Learn Analyzing Marketing Campaigns | Growth, Marketing, and Customer Insights

Marketing campaigns are essential tools for driving business growth, attracting new customers, and boosting engagement. As a startup founder, understanding how to evaluate the effectiveness of these campaigns helps you make smarter decisions about where to invest your resources. Key metrics to track include impressions (how many people saw your ad), clicks (how many people interacted), conversions (how many took a desired action, such as signing up or purchasing), conversion rate (the percentage of impressions or clicks that led to conversions), and ROI (return on investment). By analyzing these numbers, you can identify which campaigns perform best and optimize your marketing strategy.


              1234567891011
            
# Hardcoded campaign data
campaign_impressions = 5000
campaign_clicks = 400
campaign_conversions = 50

# Calculate conversion rates
click_through_rate = campaign_clicks / campaign_impressions
conversion_rate = campaign_conversions / campaign_clicks

print("Click-through rate:", round(click_through_rate * 100, 2), "%")
print("Conversion rate:", round(conversion_rate * 100, 2), "%")

The conversion rate is calculated by dividing the number of conversions by the number of clicks (or impressions, depending on your focus). This metric shows how effective your campaign is at turning interested users into customers or leads. A higher conversion rate means your campaign messaging, targeting, or offer is resonating well with your audience, while a lower rate may signal the need for adjustments.


              12345678910111213141516
            
import pandas as pd

# Data for multiple campaigns
data = {
    "Campaign": ["Spring Sale", "Summer Launch", "Holiday Promo"],
    "Impressions": [8000, 6000, 9000],
    "Clicks": [600, 500, 700],
    "Conversions": [60, 45, 80]
}

df = pd.DataFrame(data)

# Calculate conversion rate for each campaign
df["Conversion Rate (%)"] = (df["Conversions"] / df["Clicks"]) * 100

print(df[["Campaign", "Impressions", "Clicks", "Conversions", "Conversion Rate (%)"]])

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 2

Ask AI

Ask anything or try one of the suggested questions to begin our chat

Swipe to show menu


              1234567891011
            
# Hardcoded campaign data
campaign_impressions = 5000
campaign_clicks = 400
campaign_conversions = 50

# Calculate conversion rates
click_through_rate = campaign_clicks / campaign_impressions
conversion_rate = campaign_conversions / campaign_clicks

print("Click-through rate:", round(click_through_rate * 100, 2), "%")
print("Conversion rate:", round(conversion_rate * 100, 2), "%")


              12345678910111213141516
            
import pandas as pd

# Data for multiple campaigns
data = {
    "Campaign": ["Spring Sale", "Summer Launch", "Holiday Promo"],
    "Impressions": [8000, 6000, 9000],
    "Clicks": [600, 500, 700],
    "Conversions": [60, 45, 80]
}

df = pd.DataFrame(data)

# Calculate conversion rate for each campaign
df["Conversion Rate (%)"] = (df["Conversions"] / df["Clicks"]) * 100

print(df[["Campaign", "Impressions", "Clicks", "Conversions", "Conversion Rate (%)"]])

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 2

Analyzing Marketing Campaigns

1. What is a conversion rate?

2. How can Python help compare marketing campaigns?

3. Why is it important to track campaign performance?

Analyzing Marketing Campaigns

1. What is a conversion rate?

2. How can Python help compare marketing campaigns?

3. Why is it important to track campaign performance?