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?

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## 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 |

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## 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](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)

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## Modern Meta playbook (Andromeda era — 2026+)

Meta launched the **Andromeda** algorithm in 2025, which fundamentally changed Meta ads. The old p