Periodically they would retest to see if the best solutions for different users had changed. The Payments team would then store the best billing policy option as the default for that population. The Payments team had to manually segment users into populations based on their features-subscription type, geographic location, etc-then A/B test our ten or so different hardcoded rule sets to determine which performed the best for those features. These A/B tests relied heavily upon human intuition and domain knowledge to come up with a set of rules for when to charge a customer. In the last couple of years, Dropbox ran A/B tests to see if shifting when we charge customers would have an impact on the success rates of those charges. All of this, from start to finish, is handled by our Payments Platform. Once they complete their payment and become a paid customer, they enter our payment lifecycle. This all takes place behind the scenes instantly when a customer starts a Dropbox subscription. After the customer submits the form, the system collects their payment information and securely sends this info, along with the amount we want to charge them, to one of our external partners who process that type of payment information.
When a customer visits the Dropbox website and chooses to buy one of our products, we ask the customer to enter their payment information on the purchase form. The Dropbox Payments Platform manages payment processing for millions of our customers.
That’s not only bad for you the customer: It causes negative feelings about the brand, it disrupts what should be a steady flow of revenue to the business, and a customer who finds themselves shut off might decide not to come back.Īt Dropbox, we found that applying machine learning to our handling of customer payments has made us better at keeping subscribers happily humming along. It’s probably happened to you at some point: You go to use a service for which you believe you’ve got a paid subscription, only to find that it’s been canceled for non-payment.
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