Projects

Projects

Forage Virtual Experience Program

Quantium Retail Strategy & Customer Segmentation

Quantium Retail Strategy & Customer Segmentation

Statistical customer segmentation on a year of retail transaction data to evaluate purchasing behaviour and price sensitivity across demographic tiers.

Statistical customer segmentation on a year of retail transaction data to evaluate purchasing behaviour and price sensitivity across demographic tiers.

Key Findings

High-Value Segment: Mainstream–Young Singles/Couples demonstrated strong preferences for larger pack sizes and premium brands.

High-Value Segment: Mainstream–Young Singles/Couples demonstrated strong preferences for larger pack sizes and premium brands.

Statistical Proof: Independent t-tests confirmed target young and mid-age segments pay significantly more per unit (p < 0.05).

Strategic Output: Provided evidence-based promotional strategies and shelf-placement adjustments to grow category margins.

Tech Stack

R • R Markdown • dplyr • ggplot2 • data.table • tidyr

p < 0.05 Statistical significance in price-per-unit variation

1 Year Transaction & Loyalty dataset integrated

Mainstream Young Singles/Couples identified as target segment

DataCamp DataLab Project

Customer Behaviour & Return Analysis (UK E-commerce)

Customer Behaviour & Return Analysis (UK E-commerce)

An exploratory analytics project investigating e-commerce transaction patterns to isolate operational product return risks and pinpoint profitable expansion markets.

An exploratory analytics project investigating e-commerce transaction patterns to isolate operational product return risks and pinpoint profitable expansion markets.

Key Findings

Localised Return Risk: Product returns were isolated to specific products and markets rather than systemic business issues.

Localised Return Risk: Product returns were isolated to specific products and markets rather than systemic business issues.

Expansion Opportunity: Identified the Netherlands as a high-value, low-risk market ripe for targeted growth.

Financial Exposure: Flagged bulk-order transactions as a primary driver of operational loss.

Tech Stack

R • dplyr • ggplot2

Netherlands as prime growth market for expansion

Bulk Orders flagged as a primary risk for financial exposure

Independent Analytics Project

Taste of the World Café – Restaurant Menu Performance Analysis

Taste of the World Café – Restaurant Menu Performance Analysis

An end-to-end SQL and Python analysis of 12,000+ quarterly orders to evaluate menu performance, identify revenue drivers, peak sales windows, and streamline underperforming items.

An end-to-end SQL and Python analysis of 12,000+ quarterly orders to evaluate menu performance, identify revenue drivers, peak sales windows, and streamline underperforming items.

Key Findings

Top Revenue Drivers: Italian cuisine generated 31% of total revenue from only 24% of menu offerings.

Top Revenue Drivers: Italian cuisine generated 31% of total revenue from only 24% of menu offerings.

Peak Sales Window: The 12–1 PM lunch peak recorded a ~20% higher average order value than dinner, highlighting prime staffing hours.

Menu Optimisation: The top 5 dishes drove 27% of overall sales, while the lowest seller moved just 123 units, supporting immediate removal.

Tech Stack

SQL • Python (pandas) • SQLite • Google Colab

31% Revenue share driven by Italian items

+20% Higher AOV during lunch peak

12,097 Cleaned transactional orders analysed

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