Methodology
How we compute your checkout loss.
Every report uses the same formulas, applied per store. Here's what goes into the numbers, where the data comes from, and where the model has limits.
01 · Data sources
Where the numbers come from.
We aggregate public commerce signals across 1M+ stores: annual sales, payment technology stack, country, and business size. Numbers are computed per store using the formulas below.
Starter<$1Mn = 914,683
Growth$1M–$10Mn = 90,893
Scale$10M–$50Mn = 5,142
Enterprise$50M+n = 633
See aggregate Shopify checkout abandonment benchmarks: median loss by store size, drawn from this dataset.
02 · Cart and checkout
The 70% baseline.
Cart abandonment is the single largest source of leak in e-commerce. Baymard Institute's meta-research puts the average cart-abandonment rate at ~70%. We apply that to annual sales, then split 70/30 across cart and checkout.
monthly_loss_cart_and_checkout = annual_sales × 0.70 / 12 cart_loss = monthly_loss × 0.70 checkout_loss = monthly_loss × 0.30
03 · Payment failures
The layer most miss.
Failed transactions disappear from default dashboards without instrumentation. We compute a per-store failure rate from a segment baseline, modulated by the store's payment tech stack. Rates are clamped to [4%, 15%].
base_rate = { starter: 10%, growth: 9%, scale: 7%, enterprise: 5% }
rate += 1% if no BNPL signal (no Klarna/Affirm/Afterpay)
rate += 1% if no multi-PSP signal (no PayPal AND Stripe/Adyen/Braintree)
rate -= 0.5% if annual_sales ≥ $5M (Plus-grade stack proxy)
rate = clamp(rate, 0.04, 0.15)
attempted_revenue = annual_sales / (1 - rate)
payment_failures_loss = annual_sales × (rate / (1 - rate)) / 12Worked example · $5M growth store
Base rate 9% + 1% (no BNPL) + 1% (no multi-PSP) − 0.5% (≥$5M Plus proxy) = 10.5%. Monthly payment-failure loss ≈ $5M × (0.105 / 0.895) / 12 ≈ $48.9K / month.
04 · What Freway closes
How much we close.
Each stage has its own close rate, drawn from production performance across our deployments.
Cart abandonment30%Better-timed nudges and in-flow assistance bring qualified visitors back.
Checkout abandonment30%Removing friction (forced accounts, surprise costs) and real-time objection handling.
Payment failures50%Smart retry, soft-decline handling, and PSP-rail orchestration close most decline cases.
Total monthly sales closed = cart_loss × 30% + checkout_loss × 30% + payment_failures × 50%.
05 · Limitations
Where the model has limits.
- The figures are based on each store's estimated annual sales. If a store's actual revenue differs from that estimate, its loss numbers shift proportionally.
- Segment medians are aggregates, not store-specific. They're shown as peer context, not as your forecast.
- Payment-failure rates are modeled from segment baselines and tech-stack signals, not real-time auth data. The clamp to [4%, 15%] keeps the model bounded.
- Stores not in our dataset fall back to growth-segment medians and are labelled clearly in the report.
Updated 2026-05-20
06 · More methodology
The rest of the stack.
The numbers above explain the calculator. The pages below explain how Janine actually decides whether to engage a shopper, and how the commission model maps to closed checkouts.
From first visit to repeat purchase
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