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Analyzing the Impact of Shipping Modes on Product Returns at GlobalMart
e-commerce
python
problem-understanding
data-wrangling
inferential-statistics
Learning objective
- Analyze the impact of different shipping modes on the probability of product returns in an e-commerce context.
- Evaluate statistical data to identify correlations between shipping methods and customer return behavior.
- Leverage analytics to optimize shipping strategies, reduce returns, and boost customer satisfaction.
Overview
In this case study, we will delve into the investigation by GlobalMart into how shipping modes affect product returns. You will engage with real transaction data, applying statistical tools to discern whether the mode of shipping bears a significant correlation with the likelihood of returns.
Story
The scenario unfolds at GlobalMart, an e-commerce entity that has noticed an unusual pattern of product returns. Suspecting that the shipping mode might influence customers' decisions to return products, the company launches a comprehensive analysis. This investigation aims to uncover whether faster, more expensive shipping options result in fewer returns compared to standard, slower shipping methods.