CSV Data Analysis for Marketing: Campaign Data & Reporting (2025 Guide)
Jan 19, 2025•
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Marketing professionals rely heavily on CSV data exports from various platforms to analyze campaign performance, track ROI, and make data-driven decisions. Whether you're analyzing Google Ads data, Facebook campaign metrics, or website analytics, understanding how to effectively process and analyze CSV data is crucial for marketing success.
This comprehensive guide covers everything you need to know about CSV data analysis for marketing, including platform-specific data formats, analysis techniques, reporting strategies, and automation workflows. Whether you're a marketing analyst, campaign manager, or data-driven marketer, this guide will help you extract maximum value from your marketing data.
Why CSV Data Analysis Matters in Marketing
Common Marketing Data Sources
Advertising Platforms:
- Google Ads (Search, Display, Shopping, Video)
- Facebook Ads Manager
- LinkedIn Campaign Manager
- Twitter Ads
- TikTok for Business
- Microsoft Advertising
Analytics Platforms:
- Google Analytics 4
- Adobe Analytics
- Mixpanel
- Amplitude
- Hotjar
Email Marketing:
- Mailchimp
- Constant Contact
- SendGrid
- Campaign Monitor
- HubSpot
Social Media:
- Facebook Insights
- Instagram Insights
- Twitter Analytics
- LinkedIn Analytics
- YouTube Analytics
Key Marketing Metrics
Performance Metrics:
- Click-through rate (CTR)
- Conversion rate
- Cost per acquisition (CPA)
- Return on ad spend (ROAS)
- Return on investment (ROI)
Engagement Metrics:
- Impressions
- Clicks
- Likes, shares, comments
- Email open rates
- Website session duration
Financial Metrics:
- Ad spend
- Revenue
- Profit margins
- Customer lifetime value (CLV)
- Cost per lead (CPL)
Google Ads CSV Analysis
Google Ads Data Export Format
Search Campaigns Export:
Campaign,Ad group,Keyword,Match type,Impressions,Clicks,Cost,Conversions,Conversion value,CTR,Avg. CPC,Cost / conv.,Conv. rate
"Brand Campaign","Brand Keywords","company name","Exact",1000,50,25.00,5,500.00,5.00%,0.50,5.00,10.00%
"Product Campaign","Product Keywords","buy product","Broad",5000,100,150.00,8,800.00,2.00%,1.50,18.75,8.00%
Display Campaigns Export:
Campaign,Ad group,Ad,Impressions,Clicks,Cost,Conversions,Conversion value,CTR,Avg. CPC,Cost / conv.,Conv. rate
"Display Campaign","Audience 1","Banner Ad 1",10000,200,80.00,12,1200.00,2.00%,0.40,6.67,6.00%
Shopping Campaigns Export:
Campaign,Ad group,Product,Impressions,Clicks,Cost,Conversions,Conversion value,CTR,Avg. CPC,Cost / conv.,Conv. rate
"Shopping Campaign","All Products","Product A",2000,80,40.00,4,400.00,4.00%,0.50,10.00,5.00%
Google Ads Data Analysis
Campaign Performance Analysis:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def analyze_google_ads_performance(csv_file):
"""Analyze Google Ads campaign performance"""
df = pd.read_csv(csv_file)
# Convert cost and conversion value to numeric
df['Cost'] = pd.to_numeric(df['Cost'].str.replace('$', '').str.replace(',', ''), errors='coerce')
df['Conversion value'] = pd.to_numeric(df['Conversion value'].str.replace('$', '').str.replace(',', ''), errors='coerce')
# Calculate additional metrics
df['ROAS'] = df['Conversion value'] / df['Cost']
df['CPA'] = df['Cost'] / df['Conversions']
df['Profit'] = df['Conversion value'] - df['Cost']
# Campaign performance summary
campaign_summary = df.groupby('Campaign').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Cost': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum',
'CTR': 'mean',
'Avg. CPC': 'mean',
'ROAS': 'mean',
'CPA': 'mean'
}).round(2)
return campaign_summary
# Usage
performance_data = analyze_google_ads_performance('google_ads_data.csv')
print(performance_data)
Keyword Performance Analysis:
def analyze_keyword_performance(df):
"""Analyze keyword-level performance"""
# Filter for search campaigns only
search_df = df[df['Campaign'].str.contains('Search', na=False)]
# Calculate keyword metrics
keyword_analysis = search_df.groupby('Keyword').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Cost': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
# Calculate derived metrics
keyword_analysis['CTR'] = (keyword_analysis['Clicks'] / keyword_analysis['Impressions'] * 100).round(2)
keyword_analysis['Avg. CPC'] = (keyword_analysis['Cost'] / keyword_analysis['Clicks']).round(2)
keyword_analysis['Conv. Rate'] = (keyword_analysis['Conversions'] / keyword_analysis['Clicks'] * 100).round(2)
keyword_analysis['ROAS'] = (keyword_analysis['Conversion value'] / keyword_analysis['Cost']).round(2)
keyword_analysis['CPA'] = (keyword_analysis['Cost'] / keyword_analysis['Conversions']).round(2)
# Sort by performance
keyword_analysis = keyword_analysis.sort_values('ROAS', ascending=False)
return keyword_analysis
# Usage
keyword_data = analyze_keyword_performance(df)
print(keyword_data.head(10))
Time Series Analysis:
def analyze_campaign_trends(df):
"""Analyze campaign performance over time"""
# Convert date column to datetime
df['Date'] = pd.to_datetime(df['Date'])
# Daily performance trends
daily_trends = df.groupby('Date').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Cost': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
# Calculate daily metrics
daily_trends['CTR'] = (daily_trends['Clicks'] / daily_trends['Impressions'] * 100).round(2)
daily_trends['ROAS'] = (daily_trends['Conversion value'] / daily_trends['Cost']).round(2)
daily_trends['CPA'] = (daily_trends['Cost'] / daily_trends['Conversions']).round(2)
return daily_trends
# Usage
trends_data = analyze_campaign_trends(df)
print(trends_data)
Facebook Ads CSV Analysis
Facebook Ads Data Export Format
Campaign Performance Export:
Campaign name,Ad set name,Ad name,Impressions,Reach,Clicks,Spend,Conversions,Conversion value,CTR,CPC,CPM,CPP,Cost per conversion
"Brand Campaign","Audience 1","Ad 1",5000,4500,100,50.00,5,250.00,2.00%,0.50,10.00,10.00,10.00
"Product Campaign","Audience 2","Ad 2",8000,7000,150,75.00,8,400.00,1.88%,0.50,9.38,9.38,9.38
Demographics Export:
Campaign name,Age,Gender,Impressions,Clicks,Spend,Conversions,Conversion value
"Brand Campaign","25-34","Male",2000,40,20.00,2,100.00
"Brand Campaign","25-34","Female",1500,30,15.00,3,150.00
"Brand Campaign","35-44","Male",1000,20,10.00,1,50.00
Facebook Ads Data Analysis
Campaign Performance Analysis:
def analyze_facebook_ads_performance(csv_file):
"""Analyze Facebook Ads campaign performance"""
df = pd.read_csv(csv_file)
# Convert spend and conversion value to numeric
df['Spend'] = pd.to_numeric(df['Spend'].str.replace('$', '').str.replace(',', ''), errors='coerce')
df['Conversion value'] = pd.to_numeric(df['Conversion value'].str.replace('$', '').str.replace(',', ''), errors='coerce')
# Calculate additional metrics
df['ROAS'] = df['Conversion value'] / df['Spend']
df['CPA'] = df['Spend'] / df['Conversions']
df['Profit'] = df['Conversion value'] - df['Spend']
# Campaign performance summary
campaign_summary = df.groupby('Campaign name').agg({
'Impressions': 'sum',
'Reach': 'sum',
'Clicks': 'sum',
'Spend': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum',
'CTR': 'mean',
'CPC': 'mean',
'CPM': 'mean',
'ROAS': 'mean',
'CPA': 'mean'
}).round(2)
return campaign_summary
# Usage
fb_performance = analyze_facebook_ads_performance('facebook_ads_data.csv')
print(fb_performance)
Demographics Analysis:
def analyze_demographics_performance(df):
"""Analyze performance by demographics"""
# Convert spend and conversion value to numeric
df['Spend'] = pd.to_numeric(df['Spend'].str.replace('$', '').str.replace(',', ''), errors='coerce')
df['Conversion value'] = pd.to_numeric(df['Conversion value'].str.replace('$', '').str.replace(',', ''), errors='coerce')
# Demographics performance
demo_analysis = df.groupby(['Age', 'Gender']).agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Spend': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
# Calculate derived metrics
demo_analysis['CTR'] = (demo_analysis['Clicks'] / demo_analysis['Impressions'] * 100).round(2)
demo_analysis['CPC'] = (demo_analysis['Spend'] / demo_analysis['Clicks']).round(2)
demo_analysis['Conv. Rate'] = (demo_analysis['Conversions'] / demo_analysis['Clicks'] * 100).round(2)
demo_analysis['ROAS'] = (demo_analysis['Conversion value'] / demo_analysis['Spend']).round(2)
demo_analysis['CPA'] = (demo_analysis['Spend'] / demo_analysis['Conversions']).round(2)
return demo_analysis
# Usage
demo_data = analyze_demographics_performance(df)
print(demo_data)
Google Analytics 4 CSV Analysis
GA4 Data Export Format
Traffic Sources Export:
Date,Source,Medium,Campaign,Sessions,Users,New users,Bounce rate,Pages per session,Avg. session duration,Goal completions,Goal value
"2025-01-01","google","cpc","brand campaign",100,80,20,45.00%,2.5,120,5,250.00
"2025-01-01","facebook","social","product campaign",150,120,30,35.00%,3.2,180,8,400.00
Page Performance Export:
Date,Page,Pageviews,Unique pageviews,Avg. time on page,Bounce rate,Exit rate,Goal completions,Goal value
"2025-01-01","/home",500,400,120,40.00%,25.00%,10,500.00
"2025-01-01","/products",300,250,180,30.00%,20.00%,15,750.00
GA4 Data Analysis
Traffic Source Analysis:
def analyze_traffic_sources(df):
"""Analyze traffic source performance"""
# Convert goal value to numeric
df['Goal value'] = pd.to_numeric(df['Goal value'].str.replace('$', '').str.replace(',', ''), errors='coerce')
# Traffic source performance
source_analysis = df.groupby(['Source', 'Medium']).agg({
'Sessions': 'sum',
'Users': 'sum',
'New users': 'sum',
'Goal completions': 'sum',
'Goal value': 'sum'
}).round(2)
# Calculate derived metrics
source_analysis['New user rate'] = (source_analysis['New users'] / source_analysis['Users'] * 100).round(2)
source_analysis['Conversion rate'] = (source_analysis['Goal completions'] / source_analysis['Sessions'] * 100).round(2)
source_analysis['Avg. goal value'] = (source_analysis['Goal value'] / source_analysis['Goal completions']).round(2)
return source_analysis
# Usage
traffic_data = analyze_traffic_sources(df)
print(traffic_data)
Page Performance Analysis:
def analyze_page_performance(df):
"""Analyze page performance"""
# Convert goal value to numeric
df['Goal value'] = pd.to_numeric(df['Goal value'].str.replace('$', '').str.replace(',', ''), errors='coerce')
# Page performance
page_analysis = df.groupby('Page').agg({
'Pageviews': 'sum',
'Unique pageviews': 'sum',
'Avg. time on page': 'mean',
'Bounce rate': 'mean',
'Exit rate': 'mean',
'Goal completions': 'sum',
'Goal value': 'sum'
}).round(2)
# Calculate derived metrics
page_analysis['Pages per session'] = (page_analysis['Pageviews'] / page_analysis['Unique pageviews']).round(2)
page_analysis['Conversion rate'] = (page_analysis['Goal completions'] / page_analysis['Pageviews'] * 100).round(2)
page_analysis['Avg. goal value'] = (page_analysis['Goal value'] / page_analysis['Goal completions']).round(2)
return page_analysis
# Usage
page_data = analyze_page_performance(df)
print(page_data)
Email Marketing CSV Analysis
Email Marketing Data Export Format
Campaign Performance Export:
Campaign name,Send date,Recipients,Delivered,Opens,Unique opens,Opens rate,Unique opens rate,Clicks,Unique clicks,Clicks rate,Unique clicks rate,Unsubscribes,Unsubscribe rate,Bounces,Bounce rate
"Newsletter Jan 2025","2025-01-15",1000,995,200,180,20.10%,18.09%,50,45,5.03%,4.52%,5,0.50%,5,0.50%
"Product Launch","2025-01-20",500,495,150,130,30.30%,26.26%,75,65,15.15%,13.13%,2,0.40%,3,0.61%
Subscriber Data Export:
Email,First name,Last name,Signup date,Last activity,Status,Campaigns sent,Total opens,Total clicks,Last open date,Last click date
"john@example.com","John","Doe","2025-01-01","2025-01-20","Active",5,10,3,"2025-01-20","2025-01-18"
"jane@example.com","Jane","Smith","2025-01-05","2025-01-19","Active",4,8,2,"2025-01-19","2025-01-17"
Email Marketing Data Analysis
Campaign Performance Analysis:
def analyze_email_campaigns(df):
"""Analyze email campaign performance"""
# Convert rates to numeric
df['Opens rate'] = pd.to_numeric(df['Opens rate'].str.replace('%', ''), errors='coerce')
df['Clicks rate'] = pd.to_numeric(df['Clicks rate'].str.replace('%', ''), errors='coerce')
df['Unsubscribe rate'] = pd.to_numeric(df['Unsubscribe rate'].str.replace('%', ''), errors='coerce')
df['Bounce rate'] = pd.to_numeric(df['Bounce rate'].str.replace('%', ''), errors='coerce')
# Campaign performance summary
campaign_summary = df.groupby('Campaign name').agg({
'Recipients': 'sum',
'Delivered': 'sum',
'Opens': 'sum',
'Unique opens': 'sum',
'Clicks': 'sum',
'Unique clicks': 'sum',
'Unsubscribes': 'sum',
'Bounces': 'sum',
'Opens rate': 'mean',
'Clicks rate': 'mean',
'Unsubscribe rate': 'mean',
'Bounce rate': 'mean'
}).round(2)
return campaign_summary
# Usage
email_performance = analyze_email_campaigns(df)
print(email_performance)
Subscriber Engagement Analysis:
def analyze_subscriber_engagement(df):
"""Analyze subscriber engagement patterns"""
# Convert dates to datetime
df['Signup date'] = pd.to_datetime(df['Signup date'])
df['Last activity'] = pd.to_datetime(df['Last activity'])
df['Last open date'] = pd.to_datetime(df['Last open date'])
df['Last click date'] = pd.to_datetime(df['Last click date'])
# Calculate engagement metrics
df['Days since signup'] = (df['Last activity'] - df['Signup date']).dt.days
df['Open rate'] = (df['Total opens'] / df['Campaigns sent'] * 100).round(2)
df['Click rate'] = (df['Total clicks'] / df['Campaigns sent'] * 100).round(2)
# Segment subscribers by engagement
df['Engagement level'] = pd.cut(df['Open rate'],
bins=[0, 25, 50, 75, 100],
labels=['Low', 'Medium', 'High', 'Very High'])
# Engagement summary
engagement_summary = df.groupby('Engagement level').agg({
'Email': 'count',
'Open rate': 'mean',
'Click rate': 'mean',
'Days since signup': 'mean'
}).round(2)
return engagement_summary
# Usage
engagement_data = analyze_subscriber_engagement(df)
print(engagement_data)
Advanced Marketing Analytics
Cross-Platform Performance Analysis
Unified Marketing Dashboard:
def create_marketing_dashboard(google_ads_df, facebook_ads_df, ga4_df, email_df):
"""Create unified marketing performance dashboard"""
# Google Ads summary
google_summary = google_ads_df.groupby('Campaign').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Cost': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
# Facebook Ads summary
facebook_summary = facebook_ads_df.groupby('Campaign name').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Spend': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
# GA4 summary
ga4_summary = ga4_df.groupby('Campaign').agg({
'Sessions': 'sum',
'Users': 'sum',
'Goal completions': 'sum',
'Goal value': 'sum'
}).round(2)
# Email summary
email_summary = email_df.groupby('Campaign name').agg({
'Recipients': 'sum',
'Opens': 'sum',
'Clicks': 'sum'
}).round(2)
# Combine all data
dashboard_data = {
'Google Ads': google_summary,
'Facebook Ads': facebook_summary,
'Google Analytics': ga4_summary,
'Email Marketing': email_summary
}
return dashboard_data
# Usage
dashboard = create_marketing_dashboard(google_ads_df, facebook_ads_df, ga4_df, email_df)
ROI and ROAS Analysis
Campaign ROI Analysis:
def calculate_campaign_roi(df, platform):
"""Calculate ROI for campaigns"""
if platform == 'google_ads':
cost_col = 'Cost'
value_col = 'Conversion value'
elif platform == 'facebook_ads':
cost_col = 'Spend'
value_col = 'Conversion value'
elif platform == 'ga4':
cost_col = 'Cost' # Assuming cost data is available
value_col = 'Goal value'
# Calculate ROI
df['ROI'] = ((df[value_col] - df[cost_col]) / df[cost_col] * 100).round(2)
df['ROAS'] = (df[value_col] / df[cost_col]).round(2)
df['Profit'] = (df[value_col] - df[cost_col]).round(2)
# Campaign ROI summary
roi_summary = df.groupby('Campaign').agg({
cost_col: 'sum',
value_col: 'sum',
'ROI': 'mean',
'ROAS': 'mean',
'Profit': 'sum'
}).round(2)
return roi_summary
# Usage
google_roi = calculate_campaign_roi(google_ads_df, 'google_ads')
facebook_roi = calculate_campaign_roi(facebook_ads_df, 'facebook_ads')
Attribution Analysis
Multi-Touch Attribution:
def analyze_attribution_paths(df):
"""Analyze customer attribution paths"""
# Group by customer journey
attribution_data = df.groupby(['Customer ID', 'Touchpoint']).agg({
'Date': 'first',
'Channel': 'first',
'Campaign': 'first',
'Cost': 'sum',
'Value': 'sum'
}).reset_index()
# Calculate attribution weights
attribution_data['First Touch'] = attribution_data.groupby('Customer ID')['Date'].transform('min') == attribution_data['Date']
attribution_data['Last Touch'] = attribution_data.groupby('Customer ID')['Date'].transform('max') == attribution_data['Date']
# Attribution summary
attribution_summary = attribution_data.groupby('Channel').agg({
'First Touch': 'sum',
'Last Touch': 'sum',
'Cost': 'sum',
'Value': 'sum'
}).round(2)
return attribution_summary
# Usage
attribution_data = analyze_attribution_paths(df)
Marketing Reporting Automation
Automated Report Generation
Weekly Performance Report:
def generate_weekly_report(google_ads_df, facebook_ads_df, ga4_df, email_df):
"""Generate weekly marketing performance report"""
# Calculate weekly metrics
google_weekly = google_ads_df.groupby('Campaign').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Cost': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
facebook_weekly = facebook_ads_df.groupby('Campaign name').agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Spend': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
ga4_weekly = ga4_df.groupby('Campaign').agg({
'Sessions': 'sum',
'Users': 'sum',
'Goal completions': 'sum',
'Goal value': 'sum'
}).round(2)
email_weekly = email_df.groupby('Campaign name').agg({
'Recipients': 'sum',
'Opens': 'sum',
'Clicks': 'sum'
}).round(2)
# Create summary report
report = {
'Google Ads': google_weekly,
'Facebook Ads': facebook_weekly,
'Google Analytics': ga4_weekly,
'Email Marketing': email_weekly
}
return report
# Usage
weekly_report = generate_weekly_report(google_ads_df, facebook_ads_df, ga4_df, email_df)
Monthly Trend Analysis:
def analyze_monthly_trends(df, platform):
"""Analyze monthly performance trends"""
# Convert date to datetime
df['Date'] = pd.to_datetime(df['Date'])
# Group by month
monthly_data = df.groupby(df['Date'].dt.to_period('M')).agg({
'Impressions': 'sum',
'Clicks': 'sum',
'Cost': 'sum',
'Conversions': 'sum',
'Conversion value': 'sum'
}).round(2)
# Calculate month-over-month growth
monthly_data['MoM Growth'] = monthly_data['Conversions'].pct_change() * 100
monthly_data['MoM Cost Growth'] = monthly_data['Cost'].pct_change() * 100
monthly_data['MoM Value Growth'] = monthly_data['Conversion value'].pct_change() * 100
return monthly_data
# Usage
google_trends = analyze_monthly_trends(google_ads_df, 'google_ads')
facebook_trends = analyze_monthly_trends(facebook_ads_df, 'facebook_ads')
Data Visualization
Campaign Performance Charts:
import matplotlib.pyplot as plt
import seaborn as sns
def create_campaign_charts(df, platform):
"""Create visualization charts for campaign performance"""
# Set up the plotting style
plt.style.use('seaborn-v0_8')
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
# Chart 1: Campaign Performance by ROAS
if platform == 'google_ads':
cost_col = 'Cost'
value_col = 'Conversion value'
elif platform == 'facebook_ads':
cost_col = 'Spend'
value_col = 'Conversion value'
df['ROAS'] = df[value_col] / df[cost_col]
campaign_roas = df.groupby('Campaign')['ROAS'].mean().sort_values(ascending=False)
axes[0, 0].bar(range(len(campaign_roas)), campaign_roas.values)
axes[0, 0].set_title('Campaign ROAS')
axes[0, 0].set_xticks(range(len(campaign_roas)))
axes[0, 0].set_xticklabels(campaign_roas.index, rotation=45)
# Chart 2: Cost vs Conversions
axes[0, 1].scatter(df[cost_col], df['Conversions'])
axes[0, 1].set_xlabel('Cost')
axes[0, 1].set_ylabel('Conversions')
axes[0, 1].set_title('Cost vs Conversions')
# Chart 3: CTR by Campaign
campaign_ctr = df.groupby('Campaign')['CTR'].mean().sort_values(ascending=False)
axes[1, 0].bar(range(len(campaign_ctr)), campaign_ctr.values)
axes[1, 0].set_title('Campaign CTR')
axes[1, 0].set_xticks(range(len(campaign_ctr)))
axes[1, 0].set_xticklabels(campaign_ctr.index, rotation=45)
# Chart 4: Daily Performance Trend
daily_trends = df.groupby('Date')[['Impressions', 'Clicks', 'Conversions']].sum()
axes[1, 1].plot(daily_trends.index, daily_trends['Impressions'], label='Impressions')
axes[1, 1].plot(daily_trends.index, daily_trends['Clicks'], label='Clicks')
axes[1, 1].plot(daily_trends.index, daily_trends['Conversions'], label='Conversions')
axes[1, 1].set_title('Daily Performance Trend')
axes[1, 1].legend()
plt.tight_layout()
plt.show()
# Usage
create_campaign_charts(google_ads_df, 'google_ads')
create_campaign_charts(facebook_ads_df, 'facebook_ads')
Best Practices for Marketing CSV Analysis
Data Quality Management
Data Validation:
def validate_marketing_data(df, platform):
"""Validate marketing data quality"""
errors = []
# Check for missing values in critical columns
critical_columns = ['Campaign', 'Date', 'Cost', 'Conversions']
for col in critical_columns:
if col in df.columns and df[col].isnull().any():
errors.append(f"Missing values in {col}")
# Check for negative values in cost columns
cost_columns = ['Cost', 'Spend']
for col in cost_columns:
if col in df.columns and (df[col] < 0).any():
errors.append(f"Negative values in {col}")
# Check for unrealistic values
if 'CTR' in df.columns and (df['CTR'] > 100).any():
errors.append("CTR values exceed 100%")
return errors
# Usage
errors = validate_marketing_data(google_ads_df, 'google_ads')
if errors:
print("Data quality issues found:", errors)
Data Cleaning:
def clean_marketing_data(df):
"""Clean marketing data"""
# Remove rows with missing critical data
df = df.dropna(subset=['Campaign', 'Date'])
# Convert cost columns to numeric
cost_columns = ['Cost', 'Spend', 'Conversion value', 'Goal value']
for col in cost_columns:
if col in df.columns:
df[col] = pd.to_numeric(df[col].str.replace('$', '').str.replace(',', ''), errors='coerce')
# Remove outliers (values beyond 3 standard deviations)
numeric_columns = df.select_dtypes(include=[np.number]).columns
for col in numeric_columns:
if col in df.columns:
mean = df[col].mean()
std = df[col].std()
df = df[abs(df[col] - mean) <= 3 * std]
return df
# Usage
cleaned_data = clean_marketing_data(google_ads_df)
Performance Optimization
Efficient Data Processing:
def optimize_data_processing(df):
"""Optimize data processing performance"""
# Convert object columns to category if appropriate
for col in df.select_dtypes(include=['object']).columns:
if df[col].nunique() / len(df) < 0.5:
df[col] = df[col].astype('category')
# Convert numeric columns to appropriate types
for col in df.select_dtypes(include=['int64']).columns:
if df[col].min() >= 0 and df[col].max() < 255:
df[col] = df[col].astype('uint8')
elif df[col].min() >= -128 and df[col].max() < 127:
df[col] = df[col].astype('int8')
return df
# Usage
optimized_data = optimize_data_processing(google_ads_df)
Conclusion
CSV data analysis is essential for marketing success, enabling data-driven decisions and campaign optimization. By understanding platform-specific formats, implementing proper analysis techniques, and following best practices, you can extract maximum value from your marketing data.
Key Takeaways:
- Platform-Specific Formats: Each marketing platform has unique CSV export formats
- Data Validation: Always validate data quality before analysis
- Automation: Implement automated reporting workflows for efficiency
- Visualization: Use charts and graphs to communicate insights effectively
- Performance: Optimize data processing for large datasets
Next Steps:
- Choose Your Platforms: Select the marketing platforms you want to analyze
- Implement Validation: Set up data quality checks
- Create Workflows: Develop automated analysis and reporting processes
- Monitor Performance: Track key metrics and trends over time
- Optimize Campaigns: Use insights to improve campaign performance
For more CSV data processing tools and guides, explore our CSV Tools Hub or try our CSV Validator for instant data validation.