E-commerce & DTC Brands

Higher LTV and lower churn without manual retention outreach

We build a subscription retention system that predicts churn risk from skip and pause signals, surfaces the right save offer at the right moment, and stops the cancel before it happens.

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The problem

Sound familiar?

Cancellations arrive with no warning

A subscriber clicks cancel and that is the first signal you have. By the time they reach the cancel page, their intent is already firm. You need to catch the signal three steps earlier.

Your cancel flow is a static discount — every subscriber, every time

A 10% off offer shown to every subscriber regardless of their tenure, value, or reason for cancelling is not a retention strategy. It is a margin leak.

Skips and pauses are ignored as pre-churn signals

A subscriber who skips two consecutive deliveries is 4 times more likely to cancel within 60 days. If your system does not act on skip data, you are missing the clearest churn signal you have.

You have no visibility into which cohort has the worst retention

Without cohort-level retention analytics, you cannot tell whether the problem is acquisition channel, product category, or frequency cadence.

The solution

What we actually do

We instrument your Recharge or Skio subscription data, build a churn prediction model from skip and pause signals, and trigger personalised save offers at the moment of highest retention probability — before the cancel page.

What you get

What's included

Recharge or Skio data pipeline for skip, pause, and cancellation signals

Churn prediction model updated daily per subscriber

Pre-cancel intervention — personalised save offer triggered at churn-risk threshold

Cancel page optimisation with dynamic save offer matched to cancel reason

Cohort retention analytics dashboard by acquisition channel and product

A/B framework for testing save offer types (delay, discount, swap, gift)

The process

How it works

Audit

We analyse your current churn rate, skip frequency, and cancel reasons to establish baseline.

Model

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

Intervene

We deploy pre-cancel interventions and optimise the cancel flow with dynamic save offers.

Report

We track churn rate, save rate, and LTV movement and report monthly.

Proof it works

The Meatery

AI Voice CRM, Shopify multi-tenant

Read the case study

The offer

From $3,000/mo

Requires Recharge or Skio. Initial model build included in month one.

Common questions

Frequently asked

01Which subscription platforms do you support?

02Does this require changes to our Shopify theme?

03How long before we see retention improvement?

04Can we test multiple save offers simultaneously?

05Is TechEmulsion based offshore?

Ready to get started?

Let's build your subscription optimization system

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