Constructing learning models from data : the dynamic catalog mailing problem

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2003.Includes bibliographical references (p. 105-107).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.The catalog industry is a large and important industry in the US economy. One of the most important and challenging business decisions in the industry is to decide who should receive catalogs, due to the significant mailing cost and the low response rate. The problem is a dynamic one - when a customer is ready to purchase, s/he may order from a previous catalog if s/he does not have the most recent one. In this sense, customers' purchasing behavior depends not only on the firm's most recent mailing decision, but also on prior mailing decisions. From the firm's perspective, in order to maximize its long-term profit it should make a series of optimal mailing decisions to each customer over time. Contrary to the traditional myopic catalog mailing decision process that is generally implemented in the catalog industry, we propose a model that allows firms to design optimal dynamic mailing policies using their own business data. We constructed the model from a large data set provided by a catalog mailing company. The computational results from the historical data show great potential profit improvement. This application differs from many other applications of (approximate) dynamic programming in that an underlying Markov model is not a priori available, nor can it be derived in a principled manner. Instead, it has to be estimated or "learned" from available data. The thesis furthers the discussion on issues related to constructing learning models from data. More specifically, we discuss the so called "endogeneity problem" and the effects of inaccuracy in model parameter estimation. The fact that the model parameter estimation depends on data collected according to a specific policy introduces an endogeneity problem. As a result, the derived optimal policy depends on the original policy used to collect the data.(cont.) In the thesis we discuss a specific endogeneity problem, "attribution error." We also investigate whether online learning can solve this problem. More specifically, we discuss the existence of fixed point policies for potential on-line learning algorithms. Imprecision in model parameter estimation also creates the potential for bias. We illustrate this problem and offer a method for detecting it. Finally, we report preliminary results from a large scale field test that tests the effectiveness of the proposed approach in a real business decision setting.by Peng Sun.Ph.D

Determining the direct mailing frequency with dynamic stochastic programming

Both in business to business and in consumer markets direct mailings are an important means of communication with individual customers. This paper studies the mailing frequency problem that addresses the issue of how often to send a mailing to an individual customer in order to establish a profitable long-term relationship rather than targeting profitable groups of customers at every new mailing instance. The mailing frequency is optimized using long-term objectives but restricts the decisions to the number of mailings to send to the individual over consecutive finite planning periods. A stochastic dynamic programming model that is formulated for this problem is easy to solved for many applications in direct mailing. A particular implementation of the model will provide the direct mailer with controls to stimulate desired response behavior of their customers. The model is calibrated for a large non-profit organization and shows that very large improvements can be achieved by approaching the mailing strategy with the mailing frequency problem, both in the number of mailing to send and in the profits resulting from the responses.Direct marketing;Stochastic dynamic programming

Mailing catalogs : an optimization approach

Includes bibliographical references (p. 33).by Gabriel R. Bitran, Susana V. Mondschein

Models and algorithms for mailing catalogs

Includes bibliographical references (p. 38-39).by Gabriel R. Bitran

Direct Mailing Decisions for a Dutch Fundraiser

Direct marketing firms want to transfer their message as efficientlyas possible in order to obtain a profitable long-term relationshipwith individual customers. Much attention has been paid to addressselection of existing customers and on identifying new profitableprospects. Less attention has been paid to the optimal frequency ofthe contacts with customers. We provide a decision support system thathelps the direct mailer to determine mailing frequency for activecustomers. The system observes the mailing pattern of these customersin terms of the well known R(ecency), F(requency) and M(onetary)variables. The underlying model is based on an optimization model forthe frequency of direct mailings. The system provides the directmailer with tools to define preferred response behavior and advisesthe direct mailer on the mailing strategy that will steer thecustomers towards this preferred response behavior.decision support system;direct marketing;Markov decision process

Dynamic and Competitive Effects of Direct Mailings

We propose a dynamic direct mailing response model with competitive effects, where purchase and promotion history are incorporated. We then map the dynamic competitive interactions amongst the firms sending the mailings. We investigate the short- and long-run impact of a direct mailing on the revenues of the firm sending the mailing and on the revenues of its competitors. The model accounts for unobserved heterogeneity across households. We estimate the model in the charitable giving setting, as sending direct mailings represents a large part of charitable fundraising activity. Households often receive direct mailings of different charities within a short period of time and competition is highly relevant. We construct a unique database by merging the databases of three large charity organizations in the Netherlands. This results in household level data on the direct mailings received and the donations made by each household to each charity. Our results show that charitable direct mailings are short-run complements, that is, the direct mailings tend to increase the total pie that is divided among the charities. At the same time, the charitable direct mailings are long-run substitutes. In the long run they fight for a piece of the pie that households have available for charitable giving.Competition;Dynamics;Direct Mailings

Determining the direct mailing frequency with dynamic stochastic programming

Both in business to business and in consumer markets direct mailings are an important means of communication with individual customers. This paper studies the mailing frequency problem that addresses the issue of how often to send a mailing to an individual customer in order to establish a profitable long-term relationship rather than targeting profitable groups of customers at every new mailing instance. The mailing frequency is optimized using long-term objectives but restricts the decisions to the number of mailings to send to the individual over consecutive finite planning periods. A stochastic dynamic programming model that is formulated for this problem is easy to solved for many applications in direct mailing. A particular implementation of the model will provide the direct mailer with controls to stimulate desired response behavior of their customers. The model is calibrated for a large non-profit organization and shows that very large improvements can be achieved by approaching the mailing strategy with the mailing frequency problem, both in the number of mailing to send and in the profits resulting from the responses

The library and public relations

Thesis (M.S.)--Boston University, 1949. This item was digitized by the Internet Archive