A subscription business gives the predictability of a stable cash flow, which helps a company grow and make plans for the future. Some organizations base their entire business on subscriptions, for example, cable TV or SaaS providers, while others have this only as one of the product licensing options.
Since this is a successful business model, managers are trying to identify new ways to prevent customer churn, decrease the cost of customer acquisition and find the best ways to structure prices and plans. Until now, marketing research was the primary tool to answer these questions, but machine learning (ML) is becoming more effective.
How Does Machine Learning Work?
Machine learning is all about making a system recognize patterns by using vast amounts of training data. Once the system develops an understanding of the factors influencing the outcome, it can predict the probability of that result for a new set of data.
In the case of subscription billing, the outcome could be renewing the subscription or even a fraud tentative, while the influencing factors range from the price of the subscription to the neighborhood where the customer lives. Machine learning can help to create accurate predictions here. For example, if the customer has consistently renewed their subscription in the last two years, failure to do so in a particular month could be an accident, like forgetting to replace their card. On the other hand, if the company offers a free limited trial period and a customer uses the same IP but a different e-mail address to access another free round, this could be a fraud.
How to Create an ML Algorithm for Billing?
The most important preparatory step is to have the right data on hand. ML is a solution that works best for mature companies that already have years of customer logs just waiting to be put to good use. When creating an ML model for billing, previously existing commercial data has to be loaded in a data lake for further processing. If these records are stored in separate siloes, they need to be gathered in a central repository.
The next step is to think about the restrictions within which the algorithm will work. Take into consideration the length of the billing cycle, the price of the plans, even the prices of your competitors as everything could play an essential role in the outcome.
Once you have the potential factors figured out, try to create proxies for each from the available data and generate a likely model. Keep in mind that some of the variables will be eliminated as not sufficiently relevant, so it’s a good idea to start with a few dozens.
It’s now time to put the system to the test of the market data. Load about 80% of the available data in the computer and let the algorithm make its predictions. Keep the other 20% to check if it was right. To calibrate the model, you’ll need to switch between the records you have, always leaving out another 10–20% of the data.
Applications for Machine Learning in Subscription Billing
As mentioned in the introductory part of this article, there are a few applications of machine learning to improve subscription billing. Each of these can have a big impact on profitability, reducing costs and preventing fraud, among other benefits.
1. Reducing customer churn with machine learning
The topic of customer churn has to be sub-divided into two cases. First, we have the involuntary churn due to neglect or more complicated bank procedures. Secondly, we have the deliberate churn of customers who are not satisfied with the product, looking for a better price or just not needing the subscription anymore.
A recent piece talking about companies that choose subscription management states that as much as 9% of recovered revenue comes from applying machine learning to the billing process. In other words, you could be losing that percentage from your bottom line right now if you don’t use this technology.
Involuntary churn can benefit the most from machine learning, as most of these customers want to continue the relationship yet they are unaware that sometimes their credit card or bank deny the renewal. Once such a case is found, the system can identify other customers in the same situation and prompt them to renew the card or to be careful when it comes to automatic payment, especially if a two-factor authentication system is in place.
If renewal fails, the account should be deferred to a retry schedule, tailor-made for the specific case. For example, if the failure is due to an expired card, it makes no sense to prompt the customer for renewal sooner than the specified time from the issuing bank plus a few days.
2. Subscriber acquisition and plan pricing
Feeding into the model the acquisition channels attached to every account and even the campaign that brought the customer can help marketing efforts. By correlating the cost of getting a customer with the revenue generated by the same customer, the organization can make a hierarchy of the most profitable channels and redesign their marketing strategy, including assigning budgets.
Since price plays a vital role in getting new customers onboard, it is worth investigating how many of those who signed up for a discounted promotional pricing plan remained as full-paying, and what percentage has churned hunting for new opportunities.
3. Better daily management
Since an ML solution already has all the data analyzed, it can be used for cross-sells and upsells by investigating the customers’ propensity to spend more or their subsequent needs. Since such a platform can be integrated with invoicing and inventory software, it can quickly turn into a tool for business intelligence.
These are just a few ways in which AI and ML can help an organization manage its subscription-based products and ensure that the cash flow shows an upward trend.