Predicting customer churn from multi-product data
AssigneeFreshworks Inc.
InventorsSrivatsa NARASIMHA, Chetan BHAT, Sai Charan RAPOLU, Swaminathan PADMANABHAN
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Abstract
Predicting churn from a multi-product data using sequential modeling includes collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events, and creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data. This also includes correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between “churn” and “non-churn” journeys.
Claims20
A computer-implemented method for predicting churn from a multi-product data using sequential modeling, the computer-implemented method comprising:
- collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events;
- and
- creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data;
- and
- correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between “churn” and “non-churn” journeys.
A system configured to predict churn from a multi-product data using sequential modeling, the system comprising:
- memory comprising a set of instructions;
- and
- at least one processor, wherein
- the set of instructions are configured to cause the at least one processor to execute
- collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events;
- and
- creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data;
- and
- correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between “churn” and “non-churn” journeys.
A non-transitory computer-readable medium comprising a computer program that predicts churn from a multi-product data using sequential modeling, the computer program is configured to cause at least one processor to execute:
- collecting, by a journey module, data from a plurality of platforms to build a journey defined by one or more events;
- and
- creating, by a sequential deep learning (DL) model, relationship data identifying between events in a chronological order, capturing temporal dependencies present in the data;
- and
- correlating, by a churn prediction module, sequences of events to a churn event, and identifying when the churn event is going to occur based on pattern learnt to differentiate between “churn” and “non-churn” journeys.
Dependencies are inferred from the claim text.