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

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Models and techniques for hotel revenue management using a rolling horizon.

This paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays.Revenue management;Mathematical programming;Yield management

Models and Techniques for Hotel Revenue Management Using a Roling Horizon

AbstractThis paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays.mathematical programming;Revenue Management;yield management

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

Evaluating Direct Marketing Campaigns: recent findings and future research topics

This paper contains a survey of the recent literature on the evaluation of direct marketing campaigns. We give an outline of the various stages included in such a campaign. Next, we review the statistical methods most frequently used and we review the general findings from using these methods.direct marketing;target selection;evaluation;quantitative models

Understanding the role of marketing communications in direct marketing

The standard RFM models used by direct marketers include behavioral variables, but ignore the role of marketing communications. In addition, RFM models allow customer responsiveness to vary across different customers, but not across diiferent time periods. Hence, the authors first extend RFM models by incorporating the effects of marketing communications and temporal heterogeneity. Then, using direct-marketing data from a Dutch charity organization, they calibrate the proposed model, and find that it better explains customer behavior because it includes information on both the past behavior and marketing communications. More specifically, they show that direct mail communication builds goodwill, which, in turn, enhances customer's likelihood to buy. However, cumulative exposure to direct mail creates irritation, and erodes goodwill. The two opposite effects induce a cyclic pattern of goodwill formation, which repeats over four quarters. Next, the authors find that, when they control for these communications effects, the standard result - customer's likelihood to buy increases as shopping frequency increases - reverses. That is, in contrast to the extant literature, customers who donate frequently are less likely to donate in the near future. These findings are not only stable over time, but also replicate across two large data sets. Finally, the authors discuss the need for implementing pulsing strategy to mitigate irritation, and the possibility of practicing one-to-one marketing by using information on customer responsiveness, which can be estimated for each customer via the proposed model

Stochastic programming for multiple-leg network revenue management

Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced probabilistic models. Recently suggested models put even more effort into demand forecasting. We will show that the dynamic booking process rather than the demand forecasts needs to be addressed. In particular the nesting strategies applied in booking control will counter-effect the profitability of advanced estimation of booking limits and bid-prices.simulation;revenue management;mathematical programming

Comparison of response surface methodology and the Nelder and Mead simplex method for optimization in microsimulation models

Microsimulation models are increasingly used in the evaluation of cancer screening. Latent parameters of such models can be estimated by optimization of the goodness-of-fit. We compared the efficiency and accuracy of the Response Surface Methodology and the Nelder and Mead Simplex Method for optimization of microsimulation models. To this end, we tested several automated versions of both methods on a small microsimulation model, as well as on a standard set of test functions. With respect to accuracy, Response Surface Methodology performed better in case of optimization of the microsimulation model, whereas the results for the test functions were rather variable. The Nelder and Mead Simplex Method performed more efficiently than Response Surface Methodology, both for the microsimulation model and the test functions.health;simulation;optimization

Media planning by optimizing contact frequencies

In this paper we study a model to estimate the probability that a target group of an advertising campaign is reached by a commercial message a given number of times. This contact frequency distribution is known to be computationally difficult to calculate because of dependence between the viewing probabilities of advertisements. Our model calculates good estimates of contact frequencies in a very short time based on data that is often available. A media planning model that optimizes effective reach as a function of contact frequencies demonstrates the usefulness of the model. Several local search procedures such as taboo search, simulated annealing and genetic algorithms are applied to find a good media schedule. The results show that local search methods are flexible, fast and accurate in finding media schedules for media planning models based on contact frequencies. The contact frequency model is a potentially useful new tool for media planners.optimization;contact frequency;effective reach;media planning