🥖 Strategic Market Expansion: South Korean Bakery Analysis

Bridging Stakeholder Intuition with Data-Driven Reality

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🎯 Executive Summary

The Mission: Validate a market entry strategy for a South Korean bakery facing stakeholder pressure to prioritize Croissants.

The Approach: Managed the full data analysis lifecycle—from cleaning raw Kaggle transaction data to performing a comparative performance analysis of product categories.

The Pivot: The data successfully challenged the investor’s assumptions. While Croissants are a stable performer, Angbutter — a culturally resonant favorite—emerged as the primary revenue engine, outperforming Croissants by a staggering 208%.

The Impact: Delivered a data-backed recommendation that protects the bakery from an outdated "niche" strategy, ensuring the product mix aligns with actual consumer spending habits.

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📖 Background & Business Case

The Challenge: Stakeholder Bias vs. Market Reality

A South Korean bakery is currently in high-stakes negotiations with a primary investor. Drawing on market trends from the 1980s, the investor is firmly convinced that Croissants are the ultimate driver of success and has proposed a business model centered exclusively on this single product.

While stakeholder alignment is critical for securing capital, basing a 2026 business strategy on "gut feeling" and decades-old nostalgia introduces significant financial risk. As the lead analyst, my objective was to move beyond subjective "vibe" checks and provide an independent, evidence-based view of the current market.

The Strategy: Data-Driven Validation

I conducted this analysis to stress-test the investor's hypothesis. By examining 2,421 transaction records, I aimed to decode the "Sales DNA" of the bakery to determine if a Croissant-centric model would maximize ROI or if a strategic pivot was necessary to capture modern consumer demand.

Explore the Interactive Data: 📊 View the Full Google Sheets Analysis & Dashboard

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🛠️ Phase 1: Problem Definition & Context

Using the Rumsfeld Matrix, I categorized the business requirements to mitigate risk:

• Known-Knowns: Opening a bakery in the competitive Korean market requires high operational efficiency.
• Known-Unknowns: We knew which items were on the menu, but didn't know the exact customer "Rush Hours" or product-mix preferences.
• The Challenge (Stakeholder Bias): The main investor was convinced Croissants were the only path to success. My objective was to determine if this "gut feeling" aligned with market data.

Impact of Domain Knowledge: Understanding the local Korean preference for "Angbutter" (Pretzel with red beans and gourmet butter) was critical in contextualizing why Western-style staples (Croissants) might not be the #1 performer.

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⚙️ Phase 2: Data Preprocessing & Scoping

The dataset was sourced from Kaggle (modified) and consisted of 2,421 rows and 31 core features https://www.kaggle.com/datasets/hosubjeong/bakery-sales/data

• Scoping/Gap Analysis: The raw datetime column was too broad for operational planning.
• Feature Engineering: I engineered four new features—Year, Month, Day, and Hour—to perform a granular time-series analysis.
• Cleaning: Removed empty rows to ensure 100% data integrity.
• Key Fields Analyzed:
  • Temporal: datetime, day of week, hour.
  • Financial: total (Total purchase amount in Korean Won).
  • Inventory: 31 items including Angbutter, Plain Bread, Americano, Croissants, Tiramisu, etc.

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⚙️ Phase 3: Analytical Insights

📊 Time Trends & Peak Operations

• Objective: Identify peak hours and high-traffic days to optimize staffing and inventory.
• Analysis: Aggregated total income using SUMIF functions across hours and days of the week.
• Key Findings:
  • Peak Velocity: Data revealed a sharp peak at 11:00 AM.
  • Weekly Rhythm: Sunday is the highest-grossing day, suggesting the bakery serves as a "weekend destination" rather than just a weekday commuter stop.
  • Recommendation: Align labor shifts to front-load staffing between 10:00 AM and 1:30 PM.
• Visualizations:

📉 Product Popularity & Market Demand

• Objective: Determine the most popular food and beverage items to validate product-market fit.
• Analysis: Calculated total unit sales for all menu items and visualized distribution via bar charts.
• Key Findings:
  • Angbutter is the top-selling food item, significantly outperforming croissants.
  • Americanos are the most popular beverage choice, with a clear preference for hot drinks over cold ones.
• Visualizations:

🔗 Hourly Sales Dynamics

• Objective: Analyze the relationship between food and drink sales to refine afternoon strategies.
• Analysis: Computed the proportion of food versus drink sales per hour to track consumption shifts.
• Key Findings:
  • Food items consistently make up approximately 90% of total items sold.
  • Sales Decay: Food sales decrease significantly after the morning rush.
  • Resilient Beverages: Drink sales remain stable, peaking again at 2:00 PM (The "Coffee Break" surge).
  • Strategic Pivot: The bakery should shift from a "Food Focus" in the morning to a "Beverage Focus" in the afternoon to capture higher margins.
• Visualizations:

💡 Phase 4: Interpretation & Recommendations

Based on the analysis, I provided the following strategic roadmap to the stakeholders:

1. Inventory Optimization: Prioritize Angbutter production and stock-up for the 11:00 AM rush to avoid stock-outs on the highest-margin item.
2. Labor Allocation: Increase staffing for the Sunday peak and the 2:00 PM "Coffee Break" window to handle beverage demand.
3. Pivot Marketing: Instead of a "Croissant-only" shop, use Croissants as a "Staple" draw while marketing Angbutter as the "Signature" driver
4. Bundle Strategies: Launch "Americano + Angbutter" morning combos to capitalize on the 11 AM rush.

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🛠️ Technical Skills & Tooling

Core Competencies

• Platform: Microsoft Excel & Google Sheets
• Advanced Data Aggregation: Orchestrated complex datasets using SUMIFS and COUNTIFS with absolute and relative cell referencing for dynamic, error-free modeling.
• Statistical Scaling: Factored large-scale currency figures (KRW) by 1,000 to optimize data readability and dashboard aesthetics.
• Temporal Modeling: Performed time-series segmentation to identify high-velocity sales windows and operational "rush hours."

Data Visualization & BI

• Executive Dashboards: Designed clean, gridline-free reporting interfaces focused on stakeholder "Action Items."
• Advanced Charting:
  • 100% Stacked Column Charts for visualizing Product Mix and category proportions.
  • Multi-variable Line Charts to track "Sales Decay" vs. "Resilient Beverages."
  • Comparative Bar Charts for direct benchmarking (e.g., Angbutter vs. Croissants).

Methodological Frameworks

• Risk Mitigation: Applied the Rumsfeld Matrix (Known-Knowns vs. Unknowns) to define project scope and manage stakeholder bias.
• Business Intelligence: Managed the Full Data Analysis Lifecycle, moving from raw Kaggle data to a finalized strategic roadmap.
• Feature Engineering: Transformed raw datetime strings into granular time-based features (Hour, Day of Week) to unlock operational insights.

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🎓 Project Credits

This analysis was developed as part of the Data Analytics Foundations course by DeepLearning.AI on Coursera. It demonstrates proficiency in applying statistical models and business intelligence frameworks to solve real-world problems in the Retail and F&B domain.

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👤 About the Author

Ayushi Gajendra

Data Analyst | Former EdTech Co-Founder

• 7+ Years of experience in business operations, strategic growth, and entrepreneurial leadership.
• I specialize in bridging the gap between raw data and high-stakes business decisions.
• My goal is to help organizations move beyond "gut feeling" to drive growth through evidence-based strategy.

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🔗 Connect with me:

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