LRFMP MODEL-BASED ARTIFICIAL INTELLIGENCE-SUPPORTED TELECOMMUNICATIONS CUSTOMER SERVICE SYSTEM AND METHOD

Abstract

The invention relates to a system and method that identifies customer behavioral habits using an LRFMP model created by enriching the RFM model, preferred in customer segmentation, with Length and Periodicity parameters; creates customer clusters through clustering algorithms; and provides customer service support to users in different clusters via an LLM-supported artificial intelligence robot, enabling the management of requests and processes without calling a call center.

Description

1 TARIFF AI-POWERED TELECOMMUNICATIONS BASED ON THE LRFMP MODEL CUSTOMER SERVICE SYSTEM AND METHOD Technical Area The invention creates 5 different groups of products for telecommunications companies offering different services. Personalized solutions for customers using a combination of LLM (Large Language Model). It relates to a system and method that enables the provision of services. The invention specifically addresses the Length model of the RFM model, which is preferred in customer segmentation. created by enriching it with (length) and Periodicity (repetition) parameters. The LRFMP model, which identifies customer behavioral habits, uses clustering algorithms. 10 thanks to which customer clusters are formed and through an LLM-supported artificial intelligence robot Call center by providing customer service support to users in different groups It relates to a system and method that enables the management of requests and processes without requiring a search. State of the Art Today, artificial intelligence tools are used in various sectors to meet user needs. For this purpose, it is being developed with various algorithms and is widely preferred. In the telecommunications sector, customers are involved in the process of reaching a call center. They want their questions answered faster because of the delays and long waiting times they experienced. They tend to use robot-assisted systems to solve these problems. In current practices, companies providing communication services have a central mechanism. grouping customers according to their habits of using the offered service. customer groups from whom they generate high revenue and customer groups from whom they generate low revenue In an AI-powered chat environment, different responses are to be expected. Therefore, users who benefit from communication and information services, are offered services are categorized according to their frequency and variety of use. 25 It is necessary. However, in current systems, this categorization is generally limited. This is done through parameters, and this affects the services offered to customers. It falls short in terms of personalization. As a result, customers advanced artificial intelligence based that can respond to expectations more quickly and accurately systems are needed. 30 2 As a result of the research conducted on this subject, it was determined that the document numbered US20080082386A1 is “Systems and An application titled "Methods for Customer Segmentation" was found. The system, Defining segmentation rules for customer management and customer groups It focuses on the creation of the LRFMP model or telecommunications. specific topics such as providing AI-powered services tailored to a particular sector 5 It does not include. The research also included "Data" with the number US7873075B2. The application is titled "Segmentation Method in a Telecommunication System". It has been encountered. The system involves the lower layer of upper layer data units in telecommunication systems. It describes a segmentation method involving dividing something into layers. However, this The study focuses more on data transmission between protocol layers during data transfer. It focuses on segmentation, and customer segmentation or artificial intelligence. It does not cover topics such as assisted service delivery. In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, an improvement is needed in the relevant technical field. It has been made. 15 Purpose of the Invention The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The main purpose of the invention is to create a telecommunications sector with different income and usage levels. segmentation based on the LRFMP model and 20 customer groups with specific habits Personalized customer service through an AI robot powered by a Big Language Model (LLM). The goal is to create a system and method that enables the provision of communication services. In companies that provide the service, a central mechanism ensures that customers receive the service offered. They earned more income thanks to being grouped according to their usage habits. AI-powered chat with customers and low-revenue customer groups 25 It is expected that the answers given in this context will differ. The invention; the amount of payment, usage Customers' virtual habits and duration of stay in the system are taken into account. They can answer the questions or problems they encounter in the environment with an automated response system. This will enable them to find answers. Another aim of the invention is to make artificial intelligence tools widespread in the telecommunications sector. The goal is to establish a system and method that supports its use. AI robots that greet users on platforms use different algorithms. 3 Through its development, the prevalence of these tools can be increased. Included in the LRFMP model. Performing customer behavior analysis by accounting for each parameter in the field. This will be provided. In our invention, in customer analysis over a specific time period; Length (Length): The difference in days between the time the customer first and last used the service. It is expressed as follows: Recency (novelty): 5 specified for the customer's analysis study. from the end date, the last time it performed in telecommunications products and services The difference between information and frequency is the information regarding the products and services offered to the customer. It describes the frequency of use numerically. Monetary: The customer's Total amount spent on purchasing telecommunications products and services. It expresses information. Periodicity (repetition): The customer follows each other on the platform 10 It is the calculation of the standard deviation of the time spent in two different locations. LRFMP model After completing customer behavior analysis with the support of clustering algorithms The customer clustering step will be completed. In the telecommunications sector, for users... Users with different preferences and demands will be profiled for the products and services offered. After the profiling step is complete, LLM 15 is generated for users in different clusters. An AI-powered robot will be developed. This is done to increase customer satisfaction. Thanks to the provision of personalized service, the artificial intelligence robot can provide each user with... By preventing the robot from giving the same answer repeatedly, the customer's trust in the robot and the system can be increased. Another purpose of the invention is LRFMP (Length, Recency, Frequency, Monetary, Periodicity). with mobile, home internet, television, landline phone, digital services and device services 20 a system and method that encompasses the behavioral habits of benefiting customers The goal is to develop LLM-supported artificial intelligence (ALM) systems for different customer groups. Thanks to the intelligent robot, users can get help from the robot to answer their questions if needed. It will be able to find it. Thus, the artificial intelligence tool will re-evaluate based on customer questions and answers. 25 developed with algorithms and categorized according to different consumption habits Increasing customer satisfaction by providing personalized services to users will be provided. To achieve the purposes described above, the invention may be used to develop telecommunications products and services. enabling users to receive personalized customer service It is a system. Accordingly, the system is 30  a document containing customer information about the use of telecommunications products and services database network server that runs the database  Identifying user characteristics of customers using telecommunications products and services To achieve this, it performs the calculations for each parameter in the LRFMP model and 4 data on the average, minimum, and maximum values ​​of these parameters Data pre-processing by applying standardization methods user behavior analysis interface that performs,  User behavior interface including the aforementioned user behavior analysis interface server, 5  data mining models and clustering algorithms that are being worked on the number of clusters of customers with different product and service preferences applying detecting and scoring users according to L, R, F, M, P values ​​using data scoring techniques. By completing profiling processes, we identify customers using different products and services. Data mining network server that clusters data according to preferences, 10  The clustering mentioned is performed on the data mining network server. Following the process, LLMs working on clients in different clusters providing customer service through an AI-powered robot AI robot providing network server It includes. 15 The invention also provides personalized service to users of telecommunications products and services. It also includes the method by which customer service is provided. Accordingly method;  A database of customer information using telecommunications products and services network 20 saving to a database containing information running on the server,  a user behavior interface server that uses telecommunications products and services To identify customer characteristics, user behavior analysis is used. Calculate each parameter in the LRFMP model via the interface. performing and these parameters average, minimum and maximum 25 Data pre-preparation by applying data standardization methods to the values. to complete the transactions,  Data mining models running on a data mining network server By applying clustering algorithms, we can identify customers with different product and service preferences. Determining the number of clusters of customers and using data scoring techniques L, R, F, M, 30 By completing user profiling processes based on P-values, we can identify customers. clustering based on different product and service usage preferences,  The clustering mentioned is performed on the data mining network server. After the process, an AI robot will deliver the results to customers in different groups. via an LLM-powered AI robot running on the network server customer service provided It includes the steps involved in the process. The structural and characteristic features and all the advantages of the invention are given in the figures and 5 below. This becomes clearer thanks to the detailed explanation written with references to these figures. This will be understood as such, and therefore the evaluation will also take these forms and detailed explanations into account. This should be done taking that into consideration. Figures that will help understand the invention. Figure 1 is a schematic representation of the system that is the subject of the invention. 10 Explanation of Part References 1. Telecommunications products and services 2. Database 3. Database network server 4. User behavior analysis interface 15 5. User behavior analysis server 6. Data mining model 7. Data mining network server 8. Artificial intelligence robot 9. AI robot network server 20 Detailed Description of the Invention This detailed explanation describes the preferred system and method for the invention. Their structures are explained solely for the purpose of better understanding the subject. 25 6 The invention provides personalized customer service to users using telecommunications products and services (1). It is the system that enables the provision of services. Figure 1 shows the system that is the subject of the invention. A schematic representation is provided. Accordingly, the system includes telecommunications products and services. database network that runs a database containing customer information using its services (2) server (3), customers using telecommunication products and services (1) user 5 To determine its characteristics, each parameter in the LRFMP model must be calculated. performs this process and collects data on the average, minimum, and maximum values ​​of these parameters. Performing data pre-processing operations by applying standardization methods User behavior analysis interface (4), the aforementioned user behavior analysis interface (4) User behavior interface server (5), data mining running on it 10 different product and service preferences by applying clustering algorithms with models (6). Identifying the number of customer clusters using data scoring techniques (L, R, F, M, P) By completing user profiling processes based on their values, customers are offered different products and services. Data mining network server that clusters according to service usage preferences (7), The clustering operation performed on the mentioned data mining network server (7) is 15 then, LLM-powered artificial intelligence working on clients in different clusters Artificial intelligence that enables customer service to be provided via intelligence robot (8) The robot includes a network server (9). The system works on the following principle: 20 Telecommunication products and services (1) are offered to customers by telecommunication operators. This refers to products and services. Examples of these products and services include: landline phones, mobile phone lines, etc. These can include various products and services such as home internet, TV subscriptions, or digital services. 25 of these products and services with the database (2) running on the database network server (3). Information about the users benefiting from this service is stored. User behavior analysis running on the user behavior analysis server (5) behavior of users who prefer products and services offered through the interface (4). This allows for the identification of their habits. Each of the 30 parameters included in the LRFMP model... After calculation for a user, the average of these parameter values, A data standardization method is applied to the minimum and maximum values. This In this way, user data that has undergone data pre-processing is processed by the clustering algorithm. It is made ready for implementation. 35 7 The ideal data mining model (6) running on the data mining network server (7) By applying a clustering algorithm, users with different product and service preferences are identified. The number of clusters is determined. In addition, data is running on the data mining network server (7). By applying clustering algorithms thanks to mining models (6), different products and Determining the number of customer clusters with service preferences and data scoring 5 User profiling processes are completed according to L, R, F, M, P values ​​using this technique. Customers are clustered according to their different product and service usage preferences. Following the customer grouping phase, artificial intelligence is applied to users in different clusters. Customer 10 thanks to the artificial intelligence robot (8) running on the network server (9) Services are provided. Thus, users receive the same answer in existing systems. by developing an artificial intelligence robot that provides information specifically for customer groups. This ensures increased satisfaction.

Claims

8 REQUESTS 1. Personalized customer service for users using telecommunications products and services (1) It is a system that enables the provision of services, and its feature is;  a document containing customer information about the use of telecommunications products and services database (2) running database network server (3), 5  Determining the user characteristics of customers using telecommunication products and services (1) To achieve this, it performs the calculations for each parameter in the LRFMP model and data on the average, minimum, and maximum values ​​of these parameters Data pre-processing by applying standardization methods User behavior analysis interface (4), 10  User behavior analysis interface (4) included in the aforementioned user behavior analysis interface interface server (5),  data mining models (6) and clustering algorithms working on it the number of clusters of customers with different product and service preferences applying identifying and scoring users 15 based on L, R, F, M, P values ​​using data scoring techniques. By completing profiling processes, we identify customers using different products and services. Data mining network server that clusters according to preferences (7),  clustering performed on the mentioned data mining network server (7) Following the process, LLMs working on clients in different clusters 20 Customer service is provided through the assisted artificial intelligence robot (8) AI robot network server (9) It includes.

2. Personalized customer service for users using telecommunications products and services (1) It is the method that enables the provision of services, and its characteristic is; 25  a database network of customer information using telecommunications products and services (1) recording in a database (2) that runs on server (3),  a user behavior interface server (5) telecommunications products and services (1) In order to identify the user characteristics of customers using the service, the user Each parameter in the LRFMP model is 30 via the behavioral analysis interface (4). It performs the calculation and the average, minimum and of these parameters. data pre-applying data standardization methods to their maximum values completing the preparation procedures, 9  data mining running on a data mining network server (7) By applying clustering algorithms with models (6), different products and services Identifying the number of clusters of customers with specific preferences and data scoring. User profiling operations based on L, R, F, M, P values ​​using this technique. by completing it, customers are categorized into 5 different product and service usage preferences. clustering,  clustering performed on the mentioned data mining network server (7) After the process, an AI robot will deliver the results to customers in different groups. via the LLM-supported artificial intelligence robot (8) running on the network server (9) 10 It includes the steps of the process.

Images