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Higher LTV and lower churn without manual retention outreach

We predict churn from skip and pause signals. The right save offer shows before the subscriber hits cancel.

Last updated September 2026. US LLC in Wyoming. Our team works in your timezone.

The problem

Sound familiar?

Cancellations arrive with no warning

A subscriber clicks cancel. That is your first signal. Too late.

Your cancel flow is one static discount

The same 10% off for everyone, whatever their tenure or reason. That is a margin leak, not retention.

Skips and pauses are ignored as pre-churn signals

Two skipped deliveries in a row means 4 times the cancel risk within 60 days.

No visibility into which cohort has the worst retention

Without cohort data, you cannot tell if the problem is the channel, product, or cadence.

What do we actually do?

A churn model built from your Recharge or Skio skip and pause signals. Save offers show before the cancel page, when they still work.

What changed in 2026?

In 2026 subscribers skip and pause from their phone in two taps, and two skips in a row mean 4 times the cancel risk within 60 days. The cancel page is too late. The save offer has to show at the skip, matched to the reason.

What's included?

  • Recharge or Skio pipeline for skip, pause, cancel signals
  • Churn prediction model updated daily per subscriber
  • Pre-cancel save offer triggered at churn-risk threshold
  • Cancel page with dynamic offer matched to cancel reason
  • Cohort retention dashboard by channel and product
  • A/B framework for save offers: delay, discount, swap, gift

How it works

01.

Audit

We baseline churn, skips, and cancel reasons.


02.

Model

We build a churn model from your Recharge or Skio data.


03.

Intervene

We launch pre-cancel offers and a dynamic cancel flow.


04.

Report

We track churn, save rate, and LTV monthly.


Frequently asked questions

Let's build your Subscription Optimization

Requires Recharge or Skio. Model build included in month one.

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