Statistics Mini Project (Analyze a Marketing Campaign)

Goal

Given a marketing campaign dataset, you will:

compute descriptive statistics
compare two segments (A vs B)
build confidence intervals
produce a short written conclusion

Example dataset columns

user_iduser_id
variantvariant (A/B)
convertedconverted (0/1)
revenuerevenue
countrycountry

Step 1: Load

Load campaign data

import pandas as pd
 
df = pd.read_csv("data/campaign.csv")
print(df.head())

Load campaign data

import pandas as pd
 
df = pd.read_csv("data/campaign.csv")
print(df.head())

Step 2: Quick summary

Group summary

summary = (
    df.groupby("variant")
      .agg(
          users=("user_id", "nunique"),
          conversion_rate=("converted", "mean"),
          avg_revenue=("revenue", "mean"),
          median_revenue=("revenue", "median"),
      )
)
print(summary)

Group summary

summary = (
    df.groupby("variant")
      .agg(
          users=("user_id", "nunique"),
          conversion_rate=("converted", "mean"),
          avg_revenue=("revenue", "mean"),
          median_revenue=("revenue", "median"),
      )
)
print(summary)

Step 3: Visualize

Use either Matplotlib/Seaborn/Plotly:

conversion bar chart
revenue distribution comparison

Step 4: Test conversion difference (approx)

Use the “A/B Testing Basics” approach.

Step 5: Deliverable

Write a short conclusion:

Does variant B improve conversion?
Is the result practically meaningful?
Any data quality concerns?

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