US 2026/0301044 A1Published application

Automated recommendation system

AssigneeMtn Group Management Services (Proprietary) Limited

InventorsMichael Geoffrey BELL, Rizwan Haroon BHAWRA, Kavish MAHARAJ, Ankur BAJPAI

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Abstract

There is provided for an automated recommendation system and a method carried out by such a system. The automated recommendation system is capable of automating and dynamically improving the process of determining the “next best” activity, action, offer, experience, or the like, to be recommended to/for a customer.

Claims27

Dependent claims

An automated recommendation system comprising:

a data source layer which includes customer data from a plurality of data sources, the data source layer being configured such that the customer data is dynamically or periodically updated;
a processing sub-system which is communicatively coupled to the data source layer, wherein the processing sub-system is configured to process the customer data, including generating customer segment data based on at least customer behaviour data and customer lifecycle stage data obtained or derived from the data sources, wherein the processing sub-system includes or implements a recommendation engine which is configured to generate customer-specific recommendations based on a plurality of machine learning (ML) algorithms or heuristic models, or ML algorithms and heuristic models in combination applied to the processed customer data, wherein each customer-specific recommendation is indicative of a proposed best action for a specific customer or customer segment, the proposed best action being either a next offer for a business associated with the system to make to the specific customer or customer segment or a next action for the business to take in respect of the specific customer or customer segment;
and
an interface layer which is communicatively coupled to the processing sub-system to receive output indicative of the customer-specific recommendations, and which is further communicatively coupled to a plurality of user devices, thereby allowing the user devices to access the customer-specific recommendations,
wherein the processing sub-system is further configured to receive feedback data indicative of a degree or level of success associated with automated customer-specific recommendations generated by the system, the processing sub-system and recommendation engine being configured to utilise the feedback data so as to dynamically or periodically update the customer data and, as a result, the customer-specific recommendations.

2 more elements

An automated recommendation method, the method comprising the steps of:

connecting a data source layer to a processing sub-system, the data source layer including customer data from a plurality of data sources fed into the processing sub-system;
dynamically or periodically updating the customer data;
processing, by the processing sub-system, the customer data, including generating customer segment data based on at least customer behaviour data and customer lifecycle stage data obtained or derived from the data sources;
generating, by a recommendation engine forming part of or implemented by the processing sub-system, customer-specific recommendations based on a plurality of ML algorithms or heuristic models, or ML algorithms and heuristic models in combination applied to the processed customer data, wherein each customer-specific recommendation is indicative of a proposed best action for a specific customer or customer segment, the proposed best action being either a next offer for a business to make to the specific customer or customer segment, or a next action for the business to take in respect of the specific customer or customer segment;
connecting the processing sub-system to a plurality of user devices via an interface layer, the interface layer being communicatively coupled to the processing sub-system to receive output indicative of the customer-specific recommendations and communicatively coupled to the user devices thereby allowing the user devices to access the customer-specific recommendations;
receiving, by the processing sub-system, feedback data indicative of a degree or level of success associated with automated customer-specific recommendations generated by the system;
and
updating the customer data and customer-specific recommendations by the processing sub-system and recommendation engine, using the feedback data.

5 more elements

Dependencies are inferred from the claim text.

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Abstract

An automated recommendation system comprising:

a data source layer which includes customer data from a plurality of data sources, the data source layer being configured such that the customer data is dynamically or periodically updated;
a processing sub-system which is communicatively coupled to the data source layer, wherein the processing sub-system is configured to process the customer data, including generating customer segment data based on at least customer behaviour data and customer lifecycle stage data obtained or derived from the data sources, wherein the processing sub-system includes or implements a recommendation engine which is configured to generate customer-specific recommendations based on a plurality of machine learning (ML) algorithms or heuristic models, or ML algorithms and heuristic models in combination applied to the processed customer data, wherein each customer-specific recommendation is indicative of a proposed best action for a specific customer or customer segment, the proposed best action being either a next offer for a business associated with the system to make to the specific customer or customer segment or a next action for the business to take in respect of the specific customer or customer segment;
and
an interface layer which is communicatively coupled to the processing sub-system to receive output indicative of the customer-specific recommendations, and which is further communicatively coupled to a plurality of user devices, thereby allowing the user devices to access the customer-specific recommendations,
wherein the processing sub-system is further configured to receive feedback data indicative of a degree or level of success associated with automated customer-specific recommendations generated by the system, the processing sub-system and recommendation engine being configured to utilise the feedback data so as to dynamically or periodically update the customer data and, as a result, the customer-specific recommendations.

2 more elements