Works
20 projects. 20 weeks.
Every problem, real SQL.
One analytics project published every week — activation funnels, retention models, dbt stack builds, and AI analytics. Representative scenarios built with real Snowflake SQL.
Published
Coming — one per week
Feature Adoption Heatmap — Which Features Drive Retention
60% of retained users used just 2 features
Activation Rate Segmentation by Acquisition Channel
CAC payback halved by shifting to high-activation channels
30-Day Churn Early Warning System
4 of 12 flagged at-risk accounts saved ($72K ARR)
LTV/CAC Payback Model in Snowflake
True payback period was 14 months, not 8
Revenue Attribution — First-Touch vs Last-Touch vs Linear
Found content drove 38% of pipeline (invisible in last-touch)
Trial-to-Paid Conversion Funnel Analysis
Trial conversion 12% → 21% with 1 email intervention
ARR Movement Dashboard (New / Expansion / Churn)
Surfaced 23% gross churn hidden by 31% expansion
Sales Velocity Dashboard
ACV up 35% after fixing qualification criteria
dbt Project Structure That Scales — For a 5-Person Startup
47 Looker PDTs migrated to dbt in 3 weeks, zero regressions
Snowflake Cost Optimization — From $8K/mo to $2.1K/mo
74% cost reduction, $70K saved annually
Automated Data Quality Monitoring
Mean time to detect failures: 3 days → 4 minutes
Event Tracking Schema Design From Scratch
340 events → 47, full coverage of 12 key product questions
Real-Time Metrics with Snowflake Streams
30-second metric freshness vs 4-hour batch — same contract
LLM-Powered Anomaly Detection on Business Metrics
Alert false positive rate dropped 89%
Natural Language Queries on Snowflake Data
CEO self-serves 80% of questions — 10s vs 48h turnaround
AI-Generated Weekly Business Narratives from Data
Weekly metrics write-up automated: 3 hours → 10 minutes review
Customer Health Score — Predict Expansion and Churn
83% churn prediction accuracy, $210K expansion pipeline surfaced
The D-Analytics Full Stack Audit — Methodology
Full audit in 5 days → CEO greenlit $60K stack investment