Using Machine Learning to Optimize Subscription Billing

Subscription-based businesses are experiencing a boom right now. Buyer preferences are gradually shifting towards subscription-based services and products, predominantly towards Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS). According to McKinsey, the subscription e-commerce market witnessed a growth of more than 100% from 2011 to 2016. This is welcome news for these service providers. At the same time, it highlights the importance of optimizing recurring billing systems to generate higher revenues from sales.

Machine learning is gaining prominence in the subscription-based business models. The integration o

Fretty Francis works as a Content Marketing Specialist at SoftwareSuggest, an online platform that recommends software solutions to businesses. Her areas of expertise include billing solutions, ecommerce platforms, and project management software. In her spare time, she likes to travel and catch up on the latest technologies.

f machine learning in recurring payment software resulted in higher sales, reduced payment failures, and enhanced customer experience. How is this possible with machine learning? The data scientists collect information pertaining to subscription sales, which is further analyzed to create systems and software working on the principle of artificial intelligence and machine learning.

One of the simplest applications of machine learning in recurring billing is auto debit of the subscription charge, at scheduled intervals, on a prescribed date and time. This ensures that the customer doesn’t default on the payments, and enjoys services without any disruptions. The recurring billing mechanism can further be optimized by deploying machine learning and artificial intelligence, ensuring an increase in the number of transactions. Let us analyze the prospects of machine learning in subscription billing mechanism.

Intelligent Retry Schedules

You must have subscribed to a media-streaming service or any other service that requires you to renew your subscription at periodic intervals. Generally, transactions happen seamlessly but sometimes, it might fail. If the transaction fails due to an action by the customer, it is known as voluntary churn. And if it fails due to unforeseen circumstances, without any involvement of the customer, it is known as an involuntary churn.

When the service cycle nears completion, the billing software generates an invoice, triggering a payment attempt using customer’s saved payment credentials. Now let us suppose that the transaction fails. The reasons may be a decline by the payment processor or the bank, insufficient balance in customer’s account, invalid card details, and so on. The system registers this error, and it retries to collect the payment. To accomplish this task, the system may send a reminder to the customer, update payment details and initiate a new request.

Present subscription billing systems initiate payment requests at multiple intervals, as predefined in the software algorithm. The timing of these attempts is termed as “retry schedule”. Again, the payment may succeed or fail, as there is no assurance that the payment will go through. If it succeeds, you retain a customer and your revenue collection doesn’t fall. But what happens when it fails? First, you lose a sale, thereby, impacting your revenues. Secondly, you ruin your relationship with the customer. The payment failure results in service interruption, against the wish of the customer. This will further affect your credibility.

To mitigate the issue of invoice failures, machine learning can be used to redesign the subscription billing model. A mechanism is devised to determine and evaluate transactions that are successful during the retry schedule. For this, you need a huge collection of data pertaining to all transactions happened over a period of time, whether successful or not. Once you have the data, you can analyze and determine the transactions that are most likely to succeed.

Another method is to train the predictive model using the available data. You create a predictive model, test it using variables, and after successful validation, integrate it into the billing system. To train the model, we assume one dependent variable and some independent variables. The effect of independent variables on the dependent variable is analyzed to determine the best course of action. For example, a dependent variable can be an online transaction. The independent variables can be transaction time, date, type of payment method, and so on.

The predictive models attempt to collect payments at particular timings and under specific conditions, ensuring higher success rate. To further evaluate the performance of these models, a new stream of data is fed. If the predictive model analysis shows a green signal, the billing and accounting software can be integrated with a revamped retry schedule.

Machine learning is effective in detecting the fraud mechanisms as well. As per the Federal Trade Commission’s Consumer Sentinel Network Data Book, credit card fraud in the USA increased by 23% from 2013 to 2017. Machine learning can be deployed to foil any fraud attempts during an online transaction. Machine learning works on the big data related to transactions, analyzing all type of transactions. It analyzes the patterns followed during online payment frauds, thereby, taking a preventive course of action. When the subscription billing system is integrated with this model, the rate of fraud attempts reduces.

Curate Subscription Packages

Do you offer multiple services under your brand name? You cannot club them all and offer a single package as it will be too expensive for the buyers to purchase. Moreover, your services may not reach the target audience, resulting in the failure of your marketing strategy. Machine learning assists you in solving this issue. Based on the browsing and purchasing trends of customers, it identifies the key areas having a growth potential.

You can curate packages according to the requirements of the visitors. If you notice that some of your services are performing better than others, you can create a subscription package of high-performing services. This will increase your sales, increase customer acquisition rate, and generate higher revenue.

Set an Optimum Price

Machine learning can assist you in setting up appropriate prices for your products and services. This is crucial for subscription billing services as you can reap maximum benefits by targeting a specific audience. The initial step is setting up an optimum price of a product or service. How do you determine the subscription price? You have to analyze three parameters – market demand, market saturation, and buyer persona. Now you can apply artificial intelligence and machine learning to explore these aspects and establish a correlation. This will assist you in setting up a price that will rake in maximum customers.

Another factor that influences subscription billing is price localization. When implementing price localization, you can expect a rise of 30% in sales performance. Machine learning is applicable in price localization as you need to analyze the purchasing power of the local currency. It becomes crucial to determine the purchasing power as it directly influences the customer’s willingness to buy a product or service.

Since subscribing to a service is a recurring expenditure, a customer considers various factors before finalizing a decision. These factors include the buyer’s income, expenditure, lifestyle, preferences, and so on. The data analytics wing can extract datasets pertaining to these factors. Once you have the data, you can apply artificial intelligence and machine learning to create value for your customers. The value would be in terms of product or service features and competitive pricing policy.


When you streamline the subscription billing system using machine learning, reducing the invoice failure rate in the process, you ensure a seamless user experience. You don’t have to ask customers to update their billing information or resolve a payment issue. Therefore, you keep the customers out of any problem arising due to involuntary churning. Your customers will enjoy personalized & uninterrupted services, and you will receive your payments before the due date.

Fretty Francis
Fretty Francis works as a Content Marketing Specialist at SoftwareSuggest, an online platform that recommends software solutions to businesses. Her areas of expertise include billing solutions, e-commerce platforms, and project management software. In her spare time, she likes to travel and catch up on the latest technologies.
Fretty Francis

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