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Best prompts for ChatGPT for marketing using long-tail keyword clustering
12 copy-ready prompts to generate, cluster, and actionize long-tail keywords for SEO, content, PPC, local SEO, and outreach. Each prompt is specific, practical, and formatted for direct use with advanced LLMs.
GPT-5
Claude Opus 4
Claude Sonnet 4
Gemini 2.5 Pro
Gemini 2.5 Flash
You've spent hours crafting the perfect ChatGPT prompt for keyword research, only to get a generic list that feels like it came from 2015. Sound familiar? We've all been there, staring at our screens wondering why our AI-generated keywords look nothing like what actual searchers are typing into Google.
These 12 battle-tested prompts transform ChatGPT into your personal SEO strategist, generating everything from semantically clustered keywords to ready-to-publish content calendars and PPC ad groups. Instead of wrestling with vague responses and endless prompt iterations, you'll get structured, actionable outputs that plug directly into your marketing workflow. No more guesswork, no more generic lists, just copy-paste prompts that deliver professional-grade keyword strategies in minutes.
1
Expand seed keywords into long-tail list with intent and metrics
Input: {seed_keywords} (comma-separated), {country or language}. Expand into 100 long-tail keyword variations. For each keyword return: keyword, search_intent (informational/commercial/transactional/navigational), estimated_monthly_volume (numeric), keyword_difficulty (0-100), suggested_CPC_USD (numeric). Prioritize by a combination of intent and volume: show top 100 only. Output as CSV with header: keyword,search_intent,monthly_volume,keyword_difficulty,cpc_usd. Be concise and do not include extra commentary.
Take a small set of seed keywords and generate a prioritized long-tail keyword list with search intent, estimated monthly volume, difficulty, and CPC. Output as CSV.
2
Cluster keywords by semantic similarity with threshold and cluster names
Input: a newline-separated list of keywords in {language}. Cluster these into groups where intra-cluster similarity >= 0.65. For each cluster return: cluster_id, cluster_name (3-4 words), sample_keywords (top 8), cluster_size, average_intent (dominant intent), confidence_score (0-1). Output JSON array only. If a keyword fits multiple clusters, assign to the best matching cluster and mark duplicates in a 'duplicates' field referencing original cluster_id.
Group a list of keywords into semantic clusters using cosine similarity or topical intent and assign concise cluster names, plus a confidence score.
3
Map content pillar and cluster topics with titles and internal linking
Input: {pillar_topic}, {primary_keyword}, {target_country_or_language}. Produce a JSON array of 8 cluster pages. Each item: title (SEO-friendly, <= 70 chars), meta_description (120–155 chars), target_keyword, suggested_H1, recommended_internal_links (array of 2 other cluster slugs), suggested_URL_slug (kebab-case). Also output a suggested pillar page title and a short 1-sentence pillar intro. Keep descriptions actionable and concise.
Given a pillar topic, generate 6–10 cluster pages with SEO-ready blog titles, meta descriptions, target keyword, suggested H1, and internal linking to the pillar and two related clusters.
4
Generate SEO-optimized blog outline for a cluster keyword
Input: {target_keyword}, {target_word_count}, {target_audience}. Create an SEO blog outline: meta_title (<=60 chars), meta_description (140–160 chars), estimated_word_count (number), H1, H2/H3 hierarchy (with 6–10 sections), bullet points of key points per section, suggested internal_links (3 URLs), suggested_external_references (2 authoritative domains), and 5 FAQs (question + concise answer). Output only JSON.
Produce a detailed outline for a blog post optimized for a specific long-tail keyword, including headings, word-count targets, key subtopics, suggested internal/external links, and 5 FAQ schema Q&As.
5
Create a 90-day content calendar from clustered keywords
Input: JSON list of clustered keyword objects (cluster_id, cluster_name, sample_keywords). Create a 90-day calendar table with one row per content piece: publish_date (YYYY-MM-DD), content_type (blog, video, landing page, GBP post), title, target_keyword, target_audience, priority (high/medium/low), estimated_word_count, hero_image_brief, promotion_channels. Output CSV with header. Use Monday-Friday cadence, prioritize highest confidence clusters first.
Turn clustered keywords into a prioritized 90-day publishing schedule with content type, target keyword, publish date, title, author role, and promotion notes.
6
Build PPC ad groups and ad copy from clustered keywords
Input: {cluster_name}, newline list of cluster_keywords, {campaign_goal}. For the cluster produce: ad_group_name, suggested_bid_usd (recommended CPC), exact_match_keywords (comma list), phrase_match_keywords (comma list), negative_keywords (comma list), 3 ad_variations (each with headline1, headline2, description), landing_page_headline, recommended_cta. Output JSON. Keep headlines <=30 chars and descriptions <=90 chars.
Convert keyword clusters into PPC ad groups with 3 ad copy variations, recommended landing page headline, suggested exact and phrase match keywords, and negative keywords.
7
Generate FAQ schema (JSON-LD) for cluster-based questions
Input: {cluster_name}, list of top 10 cluster_keywords. Produce 6–8 FAQs (question <= 120 chars, answer 30–90 words) that match user intent. Output valid JSON-LD using the schema.org FAQPage structure only, no extra explanation. Ensure answers include the target keyword naturally once when relevant.
From a keyword cluster produce concise FAQ questions and answers and format them as JSON-LD FAQ schema ready to paste into a page.
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