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_id
• variant (A/B)
• converted (0/1)
• revenue
• country

Step 1: Load

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)

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?

The project pipeline

diagram Marketing campaign analysis pipeline mermaid

  flowchart LR
  A["Load campaign.csv"] --> B["Descriptive stats
per variant"]
  B --> C["Confidence interval
on conversion rate"]
  C --> D["A/B test
(z-test or t-test)"]
  D --> E["Written conclusion"]

Every step from this phase gets used once: load, describe, estimate, test, and conclude.

From the book

This mini project mirrors the workflow Chapter 12 describes for handing off cleaned data to a modeling library: pandas does the loading, grouping, and feature prep (groupby, agg), then a focused statistical tool (here, a hypothesis test; there, statsmodels/scikit-learn) does the actual inference. Keep that separation clear in your own analysis scripts.

🧪 Try It Yourself

Exercise 1 – Descriptive summary by variant

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Exercise 2 – CI on the conversion-rate difference

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Exercise 3 – Writing a defensible conclusion

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Next

This wraps up the Statistics phase. Carry these tools — descriptive stats, confidence intervals, and hypothesis testing — into modeling and machine learning, where the same ideas reappear as regression diagnostics and model evaluation metrics.