US 2021/0233095 A9Published application

Systems and methods for predicting subscriber churn in renewals of subscription products and for automatically supporting subscriber-subscription provider relationship development to avoid subscriber churn

AssigneeAon Global Operations SE, Singapore Branch

InventorTrevor DOHERTY

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Abstract

In an illustrative embodiment, systems and methods for predicting subscriber churn include machine learning algorithm(s) for classifying the subscriber's decision to stay with the present subscription provider or to switch (churn) to a new provider. The machine learning algorithms may include a logistic regression/neural network for modeling churn propensity in subscribers. The churn risk analysis systems and methods may identify a group of subscribers most likely to churn. Further, the churn risk analysis systems and methods may identify a group of subscribers least likely to churn. The identified subscribers may be presented to a representative of the subscription provider, for example through a user interface.

Claims20

Dependent claims

A system for predicting subscriber churn, comprising:

processing circuitry;
and
a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to
access, from one or more data sources via a network, historic attribute data representing relationships between participants in transactions for purchasing a plurality of subscription products, wherein
the participants include a plurality of subscription product providers and a plurality of subscription product subscribers, and
the historic attribute data spans a timeframe including a plurality of subscription renewal periods,
identify, based on detected correlations between items of the historic attribute data, one or more correlation features for training a churn prediction model to determine a likelihood of churn, wherein
the likelihood of churn represents a relative likelihood that a given subscriber of the plurality of subscription product subscribers will churn away from a respective provider of the plurality of subscription product providers upon product renewal of the respective subscription product of the plurality of subscription products,
for each subscriber of a plurality of current subscribers of the plurality of subscription products,
determine, based on application of one or more churn prediction attributes for the respective subscriber to the trained churn prediction model, a predicted likelihood of churn away from a respective provider of the plurality of providers,
wherein the one or more churn prediction attributes define a relationship between the respective subscriber and the respective provider in view of a respective subscription product of the plurality of subscription products, and
present, to a remote computing device of the respective provider via the network, information identifying a portion of the plurality of current subscribers, each subscriber of the portion being determined to have a respective predicted likelihood of churn identified as a high likelihood, for mitigating the predicted likelihood of churn away from the respective provider.

9 more elements

A method for predicting customer churn, comprising:

for each customer of a plurality of product customers participating in transactions to purchase a plurality of renewable products from a plurality of product providers,
determining, by processing circuitry based on application of one or more churn prediction attributes for the respective customer to a trained churn prediction model, a predicted likelihood that the respective customer will churn away from a respective product of the plurality of renewable products,
wherein the one or more churn prediction attributes define a relationship between the respective customer to a respective provider of the plurality of product providers providing the respective product, and
wherein the predicted likelihood of churn represents a relative likelihood that the respective customer will churn away from the respective product upon product renewal;
identifying, by the processing circuitry based on a portion of the one or more churn prediction attributes, one or recommended actions for mitigating the predicted likelihood of churn away from the respective product;
presenting, by the processing circuitry to a remote computing device of the respective provider of the respective product via a network, the predicted likelihood of churn and the one or more recommended actions for mitigating the predicted likelihood of churn;
and
updating, by the processing circuitry based on an accuracy of the churn prediction model in predicting the churn of the respective customer away from the respective product, one or more correlation features for training the churn prediction model,
wherein the accuracy of the churn prediction model is based on a comparison of the predicted likelihood of churn to a churn outcome for the respective customer.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/795,339Filed 19 Feb 2020
Priority: 21 Feb 2019
Published: 29 Jul 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06Q 30/02
Commerce: marketing; Price estimation or determination; Fundraising
G06Q 30/0201
Marketing; Price estimation or determination; Fundraising: market modelling; Market analysis; Collecting market data
G06N 20/00
Machine learning
G06Q 50/60
Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g.…:…