Lära Analyzing Campaign Performance | Optimizing Growth Experiments

Understanding the effectiveness of your marketing campaigns is crucial for optimizing growth strategies. Growth hackers rely on data-driven insights to determine which campaigns deliver the greatest impact and where to allocate resources. Three key metrics are central to campaign analysis: impressions, clicks, and conversions. Impressions measure how many times your campaign was seen, clicks indicate how many users engaged by clicking, and conversions show how many users completed a desired action such as signing up or making a purchase. Each of these metrics provides a different perspective on campaign performance, and together they form the foundation for evaluating and comparing growth experiments.


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import pandas as pd

# Create a DataFrame with hardcoded campaign data
data = {
    "Campaign": ["Spring Launch", "Summer Sale", "Fall Promo", "Winter Clearance"],
    "Impressions": [10000, 15000, 12000, 9000],
    "Clicks": [500, 900, 600, 300],
    "Conversions": [50, 120, 60, 30]
}

df = pd.DataFrame(data)

# Calculate click-through rate (CTR) and conversion rate (CVR)
df["Click-Through Rate"] = df["Clicks"] / df["Impressions"]
df["Conversion Rate"] = df["Conversions"] / df["Clicks"]

print(df)

In the code above, you begin by creating a pandas DataFrame with sample data for four marketing campaigns. Each campaign has values for impressions, clicks, and conversions. The click-through rate (CTR) is calculated by dividing the number of clicks by the number of impressions for each campaign. This shows what fraction of people who saw the campaign actually clicked on it. The conversion rate (CVR) is calculated by dividing the number of conversions by the number of clicks, revealing how effective the campaign is at turning interested users into customers or leads. These calculations are performed using simple arithmetic operations applied to the DataFrame columns, and the results are added as new columns, making it easy to compare performance at a glance.


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# Sort campaigns by conversion rate in descending order
sorted_df = df.sort_values(by="Conversion Rate", ascending=False)

print(sorted_df[["Campaign", "Conversion Rate"]])

1. What is a click-through rate and how is it calculated?

2. How can pandas help compare campaign performance?

3. Why is it important to sort campaigns by conversion rate?

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