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2008 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Database Marketing

Analyzing and Managing Customers

verfasst von: Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin

Verlag: Springer New York

Buchreihe : International Series in Quantitative Marketing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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 data to inform and enhance every facet of the enterprise—from branding and promotion campaigns to supply chain management to employee training to new product development. Based on decades of collective research, teaching, and application in the field, the authors present the most comprehensive treatment to date of database marketing, integrating theory and practice. Presenting rigorous models, methodologies, and techniques (including data collection, field testing, and predictive modeling), and illustrating them through dozens of examples, the authors cover the full spectrum of principles and topics related to database marketing.

"This is an excellent in-depth overview of both well-known and very recent topics in customer management models. It 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 marvelous combination of relevance and sophisticated yet understandable analytical material. It should be a standard reference in the area for many years." (Don Lehmann, George E. Warren Professor of Business, Columbia Business School)

"The title tells a lot about the book's approach—though the cover reads, "database," the content is mostly about customers and that's where the real-world action is. Most enjoyable is the comprehensive story – in case after case – which clearly explains what the analysis and concepts really mean. This is an essential read for those interested in database marketing, customer relationship management and customer optimization." (Richard Hochhauser, President and CEO, Harte-Hanks, Inc.)

"In this tour de force of careful scholarship, the authors canvass the ever expanding literature on database marketing. This book will become an invaluable reference or text for anyone practicing, researching, teaching or studying the subject." (Edward C. Malthouse, Theodore R. and Annie Laurie Sills Associate Professor of Integrated Marketing Communications, Northwestern University)

Inhaltsverzeichnis

Frontmatter

Strategic Issues

Frontmatter
1. Introduction
Abstract
Database marketing is “the use of customer databases to enhance marketing productivity through more effective acquisition, retention, and development of 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
2. Why Database Marketing?
Abstract
A basic yet crucial question is: why should the firm engage in database marketing? We discuss three fundamental motivations: enhancing marketing productivity, 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 competitive advantage as well, but further work is needed.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
3. Organizing for Database Marketing
Abstract
Quantitative analysis is endemic to database marketing, but these analyses and their implementation are not conducted in an organizational vacuum. In this 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 knowledge management. We also discuss types of database marketing strategies that precede organizational structure, as well as employee compensation and incentive issues.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
4. Customer Privacy and Database Marketing
Abstract
Probably the single most important aspect of the legal environment pertaining to database marketing is customer privacy. We examine this issue in depth. 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 example 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin

Customer Lifetime Value (LTV)

Frontmatter
5. Customer Lifetime Value: Fundamentals
Abstract
Customer lifetime value (LTV) is one of the cornerstones of database marketing. It is the metric by which we quantify the customer's long-term value to 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 of calculating LTV when customer attrition is unobserved.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
6. Issues in Computing Customer Lifetime Value
Abstract
This chapter addresses the challenging details in computing LTV that are all-too-easy to ignore. We focus particularly on the appropriate discount rate and appropriate costs. We draw from standard corporate finance and the CAPM model to derive the appropriate discount rate.We discuss the application of activity based costing (ABC) in computing costs. We advocate that the only costs appropriate for LTV calculations are those that change as a function 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
7. Customer Lifetime Value Applications
Abstract
The prior two chapters have covered the technical aspects of Lifetime Value Modeling. But how is LTV applied in the real-world and what types of questions 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin

Database Marketing Tools: The Basics

Frontmatter
8. Sources of Data
Abstract
“Data” is the first word in database marketing with good reason — the quality, impact, and ultimately, ROI of database marketing programs depend on the availability of good data. We discuss the various types of customer data available, e.g., customer demographics, transactions, and marketing actions, and the sources that provide these data such as internal records, commercially processed numbers and segmentation schemes, externally available customer lists, and primary survey data.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
9. Test Design and Analysis
Abstract
Another cornerstone of database marketing is testing. Testing provides transparent evidence of whether the program prescribed by sophisticated data analyses 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 database marketing tests; we discuss these in this chapter.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
10. The Predictive Modeling Process
Abstract
The third cornerstone of database marketing (the other two being LTV and testing) is predictive modeling. Predictive modeling is the use of statistical methods to predict customer behavior — e.g., will the customer respond to this offer or catalog? Will the customer churn in the next 2 months? Which product in our product line would be most attractive to the customer? Which sales channel will the customer use if we send the customer an email? Predictive modeling first and foremost is a process, 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin

Database Marketing Tools: Statistical Techniques

Frontmatter
11. Statistical Issues in Predictive Modeling
Abstract
Whereas Chapter 10 describes the basic process of predictive modeling, this chapter goes into depth on three key issues: selection of variables, treatment 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 models.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
12. RFM Analysis
Abstract
Recency (R), Frequency (F), and Monetary Value (M) are the most popular database marketing metrics used to quantify customer transaction history. Recency is how recently the customer has purchases; frequency is how often the customer purchases, and monetary value is the dollar value of the purchases. RFM analysis classifies customers into groups according to their RFM measures, and relates these classifications to behaviors such as the likelihood 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
13. Market Basket Analysis
Abstract
Market basket analysis scrutinizes the products customers tend to buy together, and uses the information to decide which products should be cross-sold 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 “lift” as applied to market basket analysis, and how these concepts can be translated into actionable metrics and extended.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
14. Collaborative Filtering
Abstract
Collaborative filtering is a relatively new technique to the database marketing field, gaining popularity with the advent of the Internet and the need 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 tastes to the target customer. A more recently used method is itembased collaborative filtering, which is model-based. In item-based collaborative filtering 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
15. Discrete Dependent Variables and Duration Models
Abstract
Probably the most common statistical technique in predictive modeling is the binary response, or logistic regression, model. This model is designed to predict either/or behavior such as “Will the customer buy?” or “Will the customer churn?” We discuss logistic regression and other discrete models such as discriminant analysis, multinomial logit, and count data methods. Duration models, the second part of this chapter, model the timing for an event 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.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
16. Cluster Analysis
Abstract
Cluster analysis segments a customer database so that customers within segments are similar, and collectively different from customers in other segments. Similarity is in terms of the “clustering variables,” which may be psychographics, demographics, or transaction measures such as RFM. The clusters often have rich interpretations with strong implications for which customers should be targeted with a particular offer or marketed to in a certain way. This chapter discusses the details of cluster analysis, including measures of similarity, the major cluster methods, and how to decide upon the number of clusters and their interpretation.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
17. Decision Trees
Abstract
Decision trees are a very intuitive, easy-to-implement predictive modeling technique. They literally can be depicted as a tree — a sequence of criteria for classifying customers according to a metric such as likelihood of response. The pictorial representation of the tree makes it easy to apply and communicate. This chapter discusses the methods for creating the branches of the tree, deciding how many branches the tree should have and further details in constructing decision trees.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
18. Artificial Neural Networks
Abstract
Neural network models are intriguing because they are based on the intuitive notion of mimicking the structure of neurons that constitute the human brain. More importantly to database marketers, neural networks can provide great flexibility in handling non-linearities and variable-interactions that can be important in predictive modeling applications. We describe the neural net model, how it is estimated, and more advanced forms of neural networks.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
19. Machine Learning
Abstract
Traditionally there have been two paradigms of statistical analysis — classical and Bayesian. Machine learning is essentially a third paradigm, based on algorithms that rely heavily on the speed of modern computing to derive “decision rules” that predict customer behavior. We discuss several machine learning techniques, including covering algorithms, instance-based learning, genetic algorithms, Bayesian networks, support vector machines, and committee machine methods such as bagging and boosting.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin

Customer Management

Frontmatter
20. Acquiring Customers
Abstract
All firms must build their customer base by acquiring customers. This chapter looks at the strategy and tactics for doing so. We start with the customer equity framework, which integrates customer acquisition, retention, and development. Key to that concept is the “acquisition curve,” which relates expenditures on customer acquisition to the number of customers acquired. We discuss strategies for increasing acquisition rates suggested by the acquisition curve, and then present and elaborate on a framework for developing customer acquisition programs.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
21. Cross-Selling and Up-Selling
Abstract
Cross-selling and up-selling are fundamental database marketing activities for developing customers; that is, increasing customer expenditures with the firm. Cross-selling entails selling products in the firm's product line that the customer does not currently own. Up-selling entails selling “more” (higher volume, upgrades) of products they already are buying from the company. This chapter focuses on database marketing models for cross-selling and up-selling. Included are next-product-to-buy models, which predict which product the customer is likely to purchase next, and extensions using hazard models that predict when the customer will buy. We cover data envelope and stochastic frontier models for up-selling. We conclude with a framework for managing an on-going cross-selling effort.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
22. Frequency Reward Programs
Abstract
Frequency reward programs are customer development programs based on the theme, “Buy XXX, get a reward.” “XXX” is usually a required purchase volume, and the reward can be free product, a cash rebate, or even “points” for another company's reward program.We discuss two ways that reward programs increase sales — points pressure and rewarded behavior — and the empirical evidence for each. We then review the rich economics literature that has endeavored to answer the question, “In a competitive environment, do reward programs increase firm profits?” We review several issues in designing reward programs, including the reward structure, and conclude with a review of reward programs offered by firms including Harrah's Entertainment and Hilton Hotels.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
23. Customer Tier Programs
Abstract
In today's highly competitive environment, many companies have made the strategic decision to protect and develop their most valuable customers. This strategy is implemented through customer tier programs, whereby customers are assigned to tiers — e.g., gold, silver, bronze — and accorded different levels of marketing and service depending on the tier to which they are assigned. We discuss various methods of defining the tiers and the fundamental allocation decisions firms must make in developing customers within a tier, possibly to the point where they can migrate to a higher tier. We conclude with a review of actual programs used by companies such as Bank One, Royal Bank of Canada, and Viking Office Products.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
24. Churn Management
Abstract
While database marketing activities such as cross-selling, upselling, frequency reward, and customer tier programs focus on developing the customer, there is always the fear that in the midst of these efforts, the customer will decide to leave the company, i.e., “churn.” We discuss the approaches that can be used to control customer churn, focusing on proactive churn management, where the customer is contacted ahead of when he or she is predicted to churn, and provided a service or incentive designed to prevent the customer from churning. We review predictive modeling of customer churn, and present a framework for developing a proactive churn management program.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
25. Multichannel Customer Management
Abstract
Nowhere is the potential — and challenge — for database marketing more acute than in the “brave new world” of multichannel customer management. Whereas many companies historically interacted with their customers through one channel — the bricks-and-mortar retail store, the bank branch, the company catalog, the financial advisor — today almost all companies are multichannel. This gives rise to several key questions and management issues; for example, “Is the multichannel customer a better customer?” “If so, why?” “Should we encourage our customers to be multichannel?” We have just begun to understand questions such as these, and this chapter reviews what we know and do not know. We discuss the multichannel customer in depth, including the association between multichannel usage and sales volume. We also discuss the factors that influence customers' channel choices, the phenomenon of research shopping and the impact of channel introductions on firm revenues. We present a framework for developing multichannel customer strategies, and conclude with industry examples of multichannel customer management.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
26. Acquisition and Retention Management
Abstract
While customer acquisition and retention programs are important in their own right, the firm needs to manage acquisition and retention in a coordinated fashion. This chapter addresses how companies should allocate their efforts to acquisition and retention. We discuss the models that are relevant to this task, and then optimize several of them to demonstrate their value and gain insights on when the company should allocate more resources to either acquisition or retention. We show for example that the adage, “It's cheaper to retain than acquire a customer, so we should spend more on acquisition,” needs to be sharpened considerably before it can be used to guide acquisition and retention spending. We conclude by introducing the “Customer Management Marketing Budget,” a tool for planning acquisition and retention expenditures over time.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin

Managing the Marketing Mix

Frontmatter
27. Designing Database Marketing Communications
Abstract
When all the LTV calculations, predictive modeling, and acquisition and retention planning have been done, the firm ultimately must communicate with the customer. In this chapter, we discuss how to design database marketing communications. We discuss the planning process for designing communications, and devote most of the discussion to copy development and media selection. We pay special attention to “personalization,” or individualizing the message that is communicated to each customer.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
28. Multiple Campaign Management
Abstract
Many database marketing programs are constructed as one-shot efforts - they determine the best campaign to implement now. However, more recently, academics and companies have recognized that the actions we take now influence what actions we will be compelled to take in the future, and if the current actions are not managed correctly, these future actions will not be successful. The key is to manage the series of communications holistically, taking into account the future as we design the current campaign, and to do so at the customer level. This chapter discusses “optimal contact models” for managing a series of campaigns. Many of the examples we draw on involve the catalog industry, although we also discuss examples involving e-mails, product magazines, promotional discounts, and even online survey panel management.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
29. Pricing
Abstract
The database marketing environment presents many challenges involving pricing. How should we coordinate acquisition pricing and retention pricing? How should we price when we want to re-activate customers? How should we use database marketing to price discriminate? This chapter reviews models and methods for providing insights on these questions. We point out that pricing cannot be considered in a vacuum; for example, that customer quality expectations play a key role in acquisition pricing.
Robert C. Blattberg, Byung-Do Kim, Scott A. Neslin
Backmatter
Metadaten
Titel
Database Marketing
verfasst von
Robert C. Blattberg
Byung-Do Kim
Scott A. Neslin
Copyright-Jahr
2008
Verlag
Springer New York
Electronic ISBN
978-0-387-72579-6
Print ISBN
978-0-387-72578-9
DOI
https://doi.org/10.1007/978-0-387-72579-6