• 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.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.1 no.1
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• pp.56-78
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• 1998

Objective: Chronic hepatitis B infection (CHB) occurs in 6% to 10% of population in Korea. In ethinic communities where prevalence of chronic infection is high such as Korea, transmission of hepatitis B infection is either vertical (ie, by perinatal infection) or by close family contact (usually from mothers or siblings) during the first 5 years of life. The development of chronic hepatitis B infection is increasingly more common the earlier a person is exposed to the virus, particularly in fetal and neonatal life. And it progress to cirrhosis and hepatocellular carcinoma, especially in severe liver damage and perinatal infection. Histopathology of CHB is important when evaluating the final outcomes. A numerical scoring system which is a semiquantitatively assessed objective reproducible classification of chronic viral hepatitis, is a valuable tool for statistical analysis when predicting the outcome and evaluating antiviral and other therapies. In this study, a numerical scoring system (Ludwig system) was applied and compared with the conventional histological classification of De Groute. And the comparative analysis of cinical findings, family history, serology, and liver function test by histopathological findings in chronic hepatitis B of children was done. Methods: Ninety nine patients [mean age=9 years (range=17 months to 16 years)] with clinical, biochemical, serological and histological patterns of chronic HBV infection included in this study. Five of these children had hepatocelluar carcinoma. They were 83 male and 16 female children. They all underwent liver biopsies and histologic evaluation was performed by one pathologist. The biopsy specimens were classified, according to the standard criteria of De Groute as follows: normal, chronic lobular hepatitis (CLH), chronic persistent hepatitis (CPH), mild to severe chronic active hepatitis (CAH), or active cirrhosis, inactive cirrhosis, hepatocellular carcinoma (HCC). And the biopsy specimens were also assessed and scored semiquantitatively by the numerical scoring Ludwig system. Serum HBsAg, anti-HBs, HBeAg, anti-HBe, anti-HBc (IgG, IgM), and HDV were measured by radioimunoassays. Results: Male predominated in a proportion of 5.2:1 for all patients. Of 99 patients, 2 cases had normal, 2 cases had CLH, 22 cases had CPH, 40 cases had mild CAH, 19 cases had moderate CAH, 1 case had severe CAH, 7 cases had active cirrhosis, 1 case had inactive cirrhosis, and 5 cases had HCC. The mean age, sex distribution, symptoms, signs, and family history did not differ statistically among the different histologic groups. The numerical scoring system was correlated well with the conventional histological classification. The histological activity evaluated by both the conventional classification and the scoring system was more severe as the levels of serum aminotransferases were higher. In contrast, the levels of serum aminotransferases were not useful for predicting the degree of histologic activity because of its wide range overlapping. When the histological activity was more severe and especially the cirrhosis more progressing, the prothrombin time was more prolonged. The histological severity was inversely related with the duration of seroconversion of HBeAg. Conclusions: The histological activity could not be accurately predicted by clinical and biochemical findings, but by the proper histological classification of the numerical scoring system for the biopsy specimen. The numerical scoring system was correlated well with the conventional histological classification, and it seems to be a valuable tool for the statistical analysis when predicting the outcome and evaluating effects of antiviral and other therapies in chronic hepatitis B in children.