Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining

In this article, an in-depth study and analysis of the precision marketing approach are carried out by building an IoT cloud platform and then using the technology of big data information mining. The cloud platform uses the MySQL database combined with the MongoDB database to store the cloud platform data to ensure the correct storage of data as well as to improve the access speed of data. The storage method of IoT temporal data is optimized, and the way of storing data in time slots is used to improve the efficiency of reading large amounts of data. For the scalability of the IoT data storage system, a MongoDB database clustering scheme is designed to ensure the scalability of data storage and disaster recovery capability. The relevant theories of big data marketing are reviewed and analyzed; secondly, based on the relevant theories, combined with the author’s work experience and relevant information, a comprehensive analysis and research on the current situation of big data marketing are conducted, focusing on its macro-, micro-, and industry environment. The service model combines the types of user needs, encapsulates the resources obtained by the alliance through data mining for service products, and publishes and delivers them in the form of data products. From the perspective of the development of the telecommunications industry, in terms of technology, the telecommunications industry has seen the development trend of mobile replacing fixed networks and triple play. The development of emerging technologies represented by the Internet of Things and cloud computing has also led to technological changes in the telecommunications industry. Operators are facing new development opportunities and challenges. It also divides the service mode into self-service and consulting service mode according to the different degrees of users’ cognition and understanding of the service, as well as proposes standardized data mining service guarantee from two aspects: after-sales service and operation supervision. A customized data mining service is a kind of data mining service for users’ personalized needs. And the intelligent data mining service guarantee is proposed from two aspects of multicase experience integration and group intelligence. In the empirical research part, the big data alliance in Big Data Industry Alliance, which provides data mining service as the main business, is selected as the research object, and the data mining service model of the big data alliance proposed in this article is applied to the actual alliance to verify the scientific and rationality of the data mining service model and improve the data mining service model management system.

A framework for knowledge retention to support business continuity in the cross-border mergers of the telecommunications industry in Lesotho

Purpose The purpose of this study is to investigate a framework for knowledge retention to support business continuity in cross-border mergers of the telecommunications industry in Lesotho. Design/methodology/approach This study applied a qualitative case study, with data collected through interviews from a purposively selected sample of staff members who held managerial positions. Information in this study was partially extracted from the PhD thesis of Dr Tseole ET supervised by Prof Ngulube P at the University of South Africa completed in 2021. Findings The study discovered that a considerable amount of knowledge may have been lost because employees who either left the organisation or those who were apparently forced to resign during the process had left without any proper knowledge retention arrangements. Research limitations/implications The framework proposed in this study may be used in framing future studies as a theoretical framework. The study also provides new literature for review and discussion of background in future related studies. Practical implications The framework provided in the study may be used as a benchmark in the knowledge management industry and/or organisations for policy development or improvements and implementation of knowledge retention strategies. Social implications Through recommendations and framework provided by this study, organisations will be able to improve their services to their sphere of influence in the surrounding communities. So, community will be receiving an improved and good service at all the times. Originality/value A framework for facilitating knowledge retention in the cross-border mergers of the telecommunications industry is therefore proposed and the researchers believe it will be helpful to the organisation for improving knowledge retention going forward, particularly in the merger process.

Customer Decision Prediction Using Deep Neural Network on Telco Customer Churn Data

Customer churn is the most important problem in the business world, especially in the telecommunications industry, because it greatly influences company profits. Getting new customers for a company is much more difficult and expensive than retaining existing customers. Machine learning, part of data mining, is a sub-field of artificial intelligence widely used to make predictions, including predicting customer churn. Deep neural network (DNN) has been used for churn prediction, but selecting hyperparameters in modeling requires more time and effort, making the process more challenging for the researcher. Therefore, the purpose of this study is to propose a better architecture for the DNN algorithm by using a hard tuner to obtain more optimal hyperparameters. The tuning hyperparameter used is random search in determining the number of nodes in each hidden layer, dropout, and learning rate. In addition, this study also uses three variations of the number of hidden layers, two variations of the activation function, namely rectified linear unit (ReLu) and Sigmoid, then uses five variations of the optimizer (stochastic gradient descent (SGD), adaptive moment estimation (Adam), adaptive gradient algorithm (Adagrad), Adadelta, and root mean square propagation (RMSprop)). Experiments show that the DNN algorithm using hyperparameter tuning random search produces a performance value of 83.09 % accuracy using three hidden layers, the number of nodes in each hidden layer is [20, 35, 15], using the RMSprop optimizer, dropout 0.1, the learning rate is 0.01, with the fastest tuning time of 21 seconds. Better than modeling using k-nearest neighbor (K-NN), random forest (RF), and decision tree (DT) as comparison algorithms.

Administrative internal control system and performance effect

The study focused on effect of admin internal control on performance in telecommunications industry in South South and South East Nigeria. The study employed a descriptive survey research design. Instrument adopted for the study is a structured questionnaire based on simple random sampling method. In addition, interview was also employed to serve as a compliment for the questionnaire. Reliability of the research instrument was tested using the Cronbach Alpha method which revealed that the instrument is reliable. The result of Durbin Watson revealed that the data is free from autocorrelation. Data analysis was done using both descriptive and inferential analysis technique. Descriptive statistics was used to give insight on the respondents profile while inferential statistics was used in the conducting of hypotheses. The five research questions for the study were analyzed using multiple regression model. The study shows that the five null hypotheses were rejected while the five alternative hypotheses were accepted (organization’s internal control environment (p=0.000<0.05, ?=0.890); risk assessment (p=0.000<0.05, ?=0.242); control activities (p=0.001<0.05, ?=0.092); information and communication (p=0.000<0.05, ?=0.115); monitoring (p=0.000<0.05, ?=0.307)). The study concluded that organization’s internal control environment, risk assessment, control activities, information and communication, and monitoring aid and has a positive influence on organizational performance.

Effect of Relationship Marketing on Customer Retention in the Telecommunications Industry

This study centered on the effect of relationship marketing on customer retention in the telecommunications industry. It was conducted to assess the effects of customer care, communication, trust-building and service quality on customer retention in the telecommunications context. The study adopted survey research design. A structured questionnaire was used to obtain primary data from 198 customers of MTN Nigeria Plc and Globacom Nigeria Plc in Calabar. The data were analyzed and interpreted using descriptive statistics, while the hypotheses developed for the study were tested using multiple linear regression. Consequently, the findings of the study revealed that customer care, communication, trust building and service quality had significant positive effects on customer retention of telecommunication firms in Calabar. Therefore, the study recommended that: telecommunications companies should strengthen their customer care capability by using trained service professionals to elicit and promptly resolve customers enquiries and complaints; telecommunications companies should improve communications with customers by opening up more channels such as phone calls, direct messaging, social media and email through which information can be transmitted to subscribers to enhance informed patronage decisions; and it is imperative for telecommunications companies to consolidate customers’ trust in their delivery capabilities by demonstrating through effective service delivery that they are capable of satisfactorily meeting the service needs of subscribers.

Customer churn prediction for telecommunication industry: A Malaysian Case Study

Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry. Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables. Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction. Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.

Peran Mediasi Job Satisfaction dan Employee Engagement Pada Compensation Terhadap Job Performance Di PT Telkom Indonesia Regional Jabodetabek Banten

During the technological era, it had many impacts on various industries in Indonesia, one of which was the telecommunications industry which experienced a drastic increase in customer demand due to work from home instructions that could make working from anywhere so that discussions and giving work orders were not limited by space and time. This makes working hours longer and erratic, causing changes to routines and causing job performance to be disrupted, so an appropriate compensation strategy is needed by taking into account job satisfaction and employee engagement. Therefore, it is interesting to study how these factors affect job performance. This study aims to examine the effect of compensation given by the company on job performance mediated by job satisfaction and employee engagement. This research is a quantitative research with random sampling which involved 187 employees of PT Telkom Indonesia Regional Jabodetabek Banten. The analytical tool in this study uses SEM-PLS. The results of the study have novelty, namely compensation does not have a direct significant effect on job performance, but can be through employee engagement mediation. In addition, this study shows that job satisfaction has no significant effect on job performance either directly or as a mediating variable. The implication for the company is to know the importance of increasing compensation through employee engagement to improve job performance. From the results of the study, it is recommended to study more deeply related to other factors that affect job performance in other industries Keyword: Compensation, Job satisfaction, Employee Engagement, Job Performance