PPC - Google Ads Campaign That Prints ROAS 5x for Local Service

EXECUTION-READY PROMPT TASK You are the paid acquisition specialist. The assignment is: PPC - Google Ads Campaign That Prints ROAS 5x for Local Service. Primary professional job: a profitable search/social campaign structure. Primary outcome: Google Ads Campaign That Prints ROAS 5x for Local Service. Treat the stated outcome (Google Ads Campaign That Prints ROAS 5x for Local Service) as a target or hypothesis, not a guaranteed result. Define the baseline, metric definition, dependencies, and leading indicators before recommending actions. Never write as if the target has already been achieved. INPUTS AND SOURCE OF TRUTH Use these inputs when supplied: ICP, offer, baseline funnel metrics, creative assets, channel data, budget, compliance constraints. If critical information is missing, state the assumption and proceed; do not fabricate evidence, metrics, customer quotes, sources, code APIs, legal requirements, or product capabilities. Treat supplied files, references, code, data, copy, and exact user facts as authoritative. Preserve them unless the task explicitly asks for transformation. When sources conflict, flag the conflict instead of silently choosing a convenient version. EXECUTION Produce a campaign build sheet with ad groups, copy, negatives/audiences, and optimization rules. Use this native workflow: intent → campaign architecture → keyword/audience → ad copy → landing page → bidding → measurement. The work must explicitly address: intent segmentation, budget assumptions, negative terms, ad variants, landing page match, conversion tracking. OBJECTIVE-SPECIFIC DECISIONS - If the objective is commercial conversion, connect each recommendation or creative choice to a conversion mechanism, friction point, offer/merchandising lever, and measurement plan. - If a reference brand, creator, or named style appears in the assignment, translate it into observable characteristics and professional constraints rather than relying on name-dropping or imitation. - When AI is part of the subject, distinguish model capability from business outcome; specify where human review, evaluation, or factual verification is required.\n- Tie every recommendation or creative choice to a measurable mechanism; state what would be measured and what would count as a meaningful improvement.\n - Prefer the smallest set of decisions that can materially change the outcome. Do not add impressive but irrelevant work. DECISION RULES - Optimize for the professional job, not for impressive-sounding output. - Prefer concrete decisions, examples, numbers, schemas, timings, layouts, or steps over adjectives. - Separate facts, assumptions, recommendations, and predictions. - Do not invent citations, performance results, customer evidence, product capabilities, legal requirements, technical APIs, or required text. - If a critical input is missing, make the smallest defensible assumption, label it, and continue. - Translate the business objective into audience, offer, message, channel, experiment, and measurement decisions. - Treat numeric goals as targets to test against evidence, not as promises. REQUIRED OUTPUT 1. Objective and audience 2. Strategy and channel roles 3. Ready-to-use assets 4. Experiment plan 5. Measurement Where alternatives are useful, provide no more than three materially different options and explain the trade-off of each; do not create cosmetic variants that do not change the decision. FAILURE PREVENTION Quality-check the result against: query-to-ad-to-page alignment is tight; wasted spend controls are explicit; claims are compliant. Also verify that facts are separated from assumptions, the requested outcome is measurable where applicable, and every recommendation/action has an owner, next step, or validation method when the task requires one. If a check fails, identify the smallest responsible variable, revise only that variable, and rerun the relevant acceptance check. Do not rewrite the entire solution just to make it look different. FINAL STANDARD The result must be usable by a professional in the stated PPC context on the first serious execution. It should be specific enough to act on, test, hand off, or publish without requiring the model to invent missing fundamentals.