Payment analytics
Historical transaction volumes span regions, currencies and payment methods, making payment analytics essential for identifying long-term commercial patterns through custom dashboards, structured exports, historical queries and integrations with business intelligence tools.
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Payment analytics provides granular insights into your transaction data. Understand performance across payment methods, acquirers, and geographic regions.
Identify trends, optimise conversion funnels, and make informed decisions to improve your payment processing efficiency and profitability.
Robust payment analytics provides a complete overview of transactions across Cardflo’s acquirer network, allowing merchants to unify their reporting. Track approval rates, payment channels, and network performance to optimise processing and ensure operational efficiency.
Payment analytics overview
Payment analytics serves as the foundational layer for interpreting raw transaction data within the payment stack. By aggregating data points from various sources, including acquirers, gateways, and card schemes, merchants can gain a granular view of their financial operations.
The process involves deconstructing complex datasets to isolate variables such as Merchant Category Codes (MCC), Bin types, and issuer geographic locations. This level of visibility is necessary for identifying inefficiencies in the authorisation process and understanding the cost structures associated with different payment methods.
Businesses use these insights to monitor the performance of their routing logic and to evaluate the success of authentication protocols like Strong Customer Authentication (SCA). While raw data often resides in disparate silos across multiple PSPs, a centralised analytics framework allows for a unified assessment of transaction flows, settlement periods, and dispute ratios.
This data facilitates more informed discussions with financial partners regarding processing fees and service level agreements.
How payment analytics works
Data aggregation and ingestion
Normalised transaction data is collected from all connected acquirers and payment service providers. This includes technical meta-data such as decline codes, authorisation timestamps, and scheme responses. By centralising these disparate feeds, the system creates a single source of truth for cross-platform performance comparisons and treasury reconciliation tasks.
Segmentation and attribute mapping
Transactions are categorised based on specific attributes including geographic region, device type, and payment method. The system maps specific BIN ranges to identify card levels, such as commercial or consumer, alongside the issuing bank's territory. This segmentation allows merchants to observe patterns within specific customer groups or regional markets.
Conversion and funnel analysis
The system tracks a payment's journey from the initial checkout intent through to final settlement. It identifies at which stage drop-offs occur, whether during 3DS authentication, gateway processing, or due to issuer-side declines. Monitoring these stages helps in diagnosing technical friction points within the checkout flow.
Authorisation and decline auditing
Machine learning or rule-based filters categorise declines into soft and hard categories. By analysing specific error codes like 'insufficient funds' versus 'do not honour', merchants can determine where to apply retry logic or account updater services to recover potentially lost revenue.
Reporting and export functionality
Historical and real-time data is presented through dashboards or exported via API for internal business intelligence tools. This ensures that financial controllers and developers can access the specific metrics they require, such as net settlement values or rolling reserve statuses, for accurate financial forecasting.
Why payment analytics matters
Optimising card authorisation rates
Understanding why transactions fail is essential for maintaining a healthy conversion rate. Payment analytics allows merchants to isolate specific declines related to technical errors or authentication failures. By identifying patterns in issuer behaviour, businesses can adjust their processing parameters or routing strategies to suit the preferences of specific card schemes, potentially reducing the frequency of false positives in fraud detection systems.
Managing processing and scheme costs
Payment processing involves complex fee structures including interchange, scheme fees, and acquirer markups. Analytics provide visibility into the total cost of acceptance for different payment methods. By analysing the distribution of card types and geographic origins, merchants can identify if they are being charged correctly for cross-border transactions and determine if local acquiring or alternative payment methods could reduce expenses.
Payment analytics use cases
Store estate seasonality analysis
Multi-national retailers need to compare historical card volumes across stores, regions and currencies while separating Christmas peaks, local holidays and promotional periods. Cardflo provides custom payment analytics views and aggregate exports for BI tools, allowing data teams to query consistent time ranges and model regional seasonality.
Subscription trend analysis
Omnichannel merchants need to analyse how Visa, Mastercard, Apple Pay, Google Pay and local payment method shares change across channels and reporting periods. Cardflo supplies custom dashboards and structured exports, enabling payments managers to examine historical tender mix by market, currency, device and business unit within their BI environment.
Regional authorisation trend modelling
Enterprise payments teams need to study historical authorisation rates by country, card type, currency and response code without reducing complex patterns to a single portfolio average. Cardflo aggregates gateway data into configurable analytics views and exportable datasets, helping analysts model regional trends over selected months, quarters or trading seasons.
Corporate data warehouse feeds
Large organisations need standardised payment datasets from multiple brands, MIDs and sales channels for ingestion into corporate data warehouses and BI models. Cardflo provides configurable aggregate exports in suitable data formats, with consistent dimensions and historical querying that help analysts maintain repeatable reporting across business units and reporting periods.
Payment analytics by the numbers
Industry observations suggests that optimising routing and retry logic based on analytics can lead to an uplift in authorisation rates within this range for high-volume merchants.
Businesses using analytics to identify geographic volume often find that switching from cross-border to local acquiring reduces processing fees by this typical industry margin.
Merchant accounts are generally expected to maintain a chargeback-to-transaction ratio below this level to avoid monitoring programmes from major card schemes.
Methodology: these figures are illustrative ranges drawn from published industry data and observed merchant cohorts, not guarantees. Actual results depend on your risk profile, card mix, geography and acquiring setup, and are confirmed only in your own pricing and approval terms.
Related terms
Talk to our team about a live rollout across our acquirer partners' rails.
What you get with Payment analytics
- Break down authorisation success rates by specific issuing bank and geographic region.
- Categorise decline reasons into actionable groups to distinguish between soft and hard declines.
- Monitor real-time transaction throughput to identify potential gateway or acquirer downtime.
- Evaluate the financial impact of Strong Customer Authentication on overall checkout conversion.
- Analyse the average transaction value across different payment methods and customer segments.
- Track chargeback and dispute ratios to remain within card scheme compliance limits.
- Compare the cost of acceptance between interchange-plus and blended pricing models.
- Review historical settlement timelines to improve corporate cash flow and treasury projections.
- Assess the effectiveness of fraud tools by monitoring false positive and conversion rates.
- Audit technical performance of network tokens compared to standard card-on-file transactions.
A short scoping call, then a written plan for your MIDs.
Questions about Payment analytics
What is the difference between an authorisation rate and a conversion rate in payment analytics?
The authorisation rate specifically refers to the percentage of payment requests sent to the issuer that receive a 'successful' response. This is a technical metric reflecting the performance of the payment stack.
The conversion rate is a broader business metric, representing the percentage of total website visitors who successfully complete a purchase. Payment analytics focuses on the authorisation rate to identify issues like 3DS friction, incorrect card details, or issuer-side technical refusals that impact the final conversion.
How can analytics help in reducing the total cost of payment processing?
Analytics provide transparency into the component costs of a transaction, such as interchange and scheme fees. By analysing data, a merchant may find they are paying high cross-border fees for a specific region where they have high volume.
This insight could support a business case for establishing a local legal entity and using a local acquirer. It also helps identify if a merchant is qualifying for lower interchange rates by providing the correct data levels, such as Level 2 or Level 3 data.
Why is it necessary to monitor decline codes at a granular level?
Generic decline messages like 'declined' provide no path for recovery. Granular analytics reveal specific raw response codes such as 05 (Do not honour), 51 (Insufficient funds), or 62 (Restricted card).
By categorising these, a merchant can implement logic to automatically retry cards with temporary issues (soft declines) while avoiding the costs of retrying cards that are definitively blocked or invalid (hard declines), thereby protecting their reputation with card schemes.
Can payment analytics help in detecting and preventing friendly fraud?
Yes, by tracking dispute and chargeback data alongside customer behaviour, merchants can identify patterns associated with friendly fraud.
For example, if certain products or customer segments show a high frequency of 'product not received' claims despite confirmed delivery data, the risk team can adjust their rules.
Analytics also allow for the monitoring of retrieval requests, providing an early indication that a customer may be questioning a transaction before it escalates to a formal chargeback.
How does 3D Secure 2 impact transaction data and reporting?
Under PSD2 and SCA regulations, moving to 3DS2 introduces new data points. Analytics can show the split between frictionless flows, where the customer is authenticated without interaction, and challenge flows.
Monitoring these metrics is vital as a high challenge rate can lead to abandonment. Analytics help ensure that the exemption flags used by the merchant are being respected by the issuer, avoiding unnecessary friction for the cardholder.
What role does BIN analysis play in payment reporting?
The Bank Identification Number (BIN) is the first six to eight digits of a card number.
Analysing this data allows a merchant to identify the card brand, the issuing bank, the country of origin, and the card category (e. g. , prepaid, debit, credit, or corporate).
This information is critical for understanding why certain transactions might have higher fees or lower authorisation rates, and it can be used to inform routing decisions or to apply surcharges where permitted.
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