Smart Retries
Enhance payment success rates using Yuno's Smart Retries. Utilize machine learning to optimize transaction retry attempts.
Yuno's Smart Retries employ advanced machine learning techniques to identify the optimal timing for retrying a declined recurring credit card payment. By analyzing extensive data points and transaction attributes, it improves the probability of payment success, ensuring a steady flow of revenue and reducing involuntary subscriber churn.
Key advantages
- Data-driven decision-making: Utilizes machine learning to establish the most effective retry schedule.
- Enhanced revenue retrieval: Boosts the likelihood of successfully collecting payments.
- Decreased subscriber turnover: Reduces disruptions and involuntary subscriber departures.
- Flexible logic: Customizes retry schedules to maximize success opportunities.
- Comprehensive approach: Complements other revenue recovery strategies.
Retry schedule
Yuno implements a personalized approach for each unsuccessful payment, informed by continuous analysis and machine learning. This approach evolves over time, striving to pinpoint the optimal moment for reattempting a transaction.
Event | Deadline after the first try |
---|---|
First try | - |
Second try | 5 minutes |
Third try | 60 minutes |
Fourth try | 5 hours |
Fifth try | 24 hours |
Sixth try | 48 hours |
Seventh try | 96 hours |
Customization
Since every business model varies, we enable merchants to define specific rules to enhance the flexibility of our retry schedule. While you create the subscription object you can use the retries
structure to make adjustments:
Parameter | Type | Description | Example |
---|---|---|---|
retry_on_decline | bool | If we should retry a payment or not after a first decline. False by default. | TRUE |
amount | number | The number of retries that the subscription plan will have to completion. If not set, or higher than 7, 7 will be defined as default. Max: 7 | 4 |
Updated about 2 months ago