Zuora, Inc.

Receive a first order, the first order being associated with a subscription, and the first order being associated with one or more first order actions. Generate, based on the first order and the one or more first order actions, one or more first order metrics. Generate a multi-tiered subscription data object, a first tier of the multi-tiered subscription data object including the first order, a second tier of the multi-tiered subscription data object including the one or more first order actions, and a third tier of the multi-tiered subscription data object including the one or more first order metrics. Receive a first request for a first report. Generate, based on the one or more first order metrics of the multi-tiered subscription data object, one or more first multi-tiered subscription object reports. Present the one or more first multi-tiered subscription object reports.

A multi-tenant system includes hardware processors that ingest a dataset including transaction records, each of the transaction records including a field value and attribute values of different attributes. The hardware processors test candidate stratification mechanisms on the dataset, each candidate stratification mechanism stratifying the dataset according to a different attribute. The hardware processors determine a test value from field values within each stratification of each candidate stratification mechanism. The hardware processors evaluate each candidate stratification mechanism based on metrics following implementation of the candidate stratification mechanism on the dataset. The one or more metrics indicate a proportion of conforming field values that are within a threshold range of a corresponding test value. The hardware processors select a particular stratification mechanism from the candidate stratification mechanisms based on the one or more metrics, stratify the dataset according to the particular stratification mechanism, and resolve conforming values from the stratified dataset.

The multi-tenant system includes one or more hardware processors that obtain consumption records, the consumption records including consumption event attributes and consumption event attribute values. The one or more hardware processors recognize schemas of the obtained consumption records, generate a consumption dataset to compile the obtained consumption records based on the recognized one or more schemas, determine from the generated consumption dataset cardinalities of each of eligible consumption event attributes, generate a cubed consumption dataset from the consumption dataset based on the determined cardinalities, route the cubed consumption dataset to one or more directories of a datastore, perform analytics on the cubed consumption dataset, and output the analytics results.

The multi-tenant system includes one or more hardware processors that extract standard fields from one or more objects stored in source database systems, the standard fields defining standard subscription attributes. The hardware processors map the standard fields to records within a destination database system, obtain customized fields defining tenant-selected subscription attributes, obtain a mapping of the customized fields to the records; append the standard fields to the customized fields to form appended fields; schedule a pipeline to ingest the appended fields into the records within the destination database system, and ingest the appended fields into the records within the destination database system in order to stage the appended fields into the destination database system.

The multi-tenant system includes hardware processors that obtain subscription records including subscription identification parameters and preloaded resource parameters, generate preloaded resource objects based on the preloaded resource parameters, and obtain drawdown events from a drawdown event message queue. The drawdown events include drawdown event fields indicative of a consumption quantity and a first subset of subscription identification parameters of a particular subscription record. The hardware processors identify a particular preloaded resource object, perform a selective drawdown on the particular preloaded resource object, and publish a completed drawdown event within a completed drawdown message queue. The hardware processors further obtain consumption events from a consumption event message queue, the consumption events including consumption event fields indicative of the consumption quantity and the first subset or a second subset of the subscription identification parameters. The hardware processors compute a rating, and adjust the computed rating based on the completed drawdown event.

A system and method for calculating variable consideration for performance obligations. The method can involve, checking a database for historical transaction data, accessing the historical transaction data, and applying one or more predetermined variable consideration rules to the historical transaction data. The method can include automatically analyzing the historical transaction data generating an analysis report based on the data. The method can include uploading predetermined corrections to variable consideration transactions to a database or other storage medium and applying the corrections to historical performance obligation transaction data. The method can include determining whether variable consideration changes should be applied to individual transaction lines within the variable consideration transactions.

Store a subscription dataset for each tenant of a multi-tenant system, each of the subscription datasets having a common data format native to the system, the subscription datasets including billing data and not behavioral data. Determine primary features from a particular subscription dataset. Derive secondary features from the primary features. Generate a churn prediction model based on the primary features and the secondary features. Obtain a second subscription dataset, the second subscription dataset comprising billing data that is more recent than the particular subscription dataset. Identify, using the particular churn prediction model and the second subscription dataset, one or more subscribers as a churn-risk. Report the one or more subscribers identified as a churn-risk.

The multi-tenant system includes one or more hardware processors that extract standard fields from one or more objects stored in source database systems, the standard fields defining standard subscription attributes. The hardware processors map the standard fields to records within a destination database system, obtain customized fields defining tenant-selected subscription attributes, obtain a mapping of the customized fields to the records; append the standard fields to the customized fields to form appended fields; schedule a pipeline to ingest the appended fields into the records within the destination database system, and ingest the appended fields into the records within the destination database system in order to stage the appended fields into the destination database system.

A method of managing data for a client in a computer network, wherein the client has an associated product/service account maintained by a client data management system. The product/service account specifies at least one product/service to be assessed according to a defined plan having assessment data objects which in turn have attributes such as an assessment type, a trigger condition, a unit of measurement, and a cost per unit of measurement. The method includes defining an assessment data segment associated with an assessment data object, the assessment data segment being a time-based application of the assessment data object, and includes attributes such as its associated assessment data object and the assessment data object's attributes, an effective start time, a quantity of units, and a cost. The client's use of the product/service is monitored and assessment events are generated according to the defined assessment data objects and segments.

A multi-tenant system, comprises a main storage system including: a monolithic database storing global records associated with global objects, each global object including global fields common for all tenants; a monolithic application configured to process a particular global record storage request by instructing the monolithic database to store particular global field values of the particular global record for a particular tenant, and to process a particular global record fetch request by instructing the monolithic database to retrieve the one or more particular global field values; a custom object storage system including: a custom object database configured to store custom records associated with one or more custom objects, each custom object including one or more custom fields for a tenant; a custom object record service configured to process a particular custom record storage request by instructing the custom object database to store one or more particular custom field values for the tenant, and to process a particular custom record fetch request by instructing the custom object database to retrieve the one or more particular custom field values; and a query engine configured to receive a query, fetch relevant global records from the monolithic database, fetch relevant custom records from the custom object database, and generate a query response.