The objective of this project was to evaluate the impact of Eniac's permanent discount model on its financial performance (2017-2018). By analyzing 46,000+ orders, I identified the optimal discount threshold to maximize revenue while halting unnecessary margin erosion.
Analysis reveals that discounting has shifted from a tactical lever to a permanent state. 93.1% of all order lines were sold with a markdown, indicating a high dependency on price-cutting that may dilute brand equity in the long run.
Through scenario modeling, I identified a clear Optimal Revenue Zone. Revenue peaks at a moderate discount depth of 20-25%. Beyond 30%, additional discounts fail to generate incremental volume and lead to a sharp decline in net margins.
Sales spikes are primarily synchronized with major calendar events (e.g., Black Friday, Q4 holidays) rather than the depth of the discount itself. Outside of peak periods, the correlation between discount size and daily revenue remains statistically weak.
Figure 1: Revenue Seasonality Trends |
Figure 2: Discount Depth Volatility |
- Language: Python (Pandas, NumPy)
- Visualization: Matplotlib, Seaborn
- Analytics: ETL (Data Cleaning), Correlation Analysis, Time-Series Analysis, Scenario Modeling
- Domain: E-commerce / Revenue Management
discount_analysis.ipynb— Full end-to-end Python pipeline (Data cleaning, processing, and visualization).discount_strategy_presentation.pdf— Executive summary with visual storytelling and strategic data-driven recommendations.
- Reallocate the marketing budget to high-seasonality periods where price elasticity is naturally higher.
- Cap general discounts at the 20-25% range to maintain a healthy balance between volume and profit.
- Protect "Premium Anchors" (e.g., Apple hardware) from deep discounts, using accessories as the primary "Traffic Magnets."