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🛒 About This Project

project = {
    "name"        : "Quantium Data Analytics Job Simulation",
    "platform"    : "Forage",
    "company"     : "Quantium — Australia's Leading Analytics Firm",
    "completed"   : "March 22nd, 2026",
    "tasks"       : 3,
    "dataset"     : "Retail Chip Category | FY2018-19",
    "tools"       : ["Python", "Pandas", "NumPy", "SciPy", "Matplotlib", "Seaborn"],
    "skills"      : ["Customer Analytics", "Uplift Testing", "Pearson Correlation",
                     "t-test", "95% CI", "Pyramid Principle", "Commercial Reporting"],
    "outcome"     : "🏆 Certificate Issued — Data Analytics Job Simulation"
}

💡 "Replicating the real work of a Quantium Data Analyst — turning raw retail data into commercial decisions that drive business growth."

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📊 Key Metrics

💰 Total Revenue	🛍️ Transactions	👤 Customers	🧾 Avg Spend/Trip
$1.8M	245,255	71,287	$7.36

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📁 Repository Structure

📦 Quantium-Data-Analytics-Simulation/
│
├── 📓 Quantium_Task1.ipynb           → Customer Analytics
├── 📓 Quantium_Task2.ipynb           → Uplift Testing & Experimentation
├── 📊 fig_monthly_transactions.png
├── 📊 fig_customer_segments.png
├── 📊 fig_sales_by_lifestage.png
├── 📊 fig_sales_by_premium.png
├── 📊 fig_sales_heatmap.png
├── 📊 fig_customer_count_heatmap.png
├── 📊 fig_avg_spend_heatmap.png
├── 📊 fig_top_brands.png
├── 📊 fig_pack_size_sales.png
├── 📊 fig_avg_txns_heatmap.png
├── 📊 fig_brand_preference.png
├── 📊 fig_pack_preference.png
├── 📊 fig_store77_*.png              → Store 77 Trial Charts
├── 📊 fig_store86_*.png              → Store 86 Trial Charts
├── 📊 fig_store88_*.png              → Store 88 Trial Charts
└── 📄 README.md

⚠️ Note: Raw data files excluded per Quantium's confidentiality policy.

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🎯 Task 1 — Customer Analytics

Methodology

Step	Action
🧹 Data Cleaning	Parsed dates, extracted pack sizes via regex, removed salsa & outliers
⚙️ Feature Engineering	Extracted brand names, merged transaction + customer datasets
👥 Segmentation	Analysed 7 lifestage segments × 3 premium tiers
📈 Sales Analysis	Monthly trends, brand performance, pack size preferences
📐 Statistics	Avg spend and frequency comparison across all segments

🔍 Key Findings

📌 Mainstream customers      → drive 38.8% of revenue ($700K)
📌 Older Families            → highest avg spend $34 AND frequency 4.7x/year
📌 Young Families            → $32 avg spend, over-index on Smiths & Red Rock Deli
📌 Kettle brand              → #1 at 21.6% of chip sales ($390K) — 2x nearest competitor
📌 175g pack                 → drives 26.9% of all chip revenue — universally preferred
📌 December festive peak     → clearly visible | February dip reflects shorter month

📸 Task 1 Visualizations

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🧪 Task 2 — Uplift Testing & Experimentation

Methodology

Step 1 → Calculate monthly metrics per store (Sales, Customers, Txns, AvgPrice)
Step 2 → Filter to pre-trial period (Jul 2018 – Jan 2019)
Step 3 → Match control stores using Pearson Correlation
Step 4 → Scale control store metrics to trial store magnitude
Step 5 → Apply t-tests with 95% Confidence Intervals (Feb–Apr 2019)
Step 6 → Compare trial store performance vs CI bands

📊 Results Summary

Trial Store	Control Store	Sales Significant	Customers Significant
Store 77	Store 233	✅ Mar & Apr (t=7.3, 12.5)	✅ Mar & Apr (t=13.5, 30.8)
Store 86	Store 155	✅ Mar & Apr	✅ All 3 months (t=11.8, 20.9, 5.7)
Store 88	Store 237	✅ Mar & Apr (t=6.6, 5.8)	✅ Mar & Apr (t=17.9, 9.8)

🔑 Key Insight: Primary driver of uplift = more customers visiting — not higher spend per visit.

📸 Task 2 Visualizations

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📋 Task 3 — Analytics & Commercial Application

Pyramid Principle Structure

🔺 CONCLUSION FIRST
└── Trial layout drives significant uplift → Recommend full rollout
    │
    ├── 📊 EVIDENCE 1 — Task 1 Insights
    │     Customer segments, brand dominance, pack size preferences
    │
    ├── 🧪 EVIDENCE 2 — Task 2 Results
    │     Statistically significant uplift in all 3 trial stores
    │
    └── 💡 RECOMMENDATIONS — 4 Next Steps for H2

4 Strategic Recommendations

#	Recommendation	Data Behind It
1️⃣	Target Older & Young Families	Highest spend ($33-34) + frequency (4.5-4.7x/year)
2️⃣	Protect Kettle's Shelf Position	21.6% revenue — eye-level + December push
3️⃣	Anchor on 175g Pack	26.9% of all sales — use as promo anchor SKU
4️⃣	Roll Out Trial Format	Significant uplift confirmed — target 10-15 stores

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🛠️ Tech Stack

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💼 Skills Demonstrated

Analytics	Statistics	Business
✅ End-to-end EDA	✅ Hypothesis Testing (t-test)	✅ Pyramid Principle Reporting
✅ Customer Segmentation	✅ 95% Confidence Intervals	✅ Commercial Recommendations
✅ Feature Engineering	✅ Pearson Correlation Matching	✅ Stakeholder Communication
✅ Data Visualisation	✅ Uplift Testing	✅ KPI Definition

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🏆 Certificate of Completion

Field	Details
🏅 Certificate	Data Analytics Job Simulation
🏢 Issued By	Quantium via Forage
📅 Completed	March 22nd, 2026
🔑 Credential ID	9SZbzY89Z5gHe2s4e
👤 User Verification	hbKXAXgYSyRv56eS5
🔗 View Certificate	Click Here

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👨‍💻 Author

Devesh Shukla Data Analyst | Data Scientist | Builder

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