Titlebook: Database Marketing; Analyzing and Managi Robert C. Blattberg,Byung-Do Kim,Scott A. Neslin Textbook 2008 Springer-Verlag New York 2008 CEO.D

挑染

書目名稱Database Marketing影響因子(影響力)




書目名稱Database Marketing影響因子(影響力)學(xué)科排名




書目名稱Database Marketing網(wǎng)絡(luò)公開度




書目名稱Database Marketing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Database Marketing被引頻次




書目名稱Database Marketing被引頻次學(xué)科排名




書目名稱Database Marketing年度引用




書目名稱Database Marketing年度引用學(xué)科排名




書目名稱Database Marketing讀者反饋




書目名稱Database Marketing讀者反饋學(xué)科排名

pester

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

ADORE

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.

小鹿

Grievance

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.

podiatrist

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.

podiatrist

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.

萬(wàn)花筒

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.

debris

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.

緊張過(guò)度