An application on customer relationship management

Digital transformation is a keyword leading the current industrial transformation for optimized business decision-making. This paper proposes an integrated machine learning framework implemented on Customer Relationship Management (CRM) in order to achieve enhanced customer satisfaction by hyper-personalized targeting. We applied Gradient Boosting Machine (GBM) classification to predict customer repurchase, XGBoost(eXtreme Gradient Boosting) model to predict the personalized purchase cycle, Self-Organizing Map(SOM) and Association Rule to develop customer segments and brand recommendation marketing plans. ;최근 빅데이터, AI 등 ICT 기술 기반의 경영의사결정의 중요성이 대두되는 ‘디지털 혁신’의 흐름은 IT기업을 넘어 제조업, 유통업, 통신업, 에너지 산업으로 확장되고 있다. 본고는 기업의 고객관계관리(CRM)에 있어 머신러닝, 딥러닝 기반의 초 개인화된 고객 예측 모형을 구현하고자 한다. GBM(Gradient Boosting Machine) 분류모형을 통해 고객의 재 구매 여부를 예측했고, 심층신경망(DNN) 모형으로 개인별 구매 주기를 예측하였다. 또한, 자기조직화지도(Self-Organizing Map) 기법과 연관규칙(Association Rule) 데이터마이닝을 통해 고객 세분화와 수익을 강화하는 마케팅 전략을 제안하였다. 즉, 재 구매의 가능성이 높은 고객에게, 적절한 시기에, 적절한 정보를 제공하여 자동화된 고객 관리 시스템 구현과 관련한 분석기법을 제시함으로써 고객 만족 극대화를 실현하고자 한다.I. Introduction 1 II. Data Description 2 A. Data overview 2 B. Data preprocessing by three perspectives 4 C. Feature engineering and explanatory variables 6 D. Response variables 9 III. Methodology 10 A. Gradient Boosting Machine and XGBoost 10 B. Self-Organizing Map 12 C. Association Rule and Apriori algorithm. 13 IV. Results 15 A. Repurchase prediction by classification 15 B. Purchase cycle prediction by regression 21 C. Clustering and customer segmentation 29 D. Association Rule of brand items 33 V. Conclusion 37 Bibliography 38 Abstract(in Korean) 3

로봇을 이용한 사람 전완과 손목 관절 임피던스의 추계학적 추정

학위논문(박사) - 한국과학기술원 : 기계공학전공, 2012.8, [ xviii, 192 p. ]This thesis presents a practical methodology for the estimation of dynamic impedance in human forearm and wrist in order to provide an objective and quantitative measure of abnormal muscle tone and following synergy pattern for the stroke rehabilitation; to provide a quantitative foundation for the robotic therapy of forearm and wrist rotations; to observe the degree of influence of pronation-supination (PS) of the forearm to the global wrist motion. The proposed method includes the extended usage of the stochastic estimation using the internal model based impedance control (IMBIC) and a gravity compensation scheme, which was developed and used for the experiments with a spatial 3 degree of freedom (DOF) PUMA robot. Before the proposed method was applied to the human forearm and wrist impedance estimation, the stochastic estimation using the IMBIC was validated through the experiments on the spring array and the human arm with a 2 DOF Selective Compliant Assembly Robot Arm (SCARA) robot. Also, the accuracy and reliability of the proposed method were verified by realistic simulation work. After the proposed method with a PUMA robot was validated by conducting preliminary experiments on a mechanical system with a known impedance TFM, the method was finally applied to the estimation of human forearm and wrist impedance. The simulation and preliminary experiment results demonstrated that the proposed method yields accurate and reliable estimations even under substantial frictions: the multiple coherence functions exceeded 0.95 throughout the frequency range investigated. The proposed method has also demonstrated effectiveness in the estimation of human forearm and wrist impedance using conventional robots and the estimated impedance TFM demonstrated reasonable agreement with the results of previous research. The effect of couplings between PS and FE or RUD on step-tracking wrist rotations was also investigated with the estimated impedance in this thesis. The cou...한국과학기술원 : 기계공학전공