Machine Learning Paving Way To Optimize Subscription & Recurring Billing System
Subscription-based business service providers have seen a whopping rise in the growth with more than 100% from 2011 to 2016. Customer’s preference is now diverted towards services and products.
Artificial Intelligence and Machine Learning are gaining popularity in the subscription-based business services and hence it proves to be promising news for Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS). Simultaneously, it also showcases the vital stats for optimizing recurring billing systems to generate increased revenue from the sales. Inducing machine learning in the recurring payment software of any invoicing system has shown the substantial result in not only in higher sales, reduced payment transaction failure but also has enhanced customer service.
Machine Learning In Invoicing System
Applied machine learning in the invoicing system can be checked through the recurring billing feature via automatic debit of any subscription charge, at a specific interval of pre-decided date and time. This makes sure that the customer doesn’t default on the payments and can enjoy services without any interruptions. Furthermore, it can also be used to increase the number of transactions.
At the end of a service cycle, the billing software generates an invoice and attempts to debit the amount. For example, if the said transaction is failed due to any reason whether, from the customer’s end or the bank, the system will register it as an error and will re-try after a while to collect the subscription or service payment. Usually, the system generates a message specific to the failed transaction and asks customers to either update payment details and furthermore attempts a new request. For the next attempt which is termed as “retry schedule”, there is no assurance whether the transaction will be successful or not. If it goes through, you retain a customer but if it fails then not only you lose a sale but also affects your revenue as well as customer relationship due to disruption in services.
Machine learning can be introduced to redesign the subscription billing model. A working system can be established to evaluate and determine the transactions that went successfully in the second attempt or retry schedule. Once you have a huge collection of transaction data whether successful or not, you can analyze and determine the transactions that are most likely to succeed with machine learning.
Custom Subscription Packages
When offering too many services under one cap, makes your package very expensive for the customers and hence might drive away the sale. Machine learning can help you by browsing and purchasing trends of the customer. You can also club your higher-selling services and create a unique subscription package which will increase your sales, enlarge customer base and generate higher revenue.
Set an Optimum Price
Machine learning proves to be crucial in subscription billing services and can gain maximum benefits by targeting a particular audience. It can help you to set up proper prices for products and services. To decide the optimum price of subscription service, you’ll need to analyze 3 factors such as market demand, market saturation, and buyer preference. Now you can apply machine learning and artificial intelligence to comprise data and establish a correlation. This will help you to come up with a price that will get you gain maximum customers.
A cloud-based invoicing solution such as Moon Invoice, can help you optimize your business process and all of your accounting processes and moreover, it also benefits you in multifarious ways.
All this might sound expensive, but in reality, it is not. The online invoicing application from Moon Invoice comes with a 7 days free trial period to give you first-hand operational experience and functionality feel of the app. So, we do not see any reasons for you to hold back from giving this much-awaited momentum to your business.