Introduction
In this data analytics case study, I analyzed Delaware Cookie Company’s B2B sales performance to uncover trends, customer behavior, and seasonal opportunities. Using Power BI, I transformed raw sales data into actionable insights that could help the company optimize operations, retain key accounts, and expand strategically.
This project showcases my ability to extract business value from data—a skill I bring to every analytics challenge.
Check the project details on my GitHub.
Project Overview
Objective: Identify sales patterns, top customers, and seasonal demand shifts to guide inventory and marketing decisions.
Tools Used:
- Power BI (Interactive dashboards, drill-down visuals)
- DAX measures (Revenue per customer, order trends)
- Geospatial analysis (Cities served)
Key Metrics:
✔ Revenue: $6M
✔ Orders: 2,483
✔ Customers: 2,483 (1:1 order ratio → B2B focus)
✔ Cities Delivered: 5
Key Insights & Recommendations
1. Customer Concentration Risk 🎯
- Top 3 customers (Cascade Growers, Quick Bite Convenience, YT Restaurants) contributed ~57% of revenue.
- Dependency risk: Losing one key account could significantly impact revenue.
🔹 Recommendation:
- Diversify the client base by targeting similar businesses (e.g., regional chains, cafés).
- Introduce loyalty incentives (e.g., bulk discounts, exclusive flavors) for top buyers.
2. Seasonal Demand Trends 📅
- Peak shipping in March (12OK cookies), with a gradual decline until December.
- Monday was the busiest shipping day.
🔹 Recommendation:
- Launch seasonal campaigns (e.g., summer picnic packs, holiday gift boxes) to flatten demand dips.
- Optimize Monday logistics (staffing, delivery routes) to handle volume spikes.
3. Geographic Expansion Opportunities 🗺️
- Current cities: Green Bay, Huntington, Salt Lake City, Seattle (1 unnamed).
- No clear revenue breakdown, but larger markets (Seattle, Salt Lake City) likely drive higher sales.
🔹 Recommendation:
- Analyze delivery costs vs. revenue per city to prioritize expansion (e.g., Portland, Denver).
- Partner with local distributors in new markets to reduce logistics overhead.
4. Product & Order Analysis 🍪
- Cascade Growers ordered the most cookies (~0.4M), suggesting bulk purchases for resale.
- Smaller accounts (e.g., Park & Eat LLC) had minimal orders.
🔹 Recommendation:
- Bundle deals for mid-tier clients to increase order size.
- Survey low-volume buyers to identify barriers (e.g., pricing, product fit).
Data Visualization Techniques
To highlight trends, I used:
✅ Bar charts (Top customers, monthly shipments)
✅ Horizontal bar graphs (Cookie volume by client)
✅ Geographic maps (Cities served)
✅ Tooltips & filters (Interactive drill-down by month/customer)
Example: The “Cookies Shipped by Month” visual clearly shows March’s outlier demand, prompting deeper investigation.
Business Impact & Future Analysis
Immediate Actions Enabled:
✔ Targeted retention strategies for high-value accounts.
✔ Adjusted production schedules to align with Monday/March peaks.
✔ Identified growth markets for expansion.
Next Steps:
- Profitability analysis (Are bulk orders eroding margins?)
- Customer segmentation (Do restaurants order differently than grocers?)
- Promotion ROI tracking (Do discounts drive incremental sales?)
Why This Matters for My Portfolio
This project demonstrates my ability to:
🔸 Translate raw data into strategic recommendations.
🔸 Design intuitive dashboards for stakeholder decision-making.
🔸 Identify risks (e.g., customer over-reliance) proactively.
Conclusion
Data analytics isn’t just about numbers—it’s about finding stories that drive change. For Delaware Cookie Company, the insights revealed untapped markets, operational efficiencies, and customer growth opportunities.
Check the project details on my GitHub.