Email Marketing Funnel & Sender Reputation Simulator

Campaign Settings

Send frequency 3 / week

Subject-line quality 55%

Personalization / segmentation 40%

Live Funnel Stats

Open rate

0.0%

Click-through rate

0.0%

Sender reputation

List size

160

Funnel Legend

Landed in spam Delivered, not opened Opened, no click Clicked

How it works

Each of the ~160 simulated subscribers has a fixed affinity a_i (how relevant your content is to them) and a fatigue level f_i that rises with every send and decays between sends. A campaign send resolves in three stages:

Inbox placement:  P(inbox) = clamp(0.5 + (Reputation-50)/140, 0.05, 0.97)
Open (if inbox):  P(open)  = clamp(0.15 + 0.55 * subjectQ * a_i * (1-f_i), 0, 0.95)
Click (if open):  P(click) = clamp(0.05 + 0.50 * personalization * a_i, 0, 0.9)

Fatigue accumulates as f_i += 0.12·(1 − 0.5a_i) on every send and decays as f_i ·= e^{−0.6·Δdays} between sends, where Δdays = 7 / send-frequency — mailing more often leaves less time to recover, exactly like real subscriber fatigue.

Sender reputation moves toward the campaign's engagement rate and drops sharply on spam complaints — the same engagement-based filtering Gmail/Outlook use to decide inbox vs. spam placement:

Reputation += 8 * (openRate - 0.35) - 3 * complaints
Unsubscribe: P(unsub) = clamp(0.01 + 0.05*f_i*(1-0.7*personalization), 0, 0.3)

Send frequency — more sends per week means less recovery time between them, raising fatigue and unsubscribe risk.
Subject-line quality — drives the open decision directly.
Personalization / segmentation — drives click-through and cuts fatigue-driven unsubscribes.
Particles animate from the Send node to whichever stage they stopped at — spam (below), inbox-only, opened, or clicked (above) — one wave per campaign.

Day 0 Sent: 0 campaigns

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