A system and method for minimizing customer churn in device service businesses commences with execution of a customer service contract. Ongoing customer data capture is made for each contract. Customer data includes contract events, environmental events, service events, device usage analytics and personnel events. Machine learning is applied to captured customer data, which machine learning is based on a state of customer data at the time of contract determination. Customer data is assigned weights, and aggregate data for each customer is compared to a preselected threshold level. Customers above a threshold are deemed happy and customers below the threshold are deemed to be at risk. Remedial measures relative to at risk customer data generates levels of automated remediation followed by remedial measure suggestions to an administrator when not sufficiently successful.

The proposed G-NOA framework that is based on the goals set by the customer, accepts an input configuration with all necessary details from the customer. This framework supports multi-tenancy in a customer-centric fashion to facilitate modules of various businesses. The framework also has the capability of working according to a specific module of an organization and recommends the suitable NOA for that module. This is performed using the proposed Time-Effective Reinforcement Learning (TE-RL) model of the relevant module. The enhanced version of the TE-RL model namely Enhanced TE-RL helps in defining the state with multiple dimensions and in using ANN for predicting transition probabilities of states. The TE-RL model and the Enhanced TE-RL model are defined with time effective parameters like Time_Sliced_State (TSS), Enhanced-Time_Sliced_State (E-TSS) and Time_Sensitive_Action (TSA) for precise and accurate NOA recommendation. The model performs appropriate policy estimation and policy tuning using TSS, E-TSS and TSA parameters.

The proposed G-NOA framework that is based on the goals set by the customer, accepts an input configuration with all necessary details from the customer. This framework supports multi-tenancy in a customer-centric fashion to facilitate modules of various businesses. The framework also has the capability of working according to a specific module of an organization and recommends the suitable NOA for that module. This is performed using the proposed Time-Effective Reinforcement Learning (TE-RL) model of the relevant module. The enhanced version of the TE-RL model namely Enhanced TE-RL helps in defining the state with multiple dimensions and in using ANN for predicting transition probabilities of states. The TE-RL model and the Enhanced TE-RL model are defined with time effective parameters like Time_Sliced_State (TSS), Enhanced-Time_Sliced_State (E-TSS) and Time_Sensitive_Action (TSA) for precise and accurate NOA recommendation. The model performs appropriate policy estimation and policy tuning using TSS, E-TSS and TSA parameters.

The present disclosure is directed to a novel system for using a distributed register to generate, manage, and store data for interest-pooled time deposit resource accounts. The invention leverages a pooled resource account approach, allowing for multiple disparate resource accounts to benefit from an enhanced interest return by pooling resource accounts. The system components of the invention contemplate the use of distributed register technology to provide a verified ledger of information related to one or more resource accounts, as well as store system data, user data, and metadata related to the movement and management of resources. By using a distributed register approach to store and verify data related to time-dependent resource account services, the invention provides an automated system and methods for enhancing the flow of sensitive verified information, reducing the need for manual review and increasing the speed at which various resource account services can be validated and executed.

A device monitors a communication between a user associated with a user device and a service representative associated with a service representative device, and causes a natural language processing model to perform a natural language processing analysis of a user input of the communication to identify a topic associated with the communication. The device determines a first score associated with the topic, and determines a second score associated with enabling the communication, where the first score and second score indicate a service performance score of an entity. The device causes a sentiment analysis model to perform a sentiment analysis of the communication to determine a sentiment score indicating a level of satisfaction the user has relative to the topic. The device updates a transaction protocol associated with the topic based on the service performance score, and/or updates a communication processing protocol associated with the communication based on the sentiment score.

A method of providing artificial intelligence (AI) functionality to target legacy customer outreach platforms of a plurality of tenant enterprises includes storing a plurality of AI templates, each of which is associated with one or more AI routines, generating a campaign object associating one or more of the AI templates with a tenant enterprise from among the plurality of tenant enterprises, transforming a communication on a switching network associated with the tenant enterprise according to the one or more AI templates associated with the campaign object, and providing the transformed communication to a target legacy customer outreach platform of the tenant enterprise.

Natural Language Processing (NLP) techniques are used to facilitate inferring actionable insights from interactions. Customer data from various communication channels can be used to determine, for example, voice-related aspects from sentiment analysis, intent analysis, Semantic Conscious Word Extraction (SCWE), emotion analysis, and contextual summarization. These analysis results can also be used, along with customer profile information, for deriving deep customer insights. The deep customer insight driven analysis can include multivariate customer fragmentation, setback accountability analysis, potential contender analysis, and propulsive business planning.

Natural Language Processing (NLP) techniques are used to facilitate inferring actionable insights from interactions. Customer data from various communication channels can be used to determine, for example, voice-related aspects from sentiment analysis, intent analysis, Semantic Conscious Word Extraction (SCWE), emotion analysis, and contextual summarization. These analysis results can also be used, along with customer profile information, for deriving deep customer insights. The deep customer insight driven analysis can include multivariate customer fragmentation, setback accountability analysis, potential contender analysis, and propulsive business planning.

An achievement platform may be maintained using a database system. A user interface may be displayed on a device of an authorized user of the achievement platform. The user interface may be configurable to allow the authorized user to create or modify customizable awards that are achievable based on customizable logic definable by the authorized user. The authorized user may be affiliated with a first one of a plurality of organizations implementing an application or service. Input, to create a first award based on first customizable logic may be processed. The first customizable logic may be configured by the authorized user such that satisfaction of conditions causes designated users associated with the application or service to be presented with the first award. It may be determined that a first user affiliated with the first organization has satisfied the conditions. The first user may be presented with the first award.

Provided is a server device (101) including: a transmitting/receiving section (201) for receiving, from a household electric appliance (102), an identifier for identifying the household electric appliance (102) and an operation status of the household electric appliance (102); an updating section (207) for estimating a consumption status of a consumer good using the received operation status so as to update the consumption status of the consumer good stored in a utilization information accumulating section (205) based on the estimated consumption status of the consumer good; and a determination section (206) for deciding whether or not to transmit a confirmation message for confirming the consumer good being used with the household electric appliance, based on the updated consumption status of the consumer good.