[논문]군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로

리우룬칭; 이영찬; 무홍레이

doi:10.15813/kmr.2018.19.4.004

군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로
Customer Classification and Market Basket Analysis Using K-Means Clustering and Association Rules: Evidence from Distribution Big Data of Korean Retailing Company 원문보기

지식경영연구 = Knowledge Management Research, v.19 no.4, 2018년, pp.59 - 76

리우룬칭 (Global Business, Dongguk University) , 이영찬 (Business Analytics, Dongguk University) , 무홍레이 (Global Business, Dongguk University)

Abstract ▼ AI-Helper

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

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문제 정의

2000), and the application of data mining tools for CRM is worth pursuing in a customer-centric economy. And this study aims to facilitate SMEs to maintain the loyal customers or VIPs by providing effective rules and patterns. Un-satisfaction or customers’ churn will lead to unexpected loss, including both of the financial loss and nonfinancial loss.

제안 방법

According to the results, we defined VIPs on the basis of RFM values, and develop segmentation models that classify customers into VIPs or Non-VIPs groups. Simultaneously, we propose a decision-making framework which combines a trend prediction model.
After segmenting customers into VIPs using RFM values and K-means clustering, our study would use the market basket analysis to identify VIPs characteristics and purchase patterns to help SMEs develop strategies. The most important application of the market basket analysis is the association rule.
First of all, we pre-processed the customer’s data by deleting the duplicated records and those with many missing values or inaccurate values.
The result of output will be used as input variables for the prediction. In this study, once the VIP classification model is built, we attempt to use MBA method to find association rules among the item categories from VIPs created by the classification model.
Second, this study mainly analyzes customers’ behaviors from the perspective of SMEs, thus filled the gap in the SMEs context.
Specifically, the purpose of this study is to segment the overall customers of a given Korea SME’s customer data into VIPs and Non-VIPs through the method of recency, frequency, and monetary (hereafter RFM).
The first academic implication is that this study successfully provides effective rules and new patterns by integrating the data mining techniques with CRM. The application of data mining techniques in CRM is an emerging trend in the industry.
Then, analyze VIP’s transaction data to identify the most effective rules and patterns by adopting various data mining techniques such as K-means clustering algorithm, association rules.
Then, standardized RFM scores to build clustering models. Thirdly, using K-means algorithm to evaluate and choose the best clustering model to determine the VIPs by selecting the important variables and drop those with low correlations with the target variables. It is important to identify and select variables which are taken on a good explanatory power.
This study also analyzed the main purchase item categories of VIPs to help better understand association rules, which are listed in [Figure 4]. According to the figure, Basic Cosmetics accounted for 16%, Organic Vegetable accounted for 10%, Fruits and Vegetable accounted for 6% which indicated that VIPs mainly focus on cosmetic, women wear, kids’ products, food etc

대상 데이터

What is more, the R value of cluster 3 is less than cluster 2 which means it could bring more economic benefits than cluster 2. Finally, the VIPs are 227 among the 1,866 customers.
The dataset in this study is adopted from a shopping mall in Korea that is willing to provide their customer’s transaction data.
online or in the physical store and the entire data includes customer’s basic information and their purchase behavior information from June 2015 until June 2016. This study extracted 2,000 customers and 51,080 transaction data as basic data from customer groups of target shop. Contents of the dataset are summarized in [Table 1].
Thus, this study adopted Analytic Hierarchy Process (AHP) and Expert Consultation Method (Delphi) to determine the weight of each indicator. To conduct an AHP survey, we invited 15 professors who are CRM expert. After pairwise comparison, we checked the consistency ratio and calculated geometric mean of the number of pairwise comparisons.

이론/모형

However, it is not accurate enough to estimate VIPs only based on RFM values. Thus, this study adopted Analytic Hierarchy Process (AHP) and Expert Consultation Method (Delphi) to determine the weight of each indicator. To conduct an AHP survey, we invited 15 professors who are CRM expert.

후속연구

Fourth, in the process of this study, it could be found that the lack of data would affect the accuracy of the prediction model. Finally, this study should continue and observe whether the strategies produced by actionable knowledge obtained by data mining are effective.

참고문헌 (20)

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원문보기 상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로
Customer Classification and Market Basket Analysis Using K-Means Clustering and Association Rules: Evidence from Distribution Big Data of Korean Retailing Company 원문보기

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군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로 Customer Classification and Market Basket Analysis Using K-Means Clustering and Association Rules: Evidence from Distribution Big Data of Korean Retailing Company 원문보기

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군집분석과 연관규칙을 활용한 고객 분류 및 장바구니 분석: 소매 유통 빅데이터를 중심으로
Customer Classification and Market Basket Analysis Using K-Means Clustering and Association Rules: Evidence from Distribution Big Data of Korean Retailing Company 원문보기

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