metaflow-marketing-skills

Optimize budget allocation across paid media channels using Marketing Efficiency Ratio (MER), marginal ROAS analysis, diminishing returns modeling, and incrementality-informed decisions.

AI search and citation behavior change quickly. The frameworks below use the best available public data as of early 2026 (Google surfaces, ChatGPT, Perplexity, Gemini, and large-sample studies). Re-check assumptions as platforms update.

Plan, write, and maintain content that wins purchase-near buyers in both organic search and AI answer engines. BOFU content is structurally different from TOFU — the reader has accepted they need to decide; the page's job is to resolve the decision. Get it right and a single deep page can outperform dozens of informational posts on revenue contribution.

Analyze paid media performance across dimensions, funnels, cohorts, and channels — turning data into actionable insights and business narratives.

Systematic 70+ checkpoint audit covering tracking, structure, keywords, ads, bidding, budgets, audiences, and competitive positioning — with ICE-prioritized recommendations.

Build and configure Google Ads campaigns across all 10+ campaign types — from campaign type selection through settings, targeting, creatives, and launch.

Research, evaluate, mine, and manage keywords for Google Ads — from initial discovery through ongoing search term optimization and negative keyword management.

Optimize running Google Ads campaigns — bid strategy tuning, budget reallocation, Quality Score improvement, ad copy iteration, audience refinement, placement management, and scaling.

Select, implement, and manage Google Ads scripts to automate PPC tasks at scale — n-gram analysis, bid management, budget pacing, anomaly detection, and performance reporting.

Optimize product feeds for Shopping and Performance Max campaigns — from fixing disapprovals to building custom label strategies that power profitability-based bidding.

Build backlinks, earned media, and brand mentions that move both traditional rankings and AI visibility. The dominant modern tactic is digital PR anchored to original data; the fastest wins often come from unlinked mention reclamation.

Help local and service-area businesses win the map pack, organic local results, and — increasingly — local answers in AI tools. Local search has its own rules: proximity, prominence, and relevance, each expressed through a different set of signals than classic web SEO.

Systematic 75+ checkpoint audit based on the Sam Tomlinson framework: ICP validation → signal infrastructure → account structure → audiences → creatives → measurement — with ICE-prioritized recommendations.

Build and configure Meta Ads campaigns across all ODAX objectives — from objective selection through audience strategy, placement, budget, and creative launch.

Build a systematic creative testing, production, and rotation pipeline for Meta Ads — prevent fatigue, scale winners, and maintain performance through structured experimentation.

Optimize running Meta Ads campaigns — audience refinement, budget scaling, learning phase management, placement optimization, automation rules, and creative rotation coordination.

Meta ads planning playbook: objectives, structure, audiences, creative, testing cadence, Pixel/CAPI measurement. Does not assume live Marketing API access.

Implement and validate Meta Pixel + Conversions API (CAPI) for maximum signal quality — the single biggest lever for Meta Ads performance via the Andromeda ranking system.

Turn a draft or existing page into something both Google *and* AI answer engines can index, rank, and cite. This skill operates at the single-page level — keyword research and strategy are inputs, not outputs.

Build performance reports, dashboards, and stakeholder communications that turn ad data into business narratives — client reports, QBRs, audit deliverables, and executive summaries.

Google and Microsoft paid search planning: structure, keywords, match types, negatives, RSAs, bidding lens, and landing-page alignment for conversion-focused spend.

Build a prompt library that reveals how a brand shows up across the full buyer journey in AI search systems. The library is designed for actual LLM visibility tracking, not theoretical SEO keyword research.

Design content systems that earn both Google rankings *and* AI citations. The core insight: AI answer engines read content differently than humans — they extract the highest-information-gain sentences and treat H2 headings as prompts. The "ski ramp" structure optimizes for both.

Build a keyword universe that reflects how both humans *and* AI answer engines search. Traditional keyword research looks at standalone terms; modern research also maps the **fan-out sub-queries** AI tools generate when breaking down a user prompt.

Long-form SEO and AEO methodology bundle; read SEO_AND_AEO_KT.md for the full corpus.

Build reports that stakeholders trust, rooted in data sources that are actually reliable. Modern SEO measurement has to reckon with a very specific constraint: **the primary data source (GSC) is ~75% incomplete**, and the AI visibility layer is **probabilistic**, not deterministic. Building reports that ignore these realities produces false confidence.

Diagnose and prioritize the technical issues blocking a site from being crawled, indexed, ranked, and cited. Modern technical SEO has two audiences: Google's crawler/indexer and AI answer engines (GPTBot, PerplexityBot, ClaudeBot, etc.). Both need HTML-visible content and a clean crawl path.