From Declined to Delivered:
The Merchant Playbook
for High-AOV Recovery

Audit your decline funnel

Before you install anything, you need a baseline. Most merchants don't have a clean read on their decline cohort — declines get lumped into a generic "cart abandonment" bucket that hides the recoverable subset. The first job is to pull the data and segment it properly.

Pull 90 days of payment data from your PSP (Stripe, Adyen, etc.) and segment declines by:

• AOV band — group by $0–$500, $500–$1,500, $1,500–$3,000, $3,000–$7,500, $7,500+. Decline rates rise non-linearly with AOV; you need this granularity.
• Decline reason code — separate insufficient_funds and limit_exceeded (the recoverable cohort) from card_declined_generic, fraud_blocked, expired_card, address_mismatch (not the recoverable cohort).
• Customer cohort — new vs. returning. Returning customers' declines are nearly always credit-limit related (fraud rules trust them); new customers have a wider decline reason mix.

The output is a single number: your addressable decline cohort size. For most high-AOV merchants this lands at 4–8% of attempts on AOV bands above $1,500.

Segment the high-AOV cohort

Not every dollar of decline GMV is created equal. Some cohorts have higher CAC waste behind them. Some have higher LTV implications when they convert. The Week 2 work is connecting the decline data from Step 1 to your customer-economics data so you know which AOV band × vertical × traffic source combination is the most expensive to lose.

Cross-reference the decline cohort against three datasets:

• CAC by source. Paid social CAC at $80–$150 means every lost paid-social customer is double-loss: lost revenue + wasted acquisition spend. Factor this in.
• LTV by cohort. First-purchase declines have a hidden cost: ~30% of declined first-time customers never come back. Their LTV is zero. Returning-customer declines retain LTV better but still carry friction cost.
• Vertical recovery rate (use the benchmark numbers from our recovery benchmarks post). Need-driven verticals recover 70%+; want-driven recover 50%. Apply the right multiplier per cohort.

The output of Week 2 is a prioritized cohort matrix: which AOV-band × vertical pairs deliver the highest expected recovery yield. This becomes your soft-launch target in Step 3.

Install recovery at the decline boundary

Step 3 is the technical install. Quarvo runs as a Stripe Connect platform on top of your existing Stripe account — the merchant doesn't change processors, doesn't reroute funds, doesn't restructure their checkout. The recovery surface only triggers when a card declines for an insufficient-credit reason.

Three integration paths exist depending on your stack:

Whichever path you take, the soft-launch flow is the same. Phase the rollout to maintain a control group:

Measure topline lift, not just recovery rate

The most common measurement mistake at this stage is reporting recovery rate as the success metric. Recovery rate is operational — it tells you whether the surface is working. The metric that matters for the business case is topline lift on high-AOV revenue.

Track three layers, each one for a different audience:

The third metric is the one to lead with. "Recovery rate is 62%" means nothing to a CEO. "We lifted high-AOV revenue by 4.2% with no other catalog or marketing changes" means everything.

The 60-day attribution window is important: this is when the recovery cohort is large enough that the lift becomes statistically reliable. Earlier than that and the variance dominates the signal. Beyond that, you can begin tracking second-order effects:

• Repeat-purchase rate from recovered customers. First-time buyers who almost-decline-then-recover have shown to repeat at 1.3–1.5x the rate of normal first-time buyers — likely because the rescued purchase has stronger emotional anchoring.
• Reduction in customer support load. The "why was my card declined?" ticket category drops sharply. Most pilots see 30–50% fewer payment-related tickets.
• NPS / brand sentiment. Harder to measure rigorously but surveyable. Customers who recover via Quarvo report meaningfully higher satisfaction with the merchant than the broader baseline.