Lære Automating A/B Test Analysis | Automating Marketing Tasks with Python

A/B testing is a core strategy in marketing for comparing two or more variants of a campaign, such as different versions of an email or landing page, to determine which performs better. By splitting an audience into groups and exposing each group to a different variant, marketers can use data to guide decisions. Rapid, automated analysis of A/B test results is crucial: it allows you to quickly identify the winning variant and make timely changes to campaigns, maximizing their effectiveness.


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import pandas as pd

# Sample A/B test results data
data = {
    "variant": ["A", "B", "C"],
    "impressions": [1000, 950, 980],
    "conversions": [120, 150, 110]
}

df = pd.DataFrame(data)

def calculate_conversion_rates(df):
    df = df.copy()
    df["conversion_rate"] = df["conversions"] / df["impressions"]
    return df[["variant", "impressions", "conversions", "conversion_rate"]]

conversion_results = calculate_conversion_rates(df)
print(conversion_results)

The function calculate_conversion_rates computes the conversion rate for each variant by dividing the number of conversions by the number of impressions. This metric helps you quickly spot which variant performed best. Automating this process ensures that you can rapidly process new test results, avoid manual calculation errors, and consistently identify the top performer. Automation is particularly valuable when tests involve many variants or when frequent analysis is required, freeing up your time for more strategic tasks.


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# Identify and highlight the winning variant
def highlight_winner(df):
    df = df.copy()
    winner_idx = df["conversion_rate"].idxmax()
    df["is_winner"] = False
    df.loc[winner_idx, "is_winner"] = True
    for idx, row in df.iterrows():
        winner_mark = " <--- Winner" if row["is_winner"] else ""
        print(
            f'Variant {row["variant"]}: '
            f'Impressions={row["impressions"]}, '
            f'Conversions={row["conversions"]}, '
            f'Conversion Rate={row["conversion_rate"]:.2%}{winner_mark}'
        )

highlight_winner(conversion_results)

1. What is the main goal of an A/B test in marketing?

2. Which metric is most important when comparing A/B test variants?

3. Fill in the blank: The variant with the highest ______ rate is typically considered the winner.

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Suggested prompts:

Can you explain how the winner is determined in this code?

What if there is a tie between variants?

How can I visualize these results with a chart?

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