This project explores customer response patterns to marketing campaigns using the 4Ps of Marketing — Product, Price, Place, and Promotion. The dataset includes customer demographics, purchase behavior, and campaign engagement, providing insights into how different customer segments respond to various marketing efforts.
This analysis focuses on:
- Segmenting customers based on age, income, education, and family structure.
- Visualizing spending patterns across product categories.
- Investigating the effectiveness of marketing campaigns.
- Testing hypotheses (e.g., Do customers with kids prefer online shopping?).
Using Seaborn and Plotly, interactive and static visualizations reveal how customer demographics influence marketing campaign acceptance.
- 📁 marketing-campaign-analysis/
- 📄
marketing_data.csv - 📄
analysis.ipynb - 📁 images/
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income_spending_product.png - 📊
age_response_pattern.png - 📊
customer_acceptance.png - 📊
spending_vs_household.png - 📊
sales_distribution.png - 📊
education_complaint_customer.png
- 📊
- 📄
README.md(You 🫵 are here!)
- 📄
| Variable | Description |
|---|---|
| ID | Customer's unique identifier |
| Year_Birth | Customer's birth year |
| Education | Customer's education level |
| Marital_Status | Customer's marital status |
| Income | Customer's yearly household income |
| Kidhome | number of small children in customer's household number of teenagers in customer's household |
| Teenhome | Number of teenagers in customer's house |
| Dt_Customer | Date of customer's enrollment with the company |
| Recency | Number of days since the last purchase |
| MntWines | Amount spent on wine in last 2 years |
| MntFruits | Amount spent on fruits in last 2 years |
| MntMeatProducts | Amount spent on meat products in last 2 years |
| MntFishProducts | Amount spent on fish products in last 2 years |
| MntSweetProducts | Amount spent on sweet products in last 2 years |
| MntGoldProds | Amount spent on gold in last 2 years |
| NumDealsPurchases | Number of purchases made with discount |
| NumWebPurchases | Number of purchases made through company's website |
| NumCatalogPurchases | Number of purchases made using catalog |
| NumStorePurchases | Number of purchases made directly in store |
| NumWebVisitsMonth | Number of visits to company's website in the last month |
| AcceptedCmp1 | 1 if the customer accepted the 1st campaign, 0 otherwise |
| AcceptedCmp2 | 1 if the customer accepted the 2nd campaign, 0 otherwise |
| AcceptedCmp3 | 1 if the customer accepted the 3rd campaign, 0 otherwise |
| AcceptedCmp4 | 1 if the customer accepted the 4th campaign, 0 otherwise |
| AcceptedCmp5 | 1 if the customer accepted the 5th campaign, 0 otherwise |
| Response | 1 if the customer accepted the last campaign, 0 otherwise |
| Complain | 1 if customer complained in the last 2 years |
| Country | Customer's location |
and the dataset already in the current repository.
- Python
- Pandas
- Seaborn
- Plotly
- Matplotlib
- Scipy (for hypothesis testing)
- Majority of customers are middle-aged to older adults
- Highest selling product is Wine
- Highest age group accepted campaign are
$25-34$ . - Spending tends to decrease as the number of children increases.
- Majority of the individuals preffered offline shopping
- H1: Older people are not as tech-savvy and prefer shopping in-store. ✅
- H2: Customers with kids prefer to shop online. ❌
- H3: Other channels cannibalize store purchases. ❌
- H4: US fares significantly better than the rest of the world in total purchases.❌
Vishal Verma
🔎 Passionate about Data Visualization, Data Science, and Artificial Intelligence
📫 Connect with me on LinkedIn
This project offers a hands-on approach to learning data analysis while uncovering valuable insights from real-world marketing data.
If you enjoyed this project, consider giving it a ⭐.