Software / Payment Recovery

Payment Recovery

The Best Failed Payment Recovery Built for Shopify

Implement measurable workflows, not one-off campaigns, to continuously optimize your payment recovery using AI-powered features and expert level support.

One System for Retry Logic, Dunning and Site Experience

A failed payment is a sequence, not a moment. MaxLTV runs all of it, so each piece acts on what the others have already done.

  • Deploy faster. The whole sequence ships at once, not tool by tool.
  • Test and learn. An experiment moves the retry timing and the message together, and returns a single result.
  • Recover more. MaxLTV can notify the customer, offer an incentive, improve card update UX, place the charge, and measure outcomes, so you recover more payments.

More in Your Dunning Emails and Retry Logic Belong Together.

01 · AI-powered retry logic

We retry when the card will actually authorize

Every decline carries a reason. We read it and treat it accordingly, instead of charging the same card on the same clock until the sequence runs out.

  • Decline-code intelligence: Leading AI models classify each failure and place the retry when that card is most likely to authorize, not on a fixed interval that ignores when the account gets funded. We don’t retry cards that will never authorize.
  • Backup payment methods: When the primary card is dead, we automatically attempt the other payment methods already on file rather than waiting on the customer to act.
  • Tiered discounts at the charge: After repeated declines we retry at a reduced amount. A smaller charge clears a thin balance that the full amount won’t, and a recovered subscriber at a discount beats a lapsed one at full price.

Learn more about passive churn and decline analysis

Card errors and decline handling in MaxLTV

02 · Dunning communication

Every message is personalized to the subscriber

The fastest recovery is the customer fixing the card themselves. Email and SMS both run from this layer, and everything here exists to make that easy and worth doing now.

  • Personalized flows: Messaging adapts to the decline reason, the product they subscribe to, their history and their value. An expired card and an empty account are not the same conversation.
  • Incentives to inspire action: Updates are incentivized, not nagged for. Customers get something for fixing the card now, and the moment they do, the charge goes through.
  • Your emails, your control: Every message is editable from the app and sent from your own verified domain. You see what goes out, and you change it without filing a ticket.

Read our dunning best practices, including the 30-day sequence

Update payment method without logging in

MaxLTV can test offers to optimize your recovery rates

Variant A

“10% off if you update in the next three days”

Variant B

“Save $5 if you update your card in the next 24 hours”

Failed payment banner on a subscription site, reading we could not process your last payment, with an Update my card button

03 · Website notifications

We reach them on your site and reduce friction

Email is one channel and it is crowded. A subscriber who ignored three emails still visits your store, and when they do they are already engaged and already signed in.

  • Payment failure banners: Subscribers with an open failure see it the moment they land, in context, with a one-click path to fix it. No searching their inbox for an email they never opened.
  • Card updates without a login: No password reset, no account page, no hunting for the subscription. Fewer steps means more completed updates, which is the whole game.
  • Prompts for any segment you can target: The same on-site system runs messages for whoever you choose: subscribers your churn model flags as high risk, lapsed customers who come back to browse, or anyone carrying a tag you set.

04 · Decline intelligence

AI Intelligence with Human Judgement

Top AI models classify declines, look at user behavior, and propose what to try next. Every change is queued for testing and approved by a human.

Monthly Tests to Improve Retry Logic

Each month the model reads your recoveries and proposes experiments. Each runs as a change set against your live configuration: new failures split between control and variant, one test at a time.

Dunning Email Experiments and Analysis

Flows run variants side by side. Sends, opens and clicks sit beside recovery rate, so when recovery moves you can tell whether the timing changed or the message did.

Humans in the Loop

A suggested rule arrives switched off; until someone enables it, that decline is handled at full price only. Experiments start when a person approves and land when a person adopts. The model can never reclassify a decline, disable a rule or reorder your table.

Frequently asked questions

How is MaxLTV’s retry logic different from my platform’s?

Most retry logic runs on a fixed schedule that ignores why the charge failed. MaxLTV classifies every decline and places the retry when that card is most likely to authorize, falls back to the other payment methods already on file when the primary card is dead, and retries at a reduced amount after repeated declines.

How is this different from the dunning built into my subscription platform?

Native dunning retries on a fixed schedule and sends one email. MaxLTV manages the retry itself, placing it when the card will actually authorize, then personalizes every message to the subscriber, prompts them on your site, and reports recovery split by retries versus card updates.

Do I have to migrate off Recharge, Skio, Stay.ai or Ordergroove?

No. MaxLTV installs as a Shopify app and runs on top of your subscription platform, with no migration and no checkout change. MaxLTV takes over retry timing and subscriber messaging; your platform keeps managing the subscriptions themselves.

What does the AI actually decide?

It classifies declines it has not seen before and proposes rules and experiments. It does not change your configuration. A rule the model suggests arrives switched off until a person enables it, an experiment runs only once a person approves it, and the change set is written into your live rules only when a person adopts it. The model cannot reclassify a decline type, disable a rule or reorder your rule table at all.

Why offer a discount to recover a failed payment?

Brands already offer a save when a customer clicks cancel. A subscriber about to be auto-cancelled for repeated payment failures is just as close to churning and usually gets nothing but reminders. The discount is offered late in the sequence, only to subscribers who have not acted, tied to updating the card, and with a short window so it stays an incentive rather than an entitlement.

Ready to recover more?

Bring your failed-payment export to the free churn audit. We will show you which failures are recoverable, what they are worth, and which surface is leaking most.

Background reading: dunning best practices and how to measure recovery rate. Compare: vs ChurnBuster, vs FlyCode. By platform: Recharge, Skio, Stay.ai, Ordergroove.

Free Churn Audit