ads

name ads description 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,' 'LinkedIn ads,' 'ad budget,' 'cost per click,' 'ad spend,' 'should I run ads,' 'ABM,' 'account-based marketing,' 'B2B ads,' 'lead quality,' 'negative keywords,' 'Performance Max,' 'thought leader ads,' or 'when should I kill an ad.' Use this for campaign strategy, audience targeting, bidding, and optimization. For bulk ad creative generation and iteration, see ad-creative. For landing page optimization, see cro. metadata {"version":"2.3.2"} 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 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 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 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 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 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 LinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross-channel remarketing, ABM measurement Generating Google RSAs 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 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 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 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 · conversion-tracking.md · platform-setup-checklists.md · 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 Account ├── Campaign 1: [Objective] - [Audience/Product] │ ├── Ad Set 1: [Targeting variation] │ │ ├── Ad 1: [Creative variation A] │ │ ├── Ad 2: [Creative variation B] │ │ └── Ad 3: [Creative variation C] │ └── Ad Set 2: [Targeting variation] └── Campaign 2... Naming Conventions [Platform]_[Objective]_[Audience]_[Offer]_[Date] Examples: META_Conv_Lookalike-Customers_FreeTrial_2024Q1 GOOG_Search_Brand_Demo_Ongoing LI_LeadGen_CMOs-SaaS_Whitepaper_Mar24 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 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 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 "Here's how to get 462 leads per week on autopilot" → "Here's how to get 462 dental leads per week on autopilot" / "... lawyer leads..." / "... property investment leads..." The keyword is an identity trigger for the viewer AND a targeting signal for Andromeda Dramatically drops CPL and opens audience pockets you couldn't reach with a generic ad AI variant farming (the 100-people test) Take your winning ad Feed to Claude/ChatGPT/Kong with the prompt: "I want you to read this ad and be the author. If I show the next ad I'm going to ask you to write to 100 people, not 1 in 100 would be able to tell you it's written by a different person. Now write this for [demographic/niche]." The output should read essentially the same with subtle relevance shifts for the target Apply in sequence: body copy → headlines → creative Drop all variants in a CBO, let Meta's AI allocate spend Zombie campaigns After running a CBO, Meta will give 80% of variants no spend Take the dead variants you have high conviction about Launch them in a separate ad set ("zombie campaign") Typically resurrects 20% as winners that Meta's first allocation passed over Don't make ads look like ads Hundreds of millions of people have ad blockers — the polished-ad aesthetic kills performance Study what content natively performs in your niche on TikTok/Instagram/YouTube → produce ads that match that aesthetic Burner account technique: create a clean Instagram/TikTok account, follow all influencers and pages in your niche, like their content. Your feed becomes a curated view of what's natively winning. Produce ads that match. If you have an organic video with millions of views, run that exact video as a paid ad — proven content + paid distribution = the highest-leverage move Creative Best Practices Image Ads Clear product screenshots showing UI Before/after comparisons Stats and numbers as focal point Human faces (real, not stock) Bold, readable text overlay (keep under 20%) Video Ads Structure (15-30 sec) Hook (0-3 sec): Pattern interrupt, question, or bold statement Problem (3-8 sec): Relatable pain point Solution (8-20 sec): Show product/benefit CTA (20-30 sec): Clear next step Production tips: Captions always (85% watch without sound) Vertical for Stories/Reels, square for feed Native feel outperforms polished First 3 seconds determine if they watch Creative Testing Hierarchy Concept/angle (biggest impact) Hook/headline Visual style Body copy CTA Campaign Optimization For hard kill/keep/scale thresholds, use the platform playbooks (see Reference Routing): the kill rules and breakeven CPL/CPC math live in b2b-paid-playbook.md , and Meta's full decision tree lives in meta-decision-system.md . Key Metrics by Objective Objective Primary Metrics Awareness CPM, Reach, Video view rate Consideration CTR, CPC, Time on site Conversion CPA, ROAS, Conversion rate Optimization Levers If CPA is too high: Check landing page (is the problem post-click?) Tighten audience targeting Test new creative angles Improve ad relevance/quality score Adjust bid strategy If CTR is low: Creative isn't resonating → test new hooks/angles Audience mismatch → refine targeting Ad fatigue → refresh creative If CPM is high: Audience too narrow → expand targeting High competition → try different placements Low relevance score → improve creative fit Bid Strategy Progression Start with manual or cost caps Gather conversion data (50+ conversions) Switch to automated with targets based on historical data Monitor and adjust targets based on results Retargeting Strategies Funnel-Based Approach | Funnel Stage | Audience | Message | Goal |