coreyhaines31/marketingskills

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. Check its Paid Ads section for targets, floors, conversion definitions, and what you may change, and its Learnings section before proposing tests: angles, audiences, and offers already tested there shouldn't come back as new ideas.

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	Why LTV:CAC is useless (4 flaws), Gross-margin payback and cohort recovery (3–12mo planning target), $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 (incl. AI Max), 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
Weekly or daily account check, "what changed," stakeholder readout	operating-check.md	Ordered six-step check, HOLD/FIX/TEST/INVESTIGATE calls, facts/hypotheses/missing data, Friday readout under 200 words
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
Analyzing or reporting on Google Ads data, search-term waste, pause/keep/scale on low volume, client-facing findings	reading-google-ads-data.md	Withheld search terms (disclosed vs total clicks), conversions vs all_conversions, click-date attribution, experiment arms, 30-day change history, zero-in-N table, break-even CVR, verified/inferred/stale, "conclusions that sound right"
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; deep dives for conversion signals and wasted-spend negatives
Agentic creative/competitive research: ad-library teardown, weekly competitor ad tracker, 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 · platform-setup-checklists.md · 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

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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

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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

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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

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Modern Meta playbook (Andromeda era — 2026+)

Meta's Andromeda algorithm (2025) made creative volume and variety the main lever: target broadly and let specific creative do the targeting, ship many cheap static concepts, and make ads look native rather than polished. The full playbook (creative volume, creative as targeting, identity keywords, AI variants, zombie campaigns, native-looking ads, running creator ads as distinct concepts with a quality optimization event, and ad sequencing) is in references/meta-andromeda-playbook.md.

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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)

1. Hook (0-3 sec): Pattern interrupt, question, or bold statement
2. Problem (3-8 sec): Relatable pain point
3. Solution (8-20 sec): Show product/benefit
4. 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

1. Concept/angle (biggest impact)
2. Hook/headline
3. Visual style
4. Body copy
5. CTA

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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:

1. Check landing page (is the problem post-click?)
2. Tighten audience targeting
3. Test new creative angles
4. Improve ad relevance/quality score
5. 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

1. Start with manual or cost caps
2. Gather conversion data (50+ conversions)
3. Switch to automated with targets based on historical data
4. Monitor and adjust targets based on results

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Retargeting Strategies

Funnel-Based Approach

Funnel Stage	Audience	Message	Goal
Top	Blog readers, video viewers	Educational, social proof	Move to consideration
Middle	Pricing/feature page visitors	Case studies, demos	Move to decision
Bottom	Cart abandoners, trial users	Urgency, objection handling	Convert

Retargeting Windows

Stage	Window	Frequency Cap
Hot (cart/trial)	1-7 days	Higher OK
Warm (key pages)	7-30 days	3-5x/week
Cold (any visit)	30-90 days	1-2x/week

Exclusions to Set Up

• Existing customers (unless upsell) and recent converters (7-14 day window)
• Bounced visitors (<10 sec)
• Irrelevant pages (careers, support)

Retarget with DIFFERENT offers (not the same one)

The conventional retargeting playbook re-shows the same product/offer to people who didn't buy. The Sabri Suby principle: the #1 reason someone didn't buy is the offer wasn't right for them. Re-showing the same thing harder doesn't help.

Instead, retarget with different products, services, or offers from your catalog:

• Visitor clicked on protein powder, didn't buy → retarget with creatine (totally different category)
• Visitor downloaded a lead magnet, didn't book a call → retarget with a different lead magnet on a related topic
• Visitor viewed pricing, didn't sign up → retarget with a free audit or assessment instead

The lift from this is often dramatic — a 2-