Email List Growth Forecast Calculator

Email list growth is a simple idea—more people join than leave—but the math of compounding churn makes the outcome unintuitive. A small daily unsubscribe rate can erase a surprising amount of acquisition over time, while a modest improvement in retention can lift the entire curve.

How to use this calculator

1. Current subscribers (S₀): your present list size (after recent cleanups if possible).
2. Daily new subscribers (N): average net new signups per day (exclude re-subscribes only if you track them separately).
3. Daily unsubscribe rate (%): the percent of your current list that unsubscribes each day. Example: 0.5 means 0.5% per day (0.005 as a decimal).
4. Days to project (t): how many days into the future you want a forecast.
5. Read the projected subscriber count and net growth; then adjust inputs to compare scenarios.

Formula: Subscriber growth model

We model your list as a discrete daily process with two forces:

• Acquisition: you add N new subscribers each day.
• Churn: you lose a constant fraction c of the current list each day.

Let:

• S₀ = starting subscribers
• N = daily new subscribers
• c = daily churn rate as a decimal (e.g., 0.5% → 0.005)
• t = number of days

Recurrence (day-by-day)

After one day:

S₁ = S₀(1 − c) + N

After two days:

S₂ = S₁(1 − c) + N, and so on.

Closed-form forecast (no simulation needed)

Iterating the recurrence yields a closed-form expression for day t (for c > 0):

If c = 0 (no churn), the model simplifies to linear growth:

S_t = S₀ + Nt

Introduction: Why an “equilibrium” appears

When churn is constant and applied to the current list, the system tends toward a steady-state (equilibrium) where daily additions are balanced by daily losses. In this model, that long-run level is:

Equilibrium subscribers ≈ N / c (for constant N and c)

That doesn’t mean your list stops growing immediately—it means the curve gradually flattens as you approach that level. Increasing N lifts the equilibrium; decreasing c both lifts it and makes you approach it faster.

Interpreting the results

• Projected subscribers: estimated list size after t days given the assumptions below.
• Net growth: St − S₀. Useful for goal-setting (“How many net adds will we have by Q2?”).
• Effective growth rate (if shown): often presented as a compounded rate over a month-equivalent period. Treat it as a summary metric for comparing scenarios, not as a guarantee of future performance.

Worked example

Suppose:

• S₀ = 1,000
• N = 25 new subscribers/day
• c = 0.5% per day → 0.005
• t = 180 days

Plugging these into the closed-form model yields a forecast of roughly 4,7xx subscribers after 180 days (exact value depends on rounding), for net growth of about 3,7xx. If you reduce churn to 0.2% (0.002) while keeping acquisition constant, the forecast rises substantially because the long-run equilibrium N/c increases from 5,000 to 12,500.

Scenario comparison table

The table below keeps S₀ = 1,000, N = 25/day, and t = 180 days, while varying churn. It illustrates how sensitive long-range outcomes are to retention:

Assumptions & limitations (read before relying on the forecast)

• Constant daily rates: assumes the same N and c every day. Real programs have seasonality, launches, and deliverability swings.
• Churn is applied to the current list: losses are modeled as c × S per day (a percentage of the list), not a fixed number.
• Unsubscribes only: does not separately model hard bounces, spam complaints, inbox placement, or suppression of inactive subscribers (list cleaning).
• No capacity constraints: assumes acquisition doesn’t slow as the list grows (in reality, audience saturation and channel limits can reduce N over time).
• Average behavior: treats all subscribers identically; it does not segment by cohort quality, source, or engagement level (which often have very different churn patterns).

FAQ

Is the unsubscribe rate daily or monthly?

This calculator uses a daily unsubscribe rate. If you have a monthly churn rate, convert it to a daily equivalent (see next question).

How do I convert monthly churn to daily churn?

If cm is monthly churn as a decimal (e.g., 12% → 0.12), an approximate daily rate over 30 days is:

c_d = 1 − (1 − c_m)^1/30

This keeps compounding consistent.

What if churn is 0?

If churn is truly zero, growth is linear: St = S₀ + Nt. In practice, most lists have non-zero churn once you include unsubscribes, bounces, and list hygiene.

Why does the forecast flatten over time?

Because as the list grows, a fixed percentage churn represents a larger absolute number of unsubscribes. Eventually, daily unsubscribes approach daily new signups, slowing net growth.

How can I make forecasts more realistic?

Run the calculator in segments (e.g., one month at a time) with different N and c for promotions, seasonality, or deliverability changes, and chain the ending subscribers as the next period’s starting value.