E-commerce Growth & Commercial Strategy
Commercial analysis of channel quality, mobile funnel performance, product mix, and refund risk across a 1.7M-row dataset.
THE DECISION
How should marketing budget and product effort shift across channels, devices, and products?
APPROACH
- Analyzed six relational tables with joins, CTEs, and time-series SQL
- Compared conversion and revenue per session by channel and device
- Connected session depth, product mix, and refund behavior
- Created budget scenarios and decision guardrails
EVIDENCE & DELIVERABLES
- 1.7M public records analyzed
- Desktop conversion 8.5% versus 3.1% on mobile
- Commercial recommendation, charts, workbook, and executive deck
SELECTED WORK FILES
E-commerce Growth Strategy
Executive deck connecting channel quality, mobile conversion, product mix, refunds, and budget allocation.
213 KBCapstone Evidence Summary
A summary of the original capstone’s data model, SQL evidence, findings, recommendations, and limitations.
9 KBE-commerce Budget Allocation
Editable planning workbook for channel budgets, expected contribution, and decision guardrails.
13 KBPostgreSQL Data Model
Capstone schema for products, sessions, pageviews, orders, items, refunds, and analytical indexes.
3 KBExploratory SQL Analysis
Initial analysis of volume, channel mix, device mix, funnel conversion, and session-to-order joins.
2 KBDescriptive SQL Analysis
Business-health, channel, device, refund-risk, and repeat-session analysis supporting the capstone recommendations.
3 KBCorrelation & Metric SQL
PostgreSQL analysis of session depth, conversion, repeat-session behavior, and revenue quality.
2 KBEVIDENCE INTEGRITY
This case is presented as public learning dataset · historical analysis and planning estimates. The label distinguishes observed work from simulated impact, proxies, historical comparisons, or proposed architecture.