Performance Marketing Optimization Strategies

audience_performance.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Set random seed for reproducibility
np.random.seed(42)

# Platforms and other variables
platforms = ['Google', 'Meta', 'Bing', 'LinkedIn']
campaigns = ['Campaign A', 'Campaign B', 'Campaign C']
audiences = ['Audience 1', 'Audience 2', 'Audience 3']

# Generate synthetic data
data_list = []

for platform in platforms:
    for _ in range(100):
        entry = {
            'Platform': platform,
            'Campaign': np.random.choice(campaigns),
            'Audience': np.random.choice(audiences),
            'Impressions': np.random.randint(1000, 10000),
            'Clicks': np.random.randint(100, 1000),
            'Conversions': np.random.randint(10, 100),
            'Cost': np.random.uniform(500, 5000),
            'Revenue': np.random.uniform(1000, 10000)
        }
        data_list.append(entry)

# Create DataFrame
data = pd.DataFrame(data_list)

# Calculate performance metrics
data['CTR'] = data['Clicks'] / data['Impressions']
data['CPC'] = data['Cost'] / data['Clicks']
data['CPA'] = data['Cost'] / data['Conversions']
data['ROAS'] = data['Revenue'] / data['Cost']

# Cross-platform performance comparison
platform_performance = data.groupby('Platform').agg({
    'Impressions': 'sum',
    'Clicks': 'sum',
    'Conversions': 'sum',
    'Cost': 'sum',
    'Revenue': 'sum'
}).reset_index()

platform_performance['CTR'] = platform_performance['Clicks'] / platform_performance['Impressions']
platform_performance['CPC'] = platform_performance['Cost'] / platform_performance['Clicks']
platform_performance['CPA'] = platform_performance['Cost'] / platform_performance['Conversions']
platform_performance['ROAS'] = platform_performance['Revenue'] / platform_performance['Cost']

print(platform_performance)

# Analyze audience performance across platforms
audience_performance = data.groupby(['Platform', 'Audience']).agg({
    'Impressions': 'sum',
    'Clicks': 'sum',
    'Conversions': 'sum',
    'Cost': 'sum',
    'Revenue': 'sum'
}).reset_index()

audience_performance['CTR'] = audience_performance['Clicks'] / audience_performance['Impressions']
audience_performance['CPC'] = audience_performance['Cost'] / audience_performance['Clicks']
audience_performance['CPA'] = audience_performance['Cost'] / audience_performance['Conversions']
audience_performance['ROAS'] = audience_performance['Revenue'] / audience_performance['Cost']

print(audience_performance)

# Visualize ROAS by Platform
plt.bar(platform_performance['Platform'], platform_performance['ROAS'])
plt.title('ROAS by Platform')
plt.xlabel('Platform')
plt.ylabel('ROAS')
plt.show()

More Questions:

1. Platform Comparison: Assess which platform has higher CTR and ROAS.
2. Campaign Performance: Identify top-performing campaigns in terms of conversions and revenue.
3. Cost Efficiency: Compare CPC and CPA to determine cost efficiency across platforms and campaigns.
4. Audience Performance: Identify which audiences have the highest ROAS to focus ad spend on the most profitable segments.
5. Day of Week Trends: Determine which days yield better performance, possibly reallocating budget to high-performing days.
6. Device Analysis: Assess which devices lead to higher conversions or ROAS to optimize for those platforms.
7. Ad Type Effectiveness: Evaluate which ad formats (image, Video, Carousel) generate better engagement and conversions.
8. Correlation Findings: Understand relationships between metrics (e.g., a strong correlation between CTR and ROAS) to inform strategy.

1. PLATFORM COMPARISON

CTR Analysis:

• LinkedIn: Highest CTR at 5.42%
• Google: Second highest at 5.12%
• Bing: Third at 5.03%
• Meta: Lowest at 4.87%

ROAS Ranking:

1. Meta: 1.99 (Best performer)
2. Google: 1.93
3. Bing: 1.84
4. LinkedIn: 1.72

Key Insight: While LinkedIn leads in CTR, it has the lowest ROAS, suggesting that higher click-through rates don't necessarily translate to better return on investment.

2. CAMPAIGN PERFORMANCE

Top Performers by Conversions:

1. Meta: 5,746 conversions
2. Bing: 5,426 conversions
3. Google: 5,264 conversions
4. LinkedIn: 5,041 conversions

Revenue Generation:

1. Meta: $556,089.95

2. Google: $546,251.83

3. Bing: $519,701.40

4. LinkedIn: $501,932.79

5. COST EFFICIENCY ANALYSIS

CPC Comparison:

• Meta: $48.53 (Most efficient)
• Bing: $52.14
• Google: $53.63
• LinkedIn: $57.79 (Least efficient)

CPA Analysis:
Meta Audiences:

• Audience 2: $45.20 (Most efficient)
• Audience 3: $45.04
• Audience 1: $59.48

Google Audiences:

• Audience 2: $51.79
• Audience 3: $51.15
• Audience 1: $58.91

LinkedIn Audiences:

• Audience 2: $53.83
• Audience 1: $54.15
• Audience 3: $66.49 (Least efficient)

4. AUDIENCE PERFORMANCE

Top ROAS by Audience:

1. Meta Audience 3: 2.25
2. Google Audience 2: 2.03
3. Bing Audience 3: 1.96
4. Bing Audience 2: 1.94

Lowest Performing Audiences:

1. LinkedIn Audience 3: 1.65

2. Bing Audience 1: 1.66

3. LinkedIn Audience 1: 1.67

4. CORRELATION FINDINGS

Key Correlations:

CTR and ROAS:

• Weak negative correlation observed
• LinkedIn: Highest CTR (5.42%) but lowest ROAS (1.72)
• Meta: Lowest CTR (4.87%) but highest ROAS (1.99)
• Suggests CTR isn't a reliable predictor of ROAS

CPC and ROAS:

• Moderate negative correlation
• Platforms with lower CPC tend to have higher ROAS
• Meta exemplifies this with lowest CPC ($48.53) and highest ROAS (1.99)

Conversion Volume and Revenue:

• Strong positive correlation
• Meta leads in both conversions (5,746) and revenue ($556,089.95)
• Consistent pattern across platforms

STRATEGIC RECOMMENDATIONS:

1. Budget Allocation:

• Increase investment in Meta, particularly Audience 3
• Reduce spend on LinkedIn Audience 3
• Maintain or slightly increase Google Audience 2 investment

2. Cost Optimization:

• Implement Meta's targeting strategies across platforms
• Review LinkedIn's bidding strategy to reduce CPC
• Focus on audiences with CPA under $50

3. Performance Enhancement:
   A. Short-term actions:

• Scale Meta Audience 3 campaigns
• Optimize LinkedIn cost efficiency
• Replicate successful audience targeting from Meta across platforms

B. Mid-term strategy:

• Develop cross-platform audience testing
• Implement automated bidding rules
• Create platform-specific optimization frameworks

4. Testing Framework:

Phase 1: Audience Optimization

• Test lookalike audiences based on top performers
• Experiment with interest-based targeting
• Develop custom audience segments

Phase 2: Bid Strategy Testing

• Test automated vs. manual bidding
• Implement platform-specific bid adjustments
• Develop ROAS-based bid strategies

Phase 3: Creative Optimization

• Test ad format variations
• Implement message testing
• Optimize calls-to-action

MEASUREMENT FRAMEWORK:

Primary Metrics:

1. ROAS (Target: >1.9)
2. CPA (Target: <$50)
3. Conversion Rate (Benchmark against Meta's 10.04%)
4. Revenue per Conversion (Target: >$100)

Secondary Metrics:

1. CTR (Platform-specific benchmarks)
2. CPC (Target: <$50)
3. Audience Reach
4. Frequency