A Methodology of Customer Churn Prediction based on Two-Dimensional Loyalty Segmentation (이차원 고객충성도 세그먼트 기반의 고객이탈예측 방법론)
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- 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.
Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.
1. Research Purpose Consumers rely on various clues to evaluate their decision to patronize a retail store, and store brand is one of them (Dodds 1991; Grewal et al. 1998). As consumers find ever increasing variety of contact points connecting them to specific store, the value of experiential shopping as a means of increasing store's brand equity warrants greater attention from scholars of retail management. Retail shopping values are credited for creating not only cognitive experiences like brand knowledge but also emotional experiences such as shopping pleasure and pride (Schmitt 1999). This may be because today's consumers place emphasis on emotional values associated with shopping pleasure, lifestyle brought to life, brand relationship, and store atmosphere more than utilitarian values such as product quality and price. Many previous literature found this to be true (Ahn and Lee 2011; Mathwick et al. 2001). This brings forth important research issues and questions regarding the roles of shopping experiential values and brand equity with regard to consumer's retail patronage choice. However, despite this importance, research on this area remains quite inadequate (Hwang 2010). For this reason, this study aims to verify the relationships among experiential shopping values, retail store brand equity and tries to link that with customer loyalty by surveying large-scale discount store shoppers in Korea and China. 2. Research Contents In order to carry out the research objective, this study conducted comprehensive literature survey on previous literature by discussing major findings and implications with regard to shopping values and retail brand equity and store loyalty. For data collection, researcher employed survey-based research method where data were collected in two major cities of Korea (Seoul) and China (Bejing) and sampling frame was based on patrons of large discount stores in both countries. Specific research questions raised in this study are as follows; RQ1: How do Korean and Chinese consumers differently perceive of shopping values regarding shopping at large-sclae discount stores? RQ2: Are there differences in consumers' emotional consumption propensities? RQ3: Do Korean and Chinese consumers display different perceptions of brand equity towards large-scale discount stores? RQ4: Are there differences in relationships between shopping values and brand equity for Korean and Chinese consumers? For statistical analysis, SPSS17.0, AMOS17.0 and SmartPLS were employed. 3. Research Results The data collected through face-to-face survey conducted in Seoul and Bejing revealed appropriate data validity and reliability as a result of exploratory/confirmatory factor analysis and reliability tests, andh SEM model yielding satisfactory model fitness. The result of the study may be summarized by three main points. First, as a result of testing differences in consumption dispositions, Chinese consumers showed higher scores in aesthetic and symbolic dispositions, whereas Korean consumers scored higher in hedonic disposition. Second, testing on perceptions toward brand equity of large discount stores showed that Korean consumers exhibited more positive perceptions of brand awareness and brand image than Chinese counterparts. Third, the result of exploratory factor analysis on the experiential shopping values revealed different factors for each country. On Korean side, consumer interest value, aesthetic value, and hedonic value were prominent, whereas on Chinese side, hedonic value, aesthetic value, consumer interest value, and service excellence value were found salient. 4. Research Implications While many previous studies on inter-country differences in retailing area mainly focused on cultural dispositions or orientations to explain the differences, this study sets itself apart by specifically targeting individual consumer's shopping values from an experiential viewpoint. The study result provides important theoretical as well as practical implications for large-scale discount store, especially the impotance of fully exploring the linkage between shopping values and brand equity, which has significant influence on loyalty. Therefore, the specific implications deriving from the result shed some important insights upon the consumption values based on shopping experiences and brand equity. The differences found in store shoppers between the two countries may also provide useful insights for Korean and Chinese retailers who plan to expand their operations globally. Related strategic implications derived from this study is the importance of localizing retail strategy which is based on the differences found in experiential shopping values between the two country groups. Especially the finding that Chinese consumers value consumer interest and service excellence, whereas Koreans place importance on hedonic or aesthetic values indicates the need to differentiate the consumer's psychographical profiles when it comes to expanding retail operations globally. Particularly important will be to pursue price-orienated strategy in China in consideration of the high emphasis on consumer interests and service excellence, but to emphasize the symbolic aspects of brand equity in Korea by maximizing the brand equity associated with aesthetic values and hedonic orientations. 5. Recommendations This study focused on generic retail branded discount stores in both countries, thus making it difficult to tease out store-specific strategies based on specific retail brands. Future studies may benefit fro employing actual brand names in survey questionnaire to verify relationship between shopping values and brand-based store strategy. As with other studies of this nature, this study needs to strengthen the result's generalizability by selecting respondents from a wider spectrum of respondents.