標(biāo)題: Titlebook: Database Marketing; Analyzing and Managi Robert C. Blattberg,Byung-Do Kim,Scott A. Neslin Textbook 2008 Springer-Verlag New York 2008 CEO.D [打印本頁(yè)] 作者: 挑染 時(shí)間: 2025-3-21 18:23
書(shū)目名稱(chēng)Database Marketing影響因子(影響力)
作者: pester 時(shí)間: 2025-3-21 21:45
0923-6716 t is an absolute must for marketers who want to enrich their knowledge on customer analytics." (Peter C. Verhoef, Professor of Marketing, Faculty of Economics and Business, University of Groningen).."A marvelou978-1-4419-0332-7978-0-387-72579-6Series ISSN 0923-6716 Series E-ISSN 2199-1057 作者: ADORE 時(shí)間: 2025-3-22 00:39
Christian van Stolk,Marco Hafnerring predictions of a target customer‘s preferences are based on whether customers who like the same products the target customer likes tend to like the target product. We discuss these and several other methods of collaborative filtering, as well as current issues and extensions.作者: 小鹿 時(shí)間: 2025-3-22 04:55 作者: Grievance 時(shí)間: 2025-3-22 10:00
Customer Privacy and Database Marketingample that privacy concerns hinder e-commerce. We discuss current firm practices regarding privacy, as well as some of the major laws regarding customer privacy. We conclude with a review of potential solutions to privacy concerns, including regulation, permission-based marketing, and a strategic focus on trust.作者: podiatrist 時(shí)間: 2025-3-22 14:59
Issues in Computing Customer Lifetime Valueion of the number of customers within the particular application at hand (i.e., variable costs). We conclude with a discussion of incorporating marketing response and customer externalities in LTV calculations.作者: podiatrist 時(shí)間: 2025-3-22 20:49
The Predictive Modeling Processdictive modeling first and foremost is a ., consisting of defining the problem, preparing the data, estimating the model, evaluating the model, and selecting customers to target.We discuss the process in depth, and conclude with a review of some important long-term considerations related to predictive modeling.作者: 萬(wàn)花筒 時(shí)間: 2025-3-23 01:14
RFM Analysis of responding to a catalog or other offer. RFM analysis was probably the first “predictive model” used in database marketing. This chapter discusses the RFM framework, how it can be used and various extensions.作者: debris 時(shí)間: 2025-3-23 01:44
Discrete Dependent Variables and Duration Modelst to occur. One form of duration model, the hazard model, is particularly important because it can be used to predict how long the customer will remain as a current customer. It can also predict how long it will take before the customer decides to make another purchase, switch to an upgrade, etc. We discuss hazard models in depth.作者: 緊張過(guò)度 時(shí)間: 2025-3-23 06:44 作者: 顛簸地移動(dòng) 時(shí)間: 2025-3-23 11:37 作者: 慢跑鞋 時(shí)間: 2025-3-23 14:32 作者: Assemble 時(shí)間: 2025-3-23 20:40 作者: menopause 時(shí)間: 2025-3-23 23:10
Test Design and Analysisvide them in half, run the program for one group and not the other, compare results. However, there are several issues in designing and analyzing database marketing tests; we discuss these in this chapter.作者: CRAFT 時(shí)間: 2025-3-24 03:30 作者: critic 時(shí)間: 2025-3-24 10:10
Connected Vehicles in the Internet of Thingsample that privacy concerns hinder e-commerce. We discuss current firm practices regarding privacy, as well as some of the major laws regarding customer privacy. We conclude with a review of potential solutions to privacy concerns, including regulation, permission-based marketing, and a strategic focus on trust.作者: 真繁榮 時(shí)間: 2025-3-24 14:28 作者: 增強(qiáng) 時(shí)間: 2025-3-24 17:37 作者: 灌溉 時(shí)間: 2025-3-24 21:42
Designing for Collocated Couples of responding to a catalog or other offer. RFM analysis was probably the first “predictive model” used in database marketing. This chapter discusses the RFM framework, how it can be used and various extensions.作者: Indecisive 時(shí)間: 2025-3-25 02:11
Burnout in Primary Care Workforcet to occur. One form of duration model, the hazard model, is particularly important because it can be used to predict how long the customer will remain as a current customer. It can also predict how long it will take before the customer decides to make another purchase, switch to an upgrade, etc. We discuss hazard models in depth.作者: liposuction 時(shí)間: 2025-3-25 04:22
0923-6716 dels, techniques and methodologies for analyzing customer da.Database marketing is at the crossroads of technology, business strategy, and customer relationship management. Enabled by sophisticated information and communication systems, today’s organizations have the capacity to analyze customer dat作者: constellation 時(shí)間: 2025-3-25 11:14 作者: 贊美者 時(shí)間: 2025-3-25 13:38
V. Vijayaraghavan,J. Rian Leevinsonrganization is structured “around” the customer. We discuss key ingredients of a customer-centric organizational structure: customer management and knowledge management. We also discuss types of database marketing strategies that precede organizational structure, as well as employee compensation and incentive issues.作者: myalgia 時(shí)間: 2025-3-25 19:11
https://doi.org/10.1007/978-3-319-99214-3ls.” We present a general approach to calculating LTV using these models, and illustrate with specific examples. We also discuss the particular case of calculating LTV when customer attrition is unobserved.作者: A保存的 時(shí)間: 2025-3-25 23:08
Maria Cseh,Oliver S. Crocco,Chilanay Safarlivide them in half, run the program for one group and not the other, compare results. However, there are several issues in designing and analyzing database marketing tests; we discuss these in this chapter.作者: POINT 時(shí)間: 2025-3-26 02:34 作者: MORPH 時(shí)間: 2025-3-26 04:40 作者: jovial 時(shí)間: 2025-3-26 10:16
Why Database Marketing?ctivity, creating and enhancing customer relationships, and creating sustainable competitive advantage. We review the theoretical and empirical evidence in support of each of these motivations. Marketing productivity has the best support; there is some evidence for both customer relationships and co作者: 合乎習(xí)俗 時(shí)間: 2025-3-26 15:45 作者: 金盤(pán)是高原 時(shí)間: 2025-3-26 20:34 作者: 頭盔 時(shí)間: 2025-3-26 21:49
Customer Lifetime Value: Fundamentalso the firm. This chapter focuses on the fundamental methods for calculating lifetime value, centering on “simple retention models” and “migration models.” We present a general approach to calculating LTV using these models, and illustrate with specific examples. We also discuss the particular case o作者: 輕率的你 時(shí)間: 2025-3-27 01:10 作者: paradigm 時(shí)間: 2025-3-27 06:16
Customer Lifetime Value Applicationsions can LTV provide answers that traditional marketing analyses can not? This chapter will provide some answers to these questions. We will discuss how LTV models can be used in the real-world and describe some applications from the literature.作者: ostrish 時(shí)間: 2025-3-27 12:17 作者: 闖入 時(shí)間: 2025-3-27 16:49 作者: prostatitis 時(shí)間: 2025-3-27 19:57 作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-27 22:50
Statistical Issues in Predictive Modelingment of missing data, and evaluation of models. Topics covered include stepwise selection and principal components methods of variable selection; imputation methods, missing variable dummies, and data fusion techniques for missing data; and validation techniques and metrics for evaluating predictive作者: 的闡明 時(shí)間: 2025-3-28 05:47 作者: 受人支配 時(shí)間: 2025-3-28 07:30
Market Basket Analysis or promoted together. The term arises from the shopping carts supermarket shoppers fill up during a shopping trip. The rise of the Internet has provided an entirely new venue for compiling and analyzing such data. This chapter discusses the key concepts of “confidence,” “support,” and “l(fā)ift” as app作者: BRIEF 時(shí)間: 2025-3-28 11:32
Collaborative Filtering for “recommendation engines.” We discuss the two major forms of collaborative filtering: memory-based and model-based. The classic memorybased method is “nearest neighbor,” where predictions of a target customer‘s preferences for a target product are based on customers who appear to have similar ta作者: 說(shuō)笑 時(shí)間: 2025-3-28 18:18 作者: 受傷 時(shí)間: 2025-3-28 21:05
V. Vijayaraghavan,Rishav Agarwalof customers.” In this chapter we elaborate on this definition, provide an overview of why database marketing is becoming more important, and propose a framework for the “database marketing process.” We conclude with a discussion of how we organize the book.作者: Diuretic 時(shí)間: 2025-3-29 02:59
G?rkem Giray,Bedir Tekinerdogan,Eray Tüzünctivity, creating and enhancing customer relationships, and creating sustainable competitive advantage. We review the theoretical and empirical evidence in support of each of these motivations. Marketing productivity has the best support; there is some evidence for both customer relationships and co作者: 水槽 時(shí)間: 2025-3-29 04:05
V. Vijayaraghavan,J. Rian Leevinsonthis chapter, we discuss how companies organize to implement database marketing. The key concept is the “customer-centric” organization, whereby the organization is structured “around” the customer. We discuss key ingredients of a customer-centric organizational structure: customer management and kn作者: CHOIR 時(shí)間: 2025-3-29 10:05
Connected Vehicles in the Internet of Thingsh. Privacy is a multidimensional issue for customers, and we begin by reviewing the nature and potential consequences of these several dimensions. We discuss the evidence regarding the impact of customers‘ concerns for privacy on their behavior — there is some although not definitive evidence for ex作者: LINES 時(shí)間: 2025-3-29 14:01
https://doi.org/10.1007/978-3-319-99214-3o the firm. This chapter focuses on the fundamental methods for calculating lifetime value, centering on “simple retention models” and “migration models.” We present a general approach to calculating LTV using these models, and illustrate with specific examples. We also discuss the particular case o作者: 易改變 時(shí)間: 2025-3-29 18:13 作者: Pudendal-Nerve 時(shí)間: 2025-3-29 23:35 作者: 難理解 時(shí)間: 2025-3-30 03:43 作者: 向前變橢圓 時(shí)間: 2025-3-30 06:10
Maria Cseh,Oliver S. Crocco,Chilanay Safarlialyses actually is successful in the marketplace. Much of the testing in database marketing is extremely simple — select 20,000 customers, randomly divide them in half, run the program for one group and not the other, compare results. However, there are several issues in designing and analyzing data
