Chips Customer Insights and Purchasing Behavior Analysis

Objectives:

1. Present a strategic recommendation to the Category Manager, Julia, supported by data insights for the upcoming category review.

2. Analyze customer segments and chip purchasing behavior, identifying key metrics to describe purchasing trends.

3. Evaluate store trial performance for stores 77, 86, and 88 by comparing them to control stores.

Tasks:

1. Data Analysis and Cleaning: - Perform high-level data checks, including summaries, outlier detection, and data format corrections. - Derive extra features such as pack size and brand name. - Define metrics like total sales revenue, number of customers, and average transactions per customer to understand purchasing behavior.

2. Store Trial Evaluation: - Use the QVI_data dataset to evaluate the monthly sales performance of each trial store. - Develop a function to compare trial stores to control stores using metrics like Pearson correlations or magnitude distance. - Test if sales differences in the trial period are significant and analyze drivers of change (e.g., more customers or more purchases per customer).

3. Final Recommendations: - Based on data insights, provide a clear, commercially applicable strategy to Julia.