Proof of Work

Interactive Tools Built From Real-World Workflows

These aren't toy demos. Each tool replicates a framework I used professionally — ROAS modelling, statistical significance testing, and ICP lead scoring. All calculations are server-side Python running inside a Django backend.

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ROAS & Campaign Budget Planner
Input ad budget, CPC, conversion rate, and target ROAS. Receive instant projections for revenue, CPA, required conversion rate, max allowable CPC, and budget needed to hit your ROAS target. The same framework used across $45K/month in managed media.
  • Real-time ROAS projections
  • Max CPC & budget gap analysis
  • CPA / cost-per-conversion output
Open Tool
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A/B Test Significance Calculator
Enter visitor and conversion counts for Control (A) and Variant (B). Get chi-squared statistic, p-value, confidence percentage, conversion lift, and an automated deployment recommendation. Powered by scipy.stats on the server.
  • Chi-squared statistical test
  • Confidence level & lift %
  • Minimum sample size estimate
Open Tool
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ICP Lead Scoring Simulator
Enter prospect attributes: company size, industry vertical, budget range, purchase timeline, decision-maker role, and observed intent signals. Receive a weighted ICP score (0–100), tier (A/B/C/D), and a bespoke outbound sequence recommendation.
  • Weighted 6-factor scoring model
  • Tier classification A → D
  • Step-by-step outbound sequence
Open Tool
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How These Were Built

Each tool is a Django view that accepts a JSON POST request, runs the calculation in Python (including real scipy.stats chi-squared tests for the A/B calculator), and returns a JSON response rendered by vanilla JS. The stack: Django 4.2 · Python 3.11 · scipy · PostgreSQL · Railway. No black boxes — the same statistical rigour applied to 25+ concurrent A/B tests at GrowthPulse.