👨💻 Emmanuel Kipkoech Tergech
Strategic Data Analyst | SQL • Python • Excel • Power BI
Turning complex business data into actionable insights that drive measurable results.
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👨💼 Professional Summary
I am a strategic Data Analyst specializing in SQL, Python, Excel, and Power BI to solve real business problems. I extract and clean data with SQL, perform predictive modeling in Python, and create executive dashboards in Power BI to drive decision-making. My Excel expertise—pivot tables, Power Query, and scenario modeling—enables fast financial analysis and projections. I also leverage Stata and SPSS for statistical modeling and hypothesis testing. Across all tools, I focus on generating actionable insights in profitability, customer retention, cash flow, and operational performance.
📂 Featured Strategic Projects
Showcasing my top analytics projects delivering measurable business impact.
1. E-Commerce Revenue Optimization & Growth Strategy
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- Business Problem: Marketing ROI was declining due to unoptimized seasonal spend across $1.75M in transactions.
- Action: Audited transaction logs with SQL and conducted cohort analysis in Python to identify seasonal growth triggers.
- Impact: Revealed peak sales periods, enabling a $20K budget reallocation to maximize returns.
- Tools: SQL • Python • Power BI
- Explore Project Files ↗️
2. Financial Survival & Predictive Cash Flow Modeling
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- Business Problem: Evaluate 12-month runway under $2.2M logistics risk and 25% gross margin.
- Action: Built a master table from 110K orders and created Linear Regression revenue forecasts in Python, improving accuracy by 38% over naive models.
- Impact: Projected $1.13M Month-1 revenue and demonstrated via sensitivity analysis that COGS control is 3x more effective than acquisition.
- Tools: PostgreSQL • Python (Scikit-Learn) • Power BI
- Explore Project Files ↗️
3. “Leaky Bucket” Syndrome: Retention vs Acquisition Modeling
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- Business Problem: 34% churn was draining a $5M capital reserve faster than new acquisition could compensate.
- Action: Performed stress-testing in Python and modeled cash positions in Excel, comparing baseline (65.6%) vs optimized (85%) retention scenarios.
- Impact: Modeled a path to $4.61M Year-End cash, extending runway to a sustainable 18+ months.
- Tools: Python • Excel
- Explore Project Files ↗️
4. Retail Profitability & Margin Recovery Audit
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- Business Problem: Total sales of $13M masked a $64K annual profit loss in the Furniture category.
- Action: Conducted SQL and Python analysis at product level; built Power BI dashboard to visualize KPIs.
- Impact: Recommended pricing floors and discount caps, recovering lost margin and improving profitability.
- Tools: SQL • Python • Power BI
- Explore Project Files ↗️
📬 Contact & Connect
📩 tergechemmanuel@gmail.com | 📎 LinkedIn | 📍 Nairobi, Kenya