Aprenda Automating Email Campaign Analytics | Automating Marketing Tasks with Python

Email marketing remains a powerful tool for reaching and engaging customers, but understanding its effectiveness relies on tracking key metrics. The most important metrics in email marketing include open rate, which measures the percentage of recipients who open your email; click rate, which tracks how many people click on links within the email; and unsubscribe rate, which reflects the proportion of recipients who opt out of future messages. Automating the analysis of these metrics using Python saves time and ensures you can quickly identify trends and make data-driven decisions to improve your campaigns.


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import pandas as pd

# Sample email campaign data
data = {
    "campaign": ["Spring Sale", "Summer Launch", "Holiday Promo"],
    "emails_sent": [1000, 1200, 950],
    "opens": [400, 600, 500],
    "clicks": [50, 90, 70],
    "unsubscribes": [10, 15, 8]
}

df = pd.DataFrame(data)

def calculate_email_metrics(df):
    df["open_rate"] = df["opens"] / df["emails_sent"]
    df["click_rate"] = df["clicks"] / df["emails_sent"]
    df["unsubscribe_rate"] = df["unsubscribes"] / df["emails_sent"]
    return df[["campaign", "open_rate", "click_rate", "unsubscribe_rate"]]

metrics_df = calculate_email_metrics(df)
print(metrics_df)

The function calculate_email_metrics takes a DataFrame containing raw email campaign data and computes three essential metrics for each campaign. By dividing the number of opens, clicks, and unsubscribes by the total emails sent, you obtain the open rate, click rate, and unsubscribe rate. This automated calculation provides you with a clear view of campaign performance, making it easier to spot which emails are engaging your audience and which may need improvement. With these insights, you can refine your content, timing, and targeting to maximize future campaign results.


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# Generate a summary table of top-performing campaigns by click rate

# Sort the metrics DataFrame by click_rate in descending order
top_campaigns = metrics_df.sort_values(by="click_rate", ascending=False)

# Display the summary table
print("Top-performing email campaigns by click rate:")
print(top_campaigns[["campaign", "click_rate"]])

1. Which metric indicates how many recipients opened an email?

2. Why is it useful to automate the calculation of email campaign metrics?

3. Fill in the blank: To find the top-performing campaign, sort the DataFrame by ______.

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 2

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Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Suggested prompts:

Can you explain how to interpret the click rate results?

What other metrics should I consider for evaluating email campaigns?

How can I use these insights to improve future email marketing campaigns?

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