System And Method For Automatically Optimizing Customer Communication
AssigneeGoogle Inc.
InventorsDimitris Meretakis, Zigmars Rasscevskis, Vinsensius B. Vega S. Naryanto, Tom Beyer, Szabolcs Payrits, Martin Stolle, Mark Steven Schadler, Jack Willow Waldron, Ali Galip Bayrak
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Abstract
The present disclosure provides a closed loop, self-learning system that automatically optimizes what experiences should be presented to each customer. Instead of relying on rules and external targeting, it observes customer reactions to continuously improve performance and adapt to environment changes.
Claims20
A computer-implemented method of communication, comprising:
- receiving data relating to an institution and their customers;
- training a machine learning model using the received data;
- determining a context for the customer at any given time;
- selecting, using the machine learning model, one or more experiences for delivery to the customer based on the determined context and provided business goal;
- delivering the one or more experiences to the customer;
- identifying, using a measurement service, one or more customer actions;
- and
- updating the machine learning model using this measurement.
A system, comprising:
- one or more memories;
- and
- one or more processors in communication with the one or more memories, the one or more processors configured to:
- receive data relating to an institution and its customers;
- train a machine learning model using the received data;
- determine a context for the customer at a given time;
- select, using the machine learning model, one or more experiences for delivery to the customer based on the determined context and provided business goal;
- deliver the one or more experiences to the customer;
- identify one or more customer actions;
- and
- updating the machine learning model using measurements related to the one or more customer actions.
The system of claim 20, wherein each experience further includes a specific format, and wherein selecting the one or more experiences comprises matching format of the one or more experiences with a format required by the context.
A non-transitory computer-readable medium storing instructions executable by one or more processors for performing a method of communication, comprising:
- receiving data relating to an institution and its customers;
- training a machine learning model using the received data;
- determining a context for the customer at a given time;
- selecting, using the machine learning model, one or more experiences for delivery to the customer based on the determined context and provided business goal;
- delivering the one or more experiences to the customer;
- identifying one or more customer actions;
- and
- updating the machine learning model using measurements related to the one or more customer actions.
Dependencies are inferred from the claim text.