Optimize Paid Ad Campaigns for Customer Acquisition

Guides creating, optimizing, and scaling paid ad campaigns across five platforms, each with fit criteria and campaign types: Google Ads for high-intent search traffic (Search, Performance Max, Display, YouTube, Demand Gen), Meta for demand generation with visual products (Advantage+ Shopping, Lead Gen, Conversions, Traffic, Engagement), LinkedIn for B2B decision-maker targeting (Sponsored Content, Message Ads, Lead Gen Forms, Document Ads, Conversation Ads), Twitter/X for tech audiences and real-time relevance, and TikTok for younger demographics and viral video. It specifies account structure (Campaign > Ad Set > Ad) and a naming convention ([Platform]_[Objective]_[Audience]_[Offer]_[Date]), a testing-phase budget split of 70% proven campaigns to 30% new tests that shifts to scaling budgets up 20-30% at a time with 3-5 days between increases for algorithm learning, three ad-copy frameworks with worked examples (Problem-Agitate-Solve, Before-After-Bridge, Social Proof Lead), headline formulas split by search versus social, and a soft/hard/urgency CTA taxonomy.

Use it when setting up a new paid channel, choosing between platforms for a given product or audience, writing ad copy and creative, structuring audience targeting, or diagnosing underperforming campaigns. Before starting, it requires gathering campaign goals (objective, target CPA or ROAS, budget, constraints), the product/offer and landing page, the target audience and any existing customer data for lookalikes, and prior campaign history and pixel/conversion data - proceeding without these means guessing. It documents four categories of common mistakes: strategy (launching without conversion tracking, fragmenting budget across too many campaigns, optimizing for clicks instead of conversions), targeting (audiences too narrow to exit the learning phase, or too broad and wasteful, overlapping audiences competing with each other), creative (running only one ad per ad set, not refreshing creative, a mismatched ad-to-landing-page experience), and budget (spreading too thin, making big changes that disrupt algorithm learning, stopping campaigns mid-learning-phase).

Inputs are the campaign brief items gathered up front - goals, offer, audience, current state - plus platform access; outputs are a campaign structure, ad copy in the chosen framework, an audience targeting plan (Google's custom-intent/in-market/customer-match audiences, Meta's core/custom/lookalike audiences sized from 1% and layered with interests, or LinkedIn's job-function-plus-seniority-plus-company-size combinations), and a bid-strategy recommendation - manual or cost caps during the learning phase until 50+ conversions accumulate, then automated target-CPA/ROAS bidding. Optimization output diagnoses which lever to pull based on the metric that's off: a high CPA points to the landing page, targeting, or creative; a low CTR points to weak creative or a mismatched audience; a high CPM points to an audience that's too narrow or overly aggressive bidding. Retargeting output is windowed by funnel stage - 1-7 days for hot cart/trial audiences, 7-30 days for warm page-visitor audiences, 30-90 days for cold any-visitor audiences - with standard exclusions for existing customers and recent converters.

Gives platform-specific setup checklists naming concrete tracking and audience tools: Google Ads (conversion tracking, GA4 linkage, remarketing/customer-match lists, negative keywords, ad extensions), Meta (Pixel plus server-side Conversions API, custom audiences, product catalog, domain verification), and LinkedIn (Insight Tag, Matched Audiences, lead-gen form templates). It hands off to four sibling skills: copywriting for landing-page copy, analytics-tracking for conversion-tracking setup, ab-test-setup for landing-page testing, and page-cro for post-click conversion optimization.

Performance marketers and growth teams setting up or scaling paid acquisition across Google, Meta, LinkedIn, X, or TikTok who want platform-fit guidance, copy frameworks, and a metric-to-lever optimization map rather than starting from a blank ad account.