Does Speed Matter in E-Commerce? Insights from QVC Temple Analytics Challenge

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Exploring the impact of delivery speed on customer purchasing behavior in e-commerce, this analysis from the QVC Temple Analytics Challenge reveals that customers tend to purchase more when they receive their products sooner. The study highlights the advantages of working with smaller data sets for quicker testing, the importance of effective data filtering for streamlined analysis, and methods for tracking order details and customer interactions. Utilizing Python's Openpyxl for data manipulation and analysis, the research showcases the significance of timely delivery in driving online sales.

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Does Speed Matter in E-Commerce? QVC Temple Analytics Challenge Ismail Hawk-Bey, Ryan Lattieri, John Do, Nsimba Zebedi

Do Customers that receive their product sooner purchase more than customers with longer delivery times?

YES

Working with Smaller Data Working with the provided smaller data set allowed us to test our solutions faster. Larger data sets were massive

Filtering through data Spreadsheets given had many details irrelevant to analysis. Filtering allows for quicker run time Easier to read and recognize important factors

Determining Whats Necessary By keeping track of the party ID, we can keep track of how many orders this party placed Having the order date and delivery confirmation date is an obvious factor between their total orders

METHOD 1

Person Class and opening the workbook Person class contains data for number of orders, order dates, delivery dates, and time between each order and delivery. Openpyxl was used to work with the excel files.

Iterating through workbook and saving data Algorithm used to calculate time between orders and data for persons objects.

Order dates and time between each order Left hand column shows the dates that each order was placed. The right hand column shows the time between each order. A zero denotes that the package was ordered at the same time.

METHOD 2

Python Packages used Pandas Seaborn

Organizing and Cleaning of Data Rows were removed that contained a blank cell for the column Column Delivery Confirmation Date. Organized the rows based on the Order Date column.