Payment analytics

The Payments analytics section gives you a detailed view of how subscription payments perform across their lifecycle, from the first payment attempt to multi-cycle recovery in dunning.
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Loop segments payment performance into three distinct stages:

This segmentation allows you to:

In this article, we’ll first explain how Loop categorizes payments, then walk through the key metrics, followed by practical scenarios to help you interpret the data confidently.
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To correctly read Payment analytics, it’s important to understand how Loop classifies each payment attempt.
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The first attempt is the initial payment attempt for a subscription order when the subscription is not already in dunning.

Why this matters
First attempt success reflects:

A strong first attempt success rate usually means fewer downstream recovery efforts are required.

If a payment fails on the first attempt, Loop moves the order into its first recovery cycle.

First recovery cycle only includes retries within the same order cycle where the initial failure occurred.
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Why this matters
This stage shows how effective your immediate recovery setup is.
Strong first cycle recovery indicates that:

If a payment is not recovered in the first cycle, the subscription enters dunning and recovery attempts continue in subsequent order cycles.

Why this matters
Subsequent recovery cycle shows:

The Overview tab provides a consolidated view of payment performance across the full subscription payment lifecycle, from the first attempt to extended recovery cycles. It helps you understand how much revenue was attempted, successfully realized, recovered after failure, still under recovery, and ultimately lost.

Provides a high-level summary of payment performance over the last 90 days, including attempted value, success, recovered revenue, under-recovery amounts, and losses.

Insights: This snapshot is your starting point to assess the overall health of your payment system and spot changes in recovery or loss trends at a glance.

Total attempted displays the total value of all subscription payment attempts made in the last 90 days. This metric reflects total revenue exposure during the selected period and helps assess overall billing volume and growth trends.

Success represents the percentage of payment attempts that were successfully realized in the last 90 days, showing how effective payment processing has been. A declining success rate may indicate authorization issues, invalid payment methods, or provider-level friction.

Recovered indicates the total value of payments that were successfully recovered through Loop’s retry engine after initially failing. This highlights the effectiveness of recovery efforts in retrieving revenue that would otherwise have been lost.

Under recovery shows the total value of subscription payments that are still in the process of recovery. This includes orders with available retry attempts. This metric represents revenue that is still “in play” and may convert through retries or card updates.

Lost represents the amount of subscription revenue that was eventually lost. This includes revenue from skipped orders as well as subscriptions that were paused, cancelled, or expired after payment had failed. Lost revenue is calculated across all cohorts, including First attempt, First recovery cycle, and Subsequent recovery cycles. This metric reflects permanent revenue leakage.
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The Success vs Failed revenue (Last 12 months) section tracks the trend of successful, under recovery, and failed payments across the past year. This visualization helps you understand whether payment health is improving or deteriorating over time and allows you to evaluate the impact of changes to retry configurations, billing logic, or payment methods.

The First attempt metrics block displays key data on the performance of the first payment attempt for subscription orders, including success rate, number of attempts, backup attempt rate, and backup recoveries. This stage represents the cleanest signal of payment quality because no prior failures influence the outcome.

Success rate shows the percentage of payments successfully realized on the first attempt and is calculated as [realized / attempted]. Subscriptions already in dunning at the last order are not included. This metric evaluates the quality of initial payment processing and checkout health.

Backup attempt rate displays the percentage of failed orders where a backup card was attempted and is calculated as [backup attempted / total failed orders]. A higher rate indicates better backup card availability and improves the likelihood of recovering failed payments immediately.

Backup recovered indicates the total number of orders or total revenue successfully recovered through backup payment methods. This metric demonstrates the impact of enabling backup payment methods on improving recovery rates without entering prolonged recovery cycles.

The First recovery cycle metrics block shows the performance of the first recovery cycle for subscription orders. The first recovery cycle begins when the first payment attempt fails and retries are triggered on the same order based on your configured retry schedule. This stage measures how effectively failed payments are recovered before progressing into later billing cycles.

Recovery rate shows the percentage of payments recovered during the first retry cycle and is calculated as [recovered / (attempted - under recovery)]. This reflects how well initial recovery strategies perform once a payment fails.

Lost in the first recovery cycle represents the order count or subscription revenue that could not be recovered within that first cycle. This includes revenue from skipped orders as well as subscriptions that were paused, cancelled, or expired after the initial payment had failed. This metric helps you understand how much revenue leakage occurs immediately after first-cycle retries are exhausted.

Subsequent recovery cycle metrics block shows the performance of all subsequent recovery cycles. When an order does not get recovered in the first recovery cycle, attempts continue in later order cycles, referred to as the subsequent recovery cycle. This stage reflects longer-term recovery behavior and typically involves more delayed recoveries.

Recovery rate shows the percentage of payments recovered during the subsequent retry cycles and is calculated as [recovered / (attempted - under recovery)]. This reflects the potential for late-cycle recoveries after the initial cycle has failed.

Lost in the subsequent recovery cycle represents the order count or subscription revenue that could not be recovered even after extended retry attempts. This includes revenue from skipped orders as well as subscriptions that were paused, cancelled, or expired. This metric helps determine whether prolonged recovery strategies are yielding meaningful results or simply extending inevitable losses.

The Payment source distribution section displays the distribution of payment realizations across different payment methods such as Shop Pay, Credit Cards, and PayPal. For each source, you can view Attempted, Realized, Under recovery, Realization rate, First attempt success rate, First cycle recovery rate, and Subsequent cycle recovery rate.

Source represents the payment method used for processing payments. Attempted shows the total number of payment attempts made using that source. Realized indicates the number of successful payments. Under recovery reflects payments currently in retry flows. Realization shows the percentage of payment attempts successfully realized. First attempt success rate, First cycle recovery rate, and Subsequent cycle recovery rate provide stage-wise performance visibility for each payment method.

This section helps identify which payment methods are most reliable and which may require optimization or alternative handling.
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The Country wise payment distribution section shows how payment realizations are distributed across different countries. For each country, you can view Attempted, Realized, Under recovery, Realization rate, First attempt success rate, First cycle recovery rate, and Subsequent cycle recovery rate.

Country represents the geographic region where payments were attempted. Attempted reflects total payment attempts in that country. Realized shows successful payments. Under recovery indicates payments currently being retried. Realization shows the percentage successfully realized. The stage-wise recovery rates help evaluate payment behavior by region.

This section helps identify geographic patterns in authorization performance and recovery effectiveness.

The Payments over time section provides a trend analysis of payment realizations across different time periods. It shows how successful, under recovery, and failed payments fluctuate across first attempt, first recovery cycle, and subsequent recovery cycles. This visualization helps identify seasonality, spikes in failures, and the impact of retry configuration changes.

The Recovery tab provides a deeper breakdown of how failed payments are recovered and which recovery mechanisms are most effective.

The First recovery cycle metrics block shows the performance of the first recovery cycle for subscription orders. The first recovery cycle begins when the first payment attempt fails and retries are triggered on the same order based on your configured retry schedule. This stage measures how effectively failed payments are recovered before progressing into later billing cycles.

Recovery rate shows the percentage of payments recovered during the first retry cycle and is calculated as [recovered / (attempted - under recovery)]. This reflects how well initial recovery strategies perform once a payment fails.

Lost in the first recovery cycle represents the order count or subscription revenue that could not be recovered within that first cycle. This includes revenue from skipped orders as well as subscriptions that were paused, cancelled, or expired after the initial payment had failed. This metric helps you understand how much revenue leakage occurs immediately after first-cycle retries are exhausted.

Subsequent recovery cycle metrics block shows the performance of all subsequent recovery cycles. When an order does not get recovered in the first recovery cycle, attempts continue in later order cycles, referred to as the subsequent recovery cycle. This stage reflects longer-term recovery behavior and typically involves more delayed recoveries.

Recovery rate shows the percentage of payments recovered during the subsequent retry cycles and is calculated as [recovered / (attempted - under recovery)]. This reflects the potential for late-cycle recoveries after the initial cycle has failed.
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​Lost in the subsequent recovery cycle represents the order count or subscription revenue that could not be recovered even after extended retry attempts. This includes revenue from skipped orders as well as subscriptions that were paused, cancelled, or expired. This metric helps determine whether prolonged recovery strategies are yielding meaningful results or simply extending inevitable losses.
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Recovery contribution split displays the percentage contribution of different recovery methods, such as card updates and retries, in recovering failed payments during the selected time period and cycle. This section helps assess whether automated retry strategies or customer-initiated card updates are driving stronger recovery outcomes.

Recovered with Loop represents the subscription payments successfully recovered by Loop’s retry engine after initially failing. This metric indicates the efficiency of automated recovery strategies.
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Recovery contribution trend tracks the trend of recovery rate over time, showing the split of payments recovered through retries versus card updates. This helps monitor the ongoing effectiveness of recovery strategies and detect shifts in customer behaviour.
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Failure reason wise recovery contribution
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Failure reason wise recovery contribution breaks down recovery performance by leading failure reasons. For each reason, it shows Attempts, Recovered, Under recovery payments, Recovery rate, Recovery via retry, and Recovery via card update. Reason represents the specific cause of payment failure. Attempts show total failed attempts for that reason. Recovered shows successfully recovered payments. Under recovery reflects payments still being retried. Recovery rate shows the percentage successfully recovered. Recovery via retry and Recovery via card update indicate attribution by recovery method.

This section helps determine which failure reasons are most recoverable and which require different intervention strategies
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Recovery by retries
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Recovery by retries breaks down recovery performance by each retry number. It shows Retry number, Attempts, Recovered, Recovery rate, Recovery via retry, and Recovery via card update. Retry number represents the specific retry attempt within the recovery cycle. Attempts show how many payments reached that retry. Recovered shows successful recoveries at that retry. Recovery rate indicates effectiveness at that stage. Recovery via retry and Recovery via card update show attribution.

This helps identify at which retry stage most recoveries occur and whether extended retry windows are effective.
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Recovery by failure reason
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Recovery by failure reason shows the recovery rate for each failure reason along with the distribution of successful recoveries across retry numbers. It includes Recovered and Retry 1 recovered through Retry 15 recovered. These metrics show the number of payments successfully recovered after each retry attempt within the selected recovery cycle.

This section provides deep insight into whether certain failure reasons recover early, require multiple retries, or rarely recover after a certain point.
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Failures summary: An overview of payment sources and failure reasons contributing to the highest number of payment failures.

Insights: This metric helps you identify the primary sources of failure, whether due to payment provider issues, card declines, or other reasons. It’s essential for troubleshooting and improving your payment flow.
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Payment failure reasons: Distribution of payment failures across different reasons grouped by time periods.

Insights: By analyzing why payments are failing, you can target specific issues, such as expired cards or insufficient funds, and take corrective action.

​Total failures: Distribution of payment failures across different reasons grouped by time periods.

Insights: This shows the overall scope of payment failures. A high number indicates issues that need immediate attention, such as retry strategies or pay ment gateway improvements.
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The Upcoming payments section helps you monitor and manage payments that are about to be processed. It provides a list of upcoming payments along with their associated risk levels, enabling you to identify potential issues before they arise.

Filters:

Insights:

By mastering these metrics, you can move from reactive payment management to proactive revenue protection and get the most out of Loop’s payment recovery engine.
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