摘要
随着经济全球化和高新技术的快速发展,世界进入了一个崭新的知识经济时代。企业所处的竞争环境发生了深刻的变化。某些市场开始衰落、竞争对手成倍增长、产品质量和价格日趋同化。客户消费行为也日益理性化、个性化,客户驱动成为新市场的主要特征。在这种情况下,传统的CRM因无法满足企业保持和利用客户知识资产的需要而遭淘汰。客户智能成为企业获取核心竞争力的新战略。
客户智能是指企业充分运用各种智能化技术,将客户数据转换为客户知识,并利用这些知识改进客户关系、进行客户决策、提升客户价值的战略活动。将客户智能引入到企业客户管理中,能让企业科学合理地理解客户消费模式,实现主动性客户关系管理,预测客户行为和市场动向。客户智能与企业经营管理充分结合,将“以客户为中心”的理念植入企业组织内部,并使其具有更强的可操作性。客户智能涉及了客户知识从产生到分发、使用的整个过程,强调知识的流动性和决策结果的有效性。
作为一个具有多种学科交叉特性的新概念,客户智能的形成与发展吸收了客户关系管理、知识管理、商务智能、市场营销等学科的研究成果,并且以许多高级知识信息技术为支撑,比如知识工程、人工智能、数学统计、计算机技术等。其中数据挖掘是客户智能体系中最为关键的技术。数据挖掘是指从大量不完全的、有噪声的、模糊的和随机的数据中,提取隐含的,未知的,且又潜在有用的知识和模式的过程。在商业领域中,数据挖掘可以有助于分析已知的事实,揭示和预测未知的结果,能分析出实现业务所需的关键因素,并发现业务发展的机遇和趋势。由于客户智能关注的主要对象是客户知识,而数据挖掘正是知识发现与知识获取的核心技术,因此两者具有紧密的结合性。只有将数据挖掘合理地融入客户智能过程中,才能够实现从海量客户数据中及时、有效提取客户知识的目的。
本文应用各种数据挖掘技术,从多个角度、多个层面对客户智能的理论、方法和应用进行了研究。其目的在于将数据挖掘技术与客户业务目标有效结合,构建基于数据挖掘的客户智能理论方法体系,为客户知识的获取、创新及客户决策的优化提供支持。
本文正文包括六个部分,主要内容如下:
(1)客户智能理论概述
这一部分总述客户智能理论。在归纳总结相关研究成果的基础上,对客户智能基本理论框架中的各种关键问题进行了深入的分析,包括客户智能的产生背景、定义、本质、理论基础、技术基础、功能和优势。
文中从市场竞争环境的变化、“知识=资产”新观念的建立和信息技术发展带来的挑战和机遇三个方面揭示了客户智能产生的原因与背景,从战略层次指明了客户智能是运用智能分析技术产生和利用客户知识的战略活动。文中重点从客户知识和客户价值两个方面对客户智能的内涵进行了分析,并指出客户智能的本质是通过创新、使用客户知识来产生决策和行动,帮助企业获得和提高客户价值,以增强企业竞争优势。本文认为客户理论、知识管理理论和商务智能理论是客户智能的三个主要理论基础,而数据挖掘、数据仓库与OLAP和人工智能是客户智能的三大支撑技术。客户智能在企业中具有重要的作用和意义,其主要功能体现在数据管理、数据分析、知识发现和企业建模四个方面。通过发现客户知识,客户智能能为企业客户业务的分析、预测与决策提供科学依据。
(2)基于数据挖掘的客户智能体系
这一部分从不同的层面分析实现客户智能的各种关键要素,构建基于数据挖掘的客户智能体系框架。
文中指出客户智能是一个多维综合系统,涉及到企业战略管理中的各种要素和技术因素。基于数据挖掘的客户智能体系由五个层面组成。其中,理论基础层为客户智能体系的活动提供理论指导,由客户分析、客户知识管理、客户关系管理等理论与方法组成。文中重点讨论了支持客户知识挖掘的客户分析理论,如客户生命周期分析、客户价值分析、客户满意度分析、客户忠诚度分析等;数据存储层是客户智能体系的物理基础,是客户数据的源泉,包括企业内部和外部的各种信息平台与数据库系统。文中分析了不同数据源中客户数据的构成与特点;信息分析与整合层为客户智能提供全面、统一的客户信息视图。文中分析了客户信息视图的主要内容,并着重研究了通过数据仓库实现的客户信息的多维组织;知识发现层是客户智能体系活动中关键,执行知识分析、知识挖掘、知识分类、知识建模等功能,是与数据挖掘结合最紧密的部分。文中对统计回归、聚类、决策树、神经网络、关联规则挖掘、遗传算法等数据挖掘的主要方法与算法进行了分析;战略管理层是客户智能体系的最高层,包括一组客户关系管理和企业经营决策管理的理念、策略和方法。文中从战略决策和战术决策两个方面研究了以客户知识为指导的企业客户管理决策。
(3)基于数据挖掘的客户智能方法论
这一部分研究基于数据挖掘的客户智能的全过程,它以企业业务问题的分析与描述为基础,主要研究构建和应用数据挖掘模型的模式、步骤与方法,总结了基于数据挖掘的实践活动的有序步骤,构建了一套有效的、科学的方法体系。
文中指出基于数据挖掘的客户智能流程是一个互动循环的良性知识活动过程。它由发现客户业务机会、应用数据挖掘形成客户知识、根据客户知识采取决策行动和评价决策行动结果四个主要阶段组成。本文从工程的角度出发,重点对挖掘目标分析、数据准备和挖掘建模三个主要过程的活动和技术因素进行了探讨。在确定客户业务目标和数据挖掘任务目标的过程中,需要进行识别业务相关者、识别业务需求、设定业务分析目标、分析数据挖掘环境、确定数据挖掘目标、制定客户智能项目计划等主要活动。在数据准备过程中,首先需要选择合适的客户数据并进行检验,对于发现的数据问题应进行修复,还要通过变换客户数据形成适合挖掘的客户信息,最后将数据划分为建模数据集。在数据挖掘建模的过程中,首先要分析输入/输出变量的特性,选择合适的工具与算法并建立初始模型,然后对模型进行评测、改进和优化,最后还需要对模型的应用效果进行评估。
(4)基于数据挖掘的客户知识获取与预测
这一部分在前面理论方法研究的基础上,以客户生命周期为主线,将数据挖掘模型应用于客户关系管理的各个阶段,实现客户知识的分析、获取和预测。
文中研究了在不同的客户关系阶段,企业所面临的不同客户群类型,分析了围绕客户展开的各种业务活动,以及数据挖掘模型和技术与各阶段业务活动的结合。对客户盈利能力分析、客户响应预测、客户细分、客户增值消费预测和客户流失预测五个重点领域中数据挖掘的实施途径进行了分析,对数据、建模、成果运用等关键问题进行了探讨。
(5)基于数据挖掘的客户智能应用实例
这一部分将理论研究成果应用于实践,介绍了一个在客户智能中通过数据挖掘进行客户分析的应用实例。该实例运用SAS Enterprise Miner集成环境,实现市场促销中常见的响应预测。文中详细讨论和演示了从准备数据、建立模型到应用模型的全过程。
(6)总结与展望
这一部分对论文的研究进行总结,并对未来客户智能的发展趋势进行展望。
本文通过对基于数据挖掘的客户智能的研究,推导出以下三个结论:①客户智能是企业确定其独特竞争优势的一种战略选择。②客户智能是企业客户知识管理综合能力的体现。③客户智能是高级信息技术在商业领域的集成应用。文中还指出在政策、市场和技术的推动下,客户智能将迎来一个发展的高潮,其研究重点将集中在客户知识管理的智能化、客户知识管理与企业战略管理和运营业务的有效结合等方面。
Along with the economical globalization and the high and new technology fast development, the world entered a brand-new knowledge economy time. The competition environment, which the enterprise locates, has had deep transformation. Some market starts to decline, the competitors become time of growth, and the product quality and the price assimilates day by day. Customer’s consumer behavior becomes daily rationalized and personalized. Customer-driven becomes the main characteristic of the new market. In this case, traditional CRM is abolished for unable to satisfy the enterprise’s need of keeping and using customer knowledge property. Customer Intelligence becomes a new strategy for the enterprise to maintain its core competitive power.
Customer Intelligence(CI) is the strategic activity that the enterprise fully utilizes various intellectualized technologies, converts customer data into customer knowledge, and takes advantage of the knowledge to improve customer relationship, make customer decision and promote customer value. Introduce Customer Intelligence into the customer management will enable the enterprise to understand consumer patterns reasonably, be initiative in the customer relationship, and forecast the customer behavior and the market trend. Customer Intelligence is inosculated with the enterprise management, implanting the ideas of“take the customer as the center”into the organization, and enabling it to have a stronger feasibility. Customer Intelligence involves the entire process from producing to distributing and using the customer knowledge, emphasizes the knowledge fluidity and the validity in policy-making.
Being a new concept having characteristic of disciplines overlapping, Customer Intelligence has absorbed the research results of customer relationship management, knowledge management, business intelligence, market marketing etc, and takes many high-level knowledge technology as the support, such as knowledge engineering, artificial intelligence, statistics, computer technology and so on. Data Mining is the most essential technology in the Customer Intelligence system. Data Mining(DM) is the process of extracting concealed, unknown and latent useful knowledge and pattern from massive incomplete, noisy, fuzzy and stochastic data. In the commercial domain, Data Mining may be helpful to analyze known facts, uncover and forecast unknown results. It can analyze the key business factors and discover the business tendency. Because customer knowledge is the main attention object of Customer Intelligence and Data Mining is the core of knowledge discovering and acquiring, the CI and the DM have close associativity. Only when Data Mining is reasonably integrated into the Customer Intelligence process, can realize the goal of prompt and effective customer knowledge extraction from multitudinous customer data.
This paper has conducted the research from different angles on Customer Intelligence theory, method and application using kinds of Data Mining technologies. Its goal lies in the effective combination of Data Mining technology and customer business target, the construction of Data Mining-based Customer Intelligence theory and method system, to sustain the customer knowledge acquiring, innovation and the customer decision-making optimization.
The main content in this paper includes six parts as follows:
(1) Customer Intelligence Theory Outlines
This part generally introduces the theory of Customer Intelligence. In the foundation of summarizing correlated research results, it has carried on a thorough analysis to the key issues in the elementary Customer Intelligence theory frame including background, definition, essence, theory base, technology base, functions and advantages.
The paper uncovers the reason and background in which Customer Intelligence is developed from three aspects: the change of the market competition environment, the establishment of the new“knowledge = property”idea and the challenge and the opportunity brought by information technology development. The paper points out from a strategic level that Customer Intelligence is a strategic activity, which produces and uses customer knowledge by applying intelligent technologies. The paper analyzes the Customer Intelligence’s connotation from two key aspects: the customer knowledge and the customer value, and points out the Customer Intelligence’s essence, which is making decision and taking action by innovating customer knowledge, helping the enterprise to obtain and enhance the customer value, strengthening its competitive advantages. The paper thinks customer theory, knowledge management theory and business intelligence theory are the three main theory bases of Customer Intelligence. And Data Mining, data warehouse and OLAP, and artificial intelligence are its three support technologies. Customer Intelligence has great significance in enterprise. Its main functions include data management, information analysis, knowledge discovery and enterprise modeling. Through the discovery of customer knowledge, Customer Intelligence can provide scientific basis for customer business analysis and decision-making forecast.
(2) Data Mining-based Customer Intelligence Architecture
This part analyzes each kind of essential factor in the realization of Customer Intelligence from different aspects, constructs the Data Mining-based Customer Intelligence architecture.
The paper points out that Customer Intelligence is a multi-dimensional system, involving various strategic management factors and technical factors in the enterprise. The Data Mining-based Customer Intelligence architecture is composed of five layers. The theory foundation layer provides theory instructions for the Customer Intelligence activities. It contains principles and methods of customer analysis, customer knowledge management and customer relationship management and so on. The paper discusses with emphasis about the customer analysis theory supporting the customer knowledge mining, including customer life cycle analysis, customer value analysis, customer satisfaction analysis and customer loyalty analysis. The data storage layer is the physical foundation of the Customer Intelligence. It is the resource of customer data, including enterprise interior and exterior information platforms and database systems. The paper has analyzed the data constitutions and characteristics in different data sources. The information analysis and integration layer provides a comprehensive and unified customer information view for the Customer Intelligence. The paper has analyzed the primary coverage of the customer information view, and explored the way and the methods to realize customer information integration through data warehouse. The knowledge discovery layer is crucial to Customer Intelligence activities. It has the closest relations with Data Mining, carrying on knowledge analyzing, knowledge mining, knowledge classifying and knowledge modeling. The paper has discussed some typical Data Mining methods and algorithms including statistical regression, clustering, decision tree, neural network, association rules mining and genetic algorithm. The strategic management layer is the topmost layer in the Customer Intelligence architecture, which collects a set of ideas, strategies and methods of customer relationship management and enterprise decision. The paper has studied from two aspects of strategic decision and tactical decision on knowledge-guided customer management decision in enterprise.
(3) Data Mining-based Customer Intelligence Methodology
This part studies the entire realization process of the Data Mining-based Customer Intelligence. Based on the analysis and description of enterprise business problem, it studies the patterns, steps and methods of building and applying Data Mining models. It summarizes the ordinal steps of Data Mining practice, and constructs a set of effective and scientific methodology.
They paper points out that the Data Mining-based Customer Intelligence flow is a virtuous cycle of knowledge activities. It is composed of four phases, that is, discover customer business opportunity, apply Data Mining to form customer knowledge, make decisions and take action, and appraise action result. At an engineering angle, the paper has discussed the key activities and technical factors in the three main processes of mining goal analysis, data preparation and data modeling. In the process of determining customer business goal and Data Mining task goal, some major activities should be carried on, including recognizing the people related to business, recognizing the business requirement, setting the business analysis goal, analyzing the Data Mining environment, setting the Data Mining goal, form the Customer Intelligence project plan. In the process of data preparation, the right customer data should be chosen and examined at first, the problems discovered should be repaired, the data should be transformed into customer information suitable for mining, and finally the data should be partitioned into modeling set. In the process of Data Mining modeling, it should firstly analyze the features of input/output variables and choose proper methods and algorithms to build initial model, and then test, ameliorate and optimize the model, finally evaluate the model application effects.
(4) Data Mining-based Customer Knowledge Acquiring and Forecast Based on the theory and method research in the front, this part, taking the customer life cycle as the main line, applies Data Mining to realize customer knowledge analysis, acquireing and forecast in different phrase of customer relationship management.
The paper has studied the customer groups and business activities which the enterprise faces in different relation stages, as well as the combination of Data Mining and various business activities. It has explored Data Mining implementation ways in five significant fields of customer profit ability analysis, customer response forecast, customer segment, customer increment expense forecast and customer churn forecast, especially discussed the crucial issues of data, modeling and result using.
(5) Data Mining-Based Customer Intelligence Application Instance
This part applies the theory research achievement to practice, and introduces an application instance of customer analysis using Data Mining in Customer Intelligence. This instance utilizes SAS Enterprise Miner, and realizes the common response forecast in market promotion. The paper has discussed and demonstrated in detail the entire process from preparing data, establishing model to applying model.
(6) Summary and Prospect
This part summarizes the research of the paper and forecasts the trend of Customer Intelligence.
Through the research of Data Mining-based Customer Intelligence in this paper, we can draw the following three conclusions:①Customer Intelligence is one kind of strategic choice for the enterprise to ensure its unique competitive advantage.②Customer Intelligence manifest the synthesizing capability of enterprise customer knowledge management.③Customer Intelligence is an integrated application of high-level information technologies in commercial domain. The paper also points out that under the impetus of policy, market and technology, Customer Intelligence will welcome a climax of development. Its research emphasis will concentrate in the intellectualization of customer knowledge management, the effective combination of customer knowledge and enterprise strategic management etc.
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