E-Commerce Customer Behavior

2024-03-29 337 words 2 minutes

Contents

This project aims to analyze marketing data to gain insights into customer behavior, preferences, and trends. For further details, please refer to the Kaggle Notebook linked here.

E-Commerce Customer Behavior Analysis

Overview

This project aims to analyze customer behavior in an e-commerce platform using the “Consumer Behavior and Shopping Habits Dataset” obtained from Kaggle. By conducting descriptive, segmentation, product preference, and customer engagement analyses, we aim to gain insights into customer behavior, preferences, and trends.

Analysis Steps

1. Data Exploration and Preprocessing

Loaded the dataset and performed initial exploration to understand its structure and contents.
Handled missing values, outliers, and formatting issues.
Converted data types and cleaned the data as needed.

2. Descriptive Analysis

Explored demographic characteristics such as age, gender, and location.
Analyzed popular product categories, average purchase amounts, and preferred payment methods.
Examined customer engagement metrics including review ratings, subscription status, and frequency of purchases.
Analyzed promotional activities such as discounts and promo code usage.

3. Segmentation Analysis

Segmented customers into distinct groups based on demographic variables.
Analyzed shopping behavior and preferences of each segment to identify unique patterns and trends.

4. Product Preference Analysis

Investigated top-selling products, popular categories, preferred sizes, colors, and seasonal trends.
Determined products or categories driving the highest revenue and customer satisfaction.

5. Customer Engagement Analysis

Explored factors influencing customer engagement, such as review ratings, subscription status, and promotional offers.
Identified correlations between these factors and customer loyalty or lifetime value.

Conclusion

Through descriptive, segmentation, product preference, and customer engagement analyses, this project provides valuable insights into customer behavior and preferences in the e-commerce domain. These insights can guide businesses in optimizing marketing strategies, improving campaign performance, and enhancing overall customer satisfaction.

Future Work

Perform predictive modeling to forecast customer behavior and preferences.
Conduct A/B testing to evaluate the effectiveness of marketing strategies and promotional activities.
Incorporate external data sources for deeper analysis and insights.

Author

Feel free to contact me for any questions or additional information about this project.