Every card brand that touches your Stripe account carries a different cost profile. Visa might process at 2.9% while Amex takes 3.5%. Mastercard might succeed 96% of the time while Discover fails on every tenth charge. These differences compound across thousands of transactions into real money — money you are either keeping or quietly losing. This analysis breaks your Stripe payment data down by card brand so you can see exactly where the money goes and where it leaks.
Why Card Brand Performance Matters
Most merchants treat card acceptance as a binary: you either take cards or you do not. But once you look beneath the surface, each card network behaves differently in ways that directly affect your bottom line. Visa and Mastercard dominate transaction volume in most markets, but they are not interchangeable. Their interchange rates, decline patterns, chargeback tendencies, and geographic strengths all vary. Add American Express, Discover, JCB, and UnionPay to the mix and the complexity multiplies.
Consider a SaaS company processing $200,000 per month through Stripe. If Amex transactions carry a 3.5% effective fee rate while Visa sits at 2.9%, that 0.6% gap on the 20% of revenue that comes through Amex cards costs $240 per month — nearly $3,000 per year — on Amex volume alone. Now factor in that Amex might also decline at 12% versus Visa's 5%, and those failed charges represent lost subscriptions, involuntary churn, and support tickets. The numbers add up fast when you stop treating all card brands as equivalent.
This is not about dropping a card network. It is about understanding the cost and reliability profile of each brand so you can make informed decisions about payment routing, retry logic, checkout optimization, and fee negotiation. The data is already sitting in your Stripe export — you just need to slice it correctly.
What the Report Shows You
The analysis produces seven report sections, each addressing a specific dimension of card brand performance. Together they give you a complete picture of how each card network performs in your business.
Overview and Data Pipeline
The report opens with a summary of the dataset scope: how many transactions were analyzed, the date range covered, the total volume processed, and how many distinct card brands appear in your data. The preprocessing card shows how the data was cleaned — any records excluded due to missing card brand, incomplete amounts, or non-card payment methods (like bank transfers or ACH) that do not belong in a card brand comparison. This transparency matters because dirty data produces misleading brand comparisons. If 15% of your transactions have blank card brand fields, you need to know that before drawing conclusions.
Transaction Volume by Brand
This chart shows how your total transaction count and revenue break down across card brands. In most Western markets, Visa and Mastercard together account for 70-85% of card volume. The interesting question is what the remaining 15-30% looks like. If American Express makes up 18% of your transactions but only 12% of your revenue, that tells you Amex cardholders tend toward smaller purchases — which might inform pricing or bundling strategies. Conversely, if Amex drives disproportionately high average order values, the higher processing fees might be worth the trade-off.
Volume concentration also matters for statistical reliability. If Discover accounts for only 8 transactions out of 2,000, any metrics calculated for Discover — success rates, average fees, geographic distribution — are not statistically meaningful. The report flags brands with fewer than 10 transactions so you know which numbers to trust and which to treat as anecdotal.
Payment Success Rates
This is often the most actionable section of the report. It breaks down every card brand's transactions into succeeded, failed, and refunded categories. A healthy success rate for card payments is above 95%. If any brand consistently falls below 90%, something needs investigation.
The causes of brand-specific declines vary. Amex tends to have higher decline rates partly because of more aggressive fraud screening and partly because of stricter issuer-level controls. International cards often fail more than domestic ones due to 3D Secure requirements, currency conversion issues, or issuer blocks on cross-border transactions. Some declines are recoverable — a soft decline from insufficient funds might succeed on retry — while hard declines from stolen card flags are permanent.
The practical question this chart answers: which card brands are costing you the most in failed transactions? If you process 500 Amex charges per month at an 88% success rate, that is 60 failed transactions. At a $75 average order value, that is $4,500 in potentially recoverable revenue. Smart retry logic, improved 3D Secure flows, or routing adjustments could recover a significant portion.
Processing Fee Analysis
Every card charge incurs a processing fee — typically 2.5% to 4.0% depending on the card brand, card type (credit vs. debit, consumer vs. commercial), and whether the transaction is domestic or cross-border. This chart shows the average effective fee rate for each card brand, calculated as total fees divided by total successful transaction amount.
Stripe's pricing is transparent, but the underlying interchange fees are not uniform. Visa debit cards typically carry lower interchange than Visa credit cards. American Express has historically charged higher merchant fees, though the gap has narrowed. Corporate and purchasing cards from any network tend to carry premium interchange rates. By seeing the effective rate per brand, you can identify whether certain brands are disproportionately expensive and whether the volume they bring justifies the cost.
For businesses processing significant volume, even small fee differences compound. A merchant doing $1 million per month who shifts 5% of volume from a 3.4% brand to a 2.8% brand through smart routing saves $3,000 per month. This chart tells you where those opportunities exist in your specific payment data.
Transaction Amount Distribution
Box plots show the distribution of transaction sizes for each card brand. This reveals spending patterns that averages alone would hide. You might discover that Amex transactions cluster in a higher range ($100-$300) while Visa transactions spread broadly from $10 to $500. Or that Mastercard has more outlier transactions — very large or very small charges — than other brands.
These patterns matter for fraud prevention, pricing strategy, and customer segmentation. If premium card brands correlate with higher spending, that validates offering enhanced service or loyalty benefits to those customers. If a particular brand shows unusually high variance, it might indicate a mix of consumer and corporate cards that deserve separate treatment in your checkout flow.
Geographic Brand Distribution
If your Stripe data includes the card-issuing country, this section maps card brand usage by geography. The results are often eye-opening for businesses expanding internationally. Visa and Mastercard are near-universal, but their relative share varies by market. In the UK, Visa debit dominates. In Germany, Mastercard holds a stronger position. In Japan, JCB is a major player. In China, UnionPay is essential.
This geographic view answers a strategic question: are you losing sales in specific markets because customers cannot pay with their preferred card? If you see high traffic from a country but low conversion, check whether the dominant local card brand is one you accept and process efficiently. Adding local payment method support or optimizing routing for region-specific brands can increase conversion by 10-25% in underserved markets.
Brand Summary Table
The comprehensive KPI table consolidates every metric into a single view: transaction count, total volume, average transaction size, success rate, average fee rate, total fees paid, and refund rate — all broken down by card brand. This is the table you export and share with your finance team or payment operations manager. It is the executive summary of your card brand economics.
What Data Do You Need?
You need a CSV export from Stripe containing your payment transaction data. The essential columns are: card brand (Visa, Mastercard, American Express, etc.), transaction amount, processing fee, payment status (succeeded, failed, refunded), and transaction date. Optional but valuable columns include card-issuing country (for geographic analysis), payment method type (to filter out non-card transactions), net amount, and refunded amount.
Stripe makes this data easy to export. In your Stripe Dashboard, go to Payments, click Export, and download a CSV. The analysis works with raw Stripe exports — no reformatting or column renaming required. The column mapping step in the tool lets you tell it which columns correspond to which fields, handling any naming differences automatically.
For meaningful results, aim for at least 50 transactions total, with 10 or more per major card brand. Three months of payment data is a good starting point — it smooths out weekly fluctuations and gives enough volume per brand for reliable statistics. For businesses with high transaction counts, a full year of data reveals seasonal patterns in brand usage and fee rates.
Common Mistakes to Avoid
The most frequent error is including non-card payment methods in the analysis. If your Stripe account processes ACH transfers, bank debits, or Apple Pay (which may route through different networks), those transactions often have a blank card brand field. Including them inflates the "unknown brand" category and dilutes the brand-specific metrics. Filter your export to card payments before uploading, or use the payment method column mapping to let the tool handle it automatically.
Another common mistake is comparing success rates without considering volume. A card brand with 3 transactions and a 100% success rate is not outperforming Visa at 95% success across 1,500 transactions. The small-sample result is not statistically meaningful — three more transactions could drop it to 50%. The report flags low-volume brands, but it is worth keeping this in mind when interpreting results and making business decisions.
Finally, do not assume that higher fees always mean worse economics. American Express cardholders tend to spend more per transaction, return items less frequently, and have higher lifetime value in many business categories. A brand that costs 0.5% more to process but delivers 20% higher average order values is a net positive. The transaction amount distribution chart helps you evaluate this trade-off.
When to Use Something Else
If you want to compare card payments against non-card methods (ACH, bank transfers, buy-now-pay-later), you need a payment method analysis rather than a card brand analysis. Card brand analysis is specifically about the performance differences within the card network ecosystem.
If you are investigating fraud patterns, the card brand analysis shows decline rates but does not distinguish between fraud declines and technical declines. For that, you need Stripe Radar data and a fraud-focused analysis that separates legitimate blocks from processing failures.
For revenue trends over time, the MRR analysis is a better fit. And for understanding subscription churn driven by payment failures, the churn prediction module connects payment failure patterns to customer retention outcomes.
The R Code Behind the Analysis
Every report includes the exact R code used to produce the results — reproducible, auditable, and citable. This is not AI-generated code that changes every run. The same data produces the same analysis every time.
The analysis uses standard R data manipulation with dplyr for aggregation, ggplot2 for visualizations, and base R for proportion calculations and confidence intervals. Fee rates are computed as actual fee divided by transaction amount, not from published rate cards, so the numbers reflect your real effective cost including all surcharges and adjustments. Success rates include confidence intervals when sample sizes permit, so you can distinguish between genuinely different performance and statistical noise. Every step is visible in the code tab of your report, so you or your payment operations team can verify exactly what was done.