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Impacts of Social Distancing for COVID-19 on Urban Space Use in Seoul (COVID-19 사회적 거리두기가 도시공간이용에 미치는 영향)

  • Park, Hong Il;Lee, Sangkyeong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.6
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    • pp.457-467
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    • 2021
  • This paper aims to analyze changes in urban space use due to social distancing measures for COVID-19 using de facto population data in Seoul during daytime, which is estimated by Seoul Metropolitan Government and telecommunication company of KT using public big data and LTE signal data. The result of kernel density estimation and spatial autocorrelation analysis shows that the distribution patterns of de facto population in 2019 and 2020 were generally similar. This is a result of showing that the government's social distancing measures enabled a certain level of normal activities while suppressing the spread of COVID-19. However, analyzing de facto population subtracting 2019 from 2020 showed different results at the micro level. De facto population decreased in commercial areas but increased in residential areas. This means that COVID-19 social distancing measures had spatially uneven effect. The results of analyzing the effect of regional, land use, economic, educational, and accessibility characteristics on the changes of de facto population using spatial regression analysis are as follows. The higher the density of commercial facilities, the more businesses subject to regulations and schools and universities that require non-face-to-face classes, the more de facto population decreased. Conversely, it was found that de facto population increased in areas with many houses and parks due to telecommuting.

A study of Artificial Intelligence (AI) Speaker's Development Process in Terms of Social Constructivism: Focused on the Products and Periodic Co-revolution Process (인공지능(AI) 스피커에 대한 사회구성 차원의 발달과정 연구: 제품과 시기별 공진화 과정을 중심으로)

  • Cha, Hyeon-ju;Kweon, Sang-hee
    • Journal of Internet Computing and Services
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    • v.22 no.1
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    • pp.109-135
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    • 2021
  • his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

A Methodology of Customer Churn Prediction based on Two-Dimensional Loyalty Segmentation (이차원 고객충성도 세그먼트 기반의 고객이탈예측 방법론)

  • Kim, Hyung Su;Hong, Seung Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.111-126
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    • 2020
  • Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.