Lære Challenge: Campaign Analyzer | Optimizing Growth Experiments

In growth hacking, comparing the effectiveness of different marketing campaigns is crucial for optimizing your strategies. You can use Python to automate the analysis of campaign data, allowing you to quickly identify which campaigns are performing best based on metrics like click-through rate (CTR) and conversion rate. Suppose you have a set of campaigns, each with data on impressions, clicks, and conversions. By calculating the CTR and conversion rate for each campaign, you can determine which one is converting users most effectively and focus your efforts on scaling what works.


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import pandas as pd

# Create a DataFrame with campaign data
data = {
    "campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Clearance"],
    "impressions": [10000, 15000, 12000, 8000],
    "clicks": [500, 900, 480, 320],
    "conversions": [50, 120, 36, 40]
}
df = pd.DataFrame(data)

# Calculate click-through rate (CTR) and conversion rate
df["ctr"] = df["clicks"] / df["impressions"]
df["conversion_rate"] = df["conversions"] / df["clicks"]

# Print the campaign with the highest conversion rate
best_campaign = df.loc[df["conversion_rate"].idxmax()]
print("Campaign with highest conversion rate:")
print(f"{best_campaign['campaign']} (Conversion Rate: {best_campaign['conversion_rate']:.2%})")

This script begins by building a pandas DataFrame with hardcoded data for four campaigns, including the number of impressions, clicks, and conversions. It then calculates the CTR by dividing clicks by impressions and the conversion rate by dividing conversions by clicks for each campaign. Finally, it identifies and prints the campaign with the highest conversion rate, making it easy to see which campaign is most effective at turning clicks into conversions.

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Write a Python script that analyzes marketing campaign performance using pandas.

Create a pandas DataFrame from the provided campaign_data dictionary;
Calculate a new column ctr as the ratio of clicks to impressions for each campaign;
Calculate a new column conversion_rate as the ratio of conversions to clicks for each campaign;
Identify which campaign has the highest conversion_rate and assign its name (as a string) to a variable called top_campaign.

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Sektion 3. Kapitel 5

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Suggested prompts:

Can you explain how to interpret the CTR and conversion rate values?

How can I modify the script to analyze more campaigns or different metrics?

What are some best practices for using this analysis to improve future campaigns?

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              12345678910111213141516171819
            
import pandas as pd

# Create a DataFrame with campaign data
data = {
    "campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Clearance"],
    "impressions": [10000, 15000, 12000, 8000],
    "clicks": [500, 900, 480, 320],
    "conversions": [50, 120, 36, 40]
}
df = pd.DataFrame(data)

# Calculate click-through rate (CTR) and conversion rate
df["ctr"] = df["clicks"] / df["impressions"]
df["conversion_rate"] = df["conversions"] / df["clicks"]

# Print the campaign with the highest conversion rate
best_campaign = df.loc[df["conversion_rate"].idxmax()]
print("Campaign with highest conversion rate:")
print(f"{best_campaign['campaign']} (Conversion Rate: {best_campaign['conversion_rate']:.2%})")