deep-marketing-analyst_skill

Overview

This skill performs deep, cross-platform advertising analysis and turns evidence into decision-ready plans. It focuses on hypothesis testing, evidence mapping, and strategic synthesis across Meta, Google Ads, TikTok, YouTube, Amazon Ads, and DSP/programmatic. Outputs are actionable: research plans, evidence tables, ranked conclusions, and next experiments.

How this skill works

I decompose a research question into testable hypotheses, specify which platform sources to inspect, and collect measurable findings (campaign metrics, creative performance, auction signals, and competitor patterns). I assess evidence strength and conflicts, synthesize channel-aware implications, and produce a prioritized action plan with rollback conditions and validation checkpoints. Recommendations explicitly separate observed facts from assumptions and indicate confidence levels.

When to use it

• Deciding channel budget shifts tied to ROAS, CPA, or revenue targets
• Designing hypothesis-driven creative or audience tests across multiple ad platforms
• Evaluating mixed or conflicting performance signals from Meta, Google, TikTok, YouTube, Amazon, or DSPs
• Preparing decision-ready briefs for quarterly planning or board-level recommendations
• Setting up experiments to validate growth levers before scaling spend

Best practices

• Provide a clear research_question, a set of candidate hypotheses, and a decision_deadline up front
• Include platform-level metrics and at least one source of truth (server, analytics, or platform report) for each channel
• Set a confidence_target and acceptable effect sizes to avoid over-interpreting weak signals
• Keep recommendations channel-aware: separate creative cadence (Meta/TikTok) from intent capture (Google/Amazon) and audience/frequency controls (DSP)
• Always include measurable success criteria and at least one rollback or stop-loss condition when spend risk exists

Example use cases

• Stress-test a team hypothesis that broad targeting outperforms precise audiences; produce confidence-ranked conclusions and follow-up A/Bs
• Three-month competitor creative analysis across Meta and TikTok with pattern summary and tactical creative playbook
• Quarterly channel allocation decision with budget increase scenarios, risk-weighted recommendation, and escalation payload for tracking or policy issues
• Amazon vs. Google Ads audit to prioritize demand-capture tactics and listing-level experiments