# Social Media Advertising > Use when creating and managing social media ad campaigns. - Skill: `loopyluci/social-media-advertising` (Agent Skill) - Install (CLI): `npx skillmds@latest add loopyluci/social-media-advertising` - Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/social-media-advertising/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: LoopyLuci (https://skillmd.com/u/loopyluci) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/loopyluci/social-media-advertising --- # Social Media Advertising Creating, managing, and optimizing paid social media campaigns across platforms ā€” from audience targeting and creative strategy through budget management and performance analysis. ## When to Use - Running paid campaigns on Facebook, Instagram, LinkedIn, Twitter/X, TikTok - Building and testing social ad audiences - Designing ad creative and copy for social platforms - Managing ad budgets and bids across platforms - Analyzing and optimizing social ad performance ## Platform Comparison ```python PLATFORM_COMPARISON = { 'facebook_instagram': { 'best_for': 'B2C, ecommerce, brand awareness, retargeting', 'audience_size': '2.9B+ monthly active', 'ad_formats': 'Image, Video, Carousel, Collection, Stories, Reels', 'targeting': 'Demographic, interest, behavioral, custom audiences, lookalikes', 'min_budget': '$5/day', 'cost_benchmark_cpc': '$0.50-1.50', }, 'linkedin': { 'best_for': 'B2B, professional services, recruitment, thought leadership', 'audience_size': '900M+ members', 'ad_formats': 'Sponsored Content, Message Ads, Text Ads, Dynamic Ads', 'targeting': 'Job title, company, industry, skills, seniority, groups', 'min_budget': '$10/day', 'cost_benchmark_cpc': '$5-8', }, 'twitter_x': { 'best_for': 'News, events, app installs, trending topics', 'audience_size': '350M+ monthly active', 'ad_formats': 'Promoted Tweets, Trends, Accounts, Amplify', 'targeting': 'Keyword, interest, follower lookalikes, conversation targeting', 'min_budget': '$5/day', 'cost_benchmark_cpc': '$0.50-2.00', }, 'tiktok': { 'best_for': 'Gen Z, viral content, app installs, brand awareness', 'audience_size': '1B+ monthly active', 'ad_formats': 'In-Feed, Spark Ads, Brand Takeover, Hashtag Challenge', 'targeting': 'Demographic, interest, behavior, custom audiences', 'min_budget': '$10/day', 'cost_benchmark_cpc': '$1-2', }, } ``` ## Campaign Builder ```python from typing import Dict, List, Optional from datetime import datetime, timedelta class SocialAdCampaign: """Design and manage social media advertising campaigns.""" CAMPAIGN_OBJECTIVES = { 'awareness': ['brand_awareness', 'reach'], 'consideration': ['traffic', 'engagement', 'video_views', 'lead_generation'], 'conversion': ['conversions', 'catalog_sales', 'store_visits'], } def __init__(self, name: str, platform: str, objective: str, daily_budget: float, start_date: str, end_date: str = None): self.name = name self.platform = platform self.objective = objective self.daily_budget = daily_budget self.start = start_date self.end = end_date self.ad_sets = [] self.total_budget = self._calculate_total_budget() def add_ad_set(self, name: str, targeting: Dict, placements: List[str], bid_strategy: str = 'lowest_cost') -> 'SocialAdCampaign': """Add an ad set (targeting group) to the campaign.""" self.ad_sets.append({ 'name': name, 'targeting': targeting, 'placements': placements, 'bid_strategy': bid_strategy, 'budget_percentage': 100 // max(len(self.ad_sets) + 1, 1), 'status': 'draft', }) return self def _calculate_total_budget(self) -> float: if not self.end: return None start = datetime.fromisoformat(self.start) end = datetime.fromisoformat(self.end) days = (end - start).days return self.daily_budget * max(days, 1) def get_campaign_summary(self) -> str: summary = f"\nšŸ“¢ Campaign: {self.name}\n" summary += f"Platform: {self.platform} | Objective: {self.objective}\n" summary += f"Budget: ${self.daily_budget}/day" if self.total_budget: summary += f" (total: ${self.total_budget})" summary += f"\nDuration: {self.start} to {self.end or 'ongoing'}" summary += f"\nAd Sets: {len(self.ad_sets)}\n" for i, ads in enumerate(self.ad_sets, 1): summary += f"\n {i}. {ads['name']}" summary += f"\n Targeting: {ads['targeting']}" summary += f"\n Placements: {', '.join(ads['placements'][:3])}" return summary ``` ## Audience Builder ```python class AudienceBuilder: """Build and estimate social media ad audiences.""" @staticmethod def build_custom_audience(source: str, value: str, retention_days: int = 30) -> Dict: """Define a custom audience for retargeting.""" return { 'name': f'{value} ({retention_days}d)', 'source': source, # website, customer_list, app, engagement 'value': value, 'retention_days': retention_days, 'type': 'custom', } @staticmethod def build_lookalike(source_audience_id: str, lookalike_pct: float = 1.0) -> Dict: """Build a lookalike audience from a source. lookalike_pct: 1% (most similar) to 10% (broadest) """ return { 'name': f'Lookalike ({lookalike_pct}%)', 'source_audience': source_audience_id, 'lookalike_percentage': lookalike_pct, 'type': 'lookalike', } @staticmethod def estimate_reach(targeting: Dict, platform: str) -> Dict: """Estimate potential reach for targeting criteria.""" # Simplified estimation (platform APIs provide actual estimates) base_reach = { 'facebook_instagram': 1000000, 'linkedin': 500000, 'twitter_x': 300000, 'tiktok': 800000, } # Reduce reach based on targeting specificity factors = 1.0 if targeting.get('age_range'): factors *= 0.4 if targeting.get('interests'): factors *= 0.3 if targeting.get('job_titles'): factors *= 0.1 if targeting.get('custom_audiences'): factors *= 0.5 base = base_reach.get(platform, 500000) estimated_reach = int(base * factors) return { 'platform': platform, 'estimated_reach': estimated_reach, 'targeting_specificity': 'high' if factors < 0.3 else 'medium' if factors < 0.6 else 'broad', } ``` ## Performance Analysis ```python class SocialAdAnalyzer: """Analyze social media ad performance.""" METRICS = { 'ctr': 'Click-through rate (%)', 'cpc': 'Cost per click ($)', 'cpm': 'Cost per 1000 impressions ($)', 'cpa': 'Cost per acquisition ($)', 'roas': 'Return on ad spend ($)', 'frequency': 'Avg times person saw ad', 'reach': 'Unique people reached', 'impressions': 'Total times ad shown', 'engagement_rate': 'Engagements / impressions (%)', } @staticmethod def analyze(results: Dict) -> Dict: """Analyze campaign performance across metrics.""" analysis = {} spend = results.get('spend', 0) impressions = results.get('impressions', 0) clicks = results.get('clicks', 0) conversions = results.get('conversions', 0) revenue = results.get('revenue', 0) analysis['ctr'] = round(clicks / max(impressions, 1) * 100, 2) analysis['cpc'] = round(spend / max(clicks, 1), 2) analysis['cpm'] = round(spend / max(impressions, 1) * 1000, 2) analysis['cpa'] = round(spend / max(conversions, 1), 2) analysis['roas'] = round(revenue / max(spend, 1), 2) analysis['conversion_rate'] = round(conversions / max(clicks, 1) * 100, 2) return analysis @staticmethod def benchmark_check(platform: str, metrics: Dict) -> List[str]: """Compare metrics against platform benchmarks.""" benchmarks = { 'facebook_instagram': {'ctr': 0.90, 'cpc': 0.80, 'cpa': 18.00}, 'linkedin': {'ctr': 0.50, 'cpc': 6.00, 'cpa': 80.00}, 'twitter_x': {'ctr': 0.90, 'cpc': 1.50, 'cpa': 30.00}, 'tiktok': {'ctr': 1.50, 'cpc': 1.00, 'cpa': 25.00}, } bm = benchmarks.get(platform, benchmarks['facebook_instagram']) alerts = [] for metric, benchmark_value in bm.items(): if metric in metrics: value = metrics[metric] if metric in ('cpc', 'cpa') and value > benchmark_value * 1.5: alerts.append(f"āš ļø {metric.upper()} ${value} is 50%+ above benchmark ${benchmark_value}") elif metric in ('ctr',) and value < benchmark_value * 0.5: alerts.append(f"āš ļø {metric.upper()} {value}% is 50%+ below benchmark {benchmark_value}%") if not alerts: alerts.append("āœ… All metrics within healthy range") return alerts ``` ## Common Pitfalls 1. **Wrong objective** ā€” using "brand awareness" when you want conversions; match objective to funnel stage 2. **Audience overlap** ā€” running multiple ad sets with overlapping audiences causes auction competition 3. **Creative fatigue** ā€” same ad seen 5+ times drops CTR dramatically; refresh creative every 1-2 weeks 4. **Ignoring placement** ā€” automatic placements can waste budget on low-performing spots; review placement reports 5. **Mobile-unfriendly creative** ā€” most social traffic is mobile; design for small screens first 6. **No pixel/events** ā€” can't optimize without conversion tracking; install platform pixel ## Verification Checklist - [ ] Campaign objective matches funnel stage - [ ] Audience targeting defined (demographics + interests + behaviors) - [ ] Custom audiences created (website visitors, customer list) - [ ] Lookalike audiences built from top segments - [ ] Ad creative designed for mobile-first - [ ] Tracking pixel/events installed and verified - [ ] Budget and schedule configured - [ ] Performance benchmarks identified per platform - [ ] Testing plan (creative, audience, placement variants) ## See Also - social-media-content-planning ā€” organic social strategy - ppc-advertising-management ā€” cross-platform PPC management - digital-marketing-strategy ā€” role of paid social in strategy - conversion-rate-optimization ā€” optimizing ad landing pages