Strategy for Store Management Using SOM Based on RFM (RFM 기반 SOM을 이용한 매장관리 전략 도출)
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- Journal of Intelligence and Information Systems
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- v.21 no.2
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- pp.93-112
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- 2015
Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.
To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.
With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.
The duty shifts of hospital nurses not only affect nurses' physical and mental health but also present various personnel management problems which often result in high turnover rates. In this context a study was carried out from October to November 1995 for a period of two months to find out the status of hospital nurses' duty shift patterns, and preferred duty hours and fixed duty shifts. The study population was 867 RNs working in five general hospitals located in Seoul and its vicinity. The questionnaire developed by the writer was used for data collection. The response rate was 85.9 percent or 745 returns. The SAS program was used for data analysis with the computation of frequencies, percentages and Chi square test. The findings of the study are as follows: 1. General characteristics of the study population: 56 percent of respondents was (25 years group and 76.5 percent were "single": the predominant proportion of respondents was junior nursing college graduates(92.2%) and have less than 5 years nursing experience in hospitals(65.5%). For their future working plan in nursing profession, nearly 50% responded as uncertain The reasons given for their career plan was predominantly 'personal growth and development' rather than financial reasons. 2. The interval for rotations of duty stations was found to be mostly irregular(56.4%) while others reported as weekly(16.1%), monthly(12.9%), and fixed terms(4.6%). 3. The main problems related to duty shifts particularly the evening and night duty nurses reported were "not enough time for the family, " "afraid of security problems after the work when returning home late at night." and "lack of leisure time". "problems in physical and physiological adjustment." "problems in family life." "lack of time for interactions with fellow nurses" etc. 4. The forty percent of respondents reported to have '1-2 times' of duty shift rotations while all others reported that '0 time'. '2-3 times'. 'more than 3 times' etc. which suggest the irregularity in duty shift rotations. 5. The majority(62.8%) of study population found to favor the rotating system of duty stations. The reasons for favoring the rotation system were: the opportunity for "learning new things and personal development." "better human relations are possible. "better understanding in various duty stations." "changes in monotonous routine job" etc. The proportion of those disfavor the rotating 'system was 34.7 percent. giving the reasons of"it impedes development of specialization." "poor job performances." "stress factors" etc. Furthermore. respondents made the following comments in relation to the rotation of duty stations: the nurses should be given the opportunity to participate in the. decision making process: personal interest and aptitudes should be considered: regular intervals for the rotations or it should be planned in advance. etc. 6. For the future career plan. the older. married group with longer nursing experiences appeared to think the nursing as their lifetime career more likely than the younger. single group with shorter nursing experiences (
The problem addressed by this study was to determine the effect of nurse - patient agreement on the means to achieve nursing goals in the early postpartum period of primiparous mothers. It was hypothesized that the experimental treatment would result in hegher self-confidence and satisfaction in maternal role performance. This purpose was to contribute to the planning of nursing care to enhance self- confidence and satisfaction in maternal role performance and to the development of relevant nursing theory. Especially, the early postpartum period is crucial toward in recovery from childbirth and attainment of the maternal role. Maternal role attaintment is a complex social and cognitive process of stimulus -response accomplished by learning. Most women attain the maternal role sucessfully. But, some primiparous mothers experience difficultites in attainment of the maternal role due to lack of experience and knowledge. Self-confidence and satisfaction in maternal role performance are important factors in attainment and adjustment to the maternal role (Mercer, 1981a, 1981b ; Lederman, Weigarten, and Lederman, 1981 :Bobak and Jensen, 1985). Nursing is defined as behaviors of nurses add patients that attain nursing goals through action, reaction, interaction, and transaction. For attainment of nursing goals, active participating transactions must occur by agreement on the means to achieve those goals through nurse -patient mutual goal setting and establishment of their active relationships(King, 1981, Ha, 1977). Based on King's theory of goal attainment (1981), this stuy was planned as a non-equivalent control group, non -synchronized quasi -experimental design using agreement on the means to achieve nursing goals in early postpartum as the experimental treatment. The data were collected from July 20 to Sep. 1, 1991 by questionnaires with 60 primiparous mothers planing to breast feed after normal deliveries at W hospital in Pusan, Korea. The subjects were divided into a control group(conventional group) -those admitted from July 20 to Aug. 12, and an experimental group(agreement group) - those admitted from Aug. 13 to Sep. 1. The instument for agreement on the means to nursing goals in the early postpartum period included five steps - identification of disturbances of problems through action, reaction, and interaction with primiparous mothers : mutual early postpartal nursing goal setting : exploration of the means to achieve goals ; agreement on the means (self- care, ealry maternal -infant contact, performance of mothering behavior, and communicating about the infant's behavior and health condition) : implementation of the means. This instrument was developed on the basis of King's elements that lead to transactions in nurse-patient interactions. Lederman et al's (1981) scale for Confidence in ability to cope with tasks of motherhood and Lederman et al's(1981) scale for Mother's satisfaction with motherhood and infant care were used to measure self-confidence and satisfaction in maternal role performance ·with the subjects immediately after admission and on the day of discharge. Self-care performance in the experimental group was measured by self -evaluation tool developed by the investigator from the literature concerned. The tools to measure Pelf-confidence and satisfaction in maternal role performance, and the tool to measure self-evaluation of self-care performance were tested for internal reliability. Cronbach's Alphas were 0.94, 0.94, and 0.63. The data were analysed by using in S.P.S.S. computerized program and included percentage, x²-test, t-test, ANOVA, and Pearson Correlation Coefficient. The conclusions obtained from this study are summerized as follows : 1. The degree of self-confidence in maternal role performance of the total subjects group measured before the experimental treatment was above average with a mean score of 2.77(range 2.14-3.64). Out of 14 items, those with relatively high mean scores were ‘I would like to be a better mother than I am’(3.95), and ‘I have my doubts about whether I am a good mother’(2.87). Those with low mean scores were ‘I know that my baby wants most of the times’(2.28), ‘When the baby cries, I can tell what she /he wants’(2.37), and ‘I have confidence in my ability to care for the baby’(2;50). That is, the self - confidence of Primiparous mothers was considerably high in mothering, but rather low in activities concerning the infant care and understanding of the infant behavior. The degree of satisfaction in maternal role performance of the total subjects group measured before the experimental treatment was high with a mean score of 3.18(range 1.92-3.92). Out of 13 items, those with relatively high mean scores were ‘I am glad 1 had this baby now’(3.75), ‘I play with the baby between feedings when s/he is awake and quiet’(3.67), and ‘I enjoy being a mother’(3.27). Those with low mean scores were ‘I am upset about having too many responsibilities as a mother’(2.78), ‘It bothers me to get up for the baby at night’(2.82), and ‘I get annoyed if the baby frequently interrupts my activities’.(2.82), That is, the satisfaction of primiparous mothers was considerably high in mothering and infant care, but rather low in restraints in time or on the mother's self accomplishment and development. 2. Agreement on the means to achieve nursing goals in the early postpartum period included process of mutual goal setting, exploration of the means to achieve goals, and ahreement in concert means to achieve goals based on the mothers' condition, concerns, self-perception of the nurse - patient interactions. In the process of agreement, there was agreement that the means to achieve goals should be through trust and establishment of active relationships with the nurse through identification of problems according to planned nursing goals and active interaction, such as explanations, teaching, changing of opinions, acceptance or rejection of explanations, and proposing of questions. Therefore agreement on the means to achieve nursing goals in the early postpartum period appears to be an effective nursing intervention for primiparous mothers. 3. The degree of self- confidence in maternal role performance of the exprimental group was higher than that of the control group(t＝3.95, p＜0.01). Out of 14 items, those with higher score in the experimental group were ‘I would like to be a better mother than I am’(t＝1.93, p＜0.05), ‘I know that my baby wants most of the times’(t＝2.75, p＜0.01), ‘When the baby cries, 1 can tell what she/he wants’(t＝2.10, p＜0.05), ‘I have confidence in my ability to care for the baby’(t＝3.72, p＜0.01), ‘I trust my own judement in deciding how to care for the baby’(t＝1.96, p＜0.05), ‘I feel that I know my baby and what to do for him /her’(t＝2.44, p＜0.01), ‘I am concerned about being able to meet the baby's needs’(t＝2.87, p＜0.01), ‘I know what my baby likes and dislikes’(t＝3.26, p＜0.01), ‘I don't know to care for the baby as well as I should’(t＝2.07, p＜0.05), and ‘I am unsure about whether I give enough attention to the baby’(t＝3.04, p＜0.01), That is, the degree of self-confidence in mothering, activities concerning infant care, and understanding of infant behavior of the experimental group was higher than that of the control group. Therefore, the first hypothesis, that the degree of self-confidence in maternal role performance of the experimental group would be higher than that of the control group, was supported(t＝3.95, p＜0.01). 4. The degree of satisfaction in the maternal role performance of the exprimental group was higer than that or the control group(t＝2.31, p＜0.05). Out of 13 items, those with higher score in the experimental group were ‘I am glad I had this baby now’(t＝2.29, p＜0.05), ‘I enjoy taking care of the baby’(t＝2.4g, p＜0.01), ‘It is boring for me to care for the baby and do the same thing over and over’(t＝2.87, P＜0.01), ‘I am unhappy with the amount of time I have for activities other than childcare’(t＝2.51, p＜0.01), and ‘When bathing and diapering the baby, I would like to be doing something else’(t＝2.43, p＜0.01). That is, the degree of satisfaction in mothering, infant care, and restraints in time of on the mother's self accomplishment and development in the experimental group was higher than that of the control group. Therefore, the second hypothesis, that the degree of satisfaction in maternal role performance of the experimental group would be higher than that of the control group, was supported(t＝2.31, p＜0.05). 5. The third hypothesis, that the higher the degree of satisfaction in materenal role performance, the higher the degree of self-confidence in materenal role performance in the experimental group, was supported (r＝0.57, p＜0.01)
Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.