Goal: Analyze customer demographics, spending behavior, and campaign response to uncover insights that can inform marketing strategies.

Tools Used: Tableau, Excel

Source: Kaggle – Customer Personality Analysis

Records: ~2,210 customers

Columns include:

Which customer segments spend the most?

How does income level affect spending patterns?

What demographics respond best to marketing campaigns?

What is the relationship between age and product preferences?

Created Age = 2024 - Year_Birth

Created Age_category =IF(C2<21, “1-20”, IF(C2<41, “21-40”, IF(C2<61, “41-60”, IF(C2<81, “61-80”, IF(C2<101, “81-100”, “100+”)))))

Created Total_Children = Kidhome + Teenhome

Created Total_Spend = sum of all Mnt columns

Created Campaign_Response_Total = sum of AcceptedCmp1 to AcceptedCmp5 & Response

Cleaned nulls in Income

Viz Type Description Bar Chart Avg spend by education level Box Plot Income vs Total Spend Heatmap Response rate by Age and Income Stacked Bar Campaign acceptance by marital status KPI Cards Avg Income, Avg Recency, Response Rate Filters Age, Education, Marital Status

Customers aged 21–60 with high income are the top spenders.

Married and educated individuals are more likely to accept marketing campaigns.

Parents with children at home respond less to campaigns.

Wine and meat are the top spending categories across all groups.