Customer_Purchase_Behavior_Analysis - EDA

Objective:

My mission is to analyze the provided dataset, which encompasses detailed customer information and purchasing behavior, to enable your company to make informed and strategic decisions. By delving into this data, I aim to identify patterns, trends, and correlations that can offer valuable insights into customer preferences, purchasing habits, and overall behavior. This analysis will not only highlight key factors driving customer decisions but also uncover potential areas for improvement in your marketing strategies. By understanding these dynamics, my goal is to help your company optimize its marketing efforts, tailor promotions more effectively, and increase the acceptance rates of your offers. Through a thorough examination of the dataset, I seek to provide actionable recommendations that can enhance customer engagement, boost sales, and ultimately drive the company’s growth and profitability. This comprehensive approach will ensure that marketing resources are utilized efficiently and effectively, maximizing the impact of each campaign.

Gist of Dataset:

This data was gathered during last year's campaign. Data description is as follows;

• Response (target) - 1 if customer accepted the offer in the last campaign, 0 otherwise
• ID - Unique ID of each customer
• Year_Birth - Age of the customer
• Complain - 1 if the customer complained in the last 2 years
• Dt_Customer - date of customer's enrollment with the company
• Education - customer's level of education
• Marital - customer's marital status
• Kidhome - number of small children in customer's household
• Teenhome - number of teenagers in customer's household
• Income - customer's yearly household income
• MntFishProducts - the amount spent on fish products in the last 2 years
• MntMeatProducts - the amount spent on meat products in the last 2 years
• MntFruits - the amount spent on fruits products in the last 2 years
• MntSweetProducts - amount spent on sweet products in the last 2 years
• MntWines - the amount spent on wine products in the last 2 years
• MntGoldProds - the amount spent on gold products in the last 2 years
• NumDealsPurchases - number of purchases made with discount
• NumCatalogPurchases - number of purchases made using catalog (buying goods to be shipped through the mail)
• NumStorePurchases - number of purchases made directly in stores
• NumWebPurchases - number of purchases made through the company's website
• NumWebVisitsMonth - number of visits to company's website in the last month
• Recency - number of days since the last purchase

Steps Involved:

1. Data Load
2. Data Preview
3. Data Manipulation
4. Descriptive Stastics
5. Probablity Distribution
6. Insights and Customer Segmentation

Conclusion:

Online Spending: Customers visiting websites more frequently each month spend less money online, indicating issues preventing purchase completion (e.g., checkout process, UI).

In-Store Purchases: A count plot shows consumer distribution based on in-store purchases to identify significant spending groups.

Improvement Focus: Enhance in-store experiences for customers with fewer transactions by introducing loyalty programs, incentives, or unique promotions.

Loyalty Programs: High in-store purchasers often participate in loyalty programs, showing their effectiveness in customer retention.

Bar Plot Analysis: The bar plot contrasts loyalty program response at different in-store purchase levels.

High-Value Customers: Identify high-value consumer categories and tailor marketing strategies to their needs and preferences.

Communication Tactics: Adjust communication based on a thorough understanding of clients' family structures to meet diverse customer segment demands.