If you asked a random person how much artificial intelligence and machine learning have affected their lives, the answer would probably be, not much. However, machine learning is increasingly prevalent in many new innovations and is steadily increasing its footprint over many products and services.
Machine learning is the ability of AI to analyze the situations, recognize the repetitive patterns, and interpret them accordingly. And, it is happening while you are reading this article too. As you have already noticed, the moment you close the page, the system will know which pages to suggest in your Google search, or what ads to pop out here and there. Of course, this is just the most obvious example.
Now, let’s dig a bit deeper and learn a thing or two about the role of machine learning in the recurring billing process. A good number of businesses bill their subscribers on a recurring basis and machine learning is often a part of the billing process.
The Learning And Billing Process
Firstlyl, in order to properly train the machine learning system, it has to be fed with the right information. It will need some users’ subscription information. Then, the system will draw some conclusions and make a “dummy” user profile. It will include the age, gender, and similar background data, along with his or her history. Having created a general profile, the machine learning system will raise the game by adding specific customer information.
Eventually, this is how predictions and patterns are created. They are a product of a variety of scenarios which could happen. All this is extremely useful for future billing strategies, thus also reducing the churn.
Learning And Churn
Before we go into details, it has to be noted that there are two types of churn: voluntary and involuntary. In the first, a customer willingly decides to end the subscription, whereas in the second the termination is executed by the system (e.g. when a payment is rejected). The problem lies in the fact that it’s hard to tell which subscription was ended due to credit card decline, and which one was done on purpose. And this is where machine learning jumps in and helps us see the difference.
This task would be too complicated for a regular finance department, for example. There are too many credit cards, too many banks and billing terms to take into account. Machine learning will help a company realize when the best time for billing a particular customer is. For example, by analyzing millions of pieces of information the system can determine the best date when to deliver the bill, or when to retry the transaction. This helps companies recover their revenues by as much as 9%, which is far from negligible, which consequently triggered the rise of subscription management software, as they are extremely reliant on the benefits of machine learning.
Machines Against Fraudsters
Technology has incredible benefits (obviously), but it is also giving plenty of opportunities for fraudulent activities online. When a customer is tricked, that reflects the seller of the service, too. Unfortunately, too often does this resemble a lost battle. However, once again machine learning is there to save the day. The system constantly analyzes data and recognizes potential threats, despite the variations in fraudsters’ tactics.
To sum up, so far a business can say it is pretty lucky to be operating in this time when machine learning is evolving at a great rate as its benefits are beyond practical, and profitable.