Recovery Rate Benchmarks:
What to Expect After Installing Quarvo
Honest numbers, segmented properly. This is what merchants should plan for in months 1, 3, and 6 of a Quarvo deployment — by AOV band, by vertical, and by traffic source. No averages that hide the variance. The point is to set expectations that hold up under operating reality.
Of every 100 declined high-ticket transactions where Quarvo's recovery surface is shown, 60 complete via multi-card splitting. The number is consistent across pre-launch pilots and in line with the underlying behavioral mechanics — the customer has intent, has combined credit, and is still on the page.
How we define recovery rate
Before any benchmark numbers are useful, the definition has to be tight. Different vendors define recovery rate in incompatible ways — some include declines outside their addressable cohort, some include retried-and-eventually-paid transactions over multi-day windows. Both make the number meaningless.
Quarvo's definition is narrow and honest:
Recovery rate = (transactions completed via multi-card splitting) ÷ (declined transactions where the splitting surface was offered to the customer). Window: in-session, single attempt. Excludes declines where the recovery surface wasn't shown — fraud blocks, expired cards, address mismatches, networks where the customer has only one card on file.
The denominator is the addressable cohort: declines where the customer plausibly has more credit on a second card and could complete the transaction with our help. The numerator is recoveries that actually happened in the same checkout session, without the customer leaving the page. That's the number that matters for merchant economics.
Recovery by AOV band
AOV affects recovery rate in a predictable direction: higher AOV recovers slightly higher. The reason is selection bias on intent — customers who reach checkout on a $4,000 cart have, on average, more committed purchase intent than customers on a $400 cart. They're more willing to do the small extra work of adding a second card.
Recovery climbs with AOV until somewhere around $15K, where the curve flattens and variance widens. At very high AOV, the cohort gets harder to characterize — these are often B2B-style purchases where the buyer's process is multi-step (procurement approval, manual card limit increase requests, etc.) and a single in-session recovery surface no longer captures the full opportunity.
Recovery by vertical
Vertical matters more than AOV for predicting recovery rate. The driver is whether the purchase is need-driven or want-driven. Need-driven purchases (medical, professional tools, replacement appliances) recover at much higher rates than want-driven luxury.
The asymmetry is clean. A customer buying $3,200 of dental work because they need a procedure done in two weeks will do whatever it takes to complete the transaction. A customer buying a $3,200 watch because they saw it on Instagram will rationalize the decline as a "sign" half the time and walk away.
For merchants in want-driven categories, the recovery rate is still strongly positive economically — 50% recovery on a 6.5% decline cohort is still a 3% topline lift — but the modeling and expectation-setting need to reflect the reality of the buyer.
Recovery by traffic source
The third dimension that matters: how the customer arrived. Traffic source is a proxy for purchase intent at the moment of decline. Customers from organic search recover better than customers from paid social, almost regardless of vertical.
The 20-point gap between organic search and paid social is one of the most consistent findings in the data. It's not about whether the recovery option works — it does in both cases. It's about how much friction the customer is willing to tolerate after a decline. Organic search visitors will do almost anything to complete the purchase. Paid social visitors are halfway out the door before the decline even hits.
If your traffic mix is heavily weighted toward paid social — say >60% of high-AOV attempts — your blended recovery rate will land below the 60% headline number. Plan for 50–55% in that case. The economics still work strongly; just don't model the headline.
The ramp curve — months 1, 3, and 6
Recovery rates are not constant from day one. They ramp over the first 4–6 weeks as customers (and merchant teams) learn the surface exists. Here's what to expect across the early ramp:
Month 1 — the early-adopter window
Customers seeing the recovery surface for the first time take a moment to understand what's being offered. Some hesitate or abandon despite the option being right. Recovery rate runs ~55% of steady-state. The merchant operations team is also still learning — adjusting the prompt copy, refining when the surface appears.
Month 2–3 — the learning curve
Word-of-mouth among repeat customers spreads. Customers who used the split once become more likely to try it on subsequent declines. The merchant's operations team has tuned the prompt timing and language. Recovery climbs into the 70–80% of steady-state range.
Month 4–6 — steady-state
The surface is part of the merchant's normal checkout experience. New customers see it as a familiar payment option (alongside Apple Pay, etc.), and returning customers reach for it pre-emptively when they know a purchase is large. Recovery is at full benchmark — typically 58–66% depending on vertical and traffic mix.
The recovery rate at month 1 is a leading indicator, not the steady-state number. Plan for the ramp curve. Merchants who measure month 1 in isolation and conclude "the recovery rate is lower than promised" are reading the ramp, not the level.
Run your own numbers.
The ROI calculator on quarvo.io takes your AOV and monthly orders and models the recovery yield against a 60% benchmark. Adjust the inputs to match your vertical for sharper estimates.
Open the ROI calculator →// MEDIAN MERCHANT · 60% RECOVERY · 2% PER RECOVERED SALE
What "honest benchmark" means here
Three commitments behind the numbers in this post, since the temptation in launch-stage marketing is always to publish the high end of the range:
- Pilots, not aspirations. The numbers are derived from pre-launch pilots and from analogous in-page recovery surfaces in adjacent commerce. They are not modeled from theoretical maximums.
- Variance, not just medians. Every benchmark in this post includes a variance band. Anyone publishing a single point estimate without variance is either lying or hasn't run the analysis. The variance is real and operationally important.
- Cohort definitions are stated. The headline 60% number is recovery on the addressable decline cohort — not on all declines, not on all checkout failures, not on all cart abandonment. The denominator is what we can recover. We don't pad it.
When real Quarvo merchant data flows in volume, this post will be updated with the actual cohort numbers — and the methodology section above is the public commitment to keep the comparisons apples-to-apples as the data evolves.