This repository contains three tasks related to data analysis and experimentation in the retail sector. The tasks are all completed using Python on Jupyter Notebook.
The goal of this task is to analyze purchasing trends and behaviors to support a strategic recommendation for an upcoming category review. The client is interested in customer segments and their chip purchasing behavior. The task involves:
- Performing high-level data checks such as creating and interpreting summaries of the data, finding and removing outliers, and checking and correcting data formats.
- Deriving additional features such as pack size and brand name from the data.
- Defining metrics of interest to draw insights on who spends on chips and what drives spends for each customer segment.
This task involves evaluating the performance of a store trial performed in stores 77, 86, and 88. The task involves:
- Considering the monthly sales experience of each store, broken down by total sales revenue, total number of customers, and average number of transactions per customer.
- Creating a measure to compare different control stores to each of the trial stores.
- Comparing each trial and control pair during the trial period to test if total sales are significantly different in the trial period and if so, check if the driver of change is more purchasing customers or more purchases per customers etc.
- Conducting analysis on transaction and purchase behavior data, using saved output files from Task 1 & 2 to generate charts and visualizations.
- Providing key recommendations to the Category Manager, including data visualizations, key callouts, insights, and recommendations in a Power BI report and PowerPoint.