TECHNICAL FIELD

This disclosure pertains to multi-tenant computing systems. More specifically, this disclosure pertains to predicting churn in a multi-tenant computing system.

BACKGROUND

Under conventional approaches, predicting churn (that is, predicting the likelihood a customer may cancel a service) may be determined by reviewing behavioral data. For example, predicting churn analysis may be determined by reviewing how often a user accesses the service. Typically, behavioral data is managed by service provider systems and stored in service-provider-managed data warehouse systems that cannot be easily accessed by external systems. Accordingly, predicting churn in traditional systems would be computationally intensive in terms of computer resources (e.g., memory, bandwidth, processing speeds, and/or the like) and/or processing time.

SUMMARY

A claimed solution rooted in computer technology overcomes problems specifically arising in the realm of computer technology. In various embodiments, a computing system is configured to predict churn in a multi-tenant system without using behavioral data. This may, for example, reduce computing resource requirements and/or reduce the amount of processing time to predict churn in a multi-tenant system. For example, the multi-tenant system may store billing data in a data format (or, data formats) that is native to the multi-tenant system, and that data format may be common across all of the tenants of the multi-tenant system. The multi-tenant system may generate churn prediction models for each of the tenants using their respective billing data. For example, historical billing data for AT&T may be used to generate a churn prediction model for AT&T, and historical billing data for Verizon may be used to generate a churn prediction model for Verizon. Since the billing data is local to the multi-tenant system, and in a native format, the multi-tenant system does not need to obtain data in non-native formats from third-party systems, and/or integrate such data into a data format of the multi-tenant system. The multi-tenant system may use the respective churn prediction models to predict churn using current billing data. For example, the multi-tenant system may identify current AT&T subscribers that are considered a churn-risk (e.g., at risk of canceling their AT&T subscription) using the AT&T churn prediction model and the current AT&T billing data, and identify current Verizon subscribers that are considered a churn-risk using the Verizon churn prediction model and the current Verizon billing data. Since the data formats are native to the multi-tenant system and common across the tenants, the multi-tenant system may predict churn for any number of tenants of the multi-tenant system without having to integrate large amounts of external data (e.g., behavioral data). Additionally, billing datasets are typically much smaller than behavioral datasets, which may reduce the computing requirements (e.g., memory requirements, processor requirements) required to predict churn in a multi-tenant system. Furthermore, because the data is in the single native format of the multi-tenant system it is feasible to handle data errors using automated programs rather than interventions by system administrators.

Various embodiments of the present disclosure include systems, methods, and non-transitory computer readable media configured to store a respective subscription dataset for each tenant of the multi-tenant system, each of the respective subscription datasets having one or more common data formats native to the multi-tenant system, wherein the respective subscription datasets include billing data and does not include behavioral data. Determine, by a churn prediction engine of the multi-tenant system, one or more primary features from a particular respective subscription dataset of a particular tenant of the multi-tenant system. Derive, by the churn prediction engine of the multi-tenant system, one or more secondary features from the one or more primary features. Generate, by the churn prediction engine of the multi-tenant system, a particular churn prediction model based on the one or more primary features and the one or more secondary features. Obtain, by the churn prediction engine of the multi-tenant system, 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. Identify, by the churn prediction engine of the multi-tenant system using the particular churn prediction model and the second subscription dataset of the particular tenant of the multi-tenant system, one or more subscribers of the particular tenant of the multi-tenant system as a churn-risk. Report the one or more subscribers of the particular tenant of the multi-tenant system identified as a churn-risk.

In some embodiments, the systems, methods, and non-transitory computer readable media further configured to perform: generating, by the churn prediction engine of the multi-tenant system, a different churn prediction model based on the one or more different primary features and the one or more different secondary features; obtaining, by the churn prediction engine of the multi-tenant system, a third subscription dataset of the different tenant of the multi-tenant system, the third subscription dataset comprising billing data that is more recent than the different respective subscription dataset of the different tenant of the multi-tenant system; identifying, by the churn prediction engine of the multi-tenant system using the different churn prediction model and the third subscription dataset of the different tenant of the multi-tenant system, one or more different subscribers of the different tenant of the multi-tenant system as a churn-risk; and reporting the one or more different subscribers of the different tenant of the multi-tenant system identified as a churn-risk.

In some embodiments, the one or more common data formats native to the multi-tenant system are independent of third-party data formats, and are not based on an integration of third-party data.

In some embodiments, the churn prediction engine is configured to operate on the one or more common data formats native to the multi-tenant system, thereby allowing the churn prediction engine to use the same machine learning algorithm for the particular churn prediction model and the different churn prediction model.

In some embodiments, the machine learning algorithm comprises a regression analysis.

In some embodiments, the one or more primary features include date of last payment and subscription status.

In some embodiments, the one or more second features include length of time since the date of last payment.

In some embodiments, the one or more second features include any of measures and statistics of plan information keywords.

In some embodiments, the one or more second features include any of measures and statistics of subscription billing amounts.

In some embodiments, the one or more second features include any of measures and statistics of subscription history.

In some embodiments, the one or more second features include any of features derived from the use by subscribers of products from different tenants on the multi-tenants system.

In some embodiments, the particular respective subscription dataset of the particular tenant of the multi-tenant system includes a limited number of examples of churn.

These and other features of the systems, methods, and non-transitory computer readable media disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for purposes of illustration and description only and are not intended as a definition of the limits of the invention.

