Predictive Lead Scoring & Growth Funnel Simulator

Model Weights

Engagement weight w₁ 1.00

Budget-fit weight w₂ 1.00

Pursue threshold 50%

This Quarter (Q1)

Conversion rate

Model quality

Cumulative revenue growth vs Q1

Legend

Not pursued (score below threshold) Pursued, still deciding Pursued & converted Quarterly revenue bar

How it works

Each simulated lead has two hidden features — engagement (0–1) and budget fit (0–1). The true chance a lead converts follows a fixed logistic function nobody gets to see directly:

P(convert | lead) = σ(1.3·engagement + 0.9·budget − 1.1 + noise)
σ(z) = 1 / (1 + e^(−z))

Your predictive-analytics model only gets to estimate that probability from the two weights you control:

score(lead) = σ(w₁·engagement + w₂·budget − 1.1)

w₁, w₂ sliders — the feature weights your model assigns; the closer they track the true (unknown) 1.3 / 0.9, the more the model's ranking matches reality.
Pursue threshold — leads scoring above it get sales follow-up (moved to the lower funnel ring); leads below it are left alone. A low threshold chases everyone (wastes effort on weak leads); a high one only chases the safest bets (misses real opportunities).
Model quality — balanced accuracy: the average of how many true converters were correctly pursued (sensitivity) and how many true non-converters were correctly skipped (specificity). A model with badly mismatched weights hovers near 50% — no better than a coin flip.
Each quarter, revenue = converted leads × average deal size. Compounded quarter over quarter, better weight tuning translates directly into faster business growth — the orange bar chart on the right tracks cumulative revenue since Q1.

This mirrors real predictive-analytics deployments: the model never sees ground truth, only correlated features — the whole discipline is choosing weights (via regression, gradient boosting, etc.) that track the hidden outcome as closely as possible.

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