Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

A survey on mobile payment applications and adopted theoretical models

Looking at the evolution of mobile phones, communications technology, and the Internet, one can see a clear shift in their usage in the past decade as mobile payment has become an important research area in the field of information technology. However, many financial institutions have adopted mobile payments. Except that only a limited number of clients are used. Several information systems theories/models have been proposed to examine the factors that could influence user adoption. However, the literature on the field is still in its infancy.  This paper, reviews and systematically analyzes the existing mobile payment acceptance and adoption literature that include UTAUT/TAM as a theoretical model to reveal mobile payment adoption research's current situation. The current study also provides a basis for future researchers in the mobile payment adoption study, as it provides a summary of related literature in the field, the models used, and the factors that have an impact on customer intent. Accordingly, the UTAUT, TAM models, with their extensions, are one of the models most used in examining and understanding the necessary factors that could influence mobile payment applications' adoption. The research revealed that 37 factors most commonly than a literature review on factors of adoption mobile payment applications since 2015. It was found that the factors of perceived trust and perceived risks are among the most critical factors in which the models are expanded, as they have an impact on the customer's acceptance of any new technology innovation. Therefore, emphasis must be placed on the factors of perceived trust and perceived risks to increase the applicability of UTAUT, TAM models to the mobile payment context

Understanding Customer Behaviour in Restaurants based on Data Mining Prediction Technique

Customer’s behavior varies from person to person based on their segmentations, while understanding these differences is one of the key elements of success in food and beverage sector. By understanding customer’s behaviors, restaurant’s owners will be able to identify their targeted customers and will give a clear insight on their menu products. Additionally, it will allow them to target their marketing campaigns, increase the revenue and optimize the cost. Artificial intelligence applications in this field have a huge positive impact in operations of food and beverage sector and depending on of this technology will change the way of restaurant’s management. Data mining prediction model is a tool that can be used by business’s stakeholders to determine and predict the most attributes that can affect their customer behavior. Therefore, the current research finds better solutions to enhance business decision-making by the use of AI and data analytics which will help in understanding the consumer’s behaviors

Customer satisfaction with cellular network performance: issues and analysis

This research evaluated key drivers of satisfaction with cellular network performance and quantified the relative order of importance for each of the drivers. The study also validated an existing survey instrument, and explored an expanded conceptual framework that draws on service and product quality literature to suggest additional issues and attributes to investigate in future efforts to understand and model perception of cellular network quality. Additional attributes explored were expectation, level of use, other service features, and personality.;The body of published research is generally based on tracking studies that utilize univariate data analysis such as top-box and proportions (Power and Associates, 2003. The literature review in this study reaffirmed several key network attributes commonly surveyed in satisfaction surveys (network availability, coverage, drop calls, and call quality), and also determined the relative impact of each of the variables on satisfaction with network performance.;With respect to descriptive statistics, there are lots more males than female, and there are considerable differences in size and number of account sizes and types. However, descriptive results showed call quality with highest satisfaction level, followed by network availability, drop calls and coverage with mean satisfaction values of 3.68, 3.38, 3.26, and 3.02 respectively. Box-Cox transformation of the dependent variable improved the linearity of the regression model by a modest value of .6% in total variation. Multiple regression analysis was applied to examine the effects of each independent variable on network satisfaction and rank relative order of importance. Together, the independent variables explained approximately 37% of the variation in the dependent variable. With outliers removed, the model explained nearly 45.3% of total variation in network satisfaction. Network availability emerged as the most highly correlated predictor to network satisfaction, followed by coverage and call quality with regression beta values of .435, .174 and .125 respectively.;Lastly, the normality assumption of regression was met in which the residuals were normally distributed and constant variance (homoscedastic) over sets of values of the independent variables. However, studentized vs. predicted Y plot revealed a slight deviation from linearity of datapoints. Multicollinearity was also assessed and was not a problem

INFLUENCE OF PERCEIVED VALUE ON CUSTOMER RETENTION AMONG MOBILE PHONE USERS IN THE PUBLIC UNIVERSITIES IN WESTERN REGION OF KENYA

Organizations have embraced the concept customer relationship management practices since it focuses on managing relationship between its current and prospective customer base hence helping in building long lasting relationships which consequently give the organization the joy of retained customers. The specific objective of this study is to determine the effect of perceived value on customer retention. The study was guided by the social exchange theory which focused on the fundamental principle that humans in social situations choose behaviors that maximize their likelihood of meeting self-interests in those situations. Descriptive and explanatory research designs were utilized in this study and the following networks were sampled; Safaricom, Airtel, Orange and, yuMobile A questionnaire was used to collect data from sample size of 222 respondents who were sampled from the staff of public universities in the Western region which included Moi, Masinde Muliro, Maseno, Jaramogi Oginga Odinga, University of Eldoret and Kisii University. Data collected was analyzed by use of descriptive and inferential statistics. Multiple regressions were used to establish the effect perceived value and customer Retention. The results revealed that Perceived value had significant effect on Customer retention. The study recommends that service providers should put more emphasis on Customer perceived value they influence customer retention. The study provides new theoretical insight into factors influencing customer retention. JEL: L20; L22; L63 Article visualizations

Who relies on mobile payment systems when they are on vacation? A segmentation analysis

Despite the growth of mobile phone use in travel planning, the number of tourists that adopt mobile payments (m-payments) is not high. As tourist trust in m-payment has been identified as an essential factor in m-transaction behaviour, this study contributes with a segmentation and a characterization of tourists based on their trust in m-payments. An online survey of Spanish tourists who use smartphones for travel purposes was conducted to collect the data. Utilizing cluster analysis, the data indicate that heterogeneity exists and that tourists can be classified into three segments depending on their trust in m-payments: tourists with high trust in m-payments, tourists with medium trust in m-payments and tourists with low trust in m-payments. Moreover, in terms of the characterization of these three segments, Pearson´s Chi-square found that they show different demographic characteristics. While tourists who travel for pleasure three or more times per year, men, tourists aged between 25 and 34 and the self-employed are overrepresented in users with high trust in m-payments, tourists who travel for pleasure once a year, women and users older than 45 years of age are overrepresented in users with low trust in m-payments. The segments identified will allow tourism companies to adapt their m-payment strategies

The use of fitness apps on customer satisfaction and retention: the fitness centres context

The objective of this study is to analyse the use of fitness centre apps and its influence on customer satisfaction and retention. In this sense, three studies were conducted, one systematic review and two cross-sectional, quantitative studies. The systematic review was carried out using the PRISMA method. The other two studies used the extended unified theory of acceptance and use of technology (UTAUT2) as a base model. All hypothesised relationships used partial least squares structural equation modelling (PLS-SEM), with data from 1,678 fitness customers from Portugal. The results highlighted the importance of the study of technologies in customer retention. The results also support the ability of UTAUT2 in predicting the customer´s intention to use the fitness centre and that the use varies according to customer characteristics. Behavioural intentions are positively related both to the use behaviour of the fitness centre app and to customer overall satisfaction. The suggested that fitness centres invest in the use of a good application, since their use is related to customer overall satisfaction and, thus, indirectly with retention, which benefits the fitness centres

DETERMINANTS OF SERVICE QUALITY ON ISLAMIC BANKS CUSTOMER SATISFACTION IN SURAKARTA

This study aims to determine the effect of Banking Service Quality (BSQ) as a derivative of SERVQUAL research on customer satisfaction at Bank Syariah Indonesia Surakarta Branch Office. The sample was taken by convenience sampling technique and got 96 respondents. The data collection instrument is a questionnaire. The results showed that three of the six dimensions of the BSQ which include effectiveness and assurance, access and reliability have an effect on customer satisfaction. While the other three, namely price, tangibles and services portfolio have no effect on customer satisfaction