https://www.sayprimer.com/use-cases/primer-meta-lookalikes) {% embed url="" %} *** ### 1) Build your audiences in Primer **Champion (Best Customers) seed** 1. In Primer, create a **Best Customers / Champion** audience from **Closed/Won** accounts or **high-quality leads**. 2. Use [**Historical Win Rates** ](https://support.sayprimer.com/primer/building-audiences/how-to-build-an-audience/audience-summary#use-historical-opportunity-data-to-see-where-you-win)to surface firmographic patterns (industry, employee size, seniority/title, etc.). 3. (Optional) Narrow by **titles** that most often convert (e.g., Director+ in RevOps/IT Security). 4. **Sync → Meta** (destination: Facebook/Instagram). **Anti-Persona exclusions** 1. Create an **Anti-Persona** audience for companies/contacts you *don’t* want (e.g., student emails, <50 employees, irrelevant titles). 2. Add rule-based filters that reflect “non-ICP” traits (e.g., EDU domains, job seekers, freelancers, hobbyists). 3. **Sync → Meta** as exclusion lists. *** ### 2) Configure your base ad set in Meta 1. In Meta Ads Manager, create or duplicate an ad set that targets a **1% lookalike** built from your Champion seed. 2. **Disable Advantage+ Audience Expansion** to maintain a clean test boundary. 3. Leave **exclusions off** for now—this version will be your “lookalike-only” control. > Tip: Keep **creative, placements, optimization event, and budget** identical across variants. The only change you’ll test is **exclusions**. *** ### 3) Test without resetting learning: Meta **Experiments** (A/B Test) {% embed url="" %} This approach **does not reset learning** on your scaled, already-performing setup. **A. Create your two variants** * **Variant A (Control):** Your existing **1% lookalike** ad set (no exclusions). * **Variant B (Test):** **Duplicate** that ad set and **add your Anti-Persona exclusions**. Everything else stays identical. **B. Launch the A/B Test in Experiments** 1. In Ads Manager, go to **All Tools → Analyze & Report → Experiments** and choose **A/B Test**. 2. **Select your two ad sets** (A = lookalike-only; B = lookalike+exclusions). 3. Set **Schedule** (test window), **Budget split** (e.g., 50/50), and **Primary KPI** (e.g., **Qualified Signup / SQL rate / CPA**). 4. Name the test (e.g., *LLA 1% w/ vs. w/o Exclusions*), **Review**, and **Create Test**. **What Experiments does during the test** * **Evenly splits delivery** between variants and **prevents audience overlap** during the test window. * Your **original optimization history** stays with the control; the **test duplicate** learns independently. * After the test, **delivery returns to normal** and you can **pause the loser**. *** ### 4) Alternative: Test inside a CBO campaign (with caution) If you’re using **Campaign Budget Optimization (CBO)**: 1. In the same campaign, **add a new ad set** (duplicate the control) and apply **exclusions** to the **new ad set only**. 2. Let CBO allocate budget between ad sets. **Caveat:** The new ad set will enter learning. Your existing ad set generally won’t reset, but **budget shifts can create ripple effects**. Monitor closely. *** ### 5) Analyze and decide In **Experiments → Results** (or Ads Manager reporting), compare: * **Lead quality:** SQL rate, Opportunity rate, Qualified submission rate * **Efficiency:** **CPA/CPL**, CPM, CTR, CVR * **Down-funnel:** Pipeline $$ per 1,000 impressions, win rate by cohort * **Operational:** Match rate to CRM, disqualify rate (junk), form-completion rate **Decision rule of thumb** * If **exclusions** improve **SQL rate** and **CPA** without killing volume, **promote the exclusion variant**. * If volume drops too hard or CPA rises, keep lookalike-only and revisit your Anti-Persona logic (tighten only the most wasteful segments). *** ### Troubleshooting & tips * **No reset to your main performer:** Use **Experiments** so your winning ad set keeps its **learning and stability**; the *duplicate* is the one that learns. * **Keep variables isolated:** Same creative, placements, optimization event, bid strategy, and budget split. Only change **exclusions**. * **Disable Advantage+ Audience Expansion:** Maintain clean boundaries for the test. * **Test length:** Aim for **7–14 days** or until you reach **statistically meaningful** conversions (e.g., 100+ qualified leads across both arms, if feasible). * **Document seeds & exclusions:** Note seed composition (e.g., top 500 Closed/Won, last 12–24 months) and exclusion logic so results are reproducible. *** ### FAQ **Will exclusions reset the learning phase?** * **If you edit your *****live***** scaled ad set**, significant targeting changes (like adding exclusions) can trigger a reset. * Using **Experiments** with a **duplicate** protects your main ad set; only the **test variant** learns. **Can I start the test with just one ad set?** * You need **two variants** to compare. If you only have one live ad set, **duplicate it** and add exclusions to the copy for the test. **What % lookalike should I use?** * Start with **1%** for signal purity. If you need scale, test **2–5%** in follow-up experiments after you’ve proven the exclusion lift. **What should I monitor first?** * **SQL rate** and **CPA** (or cost per qualified signup). A better exclusion strategy should reduce junk leads and stabilize CPA. {% embed url="" %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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