A naukri.com initiative
Nycdatascience
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R Project: E-Commerce Customer Behavior
- This project aims to help businesses better understand what their customers want and what incentivizes them to place an order.
- The analysis is based on segmentation into different categories of customers, which considers purchasing patterns, membership type, and demographic information, such as gender and location.
- The dataset provides a comprehensive view of customer behavior within an e-commerce platform in terms of structure, with eleven columns providing a holistic overview of customer profiles and behaviors.
- Memberships are classified by spending into Bronze, Silver, and Gold for this customer set.Fig.2
- The average amount customers spend when a discount is applied indicates that Gold and Silver members are significantly more likely to take advantage of discounts compared to Bronze members.
- Regarding target demographics, the data indicates that females are more likely to take advantage of discounts than males.
- City preference and performance: Customers in Houston represent the lowest average ratings, which may partly be attributed to the economic conditions and customer sentiments. The highest ratings come from customers in San Francisco, with New York customers close behind.
- Machine learning (ML) applications for e-commerce customer behavior offer a range of powerful tools for analyzing and enhancing customer interactions. ML has the potential to transform e-commerce by providing deeper insights into customer behavior, enhancing personalization, and optimizing operational efficiencies.
- Oreste RUKUNDO developed this R Shiny App as one of his projects for the New York City Data Science Academy program.
- The R Shiny App offers a user-friendly interface with interactive data visualization and rapid prototyping.
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