Your payment profile
Defaults reflect industry baselines: 3.00% (300 bps) total payment fees and a 30 bps chargeback rate. For feasibility, scenarios start from +20 / +60 / +120 bps of authorization uplift and scale with your headroom: lower current rates earn a larger uplift (up to 3×), rates near 99% a smaller one.
Loaded hourly cost uses an upper-market assumption by region and can be edited manually. It is intended to include salary, benefits, payroll costs, and overhead.
Impact scenario
What this scenario assumes
Conservative scenario: +20 bps assumed authorization uplift (applied: +30 bps, 84% → 84.3%) at a 30% contribution margin, 5% payment-fee optimization (300 → 285 bps), 10% of disputes avoided before they become chargebacks (30 → 27 bps), 20% of manual payment work automated, and +0% interaction effects.
Calculation logic
Authorization uplift
= baseline assumption of +20 / +60 / +120 bps (Conservative / Expected / Upside) × a
headroom multiplier of (99% − current rate) / 10, bounded to ×0.5–×3 and capped at
the 99% ceiling.
Recovered approved volume
= monthly attempted volume × 12 × authorization uplift.
Approval revenue impact
= recovered approved volume × the contribution margin you enter (30% by default, or
a vertical preset). Only the margin is counted, not the gross sale.
Payment fee optimization
= annual processed volume × total payment fee rate × 5% / 10% / 15% optimization
share.
Dispute fees avoided
= disputes avoided (10% / 20% / 30% of current chargebacks) × the fee your acquirer
charges per chargeback. Payments Intelligence surfaces disputes while they are still
disputes, before they become formal chargebacks. Resolving an alert means refunding
the cardholder, so
the transaction value is not retained and is not counted here
. What is saved is the dispute fee. Lower dispute ratios and less manual case
handling are real additional benefits that this model does not put a number on.
Team time savings
= people × hours/week × 52 × loaded hourly cost × 20% / 40% / 60% automation share.
Potential interaction effects
= (approval + fee + dispute value) × a fixed coefficient of 0% / 5% / 10% by
scenario. The Conservative case claims none. The coefficient reflects two documented
feedback loops: a lower chargeback ratio improves issuer risk scoring (which
compounds the authorization uplift), and leaner fees create room for more
competitive pricing.
Total annual run rate
= sum of the five value rows.
Year-1 realized
= 70.83% of run rate. Months 1 and 2 are implementation and claim nothing, month 3
runs at 25%, month 4 at 50%, month 5 at 75%, and full run rate is reached in month
6.
Cleverbridge: cost transparency and margin control
Payments Intelligence helped Cleverbridge turn fragmented PSP and fee data into clearer cost intelligence, stronger margin analysis, and reduced manual operational work. Read the case study .
The conservative scenario models $284k in annual run-rate value, of which $201k is realized in year one after the implementation ramp. It comes from a +30 bps authorization uplift, payment fee optimization, dispute fees avoided, and team productivity.
Payments Intelligence helps teams lift authorization rates, uncover payment-fee pressure, monitor chargeback exposure, and reduce manual investigation. The levers also reinforce each other: fewer chargebacks improve issuer risk scoring, and leaner fees fund sharper pricing. IXONav turns payment questions into actionable intelligence so teams can act faster when revenue or margin is at risk.
Value breakdown
A dispute resolved early is refunded to the cardholder, so the sale is not saved. What is saved is the $25 dispute fee on each of the ~302 disputes intercepted a year, plus a lower dispute ratio and less manual case work, neither of which is counted here.
The Conservative scenario claims no interaction effects. Every currency unit in the total is attributable to a single named lever.
Rows reconcile to the total before rounding. Directional estimate only. Actual impact depends on payment mix, implementation scope, data quality, vertical, and actions taken.
12-month value projection
Cumulative view. Months 1 and 2 are implementation and claim no value; the ramp runs 25% / 50% / 75% across months 3 to 5 and reaches full run rate in month 6, so year one realizes 70.83% of the annual run rate. Fee optimization typically lands at the slower end of this, since it depends on routing or acquirer changes.
Investment and payback
Nothing here assumes a price. At these inputs the model stays net-positive in year one at any annual investment below $201.331. That test uses year-one realized value rather than the run rate, so it is deliberately the harder one to pass.
See these figures tested against your own payment data.
