Because “30 days” feels clean in a pricing table. But financially it’s one of the fastest ways to turn a high-LTV customer into a churned customer. When you ship faster than consumption, you don’t create recurring revenue — you create inventory pile-up, guilt, skip behavior, and eventually cancellations. And cancellations are the most expensive kind of churn because you already “won” the customer.
Here’s the story I see constantly.
A brand launches subscriptions and defaults everyone to 30 days. It looks good at first:
Subscription sign-ups increase.
“Recurring revenue” looks healthy.
The team celebrates an “LTV unlock.”
Then 60–90 days later, support tickets spike:
“I have too much product.”
“Can I pause?”
“I didn’t even open the last one.”
“Cancel my subscription.”
Marketing thinks the fix is better onboarding. Product thinks it’s a better bundle. Finance thinks subscriptions are “overrated.”
The truth is simpler: the cadence is wrong.
You didn’t build a subscription business. You built a forced shipment schedule that fights how customers actually use the product.
That chart is the whole game:
Red line (30d): inventory climbs → feels like waste → guilt → cancel
Green line (60d): customer hits empty → appreciates refill → stays subscribed
5 principles that change how you run subscriptions
Subscription churn is usually “pace churn,” not “product churn.”
Most customers don’t cancel because they dislike the product. They cancel because the brand made them feel stupid: too much inventory, too many boxes, too many reminders. That’s an experience failure, not a product failure.
Skipping is not retention — it’s delayed cancellation.
Skip rate is one of the cleanest early warnings. If customers are repeatedly skipping, your cadence is misaligned with consumption. If your system treats skipping as “success” because the subscription is still active, you’re reading the wrong signal.
Cadence is a unit economics lever, not a settings page.
Over-shipping increases support load, increases refund risk, increases discount pressure (“here’s a win-back offer”), and lowers net CM per customer. If you measure subscription performance only by “active subscribers,” you’ll miss the margin leak.
“Median replenish time” is not the same as “best default cadence.”
You shouldn’t ship at the median. You should ship slightly before the expected run-out window for the majority of customers — and allow flexible adjustment. The goal is: the box arrives when it feels like relief, not clutter.
Different customers consume at different speeds — your subscription should adapt.
One customer uses daily, another uses occasionally, another stockpiles. A single fixed cadence assumes everyone is identical. The winning model is: default cadence based on real data + easy self-adjustment + proactive “pace correction” when behavior signals misalignment.
Practical actions you can implement fast
Build one “Days of Supply” model per subscription product.
You don’t need perfection. Start with a pragmatic estimate:
average consumption rate (based on observed reorder timing)
typical usage variance (fast users vs slow users)
median time-to-reorder for buyers of that SKU
If your replenishment data says “most people reorder around day 55–70,” 30-day shipping is self-sabotage.
Create a cadence scorecard (and stop using “active subs” as the KPI).
Track these by cadence option (30/45/60/90):
skip rate
cancel rate
return/refund rate
support tickets per 100 subscribers
net contribution margin per subscriber over 90–180 days
The best cadence is the one that maximizes net CM and retention, not the one that ships the most boxes.
Change your default cadence to match consumption — and make “speeding up” easier than “slowing down.”
Most brands do the opposite: they default too fast and then make pausing painful. Flip it:
default to the most common replenishment window (often 45–60 days)
let fast users easily shorten cadence
nudge slow users toward longer cadence before they hit “cancel”
This protects retention without harming the customers who genuinely consume faster.
Add a “pace correction” flow at day 25–35 (before the second shipment becomes the breaking point).
This is where most cancellations start forming. Trigger a lightweight message:
“How’s your pace?”
“Want to move to 45/60 days?”
“Pause until you’re closer to empty.”
Position it as service, not as churn prevention. You’re not “saving a subscription.” You’re preventing clutter.
Treat cadence like an experiment, not a permanent rule.
Run a clean test:
Cohort A defaults to 30 days
Cohort B defaults to 60 days
Compare at 90 days: cancels, skips, refunds, support tickets, net CM
You may see fewer shipments in the 60-day cohort — and more retained customers. That’s the whole point. A smaller “monthly recurring revenue” number can still produce higher total profit over time.
The payoff: compounding, not clutter
Subscriptions are supposed to create:
predictable repeat behavior,
lower CAC dependence,
higher lifetime margin per customer.
But if your cadence is too aggressive, you’ll get the opposite:
a short-lived subscription spike,
a wave of cancellations,
and a customer file trained to associate your brand with “too much stuff.”
Align cadence to consumption, and the psychology flips:
the refill feels helpful, not wasteful — and retention becomes natural.
-Alex
Frequently Asked Questions
How do you calculate “median replenish time” cleanly—by SKU, by customer segment, by first vs repeat buyers? I’ve seen averages get skewed by power users and promo spikes.
I prefer median specifically because it’s less skewed by power users. Then I segment it at least by SKU (or SKU family) and by first-time vs repeat buyers, because reorder behavior changes once people settle into a routine. If you have enough volume, layer in a few meaningful segments (e.g., bundles vs singles, subscription vs non-subscription). The goal is a cadence that reflects how most people actually consume, not how a few super-users behave.
What’s the best way to operationalize this when consumption varies a lot by customer (household size, usage rate)? Do you offer 2–3 preset cadences, or do you let customers choose and then guide them with data?
Start with 2–3 data-backed presets (e.g., “most popular,” “lighter use,” “heavy use”) so it’s simple. Then let customers customize, but guide them with plain-language cues like “this matches how customers usually reorder.” Over time, you can personalize based on their actual behavior (skips, early reorders, pauses). The point is to reduce decision friction while avoiding a one-size-fits-none default.
Do you ever default longer?
Yes—defaulting slightly longer is often safer than defaulting too short. Too short creates immediate pressure and clutter, which drives skips and cancellations. A slightly longer default still lets “heavy users” pull forward, but it doesn’t punish the median customer. Default should protect retention, not maximize early shipments.
How do you handle bundles or multi-pack subscriptions where the “product amount” changes the cadence? A 60-day product becomes 120 days if they buy 2x — do you model replenish time in “units consumed” instead of days?
Exactly—think in units (or “days of supply”), not just calendar days. If someone subscribes to a 2-pack, the cadence should expand accordingly, otherwise you’re creating inventory buildup. The clean approach is: estimate usage rate, translate purchase size into “coverage,” then set cadence to match that coverage for the median user. It also makes your messaging clearer: “this will last about X weeks.”
What metric tells you you nailed it: lower churn, lower skip rate, fewer support contacts, higher LTV? And how long do you wait before concluding the new cadence is better?
I’d watch skip rate and churn first (they move fastest), then validate with LTV / CM1 per subscriber over a longer window. Support tickets are a great qualitative proxy too—if cadence is wrong, people complain. Give it at least one full cycle or two cycles of the new cadence (so 60–120 days depending on the product) before making a final call. The win isn’t “more shipments,” it’s “more durable subscribers with healthier margin.”
