Abstract: A system and method for providing one or more recommendations. The method encompasses identifying, target feature(s) from a set of features influencing a customer feedback data associated with customer(s) of a digital platform. The method thereafter comprises fine-tuning, a sub-system based on the target feature(s). Further the method encompasses determining, a probability of a promoter rating for the customer(s) based on the fine-tuned sub-system and the customer feedback data. The method thereafter encompasses determining, a contribution of each target feature in the probability of the promoter rating. The method further comprises generating, at least one of a customer insight and a governance parameter, based on the contribution of each target feature in the probability of the promoter rating. Further the method encompasses providing, the one or more recommendations on the digital platform based on at least one of the customer insight and the governance parameter.

Abstract: A system and method for determining market share of an organization. The method encompasses receiving, at least one of a voice of customer data, an internal data of the organization and an external data. The method thereafter leads to determining, one or more set of target features based on at least one of the voice of customer data, the internal data of the organization and the external data. Further the method comprises generating, one or more pre-trained dataset based at least on the one or more set of target features. The method thereafter encompasses receiving, at least one of a first set of feature constraints and a second set of feature constraints. Further the method comprises determining, the market share of the organization based at least on the one or more pre-trained dataset, the first set of feature constraints and the second set of feature constraints.

Abstract: A system and method for generation of a user feedback data is provided. The method encompasses extracting in real time, from social media platform(s), a set of social media posts, wherein each social media post comprises mention(s) related to e-commerce platform(s). The method thereafter encompasses categorizing, each social media post into one of a promotional post and non-promotional post. The method further comprises identifying, a sentiment associated with each social media post. Further, the method encompasses assigning, a customer experience node and/or a business unit with the social media post(s). The method thereafter leads to removing, irrelevant posts from the set of social media posts. The method then generates the user feedback data based on the removal of the at least one irrelevant post and the assigned customer experience node, the assigned business unit and/or the sentiment of each social media post.

Abstract: A method may include determining first network data corresponding to a first time period, including alarm data and incident data collected by a set of network devices of a core network during the first time period. The method may include creating, based on the first network data, an incident prediction model. The method may include receiving second network data, associated with the core network, corresponding to a second time period. The second network data may include alarm data and incident data collected by a network device, of the set of network devices, during the second time period. The method may include generating, based on the second network data and the incident prediction model, an incident prediction that includes a prediction whether the network device will experience an incident during a third time period.

Abstract: A method may include determining first network data corresponding to a first time period, including alarm data and incident data collected by a set of network devices of a core network during the first time period. The method may include creating, based on the first network data, an incident prediction model. The method may include receiving second network data, associated with the core network, corresponding to a second time period. The second network data may include alarm data and incident data collected by a network device, of the set of network devices, during the second time period. The method may include generating, based on the second network data and the incident prediction model, an incident prediction that includes a prediction whether the network device will experience an incident during a third time period.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.