POE Insights

POE Insights Dashboard Demo

This static sample highlights payments performance, surfacing quick wins across volume, approvals, and optimization value.

Static sample data
Theme-aware styling
Coverage of core insights

Overview

Performance Overview

This static sample highlights payments performance, surfacing quick wins across volume, approvals, and optimization value.

Executive Summary

Your payment performance shows an 88.5% approval rate with $4.2M monthly revenue. We uncovered $47.5K per month in optimization value that can lift approval rate to 90.8% (+2.3pp).

Approval Rate

Trend
88.5%

+1.0pp vs industry avg

Total Opportunity

Trend
$47.5K

Monthly savings potential

Transaction Volume

Trend
50,000

Analyzed transactions

Monthly Revenue

Trend
$4.2M

Processed successfully

Daily Transaction Volume

Seven-day snapshot from the static sample

Approval Rate Trend

Illustrating incremental daily lifts across the week

CIT vs MIT

CIT vs MIT Performance

Static comparison pulled from the sample dashboard

Consumer-initiated payments outpace merchant-initiated flows; scheduled MIT keeps pace while unscheduled charges remain the biggest optimization lever.

Consumer Initiated (CIT)

Customer actively authorizes payment

65%
Transaction Volume
89.8%
Approval Rate
32,500 transactions
+2.8pp vs MIT

Characteristics

  • Customer present with real-time authorization context
  • Higher 3DS engagement (42% of CIT traffic)
  • Strong fraud signals reduce false declines
  • Immediate confirmation improves user confidence

Merchant Initiated (MIT)

Merchant charges stored credentials

35%
Transaction Volume
87.0%
Approval Rate
17,500 transactions
-2.8pp vs CIT

Characteristics

  • Customer absent—relies on stored credential updates
  • Limited 3DS usage (only 8% of MIT requests)
  • Expiration and credential mismatch drive declines
  • Best fit for subscriptions and installments

Transaction Mix

Distribution of CIT, scheduled MIT, and unscheduled MIT

Approval Rate Comparison

Highlighting the runway to optimize unscheduled MIT

Scheduled MIT

88.5%

13% of total volume • 6,500 transactions

Unscheduled MIT

84.2%

22% of volume • 11,000 transactions

Optimization Gap

$22.4K monthly lift

Close unscheduled MIT to scheduled MIT performance

3DS Usage

Sample data shows limited authentication on MIT flows

Top Decline Drivers

Contrasting CIT vs MIT declines to locate policy gaps

PSP Performance

PSP Performance Snapshot

Sample metrics for Adyen, Stripe, Braintree, and Checkout.com highlight approval lift and token adoption gaps.

AdyenApproval 91.2%Token Adoption 42%
StripeApproval 88.9%Token Adoption 38%
BraintreeApproval 87.3%Token Adoption 31%
Checkout.comApproval 86.7%Token Adoption 22%

Approval Rate Distribution

How each PSP performs in the static data slice

Token Adoption

Comparing network token usage across the PSP mix

Optimization Opportunities

Optimization Opportunities Snapshot

Five sample levers ranked by potential monthly savings to illustrate prioritization.

Savings Shortlist

  • Network Tokens$15,200.0
  • 3DS Optimization$12,800.0
  • CVV/AVS$11,400.0
  • MIT Optimization$8,900.0
  • Debit Routing$5,600.0

Potential Monthly Savings

Horizontal bars make the priority order obvious

Additional Insights

Additional Insights Overview

Sample views cover hourly performance, card brand variance, token impact, and the top decline drivers.

Hourly Approval Pattern

Spotting the afternoon dip in the sample data

Card Brand Performance

Comparing success rates by network

Token vs Non-Token

Tokenization unlocks higher success in the sample

Top Decline Reasons

Ranking the most common failure triggers

Approval rate slips to 86% between 14:00 and 17:00—enable smart retries during that window.

Visa holds steady at 91.2% while Amex trails at 79.8%; tighten 3DS or risk checks on Amex portfolios.