Maven Marketing Dataset Analysis

Overview

This project analyzes the Maven Marketing dataset, which contains information on customer profiles, purchase behavior, and campaign responses. The dataset comprises various attributes such as customer demographics, purchase history, and campaign acceptance data.

Purpose

The goal of this analysis is to gain insights into Maven Marketing's customer base, identify trends in purchase behavior, and evaluate the effectiveness of marketing campaigns.

Dataset Description

The Maven Marketing dataset includes the following columns:

• 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 children in customer's household
• Teenhome: Number of teenagers in customer's household
• Dt_Customer: Date of customer's enrollment with the company
• Recency: Number of days since customer's last purchase
• MntWines: Amount spent on wine in the last 2 years
• MntFruits: Amount spent on fruits in the last 2 years
• MntMeatProducts: Amount spent on meat in the last 2 years
• MntFishProducts: Amount spent on fish in the last 2 years
• MntSweetProducts: Amount spent on sweets in the last 2 years
• MntGoldProds: Amount spent on gold in the last 2 years
• NumDealsPurchases: Number of purchases made with a discount
• NumWebPurchases: Number of purchases made through the company's website
• NumCatalogPurchases: Number of purchases made using a catalogue
• NumStorePurchases: Number of purchases made directly in stores
• NumWebVisitsMonth: Number of visits to the company's website in the last month
• AcceptedCmp1-5: Campaign acceptance indicators
• Response: Indicator for acceptance in the last campaign
• Complain: Indicator for customer complaints in the last 2 years
• Country: Customer's location

Insights and Analysis

The analysis focuses on the following aspects:

• Exploratory data analysis of customer demographics
• Examination of customer purchasing behavior across different product categories
• Evaluation of campaign performance and customer response rates
• Identification of factors influencing purchase decisions and campaign acceptance

Tools and Libraries Used

• Python (Pandas, NumPy) for data manipulation and analysis
• Matplotlib and Seaborn for data visualization
• Spyder for code execution and documentation