The Subscription Prediction API allows you to get a sense of your non-paying customers, by predicting the likelihood of whether they would subscribe to one of your plans.
Pelcro's subscription prediction algorithms are powered by an ensemble decision random forest, comprising of more than 40 decision trees, which are collectively combined to provide you with powerful predictive analytics for such a critical metric.
Random Decision Forest Structure
We adopt offline batch training for the prediction algorithm that accounts for historical data patterns within our input parameters for a number of activities and variables; such as viewed pages, page topics, total number of visits and paywalled content visits, in addition to user's metadata.
Your user's trends will change over time, people’s interests vary with the seasons and the constantly changing ongoing events, and so will their subscriptions; that's why training is a continuous process. Our machine learning modules adapts through continuous monitoring, periodic data extraction and updates to retrain our prediction models to account for any deviations within data distributions and patterns from those of the initial training sets. Depending on the nature of your business, content, products, or services, you will have the ability to retrain as often as needed for a sustainable predictive performance.
With the ability to estimate which of your products and content will likely attract more attention from potential subscribers in the future, you can timely identify and adjust your marketing strategies to put more focus on these products as your cornerstone for getting more reach. Additionally, you'll also have more awareness on which products are getting less noticed, to consider what can be done to obtain more value out of them.