The Secret Sauce Behind Zuora Collect AI

March 15, 2021

Payments are a critical aspect of the Subscription Economy, and there is a science to optimizing your collections strategies. Subscribers need the flexibility to make payments at a time of their choice through their preferred methods, and businesses must be adaptable to meet these growing needs of their customers. With that said, collections are now a strategic imperative for subscription businesses worldwide, and the best ones use their collections processes as a backbone to ensure predictable revenue growth, customer retention, and bottom-line efficiency. However, the issue right now is that collection teams are spending countless hours analyzing data to identify payment trends and best practices to ensure they leave no money on the table. But what if that time could be spent elsewhere? What if there was something that could automatically isolate the best payment retry strategies for every customer?

Zuora Collect AI is that solution.

Zuora Collect AI utilizes machine learning to assist our customers in establishing an effective retry strategy and maximizing their success in payment collection.

What is AI? 

The secret is in the sauce and Zuora has some good sauce.

Artificial intelligence is the ability for a machine to demonstrate intelligence in performing a task, usually by means of a prediction or recommendation. AI models learn about these tasks through an algorithm that constantly ingests and analyzes data to identify trends and patterns, which ultimately impact its recommendations.

There are many aspects of AI, one of the most beneficial is machine learning. Machine learning is the ability for algorithms to continually learn from ingested data. Essentially, new patterns and trends are identified and then applied when making the predictions.

What does this have to do with Zuora payments and collections?

Everything! Zuora’s data science team created our machine learning model with a goal of identifying the optimal times to retry payments specific to every customer. Optimizing the time of retry ensures our customers are able to recover revenue far more effectively, while also alleviating the stress of manual analysis and delays to identify these times. By retrying payments at the ideal time, our customers can recover failed payments at a much higher rate to increase revenue and keep more of their loyal subscribers. 

Our machine learning model taps into the robust history of Zuora’s subscription payment data. That is over twelve years of data including hundreds of billions of dollars in payments made with 35+ gateways, 20+ payment methods, and 180 currencies. When looking at the factors that most impact our customers payment success, we evaluated over fifteen characteristics specific to a customer’s transaction including payment gateway response codes, region, and time of payment. Our algorithm uses this information to continuously identify new trends, evolve, and even isolate patterns specific to our customers’ subscribers. By isolating payment patterns specific to each of Zuora’s customer’s environments, we can ensure our retry schedules are using the most relevant data for every customer and its subscribers in order to recover the maximum amount of revenue.

Leveraging AI Machine Learning

Let’s take a look at how machine learning looks when applied to a retry strategy. Two subscribers, one in North America and one in Europe, both paid with electronic payment methods but each of them received different responses from different gateways with their respective payment’s failure reason. After the initial payment fails, Zuora Collect AI’s machine learning capability, Smart Retry, ingests this information and begins its analysis to find the optimal retry schedule to yield a successful payment for a specific customer. 

The intelligence of Smart Retry allows our system to identify successful payment times unique to each subscriber. Potentially, the North American subscriber could be attempted two days later at 08:00, while the European subscriber could be retried one day later at 13:30. Both times are specific to the subscriber’s region and Smart Retry ensures a company retries each of their failed payments at the most opportune times. Collection teams no longer have to spend precious time orchestrating complex workflows in an attempt to target what their manual analysis deems optimal times.

Smart Retry not only reduces a company’s revenue leakage by saving a potentially endless supply of recurring revenue, but it also minimizes passive churn, otherwise known as involuntary churn, which is a common challenge for subscription businesses. Continuing the life of a subscriber with your business opens the door for predictable growth and more upsell opportunities in the future.

Zuora Collect to date has already helped many global businesses in the subscription economy recover over $71 million dollars. These businesses have an average invoice recovery rate 10% higher than those not utilizing a retry strategy. What’s more impressive, however, is that Zuora Collect AI early adopters like Whitepages and rankingCoach have seen up to a 10% increase on top of this to recover up to 20% more subscription revenue thanks to our machine learning capabilities! Having an effective retry strategy is not just recommended, it is mandatory. Whether you are just starting to develop a retry strategy or looking to optimize current strategies, Zuora Collect AI is your solution.

Like I said, Zuora had some good sauce. To learn more about the product, check out our solution brief.