Quantium Data Analytics Virtual Experience Program

This virtual experience program focuses on analyzing supermarket chip purchases to understand customer behavior and assess the success of a new store layout trial. The program consists of three tasks:

Task 1: Data Preparation and Customer Analytics

• Objective: Clean and analyze customer purchase behavior data.
• Files: quantium_task1.ipynb reads from QVI_purchase_behaviour.csv and QVI_transaction_data.xlsx.
• Process:
  • Data Cleaning: Converted dates from integer to datetime format, removed salsas and outliers.
  • Analysis:
    • Examined customer segments based on LIFESTAGE and MEMBER_TYPE.
    • Focused on the purchasing habits of the Mainstream Young Singles/Couples segment, analyzing chip brand and packet size preferences.
• Key Insights:
  • Top three segments by total sales: Budget Older Families, Mainstream Young Singles/Couples, and Mainstream Retirees.
  • Older families purchase more packets on average, while the Mainstream Young Singles/Couples have the largest population.
  • Across most segments, Kettles chips and 175g packets are the most popular.
  • Mainstream Young Singles/Couples are more likely to purchase Tyrells chips and larger 270g packets, particularly Twisties.

Task 2: Experimentation and Uplift Testing

• Objective: Evaluate the impact of a new store layout on sales and customer numbers.
• Files: quantium_Task2(1).ipynb reads from QVI_data.csv.
• Process:
  • Store Matching: Paired trial stores with control stores based on combined metrics using Pearson correlations and magnitude distances.
  • Hypothesis Testing: Assessed whether differences in performance between control and trial stores were statistically significant.
• Key Insights:
  • Control and trial store pairs identified: 77 with 233, 86 with 155, 88 with 40.
  • Significant sales increases in stores 77 and 86, especially in March and April 2019.
  • Significant customer number increases in stores 77 and 86.
  • No significant performance change in store 88.

Task 3: Analytics and Commercial Application

• Objective: Summarize findings and provide commercial insights.
• Output: PowerPoint report using the Pyramid Principle to communicate key insights from Tasks 1 and 2.

Dependencies

• Language: Python 3.8
• Packages: pandas, matplotlib, mlxtend, datetime, sklearn, scipy

Project Overview

The goal was to analyze chip purchasing behavior, evaluate the impact of a new store layout, and provide actionable recommendations based on customer preferences and trial performance.