WO2025079109A1

WO2025079109A1 - Method and system for management of customers telecom subscription plans

Info

Publication number: WO2025079109A1
Application number: PCT/IN2024/052048
Authority: WO
Inventors: Aayush Bhatnagar; Ankit Murarka; Jugal Kishore; Chandra GANVEER; Sanjana Chaudhary; Gourav Gurbani; Yogesh Kumar; Avinash Kushwaha; Dharmendra Kumar Vishwakarma; Sajal Soni; Niharika PATNAM; Shubham Ingle; Harsh Poddar; Sanket KUMTHEKAR; Mohit Bhanwria; Shashank Bhushan; Vinay Gayki; Aniket KHADE; Durgesh KUMAR; Zenith KUMAR
Original assignee: Jio Platforms Ltd
Current assignee: Jio Platforms Ltd
Priority date: 2023-10-11
Filing date: 2024-10-11
Publication date: 2025-04-17
Anticipated expiration: 2026-04-11

Abstract

The present disclosure relates to a system (108) and a method (500) for management of customers telecom subscription plans The system (108) includes a receiving unit (210) configured to receive a single stream of integrated data from a data integration module (306). The system (108) includes a normalizing unit (214) to normalize, the integrated data, using a pre-processor (310) to obtain an organized set of data, and storing the organized data. The system (108) includes an analyzing unit (216) to analyze, the organized data, and perform trend analysis to segregate and categorize the customers. The system (108) includes a predicting unit (218) to predict the risk of customer churn and detect anomaly using the model. The system (108) includes a generating unit (222) to generate a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn.

Description

METHOD AND SYSTEM FOR MANAGEMENT OF CUSTOMERS

TELECOM SUBSCRIPTION PLANS

FIELD OF THE INVENTION

[0001] The present invention relates to telecom subscriptions plans such as mobile subscription plans used by the customers, more particularly relates to a method and a system for management of customers telecom subscription plans.

BACKGROUND OF THE INVENTION

[0002] In general, service providers provide multiple choices of mobile subscription plans to their customers to select. In particular, customers may have multiple choices of mobile subscription plans provided by the service providers and customers can choose the best mobile subscription plan according to their needs.

[0003] In practice, the service providers track and record the customer data such as the previous and current mobile subscription plans. Thereafter, the service providers may categorize the customers based on their current and previous mobile subscription plans in order to provide customers with personalized offers and services in order to retain customers. The process of categorizing the customers based on their current and previous mobile subscription plans is time consuming and cumbersome task. Further, the service providers face challenges in identifying and addressing customer plan changes in real-time. Further, the service providers also face difficulty in predicting customer churn or upgrade opportunities.

[0004] In view of the above, there is a dire need for a system and method for efficiently managing subscription plan of customers, which ensures better utilization of customer data for providing customers with personalized offers and services thereby enhancing customer experience.

SUMMARY OF THE INVENTION

[0005] One or more embodiments of the present disclosure provide a method and system for optimal management of telecom subscription plans. [0006] In one aspect of the present invention, the system for optimal management of telecom subscription plans of customers is disclosed. The system includes a receiving unit, configured to receive a single stream of integrated data from a data integration module. The system further includes a normalizing unit, configured to normalize, the integrated data, using a pre-processor to obtain an organized set of data, and storing the organized data. The system further includes an analyzing unit, configured to analyze, the organized data, and perform trend analysis to segregate and categorize the customers based on their current subscription data using a model, deployed by an execution module. The system further includes a predicting unit, configured to predict, the risk of customer churn, and detect anomaly using the model. The system further includes a generating unit, configured to generate, a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analyzed data.

[0007] In an embodiment, the system comprises a retrieving unit, configured to retrieve one or more subscriptions data associated with one or more mobile subscribers.

[0008] In an embodiment, the retrieving unit comprises the receiving unit configured to receive the subscriptions data associated with the mobile subscriber at a graphical user interface.

[0009] In an embodiment, the system comprises a storing unit configured to store the predicted trend data, and the analyzed data in a data lake.

[0010] In an embodiment, the receiving the single stream of integrated data, comprises the retrieving unit, configured to retrieve, a multiple types of data of customers from external sources and feed data to a fault management system, wherein the data is historical data including one of, but not limited to customer onboarding, customer deactivation and system-based data. [0011] In an embodiment, the system comprises a rendering unit, configured to render insights of the analysis to a user interface, wherein the data enables segregating the customers based on their current mobile subscription plans and predicting the risk of customer churn.

[0012] In another aspect of the present invention, the method for optimal management of telecom subscription plans of customers is disclosed. The method includes the step of receiving a single stream of integrated data from a data integration module. The method further includes the step of normalizing the integrated data, using a pre-processor to obtain an organized set of data, and storing the organized data. The method further includes the step of analyzing, the organized data, and perform trend analysis to segregate and categorize the customers based on their current subscription data using a model, deployed by an execution module. The method further includes the step of predicting the risk of customer churn, and detect anomaly using the model. The method further generating a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analyzed data.

[0013] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to receive a single stream of integrated data from a data integration module. The processor is configured to normalize the integrated data, using a pre-processor to obtain an organized set of data, and storing the organized data. The processor is configured to analyze the organized data and perform trend analysis to segregate and categorize the customers based on their current subscription data using a model, deployed by an execution module. The processor is configured to predict the risk of customer churn, and detect anomaly using the model. The processor is configured to generate a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analyzed data. [0014] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all- inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0016] FIG. 1 is an exemplary block diagram of an environment for management of telecom subscription plans of customers, according to one or more embodiments of the present invention;

[0017] FIG. 2 is an exemplary block diagram of a system for management of the telecom subscription plans of the customers, according to one or more embodiments of the present invention; [0018] FIG. 3 is an exemplary block diagram of an architecture implemented in the system of the FIG. 2, according to one or more embodiments of the present invention;

[0019] FIG. 4 is a flow diagram for management of telecom subscription plans of customers, according to one or more embodiments of the present invention; and

[0020] FIG. 5 is a schematic representation of a method for management of the telecom subscription plans of the customers, according to one or more embodiments of the present invention.

[0021] The foregoing shall be more apparent from the following detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0022] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.

[0023] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.

[0024] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.

[0025] The present invention provides a unique approach of automatically/dynamically categorizing the customers based on their current mobile subscription plans, predicting the risk of customer churn. The predictions are performed by an Artificial Intelligence /Machine Learning (AI/ML) model utilizing the historical data and trend analysis, thereby providing customers with better customized mobile subscription plans in order to retain the customers with the current service providers.

[0026] FIG. 1 illustrates an exemplary block diagram of an environment 100 for management of customers telecom subscription plans, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 102, a server 104, a network 106 and a system 108 communicably coupled to each other for management of the customers telecom subscription plans.

[0027] The telecom subscription plans refer to the various service packages offered by telecommunication companies to their customers, providing access to communication services such as voice calls, text messaging, and internet/data usage. The telecom subscription plans include, but not limited to, data allowance, voice minutes, text messaging, add-on features, billing types, validity period, subscription tiers. The customers are individuals or organizations that purchase or use products or services provided by a service provider. The service providers are entities that offer communication services such as voice calling, text messaging, and internet connectivity. [0028] As per the illustrated embodiment and for the purpose of description and illustration, the UE 102 includes, but not limited to, a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 102 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 102a, the second UE 102b, and the third UE 102c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 102”.

[0029] In an embodiment, the UE 102 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device.

[0030] The environment 100 includes the server 104 accessible via the network 106. The server 104 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.

[0031] The network 106 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 106 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.

[0032] The network 106 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 106 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public- Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a V OIP or some combination thereof.

[0033] The environment 100 further includes the system 108 communicably coupled to the server 104 and the UE 102 via the network 106. The system 108 is configured to manage the customers telecom subscription plans. As per one or more embodiments, the system 108 is adapted to be embedded within the server 104 or embedded as an individual entity.

[0034] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.

[0035] FIG. 2 is an exemplary block diagram of the system 108 for management of customers telecom subscription plans, according to one or more embodiments of the present invention.

[0036] As per the illustrated embodiment, the system 108 includes one or more processors 202, a memory 204, a user interface 206, and a database 208. For the purpose of description and explanation, the description will be explained with respect to one processor 202 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 108 may include more than one processor 202 as per the requirement of the network 106. The one or more processors 202, hereinafter referred to as the processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.

[0037] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.

[0038] In an embodiment, the user interface 206 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 206 facilitates communication of the system 108. In one embodiment, the user interface 206 provides a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, the UE 102 and the database 208.

[0039] The database 208 is one of, but not limited to, a centralized database, a cloudbased database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 208 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open- source, etc.

[0040] In order for the system 108 for optimal management of telecom subscription plans of customers, the processor 202 includes one or more modules. In one embodiment, the one or more modules includes, but not limited to, a receiving unit 210, a retrieving unit 212, a normalizing unit 214, an analyzing unit 216, a predicting unit 218, a storing unit 220, a generating unit 222 and a rendering unit 224 communicably coupled to each other for management of customers telecom subscription plans.

[0041] In one embodiment, each of the one or more modules the receiving unit 210, the retrieving unit 212, the normalizing unit 214, the analyzing unit 216, the predicting unit 218, the storing unit 220, the generating unit 222, and the rendering unit 224 can be used in combination or interchangeably for management of customers telecom subscription plans.

[0042] The receiving unit 210, the retrieving unit 212, the normalizing unit 214, the analyzing unit 216, the predicting unit 218, the storing unit 220, the generating unit 222, and the rendering unit 224 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 202 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 204 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.

[0043] In one embodiment, the receiving unit 210 is configured to receive a single stream of integrated data. The single stream of integrated data refers to a unified flow of data that consolidates information from multiple sources into one coherent and continuous data stream. The integration process brings together various types of data, such as customer details, usage pattern, transaction records, and system logs, into a single stream to simplify analysis and processing.

[0044] The integrated data stream includes, but not limited to customer data, usage data, operational data and historical data. The customer data include information about customer profiles, subscription plans and payment details. The usage data includes the data such as call record, data consumption, and Short Message Service (SMS) usage. The operational data includes the information from network operations including system logs, network events and fault reports. The historical data includes past records related to customer onboarding, billing service changes or account deactivation.

[0045] The data is historical data including one of, but not limited to, customer onboarding, customer deactivation and system-based data. The customer onboarding is the process of registering a new customer into the service provider's system, enabling them to start using the telecom services. The customer onboarding includes, but not limited to, capturing customer details, verifying identity, selecting subscription plans, configuring network settings, and ensuring that all technical and administrative procedures are completed. The customer deactivation refers to the process of terminating a customer's telecom service. The customer deactivation happens for various reasons, such as the customer's decision to cancel the service, a failure to pay bills, or moving to another service provider. The system-based data refers to information generated from the telecom network and operational systems that support the delivery of services to customers. The system-based data includes, but not limited to, network logs, usage statistics, service metrics, operational data. The network logs include information related to network performance, event record and fault detection. The usage statistics includes the data that tracks how telecom infrastructure is being used such as bandwidth usage, system load and service availability. The service metrics includes the measurements that provide insights into the quality and reliability of services such as latency, call drop rates and data speed. The operational data includes the data related to day-to-day operations such as ticketing information for maintenance tasks, system health monitoring, and configurations. In an embodiment, the data is at least one of service- based data. The service -based data refers to information related to the services provided by a telecom or other service provider to its customers. The service-based data includes details about how customers use specific services, their preferences, and any issues or interactions related to the services they are subscribed to. The service-based data includes, but not limited to, usage data, service quality metrics, billing information, customer interactions, subscription details, provisioning data.

[0046] In an embodiment, the single stream of integrated data is received from a data integration module 306. Upon receiving the single stream of data from the data integration module 306, the retrieving unit 212 is configured to retrieve multiple types of data of customers from external sources. The multiple types of data of customers from external sources refers to diverse sets of customer-related information gathered from various external entities outside the telecom service providers. The multiple types of data include, but not limited to, social media data, demographic data, behavioral data, financial data, public records, etc. The external sources include, but not limited to, social media platforms, third-party data providers, financial institutions, government databases, etc. Upon retrieving the multiple types of data of customers, the data is feed to a fault management system. The fault management system refers to a system designed to detect, isolate, notify, and sometimes correct issues or faults within the network 106. The faults are at least one of hardware or software failures, performance issues etc. The key functionalities of the fault management system include, but are not limited to, fault detection, fault isolation, fault notification, fault correction, logging and reporting.

[0047] In an embodiment, the retrieving unit 212 is configured to retrieve a subscriptions data associated with a mobile subscriber. The subscription data refers to information associated with a customer's telecom subscription. The subscription data includes, but not limited to, plan details, subscription start and end date, billing information, usage statistics, service activation status. The mobile subscriber is an individual or entity that has subscribed to a mobile network service provided by a telecom operator. The mobile subscriber uses a unique Subscriber Identity Module (SIM) card to access voice, messaging, data, and other mobile services. The mobile subscriber is identified by a unique phone number such as Mobile Station International Subscriber Directory Number (MSISDN) and an International Mobile Subscriber Identity (IMSI) linked to the SIM card. Further, the retrieving unit 212 includes receiving unit configured to receive the subscriptions data associated with the mobile subscriber at a graphical user interface (GUI). The subscriptions data associated with the mobile subscriber refers to all the information related to the mobile services that a subscriber has signed up for with their telecom service provider. The subscription data includes, but not limited to, plan details, subscription status, service activation date, add-ons and features, billing information, usage data. In an embodiment, the subscriptions data associated with the mobile subscriber is received at the user interface 206. In another embodiment, the subscriptions data associated with the mobile subscriber is received through one or more requests. The one or more requests includes at least of Hypertext Transfer Protocol (HTTP) request. The one or more request is received from one or more microservices.

[0048] Upon receiving the integrated data, the normalizing unit 214 is configured to normalize the integrated data. The integrated data is normalized by using a preprocessor 310. The normalization of integrated data using the pre-processor 310 involves transforming raw, inconsistent, or heterogeneous data into a standardized and organized format, making it easier to analyze and process. The normalization of integrated data includes, but not limited to scaling data, standardization, removing data duplication, handling units and formats. The preprocessor includes data definition and data cleaning. The data definition refers to identifying and describing the attributes, formats, and types of the data that are involved in a particular data set. The data definition includes, but not limited to, data types, attributes and fields, relationships, metadata. The data cleaning includes detecting and correcting errors, inconsistencies, or inaccuracies within the data. The data cleaning includes, but not limited to, handling missing values, outlier detection and removal, removing inconsistencies, correcting errors, standardizing text. The integrated data is normalized to obtain an organized set of data and storing the organized data. The organized set of data refers to data that has been processed and structured in a way that makes it consistent, clean, and ready for analysis or further use. The organized set of data is the data has gone through normalization and pre-processing steps to eliminate inconsistencies, redundancies, and errors, resulting in a well-structured and uniform dataset.

[0049] Upon normalizing the integrated data, the analyzing unit 216 is configured to analyze the organized data. The analyzing the organized data involves examining the normalized and structured dataset to extract meaningful insights, identify patterns, and make informed decisions. The analyzing unit 216 is further configured to perform trend analysis to segregate and categorize the customers based on their current subscription data using a model. The model is at least one of an Artificial Intelligence/ Machine Learning (AI/ML) model. The trend analysis refers to the systematic examination of historical and current data to identify patterns, behaviors, and changes over time related to customer subscriptions. The trend analysis includes, but not limited to, historical subscription data, current data, usage trends, subscription changes, seasonal variations, customer grouping, behavioral analysis, automated analysis, predictive capabilities. The historical subscription data is the collection of data over a period regarding customers' subscription plans, usage statistics, payment histories, and any changes in subscription behavior. The current data refers to a realtime data on customer interactions, usage patterns and any modifications in their subscription plans. The usage trends refer to the analysis of how customer usage (voice, data, SMS) changes over time, allowing providers to identify peak usage periods or shifts in customer preferences. The subscription changes refer to the examining of the trends in customer behavior regarding switching plans (e.g., moving from prepaid to postpaid) or upgrading/downgrading their services. The seasonal variations refer to the recognition of seasonal or temporal trends that influence customer behavior, such as increased usage during holidays or promotional periods. The customer grouping refers to the segmentation of the customers into groups based on similar characteristics, behaviors, or preferences. For example, the customer grouping based on high data users, international callers, or value-conscious customers. The behavioral analysis refers to analysis of how different segments respond to trends and which segments are more likely to engage with specific services or promotions. The automated analysis refers to the analysis of large datasets, identifying trends using the AI/ML model. The predictive capabilities refer to the predicting of the customer behaviors such as potential churn rates or the likelihood of upgrading to a higher-tier subscription using historical trends. The analyzing of the organized data and performing the trend analysis are deployed by an execution module 312.

[0050] Upon analyzing the organized data and performing the trend analysis, the predicting unit 218 is configured to predict the risk of customer churn and detect anomaly using the AI/ML model. In particular, the AI/ML model identifies the customer who are likely to discontinue their subscription to the telecom service. The customer churn refers to the phenomenon where a customer discontinues or cancels their subscription to a telecom service. The predicting of the customer churn involves analyzing customer behavior and trends to identify which customers are at risk of leaving the service. In particular, the customer churn is identified by using a churn indicator. The churn indicators include, but not limited to, declining usage, service frequency, late payments, change in billing amount, support requests, negative feedback, plan downgrades, frequent plan switching etc. The anomaly refers to any data point or behavior that significantly deviates from the expected or usual pattern in customer data. The anomaly indicates unusual or irregular customer behavior, which could be positive (e.g., unexpected high usage during a specific period) or negative (e.g., sudden drop in service usage).. In an embodiment, the inputs fed into the AI/ML model includes, but not limited to, customer demographics, service usage data, billing and payment history, service quality metrics, customer support interactions data, historical churn data. The several AI/ML algorithms are typically used in churn prediction and anomaly detection, which includes, but are not limited to, logistic regression, decision tress /random forest, Gradient Boosting Machines (GBM), Neural Networks/Deep Learning, Support Vector Machines (SVM), anomaly detection algorithms. The AI/ML model executes the prediction by data preprocessing, feature extraction, model inference, prediction output. The outputs of the AI/ML model include, but not limited to, churn risk score, churn classification, anomaly detection.

[0051] Upon analyzing and predicting the data, the storing unit 220 is configured to store the predicted trend data and the analyzed data in a data lake 314. The predicted trend data refers to forecasts and projections about customer behavior and market dynamics based on historical and current data. The predicted trend data includes, but not limited to churn predictions, usage forecasts, customer engagement trends, market trends. The analyzed data encompasses the insights and findings obtained from the analysis of organized customer data. The analyzed data includes, but is not limited to, customer segmentation, usage analytics, customer satisfaction metrics, churn analysis, service performance metrics.

[0052] Upon storing the predicted trend data and the analyzed data in a data lake 314, the generating unit 222 is configured to generate a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and the analyzed data. The data visualization includes creating graphical representations of complex datasets to make the information more accessible and understandable. The data visualization includes, but is not limited to, customer segmentation charts, churn risk heatmaps, trends over time, usage patterns, customer journey mapping, comparative analysis. The key factors or one or more parameters for predicting customer churn include, but not limited to, customer demographics, service usage patterns, billing and payment history, service quality metrics, customer support interactions, subscription information, historical churn data. The customer demographics include, but not limited to, age, gender, location. The service usage patterns include, but not limited to, voice/data usage, Value-Added Services (VAS), The billing and payment history include, but are not limited to, later payments, billing complaints. The service quality metrics include, but are not limited to, dropped calls/network downtime, customer satisfaction surveys. The customer support interactions include, but are not limited to, number of complaints /support calls, resolution time. The subscription information includes, but is not limited to, contract expiry date or subscription end date, plan change requests. The historical data includes past churn behavior, predictive features. The approach to set the boundary condition includes service quality and billing. For example, if a customer's service quality score falls below a certain threshold (e.g., more than 5 dropped calls per month), they might be flagged as at risk, if a customer has more than 3 late payments in the last 6 months, they might be flagged for potential churn.

[0053] Subsequently, the rendering unit 224 is configured to render insights of the analysis to a service provider via the user interface 206. The rendering insights of the analysis to a service provider via the user interface 206 involves presenting the results of data analysis in an intuitive and accessible format, enabling the service provider to take informed actions based on the insights. The rendering the insights of the analysis process is essential for communicating valuable information about customer behavior, usage patterns, and churn risk. In an embodiment, the rendering unit 224 provides realtime insights into customer plan changes by utilizing predictive analytics to identify churn risks and upgrade opportunities. Further, the rendering unit 224 offers personalized plans and services based on categorization and automates data preprocessing to streamline processes and save operational costs. In an embodiment, realtime insights are automatically transmitted to microservice or applications via Hypertext Transfer Protocol (HTTP) JSON/XML based request. In particular, the microservice or applications are transmitting the request to the system 108 and receiving the end-response from the system 108. Thus no manual interactions is involved.

[0054] Therefore, the system 108 proactively categorizes customers based on their current plans, enabling personalized offerings. Further, the system 108 automates the data pre-processing, saving time and resources. Further, the system 108 identifies potential churn risks or opportunities for plan upgrades. The system 108 maximizes the use of customer data for targeted marketing and service customization. The system 108 provides real-time insights into customer plan changes, facilitating prompt responses.

[0055] FIG. 3 is an exemplary block diagram of an architecture 300 of the system 108 for management of customers telecom subscription plans, according to one or more embodiments of the present invention.

[0056] The architecture 300 includes a user, Graphical User Interface (GUI) login 302, a Fault Management System (FMS) instance 304, the data integration module 306, processing hub 308 and the user interface 206. The processing hub 308 includes the pre-processor 310, the execution module 312, the data lake 314, a workflow manager 316, the trend analysis 318.

[0057] In an embodiment, the user logins via the GUI login 302. In another embodiment, the user logins via the user interface 206. The subscriptions data associated with the mobile subscriber are retrieved at the GUI. In an embodiment the FMS instance 304 collects the data from various sources such as customer onboarding data, service-based data, and customer deactivation or churn data. Upon collecting the data, the FMS instance 304 transmits the data to the data integration module 306. The data integration module 306 integrates the collected data in the single stream. [0058] Subsequently, the single stream of integrated data is transmitted to the preprocessor 310 for normalizing the integrated data. The pre-processor 310 normalizes the integrated data to obtain the organized set of data and storing of the organized data. The pre-processor 310 includes the data definition and the data cleaning.

[0059] Upon normalizing the integrated data, the execution module 312 analyze the organized data. Further, the execution module 312 perform trend analysis to segregate and categorize the customers based on their current subscription data using the AI/ML model. Upon analyzing and performing trend analysis, the analyzed data is stored in the data lake 314. Upon storing the analyzed data, the workflow manager 316 manages the workflow. The workflow is the collection of the activities that is performed in order to complete a task.

[0060] Thereafter, the trend analysis 318 predicts the risk of customer churn, and detect anomaly using the Al/ML model. Upon predicting the risk of customer churn, and the detection of the anomalies, the data visualization of categorizing the customers is generated based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and the analyzed data. Upon generating, the insight of the analysis is rendered to the service provider via the user interface 206.

[0061] FIG. 4 is a flow diagram for management of customers telecom subscription plans, according to one or more embodiments of the present invention.

[0062] At step 402, the data from various sources are collected from the FMS instance 304. The data from various sources includes, but not limited to, customer onboarding data, service-based data and deactivation data.

[0063] At step 404, upon collecting the data from various sources, the data integration module 306 integrates the collected data in the single stream. [0064] At step 406, upon integrating the collected data, the pre-processor 310 normalizes the single stream of integrated data. The pre-processor 310 normalizes the integrated data to obtain the organized set of data and storing of the organized data. The pre-processor 310 includes the data definition and the data cleaning.

[0065] At step 408, upon normalizing the integrated data, the execution module 312 analyze the organized data. Further, the execution module 312 perform trend analysis 318 to segregate and categorize the customers based on their current subscription data using the AI/ML model.

[0066] At step 410, upon analyzing and performing trend analysis 318, the analyzed data is stored in the data lake 314.

[0067] At step 412, upon storing the analyzed data, the trend analysis 318 predicts the risk of customer churn, and detects anomaly using the Al/ML model. Upon predicting the risk of customer churn, and the detection of the anomalies, the data visualization of categorizing the customers is generated based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and the analyzed data

[0068] At step 414, upon generating the data visualization, the insight of the analysis is rendered to the service provider via the user interface 206.

[0069] FIG. 5 is a flow diagram of a method 500 for management of customers telecom subscription plans, according to one or more embodiments of the present invention. For the purpose of description, the method 500 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.

[0070] At step 502, the method 500 includes the step of receiving the single stream of integrated data from the data integration module 306 by the receiving unit 210. The receiving of the single stream of integrated data includes the retrieving unit 212 to retrieve the multiple types of data of customers from external sources and feed data to a fault management system. The data is historical data including one of, but not limited to the customer onboarding, the customer deactivation and the system-based data. The retrieving unit 212 retrieves the subscriptions data associated with the mobile subscriber. Further, the retrieving unit 212 includes the subscriptions data associated with the mobile subscriber at the graphical user interface.

[0071] At step 504, the method 500 includes the step of normalizing the integrated data, using the pre-processor 310 to obtain the organized set of data, and storing the organized data by the normalizing unit 214.

[0072] At step 506, the method 500 includes the step of analyzing the organized data and perform trend analysis to segregate and categorize the customers based on their current subscription data using the model, deployed by the execution module 312 by the analyzing unit 216.

[0073] At step 508, the method 500 includes the step of predicting the risk of customer churn, and detecting anomaly using the model, by the predicting unit 218. In an embodiment, the predicted trend data, and the analyzed data are stored in the data lake 314 by the storing unit 220.

[0074] At step 510, the method 500 includes the step of generating the data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and the analyzed data by the generating unit 222. Further, the insight of the analysis is rendered to the service provider via the user interface 206 by the rendering unit 224.

[0075] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer- readable instructions are executed by the processor 202. The processor 202 is configured to receive a single stream of integrated data from a data integration module 306. The processor 202 is further configured to normalize the integrated data, using the pre-processor 310 to obtain an organized set of data, and storing the organized data. The processor 202 is further configured to analyze the organized data and perform trend analysis to segregate and categorize the customers based on their current subscription data using the model, deployed by the execution module 312. The processor 202 is further configured to predict the risk of customer churn and detect anomaly using the model. The processor 202 is further configured to generate a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analyzed data.

[0076] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-5) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.

[0077] The present disclosure incorporates technical advancement that by providing detailed insights into customer behaviors and preferences, service providers can make informed decisions regarding subscription offerings, marketing strategies, and customer retention initiatives. The present invention helps in identifying at-risk customers and enable proactive engagement strategies to reduce churn rates and improve customer loyalty. The present invention improves customer satisfaction and increases the likelihood of upselling and cross-selling opportunities. The present invention reduces manual effort and associated errors by automating the data retrieval, normalization, analysis, and visualization. The present invention supports scalability, by adapting to changing customer behaviors and market dynamics. Further, the present invention proactively categorizes customers based on their current plans, enabling personalized offerings. Further, the present invention automates the data preprocessing, saving time and resources. Further, the present invention identifies potential churn risks or opportunities for plan upgrades. The present invention maximizes the use of customer data for targeted marketing and service customization. The present invention provides real-time insights into customer plan changes, facilitating prompt responses.

[0078] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS

[0079] Environment- 100

[0080] User Equipment (UE)- 102

[0081] Server- 104

[0082] Network- 106

[0083] System -108

[0084] Processor- 202

[0085] Memory- 204

[0086] User Interface- 206

[0087] Database- 208

[0088] Receiving Unit- 210

[0089] Retrieving Unit- 212

[0090] Normalizing unit- 214

[0091] Analyzing Unit- 216

[0092] Predicting Unit- 218

[0093] Storing Unit- 220

[0094] Generating Unit-222

[0095] Rendering unit- 224

[0096] GUI Login- 302

[0097] FMS instance- 304

[0098] Data integration module- 306

[0099] Processing hub- 308

[00100] Pre-processor- 310

[00101] execution module- 312

[00102] Data lake- 314

[00103] Workflow manager- 316

[00104] Trend analysis- 318

Claims

CLAIMS We Claim:

1. A method (500) for management of customers telecom subscription plans, the method (500) comprising the steps of: receiving, by the one or more processors (202), a single stream of integrated data from a data integration module (306); normalizing, by the one or more processors (202), the integrated data, using a pre-processor (310) to obtain an organised set of data, and storing the organized data; analysing, by the one or more processors (202), the organized data, and perform analysis to segregate and categorize the customers based on their current subscription data using a model, deployed by an execution module (312); predicting, by the one or more processors (202), the risk of customer churn, and detect anomaly using the model; and generating, by the one or more processors (202), a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analysed data.

2. The method (500) as claimed in claim 1, comprises retrieving, by a one or more processors (202), one or more subscriptions data associated with one or more mobile subscribers;

3. The method (500) as claimed in claim 2, wherein retrieving, comprises, receiving, by the one or more processors (202), the subscriptions data associated with the mobile subscriber at a graphical user interface.

4. The method (500) as claimed in claim 1, comprises storing, by the one or more processors (202), the predicted trend data, and the analysed data in a data lake (314); and

5. The method (500) as claimed in claim 1, wherein receiving the single stream of integrated data, comprises retrieving, by the one or more processors (202), multiple types of data of customers from external sources and feed data to a fault management system, wherein the data is historical data including one of, but not limited to customer onboarding, customer deactivation and system-based data.

6. The method (500) as claimed in claim 1, comprises rendering, by the one or more processors (202), insights of the analysis to a user interface (206), wherein the data enables segregating the customers based on their current mobile subscription plans and predicting the risk of customer churn.

7. A system (108) for management of customers telecom subscription plans, the system (108) comprises: a receiving unit (210), configured to receive a single stream of integrated data from a data integration module (306); a normalizing unit (214), configured to normalize, the integrated data, using a pre-processor (310) to obtain an organised set of data, and storing the organized data; an analysing unit (216), configured to analyse, the organized data, and perform analysis to segregate and categorize the customers based on their current subscription data using a model, deployed by an execution module (312); a predicting unit (218), configured to predict, the risk of customer churn, and detect anomaly using the model; and a generating unit (222), configured to generate, a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analysed data.

8. The system (108) as claimed in claim 7, the system comprises a retrieving unit (212), configured to retrieve one or more subscriptions data associated with one or more mobile subscriber.

9. The system (108) as claimed in claim 7, wherein the retrieving unit (212) comprises the receiving unit (210) configured to receive the subscriptions data associated with the mobile subscriber at a graphical user interface.

10. The system (108) as claimed in claim 7, the system comprises a storing unit (220) configured to store the predicted trend data, and the analysed data in a data lake (314).

11. The system (108) as claimed in claim 7, wherein receiving the single stream of integrated data, comprises the retrieving unit (212), configured to retrieve, a multiple types of data of customers from external sources and feed data to a fault management system, wherein the data is historical data including one of, but not limited to customer onboarding, customer deactivation and system-based data.

12. The system (108) as claimed in claim 7, the system comprises a rendering unit (224), configured to render insights of the analysis to a user interface, wherein the data enables segregating the customers based on their current mobile subscription plans and predicting the risk of customer churn.

13. A non-transitory computer-readable medium having stored thereon computer- readable instructions that, when executed by a processor (202), cause the processor (202) to: receive a single stream of integrated data from a data integration module (306); normalize the integrated data, using a pre-processor (310) to obtain an organised set of data, and storing the organized data; 1 analyse the organized data, and perform trend analysis to segregate and categorize the customers based on their current subscription data using a model, deployed by an execution module (312); predict the risk of customer churn, and detect anomaly using the model; and generate a data visualization of categorizing the customers based on their current mobile subscription plans and predicting the risk of customer churn based on a predicted trend data, and an analysed data.