Systems and methods for predicting churn in a multi-tenant system

A method being implemented by a computing system including one or more physical processors, storage media storing machine-readable instructions, and a plurality of tenant interfaces configured to support load balancing when multiple tenants of a multi-tenant system or multiple subscribers of the multiple tenants try to access the multi-tenant system concurrently, each tenant of the multiple tenants being associated with a subset of the plurality of tenant interfaces for load balancing, the method comprising:

• storing a respective subscription dataset for each tenant of the multiple tenants of a multi-tenant system, each respective subscription dataset having one or more common data formats native to the multi-tenant system, each respective subscription dataset including billing data of subscribers of the multiple subscribers of the tenant and not including product or service usage data of the subscribers of the tenant;
• determining, by a churn prediction engine, one or more primary features from a particular respective subscription dataset of a particular tenant of the multi-tenant system;
• deriving, by the churn prediction engine, one or more secondary features from the one or more primary features;
• generating, by the churn prediction engine, a particular churn prediction model based on the one or more primary features and the one or more secondary features, the churn prediction engine using a common machine learning algorithm capable of operating on the one or more common prediction formats native to the multi-tenant system so that the same machine learning algorithm can generate churn prediction models for different tenants of the multi-tenant system;
• obtaining, by the churn prediction engine, a second subscription dataset of the particular tenant of the multi-tenant system, the second subscription dataset comprising billing data that is more recent than the particular respective subscription dataset of the particular tenant of the multi-tenant system;
• identifying, by the churn prediction engine using the particular churn prediction model and the second subscription dataset of the particular tenant of the multi-tenant system, one or more particular subscribers of the particular tenant of the multi-tenant system as a churn-risk;
• and
• reporting the one or more particular subscribers of the particular tenant of the multi-tenant system identified as a churn-risk using at least one of the subset of the tenant interfaces associated with the particular tenant.

A non-transitory computer readable medium comprising computer readable instructions configured to, when executed, cause one or more physical processors to perform:

• providing a plurality of tenant interfaces configured to support load balancing when multiple tenants of a multi-tenant system or multiple subscribers of the multiple tenants try to access the multi-tenant system concurrently, each tenant of the multiple tenants being associated with a subset of the plurality of tenant interfaces for load balancing;
• storing a respective subscription dataset for each tenant of the multiple tenants of a multi-tenant system, each respective subscription dataset having one or more common data formats native to the multi-tenant system, each respective subscription dataset including billing data of subscribers of the multiple subscribers of the tenant and not including product or service usage data of the subscribers of the tenant;
• determining, by a churn prediction engine, one or more primary features from a particular respective subscription dataset of a particular tenant of the multi-tenant system;
• deriving, by the churn prediction engine, one or more secondary features from the one or more primary features;
• generating, by the churn prediction engine, a particular churn prediction model based on the one or more primary features and the one or more secondary features, the churn prediction engine using a common machine learning algorithm capable of operating on the one or more common prediction formats native to the multi-tenant system so that the same machine learning algorithm can generate churn prediction models for different tenants of the multi-tenant system;
• obtaining, by the churn prediction engine, a second subscription dataset of the particular tenant of the multi-tenant system, the second subscription dataset comprising billing data that is more recent than the particular respective subscription dataset of the particular tenant of the multi-tenant system;
• identifying, by the churn prediction engine using the particular churn prediction model and the second subscription dataset of the particular tenant of the multi-tenant system, one or more particular subscribers of the particular tenant of the multi-tenant system as a churn-risk;
• and
• reporting the one or more particular subscribers of the particular tenant of the multi-tenant system identified as a churn-risk using at least one of the subset of the tenant interfaces associated with the particular tenant.