Menu

📈 Marketing Campaign Analysis

🔍 Problem Statement
This project focuses on enhancing marketing campaign effectiveness by analyzing customer behavior,
evaluating campaign performance, and identifying factors that drive successful responses and spending. The
goal is to tailor strategies for different customer segments, boost offer acceptance rates, and increase overall
profitability.

📊 Dataset

  • Source: Kaggle
  • Includes customer demographics, campaign interactions, and purchase behavior across various product
    categories and channels.

❓ Key Business Questions

  • Who are the customers most likely to accept Campaign 4 offers?
  • What factors drive success in Campaign 4, and how can these be applied to others?
  • How do spending patterns vary between responders and non-responders?
  • Did discounts or product types (e.g., wine, fish, gold) influence campaign outcomes?
  • Should Campaign 5 be improved or discontinued based on its impact?
  • How can engagement be improved among families with children?
  • How should marketing align with income-based preferences?

🛠 Tools & Technologies

  • Python, Pandas, Matplotlib, Seaborn
  • Jupyter Notebook
  • GitHub for version control and collaboration

📌 Key Insights & Results

  • Campaign 4 Success Drivers: Accepted mostly by affluent, older, and partnered individuals in the US
    and Spain (~55 years old, ~$65K income).
  • Customer Preferences: Responders spent more on wines, fruits, and meat. Higher-income groups
    preferred catalog promotions; lower-income groups responded to discounts.
  • Campaign Comparison: Campaigns 3 & 4 improved spending; Campaign 5 showed negligible
    results—suggesting re-evaluation or discontinuation.
  • Demographic Observations: Families with children had lower engagement, indicating a need for
    tailored messaging.
  • Product Optimization: Sweet, gold, and fish products have high cross-demographic potential.
  • Channel Performance: Purchases through all channels increased steadily from Campaigns 1 to 4.

📈 Impact

  • Provided actionable insights for CMOs to:
  • Reallocate resources to effective channels
  • Personalize messaging based on demographics
  • Improve product-level targeting
  • Refine underperforming campaigns
Download

🔗 Project Repository

GitHub