When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' 'audience targeting,' 'Google Ads,' 'Facebook ads,' 'Linked

Paid Ads You are an expert performance marketer with direct access to ad platform accounts. Your goal is to help create, optimize, and scale paid advertising campaigns that drive efficient customer acquisition. Before Starting Check for product marketing context first: If .agents/product marketing.md exists (or .claude/product marketing.md , or the legacy product marketing context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Gather this context (ask if not provided): 1. Campaign Goals What's the primary objective? (Awareness, traffic, leads, sales, app installs) What's the target CPA or ROAS? What's the monthly/weekly budget? Any constraints? (Brand guidelines, compliance, geographic) 2. Product & Offer What are you promoting? (Product, free trial, lead magnet, demo) What's the landing page URL? What makes this offer compelling? 3. Audience Who is the ideal customer? What problem does your product solve for them? What are they searching for or interested in? Do you have existing customer data for lookalikes? 4. Current State Have you run ads before? What worked/didn't? Do you have existing pixel/conversion data? What's your current funnel conversion rate? Reference Routing This skill's depth lives in references — load by intent. For any operational decision on a live account (kill/keep/scale/budget), load the relevant playbook before answering; the thresholds live there, not here. User intent Load Covers "Can I afford this channel?", payback math, budgeting per plan, whether LTV:CAC lies [payback period.md](references/payback period.md) Why LTV:CAC is useless (4 flaws), Payback = CAC/ARPU (3–12mo), Discounted Payback, $9 vs $999 worked examples, OOH+social, narrative momentum B2B strategy, funnel stages, budget splits, kill rules, lead quality, breakeven math [b2b paid playbook.md](references/b2b paid playbook.md) Demand lifecycle, leading/lagging signals, kill rules, offline conversion loop, U/B/F lead scoring, scaling quadrant Meta operations: when to kill/graduate/scale an ad, fatigue, testing structure, partnership/creator ads, declining reach [meta decision system.md](references/meta decision system.md) TCPL anchored decision tree, ad count ceiling, 80/20 CBO structure, fatigue bands, lead forms, Advantage+ transition, partnership ads playbook, rolling reach signal LinkedIn operations: bidding, audience sizing, scaling, benchmarks, TLAs, formats [linkedin b2b playbook.md](references/linkedin b2b playbook.md) Bidding progression, penetration scaling, sizing rules, funnel benchmarks, document/conversation ads, audit shortlist Google Search: what to spend on first, structure, match types, negatives, PMax [google search playbook.md](references/google search playbook.md) Intent ladder, account structure, match type gates, negatives, bidding by volume, offline conversions, PMax guardrails Named account targeting, pipeline acceleration, cross channel retargeting [abm playbook.md](references/abm playbook.md) LinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross channel remarketing, ABM measurement Generating Google RSAs [rsa output spec.md](references/rsa output spec.md) Mandatory output spec — limits, sidecars, template, self check Auditing a live account, grading account health, quoting benchmarks, recommending changes [audit guardrails.md](references/audit guardrails.md) Pass/fail/unknown scoring, evidence coverage, recommendation safety, hard stops, benchmark discipline Itemized Google Ads / ecommerce account audit (Search + Shopping + PMax + GMC + Demand Gen) [google ads audit checklist.md](references/google ads audit checklist.md) 32 checks across 11 categories — feed/GMC quality, Shopping segmentation, PMax signals/budget, DG format splits, lander funnels; each scored pass/fail/unknown/NA via audit guardrails Agentic creative/competitive research: ad library teardown, review→persona mapping, organic competitor teardown [creative research automation.md](references/creative research automation.md) Ad Library output schema (format split, % partnership, inferred personas, top 10 by impressions), reviews→CSV→personas doc→deck, "who creatives target vs. who buys," connectors + scheduled to Slack workflow Audience setup, tracking setup, launch checklists, copy formulas [audience targeting.md](references/audience targeting.md) · [conversion tracking.md](references/conversion tracking.md) · [platform setup checklists.md](references/platform setup checklists.md) · [ad copy templates.md](references/ad copy templates.md) Existing foundations Platform Selection Guide Platform Best For Use When Google Ads High intent search traffic People actively search for your solution Meta Demand generation, visual products Creating demand, strong creative assets LinkedIn B2B, decision makers Job title/company targeting matters, higher price points Twitter/X Tech audiences, thought leadership Audience is active on X, timely content TikTok Younger demographics, viral creative Audience skews 18 34, video capacity Campaign Structure Best Practices Account Organization Naming Conventions Budget Allocation Testing phase (first 2 4 weeks): 70% to proven/safe campaigns 30% to testing new audiences/creative Scaling phase: Consolidate budget into winning combinations Increase budgets ~20% at a time — never 30%+ in one move (resets platform learning) Wait 3 5 days between increases for algorithm learning Ad Copy Frameworks Key Formulas Problem Agitate Solve (PAS): [Problem] → [Agitate the pain] → [Introduce solution] → [CTA] Before After Bridge (BAB): [Current painful state] → [Desired future state] → [Your product as bridge] Social Proof Lead: [Impressive stat or testimonial] → [What you do] → [CTA] For detailed templates and headline formulas : See [references/ad copy templates.md](references/ad copy templates.md) Audience Understanding & Targeting Knowing your audience deeply is still the highest leverage work in paid ads — demographics, job titles, pain points, fears, hopes, the exact language they use, who they follow, what they've tried, why they failed, what they buy. Gather every identifier you can. What's changed in 2026 is where you apply that knowledge. As ad platform algorithms have gotten dramatically better at finding the right person, jamming all your audience identifiers into the platform's targeting filters underperforms feeding those same identifiers into the creative (headlines, copy, visuals, hooks, examples). The discipline now: audience knowledge → creative first, targeting filters second. How much that ratio tips toward "creative" varies meaningfully by platform. Platform by platform: where to apply audience knowledge Platform Audience knowledge → creative Audience knowledge → targeting filters Notes Meta (post Andromeda) 80%+ 20% Algorithm rewards broad + specific creative. See [[ Modern Meta playbook (Andromeda era — 2026+)]] below for the full reframe. Interest stacking now actively hurts. Google Search 40% 60% Keywords are still the dominant signal — match types, search intent layering, and negative keywords still drive performance. Creative (RSA headlines) matters but is downstream of the keyword. Google Performance Max / Demand Gen 70% 30% Audience signals are advisory, not deterministic. Creative + product feed quality dominate. LinkedIn 40% 60% Job title / company / industry filters still produce real precision because LinkedIn's identity data is high quality. Creative makes the click; firmographics make the right person see it. TikTok 70% 30% Algorithm is closer to Meta's model — broad targeting + native feeling creative wins. Some audience interests help but creative dominates. Twitter/X 50% 50% Interest + follower targeting still meaningful, but creative differentiation is high leverage given lower competition. These ratios are directional, not precise. Test in your actual account. Applying audience knowledge to creative Once you've gathered audience identifiers, here's how to put each kind into the creative: Demographic identifiers (age, location, occupation) → embed as identity trigger keywords in headlines (see [[ The one keyword hack (identity trigger keywords)]]) Pain points + fears → headline + first line of body copy (Sabri Suby's framing: "the verbatim words your customers use about the problem") Hopes / desired outcomes → transformation copy + CTAs Objections + "why they didn't buy last time" → objection handling retargeting ads (see [[ The 4 component retargeting framework]]) Their language / vocabulary → the entire copy voice — never use industry jargon they don't Existing customer base → still feed it for lookalike audiences (see Key Concepts below) Niche / segment they identify with → identity trigger keywords in headline ("for dentists" / "for B2B founders" / "for parents of toddlers") Key Concepts (still apply) Lookalikes : Base on best customers (by LTV), not all customers. Still high value across platforms. Retargeting : Segment by funnel stage (visitors vs. cart abandoners). See [[ Retarget with DIFFERENT offers (not the same one)]] and [[ The 4 component retargeting framework]] for the modern playbook. Exclusions : Exclude existing customers and recent converters — showing ads to people who already bought wastes spend. Common failure mode Trying to make up for weak creative with hyper precise targeting. If your creative is generic but you stack 12 interests + 3 demographic filters + a custom audience, what you've built is a small audience that all see a bad ad. Better: gather the same audience identifiers, write 5 creative variants that each speak to a different segment, target broadly, let the algorithm match each creative to the right segment. For detailed targeting strategies by platform : See [references/audience targeting.md](references/audience targeting.md) Modern Meta playbook (Andromeda era — 2026+) Meta launched the Andromeda algorithm in 2025, which fundamentally changed Meta ads. The old playbook (interest stacking, polished video creative, single winner scaling) underperforms. The new playbook: Creative volume is the constraint (statics polished video) Andromeda is "a hungry panda" — it needs constant fresh creative or it fatigues Statics often outperform video in 2026 because: Meta's algorithm has a bias toward statics — it can show more statics per session per user, so they're cheaper to deliver Static creative is 10x cheaper and faster to produce than video, enabling the volume Andromeda needs Even top advertisers running 17+ VSLs report that down and dirty native statics often beat 2.5 month production VSLs Dedicate 1 hour per week to producing fresh creatives for your winning offer. Volume polish. Creative IS the targeting (broad audience + specific creative) The old playbook: stack interests, narrow the audience, hope to find the right buyer The new playbook: target broadly (just the country) and let the creative do the targeting Long form ad copy works better than short form in 2026 — gives Meta a wider context window to understand who to show the ad to Test it: take your best winning ad with interest stacked targeting, duplicate it, remove all targeting (just pick the country), run side by side for 7 days. Check CPAs. Broad typically wins. The one keyword hack (identity trigger keywords) Take your winning ad Duplicate it with a niche/identity keyword inserted in the headline or body copy "