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Paid Advertising Strategy — Meta, Google & Beyond

The Prompt
You are a performance marketing strategist and paid media expert. Build a profitable paid advertising system.

Business type: [eCommerce / SaaS / Lead gen / App / Local / B2B]
Platforms: [Meta / Google Search / Google Shopping / YouTube / TikTok / LinkedIn]
Monthly ad budget: [$]
AOV or LTV: [$]
Target CPA or ROAS: [$]
Audience: [Demographics, interests, behaviors]

Deliver: platform selection rationale, account structure, audience strategy (cold/warm/hot), creative angles, ad-copy frameworks (5 variations), bidding strategy, budget allocation, A/B testing methodology, attribution model, and a scaling playbook.
The Output

Below is a profitable paid advertising system you can plug your specifics into. I’ll keep it tight and structured.

0) Inputs (fill these first)

  • Business type: eCommerce / SaaS / Lead gen / App / Local / B2B
  • Monthly ad budget: $___
  • AOV or LTV: $___
  • Target: CPA $ or ROAS
  • Audience: (who + what pain/desire + where they hang out)

Core math

  • If CPA target: Required CVR + CPC must make CPA workable.
  • If ROAS target (eCom): Break-even ROAS = 1 / gross margin (adjust for shipping/returns/ops).
  • If SaaS: Allowable CAC = LTV * contribution margin * payback constraint.

1) Platform selection rationale (pick based on intent + creative + budget)

Use this decision rule:

High purchase intent (capture demand)

  • Google Search: best for “I want it now” queries (brand + non-brand).
  • Google Shopping (eCom): best when you have competitive pricing + solid feed + sufficient SKU volume.

Demand creation (prospecting)

  • Meta: strongest for broad scale + fast creative iteration (most businesses).
  • TikTok: strongest for UGC-style creative + impulse + cheaper reach (works when you can make lots of creatives).
  • YouTube: strongest for authority + mid-funnel education + retargeting with video.

High-ticket B2B / narrow ICP

  • LinkedIn: best when job title/company targeting matters and you can monetize lead-to-close properly.

Default profitable stack (most cases)

  • eCommerce: Shopping + Meta (add Search + YouTube as budget grows)
  • Lead gen: Google Search + Meta (LinkedIn only if B2B ICP is strict)
  • SaaS: Search + Meta (YouTube for problem-aware education; LinkedIn for ABM-ish)

2) Account structure (clean, scalable)

Meta (Campaigns)

  1. Prospecting (Cold)
    • 1 campaign per objective (Sales/Leads)
    • 2–4 ad sets max (keep consolidation)
  2. Retargeting (Warm/Hot)
    • Separate campaign
  3. Existing customers (optional)
    • Upsell/cross-sell, exclude from prospecting if needed

Google Search

  • Brand Search (defensive, cheap conversions)
  • Non-brand Search (core growth)
  • Competitor (optional)
  • DSA (optional for scale)

Shopping

  • Performance Max / Shopping split by:
    • Best sellers vs long-tail
    • Margin tiers
    • Price competitiveness

3) Audience strategy (cold / warm / hot)

Cold (prospecting)

  • Broad + strong creative (default winner on Meta/TikTok)
  • 1–2 interest stacks only if broad is unstable (keep simple)
  • Lookalikes (only if you have strong conversion volume/quality)

Exclusions: recent purchasers/leads (time window depends on cycle)

Warm (engaged)

  • Video viewers (25–75%)
  • IG/FB engagers (7–30 days)
  • Site visitors (7–30 days), excluding checkout-start if you want separation

Hot (high intent)

  • Add to cart / initiate checkout (3–14 days)
  • Pricing page viewers (SaaS)
  • Form start but not submit (lead gen)
  • Abandoned checkout (eCom)

Rule: Warm/hot should have different messaging (proof, objections, offer, urgency), not just the same ads.

4) Creative angles (what to test first)

Pick 6–10 angles and build 3–5 variations each.

  1. Problem → relief (before/after)
  2. Proof (reviews, results, case study, numbers)
  3. Mechanism (why it works, “the method”)
  4. Comparison (vs alternatives, “why not X”)
  5. Offer framing (bundle, guarantee, free trial, demo)
  6. Objection destroyer (price, time, risk, complexity)
  7. Founder/authority (why we built this)
  8. UGC testimonial (raw, specific, believable)
  9. Use-case / scenario (“when you need this is…”)
  10. Speed + simplicity (“in 10 minutes…”)

5) Ad-copy frameworks (5 variations you can reuse)

Use these as templates (swap in your product + outcome + proof).

  1. PAS (Problem–Agitate–Solve)
  • Problem: “Still dealing with ___?”
  • Agitate: “It costs you ___.”
  • Solve: “Use to get in ___.” + CTA
  1. 4U (Urgent, Unique, Useful, Ultra-specific)
  • “Get without in days. Here’s how: .” + CTA
  1. Proof-led
  • “ people got .”
  • “Average result: ___.”
  • “Try ___ (risk reducer).” + CTA
  1. Objection-first
  • “Don’t buy if .”
  • “But if you want , this is built for .” + CTA
  1. Feature → benefit → outcome
  • Feature: “___”
  • Benefit: “So you can ___”
  • Outcome: “Which means ___” + CTA

6) Bidding strategy (simple defaults that usually work)

Meta

  • Start: Highest volume conversion event you can reliably optimize (Purchase / Lead / Complete Registration)
  • If unstable: optimize higher in funnel briefly (ATC) only as a learning bridge
  • Scale:
    • If CPA is stable: increase budgets 10–20% per day
    • If volatile: duplicate winners into new ad set/campaign rather than huge budget jumps

Google Search

  • Start:
    • If you have conversion volume: Max Conversions → tCPA (or Max Conversion Value → tROAS for eCom)
    • If low volume: Manual CPC / ECPC short-term until enough data

Shopping / PMax

  • Feed quality first (titles, GTIN, images, pricing, shipping)
  • Split by margin/bestsellers if ROAS control is needed

7) Budget allocation (by stage)

Use this baseline split, then adjust weekly:

If you need growth

  • 60–75% Cold
  • 15–25% Warm
  • 10–15% Hot

If you’re efficiency-constrained (tight CPA/ROAS)

  • 50–60% Cold
  • 20–30% Warm
  • 15–20% Hot

Cross-platform rule: always fund demand capture (Search/Shopping) before aggressively scaling pure prospecting.

8) A/B testing methodology (how to not waste budget)

Test hierarchy (only one level at a time):

  1. Creative angle (biggest lever)
  2. Hook + first 3 seconds (video)
  3. Offer (bundle, guarantee, trial, pricing)
  4. Landing page (message match)
  5. Audience (only after creative works)

Practical rules:

  • Minimum test length: 3–7 days (or until you hit a meaningful conversion count)
  • Kill criteria: spend 1–2x target CPA with no conversion (depends on funnel)
  • Promote winners: move into a “Winners” ad set/campaign and keep iterating variants

9) Attribution model (decision-making you can trust)

Use a blended model:

  • Platform reporting: directional (good for creative/audience inside platform)
  • GA4 / Shopify / CRM: cross-channel view
  • North Star: blended CAC / MER (eCom: MER = Total revenue / Total ad spend)
  • Holdouts (optional): geo or time-based tests when scaling

Windows (defaults)

  • eCom: 7-day click (Meta), plus blended MER weekly
  • Lead gen/B2B: track to qualified lead + close rate, not just CPL

10) Scaling playbook (step-by-step)

Scale only after you have:

  • 1–3 proven angles
  • 3–5 proven creatives per angle
  • Stable CPA/ROAS for 7–14 days

Scaling levers (order)

  1. Creative volume: add 5–10 new creatives/week (highest ROI lever)
  2. Budget up slowly: +10–20% daily on stable campaigns
  3. Expand placement/inventory: Advantage+ placements / broaden targeting
  4. Add new platform (only when core channel is stable)
  5. Offer engineering: bundles, upsells, annual plans, lead magnets

Protect efficiency

  • Separate Testing vs Scaling campaigns
  • Retargeting caps: don’t let it consume budget if audience is small
  • Refresh creatives when frequency rises and CPA climbs

If you paste your filled inputs (business type, budget, AOV/LTV, target CPA/ROAS, audience), I’ll lock this into a specific platform mix + exact campaign/ad set structure + budget numbers.

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