ChatGPT Prompts for Google Ads

<context> Product: [PRODUCT] Product lines / services: [LIST] Geographies: [LOCATIONS] Monthly budget: [BUDGET] Goal: [GOAL] </context> <task> 1. Design a full account structure: campaigns by intent/product line, ad groups by keyword theme. 2. Recommend campaign types (Search, Performance Max, Shopping, Brand) and how to split budget across them given [BUDGET]. 3. Decide whether to segment campaigns by geography, device, or match type and justify each split. 4. Define a clear naming convention for campaigns and ad groups. 5. Output a tree diagram (text) of the recommended account hierarchy. </task>

<context> Brand name: [BRAND] Product: [PRODUCT] Competitors bidding on my brand: [YES/NO] Goal: [GOAL] Budget: [BUDGET] </context> <task> 1. Explain why brand and non-brand belong in separate campaigns and the metrics each should be judged on. 2. Recommend a budget allocation between brand and non-brand for my goal. 3. Decide whether to defend the brand campaign given competitor bidding behavior. 4. Propose bidding strategies for each (brand often Target Impression Share; non-brand often Target CPA/ROAS). 5. List the negatives to cross-apply so brand and non-brand do not cannibalize each other. </task>

<context> Goal: [GOAL] Conversion volume last 30 days: [NUMBER] Target CPA or ROAS: [TARGET] Budget: [BUDGET] </context> <task> 1. Recommend the best bidding strategy (Manual CPC, Maximize Clicks, Maximize Conversions, Target CPA, Target ROAS, Maximize Conversion Value) for my goal and data volume. 2. Explain the conversion-data threshold each strategy needs to perform. 3. Propose a migration path: which strategy to start with and when to graduate to a smarter one. 4. Warn about the learning-period volatility and how long to wait before judging results. 5. Set a realistic starting target given my historical data. </task>

<context> Product: [PRODUCT] Serviceable locations: [LOCATIONS] Customer behavior notes: [WHEN/WHERE THEY BUY] Budget: [BUDGET] Goal: [GOAL] </context> <task> 1. Recommend location-targeting settings (presence vs. presence-or-interest) and why. 2. Propose location bid adjustments based on likely value differences between regions. 3. Build a starting ad-schedule (dayparting) hypothesis aligned to when my customers convert. 4. Flag locations to exclude to protect [BUDGET] (low intent, out of service area). 5. Note which targeting decisions to revisit once conversion data accumulates rather than guessing now. </task>

<context> Product: [PRODUCT] Have product feed / Merchant Center: [YES/NO] Creative assets available: [IMAGES/VIDEO/TEXT] Budget: [BUDGET] Goal: [GOAL] </context> <task> 1. Recommend whether to run Performance Max, Search-only, or both in parallel for my situation. 2. Explain how PMax and Search interact and how to prevent PMax from eating my Search traffic. 3. List the asset groups and signals PMax needs to perform, and whether I can supply them. 4. Propose a budget split and a 30-60 day test design to compare the two. 5. Define guardrails (brand exclusions, account-level negatives) to keep PMax on-strategy. </task>

<context> Product: [PRODUCT] Key benefits: [BENEFITS] Differentiators: [WHAT MAKES IT UNIQUE] Offer/CTA: [OFFER] Target keyword theme: [THEME] Goal: [GOAL] </context> <task> 1. Write 15 distinct RSA headlines (max 30 characters each, count them) covering: benefit, feature, social proof, offer, urgency, and keyword-insertion variants. 2. Ensure at least 3 headlines include the target keyword theme for relevance/Quality Score. 3. Make headlines pinnable into position 1 (brand/offer) and position 2 (benefit) where appropriate, and label which to pin. 4. Avoid duplicate phrasing so Google can test true variety. 5. Output as a numbered list with character counts shown. </task>

<context> Product: [PRODUCT] Key benefits: [BENEFITS] Proof points: [STATS/AWARDS/REVIEWS] Offer/CTA: [OFFER] Goal: [GOAL] </context> <task> 1. Write 4 RSA descriptions (max 90 characters each, count them) that each lead with a benefit and end with a clear CTA. 2. Vary the angle across the four: outcome-focused, risk-reversal, proof-driven, and urgency. 3. Include the offer in at least one description and a proof point in another. 4. Keep language active and specific; no vague superlatives without proof. 5. Output as a numbered list with character counts shown. </task>

<context> Ad group keyword theme: [THEME] Keywords: [PASTE KEYWORDS] Product: [PRODUCT] Landing page promise: [PROMISE] Goal: [GOAL] </context> <task> 1. Write a complete RSA (15 headlines, 4 descriptions) tightly matched to this single keyword theme. 2. Mirror the searcher's exact language from the keywords in the headlines for relevance. 3. Ensure the ad promise matches the landing page promise so the scent stays consistent. 4. Flag any headline/description that would create a mismatch with the landing page. 5. Output the full ad plus a one-line note on expected Quality Score impact. </task>

<context> Product: [PRODUCT] Target customer: [TARGET CUSTOMER] Key benefits: [BENEFITS] Goal: [GOAL] </context> <task> 1. Write two complete RSAs: one leaning emotional (fear of missing out, status, relief) and one leaning rational (price, specs, time saved, ROI). 2. Keep both within RSA character limits and label each headline's angle. 3. Explain which audience segment each version likely resonates with. 4. Define how to A/B these (ad variation experiment) and what metric decides the winner. 5. Predict which is likely to win for my goal and why. </task>

<context> Industry: [INDUSTRY] Draft ad copy: [PASTE HEADLINES + DESCRIPTIONS] Claims made: [LIST CLAIMS] Goal: [GOAL] </context> <task> 1. Review the draft ad against common Google Ads policies (prohibited claims, superlatives needing proof, restricted-industry rules, trademark use). 2. Flag any phrase likely to trigger disapproval and explain why. 3. Rewrite each flagged line into a compliant version that keeps the persuasive intent. 4. Note any claims that require visible proof or disclaimers on the landing page. 5. Output a before/after table of flagged lines. </task>

<context> Product: [PRODUCT] Key pages/offers: [PAGES] Goal: [GOAL] </context> <task> 1. Write 8 sitelink assets: each with a link text (max 25 chars, count it) and 2 description lines (max 35 chars each, count them). 2. Cover distinct destinations: pricing, features, demo/trial, case studies, specific use cases. 3. Align each sitelink to a step in the buyer journey for my goal. 4. Avoid overlap with the main ad headlines so sitelinks add new information. 5. Output as a table: link text, description 1, description 2, destination, character counts. </task>

<context> Product: [PRODUCT] Features/benefits: [LIST] Trust signals: [GUARANTEES/SUPPORT/SHIPPING] Goal: [GOAL] </context> <task> 1. Write 10 callout assets (max 25 chars each, count them) highlighting trust and differentiators (e.g. free shipping, 24/7 support, no contract). 2. Recommend the best structured snippet header (e.g. Types, Brands, Features, Services) for my product and list 6-8 values under it. 3. Ensure callouts do not duplicate headlines or sitelinks. 4. Prioritize the callouts most likely to lift CTR for my goal. 5. Output callouts and snippets in separate labeled lists with character counts. </task>

<context> Product: [PRODUCT] Current offer: [OFFER] Price points: [PRICES] Goal: [GOAL] </context> <task> 1. Draft a promotion asset for my current offer (occasion, monetary or percent discount, promo code, valid dates). 2. Build a price asset set: 3-8 items with header (max 25 chars), description (max 25 chars), and price, each linked to its destination. 3. Recommend which products/tiers to feature in the price asset to push my goal. 4. Ensure all copy is within Google Ads asset limits and count the characters. 5. Note start/end scheduling so the promotion asset auto-expires. </task>

<context> Product: [PRODUCT] Brand visual style: [STYLE NOTES] Available assets: [WHAT IMAGES YOU HAVE] Goal: [GOAL] </context> <task> 1. Recommend the image asset types and aspect ratios Google Ads needs (1.91:1, 1:1, and logo 1:1 / 4:1). 2. Describe 6 distinct image concepts that would suit my product and goal (product shots, lifestyle, before/after, results). 3. Advise on what makes a high-performing search image asset (clear subject, minimal text, good contrast). 4. Provide alt-text style descriptions a designer could brief from. 5. Flag common image-asset disapproval reasons to avoid. </task>

<context> Product: [PRODUCT] Lead qualification needs: [WHAT YOU NEED TO QUALIFY] Phone support hours: [HOURS] Goal: [GOAL] </context> <task> 1. Decide whether a lead-form asset, call asset, or both fit my goal and explain the trade-offs. 2. Draft lead-form copy: headline, business name, description, and the qualifying questions to ask (without over-asking and tanking volume). 3. Recommend a call asset schedule aligned to my support hours and set call-conversion tracking expectations. 4. Note the privacy/consent requirements for lead-form data. 5. Output a setup checklist for both assets. </task>

<context> Product: [PRODUCT] Who it is NOT for: [NON-CUSTOMERS] Free alternatives / DIY terms to avoid: [TERMS] Goal: [GOAL] </context> <task> 1. Build a foundational negative keyword list grouped by category: irrelevant intent, free/DIY seekers, job seekers, wrong audience, and competitor brands (if not conquesting). 2. For each negative, state the match type (broad/phrase/exact negative) and why. 3. Recommend which negatives belong at account level vs. campaign level. 4. Flag any negatives that could accidentally block valuable traffic and how to scope them. 5. Output as a paste-ready list grouped by application level. </task>

<context> Product: [PRODUCT] Search terms report: [PASTE TERMS WITH COST/CONVERSIONS] Goal: [GOAL] </context> <task> 1. Analyze the pasted search terms: identify wasted spend (clicks/cost, zero conversions, low intent). 2. Recommend specific negative keywords to add, with match type, to stop the waste. 3. Identify high-performing terms worth promoting to their own exact-match keyword/ad group. 4. Spot intent themes I am missing that I should expand into. 5. Output two lists: "add as negative" and "promote to keyword", each with reasoning. </task>

<context> Product: [PRODUCT] Ideal customer profile: [ICP] Existing customer data available: [CRM/REMARKETING LISTS?] Goal: [GOAL] </context> <task> 1. Recommend which audience signals to layer onto Search campaigns (in-market, affinity, custom segments, remarketing) for my goal. 2. Specify whether to use each audience in "observation" or "targeting" mode and why. 3. Propose custom-segment definitions (by URL, search behavior, or interest) tailored to my ICP. 4. Suggest bid adjustments to test once observation data accrues. 5. Output an audience layering plan by campaign. </task>

<context> Product: [PRODUCT] Sales cycle length: [LENGTH] Customer lists available: [LISTS] Goal: [GOAL] </context> <task> 1. Design a remarketing strategy: which site visitors to re-engage, membership durations, and the message shift vs. cold traffic. 2. Recommend Customer Match uploads (past buyers, churned, high-value) and how to use each (target, exclude, or bid up). 3. Define exclusion audiences to stop paying for users who already converted. 4. Align list membership windows to my sales cycle length. 5. Output a remarketing/Customer Match matrix: audience, action, message angle, duration. </task>

<context> Campaign types running: [SEARCH/DISPLAY/PMAX] Brand safety concerns: [CONCERNS] Goal: [GOAL] </context> <task> 1. Recommend account-level placement and content exclusions to protect brand safety and budget (low-quality apps, sensitive content, parked domains). 2. For Display/PMax, list the content-suitability and placement categories I should exclude by default. 3. Explain how to audit the placement report and what signals indicate a placement to cut. 4. Recommend a review cadence for placements/exclusions. 5. Output a starter exclusion list plus an ongoing audit checklist. </task>

<context> Product: [PRODUCT] Ad copy: [PASTE ADS] Landing page content: [PASTE OR DESCRIBE PAGE] Goal: [GOAL] </context> <task> 1. Audit message match between the ad and the landing page (headline, offer, CTA, proof). 2. Flag every mismatch that breaks the scent from click to conversion. 3. Recommend landing-page edits to lift conversion rate and Quality Score (above-fold promise, form length, trust signals, speed). 4. Identify missing elements the ad promised but the page lacks. 5. Output a prioritized fix list ranked by likely conversion impact. </task>

<context> Account goal: [GOAL] Budget: [BUDGET] Current performance: [KEY METRICS] Goal: [GOAL] </context> <task> 1. Build a weekly Google Ads optimization checklist (search terms review, bid/budget pacing, negative additions, ad asset performance, audience signals). 2. For each task, state what metric to look at and the threshold that triggers action. 3. Separate "do weekly" from "do monthly" and "do quarterly" tasks. 4. Recommend what NOT to touch (avoid over-optimizing during learning periods). 5. Output the routine as a checklist with time estimates per task. </task>

<context> Campaign: [CAMPAIGN] Goal: [GOAL] Metrics: impressions [X], CTR [X], CVR [X], CPA/ROAS [X], impression share [X] Problem: [WHAT IS WRONG] </context> <task> 1. Diagnose the likely root cause from the metrics: is it a reach, relevance, conversion, or bidding problem? 2. Walk the funnel: low impressions vs. low CTR vs. low CVR vs. high CPA each points to different fixes. 3. Recommend the 3 highest-leverage changes to make first and what to expect from each. 4. Identify which metric to watch to confirm the fix is working. 5. Warn me away from changes that would just reset learning without addressing the cause. </task>

<context> Total budget: [BUDGET] Campaign performance: [CAMPAIGN: CPA/ROAS, SPEND, CONVERSIONS] Goal: [GOAL] Scaling appetite: [HOW AGGRESSIVE] </context> <task> 1. Rank campaigns/ad groups by efficiency (CPA or ROAS vs. target) and identify where to shift budget. 2. Recommend how much to scale winners without breaking Smart Bidding (e.g. 15-20% budget steps, wait for re-stabilization). 3. Identify campaigns capped by impression share that could absorb more budget profitably. 4. Flag losers to pause or restructure and reallocate their spend. 5. Output a reallocation table and a 30-day scaling timeline. </task>

<context> What I want to test: [HYPOTHESIS] Campaign: [CAMPAIGN] Current baseline: [METRIC] Goal: [GOAL] </context> <task> 1. Turn my idea into a clear, testable hypothesis with a single variable changed. 2. Recommend the right Google Ads experiment type (campaign experiment, ad variation, drafts) and the traffic split. 3. Define the primary success metric and the minimum data/duration before calling a result. 4. Estimate whether my conversion volume can reach significance and, if not, suggest a simpler test. 5. Output an experiment brief: hypothesis, setup, metric, duration, decision rule. </task>