Nissan Levin

Applying neural computing to target marketing

Database marketing uses the power of data and information technology in the pursuit of personal marketing of products and services to consumers, based on their preferences and needs. We explore the feasibility of using neural computing as a means for targeting audiences for promotion through the mail, from among a list of customers in a database, either as an alternative and/or as a supplement to discrete-choice logistic regression models. Detailed numerical examples involving realistic data are used throughout to support the analysis and demonstrate the results. It is shown that, at least for the data used in this study the fit achieved for both methods is approximately the same, but the process of configuring and setting up a neural network for a database marketing application is not straightforward and may require extensive experimentation and computer resources. The results are therefore not encouraging for the neural net approach.

The Impact of Breaks on Forgetting When Performing A Repetitive Task

Commonly used learning curve models assume that a repetitive task is performed continuously, disregarding the existence of possible break periods between consecutive repetitions. Since these breaks generate forgetting, actual performance will be inferior to the performance forecasted by typical learning curve models. This paper describes and analyzes a laboratory experiment designed to investigate the nature of forgetting in a working environment. The results of the experiment indicate that the degree of forgetting is a function of the break length and the level of experience gained prior to the break. The study investigated the impact of breaks within a range of one to eighty two days. The performance deterioration due to the breaks was just a few percentage points for a single day break and up to 70 percentage points for the longest breaks. A power curve was identified as a proper forgetting model to depict the relationship between break length, performance time before the break and the degree of forget...

Issues and problems in applying neural computing to target marketing

Applying a neural network (NN) to the targeting and prediction problems in target marketing poses some unique problems and difficulties unparalleled in other business applications of neural computations. We discuss several of these issues in this article, as applied to solo mailings, offer remedies to some, and discuss possible solutions to others. A numerical example, using NN backpropagation models and involving realistic data, is used to exemplify some of the resulting issues.

Continuous predictive modeling—A comparative analysis

Abstract We evaluate the performance of several predictive models to analyze continuous response vis-a-vis the performance of a discrete-choice logistic regression model. The models were evaluated based on three measures—profitability analysis, goodness-of-fit criteria, and prediction accuracy. The evaluation was conducted on a real application involving an honme equity loan campaign in the banking industry. The implications of the results for decision making are also discussed.

Joint production planning and product delivery commitments with random yield

We consider a producer who turns a raw material into a product. Before embarking upon production, the producer has to consider the quantity of raw material to order and the finished product delivery commitments; the actual amount produced is a random multiple of the amount of raw material ordered. A concave expected profit function is introduced which gives rise to simple formulas for determining the optimal quantities to order and to commit for delivery. We also analyze the relations between the optimal quantities to order and to commit, the expected amount received and production capacity. We show that among several vendors of the raw material, there exists a preferred one, no matter what the producers cost parameters, if and only if the random multiple associated with that vendor is dominant in the sense of the second-degree stochastic order.

Time Step vs. Dynamic Optimization of Generation-Capacity-Expansion Programs of Power Systems

We derive conditions under which the time-step, or the myopic, approach to generation capacity planning in the power industry yields solutions identical to the solution obtained by an equivalent dynamic model that views the capacity expansion program simultaneously over time. The conditions are derived for thermal power systems for which the capacity expansion program is formulated using a convex, nonlinear mathematical programming model.

A learning curve model for an equivalent number of units

Conventional learning curve models are able to deal only with past data that includes an integer number of cycles and time per cycle. However, in real-life situations, the data collected are of a different nature: periodical information, which includes total work time, in-process inventory and completed units, for example. In such circumstances it would be wrong to consider just the completed units, disregarding the in-process inventory. The concept of Equivalent Number of Units (ENU) has been introduced to permit one to sum up all the work performed, and express it in a manner that allows one to use the learning curve model for such cases. Also, time per unit may not be given for single units and the ENU produced per reported period may not be an integer. In order to solve the above problems, a simple procedure was developed for periodical non-integer data. The end result of the procedure is the product learning curve parameters.

Optimal Mix Algorithms with Limited-Energy Plants

In this paper we extend the optimal mix algorithm to include any number of existing thermal units and energy-limited plants (LEP) and one new LEP. The problem is formulated as a nonlinear programming problem and solved by first identifying whether the LEP is loaded separately or jointly with the existing-thermal-unit (EMU) and then finding the loading point of the LEP and the existing thermal units by solving a series of auxiliary problems. The algorithm is demonstrated by solving a few numerical examples.

Data Mining for Target Marketing

Targeting is the core of marketing management. It is concerned with offering the right product/service to the customer at the right time and using the proper channel. In this chapter we discuss how Data Mining modeling and analysis can support targeting applications. We focus on three types of targeting models: continuous-choice models, discrete-choice models and in-market timing models, discussing alternative modeling for each application and decision making. We also discuss a range of pitfalls that one needs to be aware of in implementing a data mining solution for a targeting problem.

Calculating the regression‐to‐the‐mean effect: A comparative analysis

Abstract Various methods are compared to calculate the regression-to-the-mean (RTM) effect in segmentation analysis, based on the results of a test mailing, distinguishing between the case of no-prior, non-parametric, and parametric knowledge on the distribution of the response rates of segments across the list. The advantages and disadvantages of each method and its implication for decision making are discussed.