🧠 Quantium Data Analytics Virtual Experience – Forage

A data analytics project completed as part of the Quantium Job Simulation on Forage. This simulation mimics real-world retail challenges and demonstrates how data-driven insights can support customer segmentation, product strategy and store performance evaluation.

This experience involved analyzing retail transaction and customer data to deliver insights in three core areas:

🧾 Customer Behavior Analysis
📈 Trial vs Control Store Performance Evaluation
📊 Data Storytelling & Reporting

📌 Project Overview

The objective of this simulation was to analyze snacking chips customer data, evaluate a trial store layout intervention and provide actionable insights for Quantium’s retail client.

📂 Project Breakdown

Task 1: 🧾 Customer & Sales Analysis

Objective: Understand customer purchasing behavior and identify key segments.

Steps:

Analyzed transaction and customer datasets
Cleaned and joined transaction and customer datasets
Analyzed chip units sold and sales by identified customer segments
Created visualizations to explore behavioral trends

Results:

Identified Mainstream Young Singles & Couples as primary category shoppers
Found growth potential among Young and Older Families
Observed consistent affluence across life stages but higher engagement from family groups

Files:

Dataset Files: QVI_purchase_behaviour, QVI_transaction_data
Code File: Data Exploration and Analysis - Task 1.Rmd
Code with Output/Results File: Data Exploration and Analysis - Task 1.pdf

Task 2: 🧪 Trial vs Control Store Analysis

Objective: Find control stores based on the prior performance of the trial stores

Steps:

Defined performance metrics (e.g., total sales, number of customers)
Selected Control Stores based on trend similarity (not averages)
Conducted comparative time series analysis
Used ggplot2 to visualize trends pre and post-trial

Results:

Identified control stores for the trial stores
Delivered recommendations based on sales lift and engagement patterns

Files:

Dataset File (Created after running code in Task 1): QVI_data.csv
Code File: Retail Strategy and Analytics - Task 2.Rmd
Code with Output/Results File: Retail Strategy and Analytics - Task 2.pdf

Task 3: 📊 Insights & Reporting

Objective: Structure insights using the Pyramid Principle and present to stakeholders.

Steps:

Synthesized findings into a business-focused narrative
Created polished data visualizations and slide summaries
Compiled a report with recommendations and key takeaways

Results:

Final report (R Markdown to PDF)
Slide-ready visuals
Strategic insights for scaling the layout intervention across stores

Files:

PowerPoint Presentation: Presentation_BRAND - Task 3.pptx
File for client: Presentation_BRAND - Task 3.pdf

📊 Technologies Used:
R (dplyr, tidyr, ggplot2, ggmosaic, lubridate, scale, )
RMarkdown for reproducible reporting
PowerPoint for stakeholder communication
PDF for creating final files and reports

📈 Business Impact:

Data supports scaling the layout redesign to stores with similar profiles
Customer segmentation enables more targeted promotions
Insights help optimize store experience and chip category performance

👤 Author

Siddharth Gada
📧 Email: gadasiddharth@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/siddharthgada/

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