LoopyLuci Social Media Advertising
Creating, managing, and optimizing paid social media campaigns across platforms — 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
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
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"\n📢 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
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
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"⚠️ {metric.upper()} ${value} is 50%+ above benchmark ${benchmark_value}")
elif metric in ('ctr',) and value < benchmark_value * 0.5:
alerts.append(f"⚠️ {metric.upper()} {value}% is 50%+ below benchmark {benchmark_value}%")
if not alerts:
alerts.append("✅ All metrics within healthy range")
return alerts
Common Pitfalls
- Wrong objective — using "brand awareness" when you want conversions; match objective to funnel stage
- Audience overlap — running multiple ad sets with overlapping audiences causes auction competition
- Creative fatigue — same ad seen 5+ times drops CTR dramatically; refresh creative every 1-2 weeks
- Ignoring placement — automatic placements can waste budget on low-performing spots; review placement reports
- Mobile-unfriendly creative — most social traffic is mobile; design for small screens first
- No pixel/events — 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 — organic social strategy
- ppc-advertising-management — cross-platform PPC management
- digital-marketing-strategy — role of paid social in strategy
- conversion-rate-optimization — optimizing ad landing pages