Deriving Target Selection Rules from Endogenously Selected Samples

One of the aims of direct marketing in practice is to target the most profitable customers in the database at hand. This selection is often done based on observed behavior in the past. As a consequence, databases arising from the responses to direct mailings are not a random sample from all potential respondents. When not all heterogeneity is observed, part of the target selection rule will be based on the unobserved heterogeneity, so selection is endogenous. Treating an endogenously selected sample as a random sample results in inconsistent parameter estimates, which in general also harms the predictive performance of the model. We develop an adjustment to the likelihood of the model that corrects for the endogenous sample selection. We apply this technique to the selection of mail targets for a charitable organization. In the application we also show that, based on a model for the response rate and the amount donated simultaneously, we can create a target selection rule that maximizes expected revenues. Such a selection rule outperforms selection rules based on response rates or donated amount only. The traditional approach of maximizing response is therefore not the optimal approach to target selection.econometric models;direct marketing;target selection;endogeneity;sample selection

Modeling charity donations: target selection, response time and gift size

Charitable organizations often consider direct mailings to raise donations. Obviously, it is important for a charity to make a profitable selection from available mailing lists, which can be its own list or a list obtained elsewhere. For this purpose, a charitable organization usually has to address the following four questions:1. Who should we send a mailing?2. Who is likely to respond to that mailing?3. How much time will it take for such an individual to respond?4. How much money will this individual donate?Several techniques for addressing one or more of these questions have been suggested in the literature. For example, Bult and Wansbeek (1995) develop a model that addresses question 2. Otter et al. (1997) develop a model that jointly considers questions 2 and 4. In practice one often relies on techniques such as RFM-based decision rules (Bauer 1988) in order to answer question 1.In this paper we develop a model which enables a charitable organization to make an optimal selection from its own mailing list, while simultaneously considering the four questions above. Hence, our model consists of four components with a possible non-zero correlation structure. The explanatory variables in each of these components are RFM-type variables, wherethese are allowed to have different effects on the various variables to be explained. In particular, we show that the first component is essential when a target selection model is applied on a database. Neglecting this component can generate a substantial bias in the results of subsequent analysis. The various model parameters are estimated by maximum likelihood.We illustrate our model using a random drawing of about 5,300 individuals from the database of a large Dutch charitable organization. Our empirical results indicate the relevance of the non-zero correlation across the model components,and the relevance of taking account of the target selection part. We find some RFM variables to have effects with opposite signs on the probability to respond, the time for response and the donation. It is found that the most profitable individuals are not the ones who have maximum scores on the RFM variables. We conclude with a discussion of various further research topics.censored regression;target selection;duration model;charity donations;time to response

The Trade and FDI Effects of EMU Enlargement

This paper considers the nature and the distribution of trade and FDI effects of a potential enlargement of the European Monetary Union (EMU) to the ten countries that obtained EU membership in 2004. Intuitively, the implementation of a single currency for these countries means replacing several fluctuating currencies by a common currency. This gives rise to both “level” and “risk” effects of reduced currency movements on trade and investment. Another factor is the nature of the link between trade and FDI. This is also important not only because cross-border factor flows are becoming increasingly important, but also the international trade literature has long recognized that cross-border factor flows and trade in goods and services can be substitutes or complements. Given this background, one-way and two-way error component gravity models are estimated to examine for these theoretical expectations within a dataset of unbalanced panel data that combines bilateral trade flows among 29 countries and the distribution of outward FDI stocks among these countries (including the 10 new EU members). The data generally cover the period from 1990 to 2004. Our empirical results convincingly support: (i) a complementarity between trade and investment, (ii) a relationship between trade and exchange rate volatility that depends on the sign of bilateral trade balances, (iii) a positive effect of EU on trade and investment, and (iv) a positive effect of EMU on foreign investment. Using a simulation-based technique, we find that estimates of FDI effects of EMU range between 18.5 percent for Poland and 30 percent for Hungary

Modeling charity donations: target selection, response time and gift size

Charitable organizations often consider direct mailings to raise donations. Obviously, it is important for a charity to make a profitable selection from available mailing lists, which can be its own list or a list obtained elsewhere. For this purpose, a charitable organization usually has to address the following four questions: 1. Who should we send a mailing? 2. Who is likely to respond to that mailing? 3. How much time will it take for such an individual to respond? 4. How much money will this individual donate? Several techniques for addressing one or more of these questions have been suggested in the literature. For example, Bult and Wansbeek (1995) develop a model that addresses question 2. Otter et al. (1997) develop a model that jointly considers questions 2 and 4. In practice one often relies on techniques such as RFM-based decision rules (Bauer 1988) in order to answer question 1. In this paper we develop a model which enables a charitable organization to make an optimal selection from its own mailing list, while simultaneously considering the four questions above. Hence, our model consists of four components with a possible non-zero correlation structure. The explanatory variables in each of these components are RFM-type variables, where these are allowed to have different effects on the various variables to be explained. In particular, we show that the first component is essential when a target selection model is applied on a database. Neglecting this component can generate a substantial bias in the results of subsequent analysis. The various model parameters are estimated by maximum likelihood. We illustrate our model using a random drawing of about 5,300 individuals from the database of a large Dutch charitable organization. Our empirical results indicate the relevance of the non-zero correlation across the model components, and the relevance of taking account of the target selection part. We find some RFM variables to have effects with opposite signs on the probability to respond, the time for response and the donation. It is found that the most profitable individuals are not the ones who have maximum scores on the RFM variables. We conclude with a discussion of various further research topics

A real-time plant discrimination system utilising discrete reflectance spectroscopy

An advanced, proof-of-concept real-time plant discrimination system is presented that employs two visible (red) laser diodes (635. nm, 685. nm) and one near-infrared (NIR) laser diode (785. nm). The lasers sequentially illuminate the target ground area and a linear sensor array measures the intensities of the reflected laser beams. The spectral reflectance measurements are then processed by an embedded microcontroller running a discrimination algorithm based on dual Normalised Difference Vegetation Indices (NDVI). Pre-determined plant spectral signatures are used to define unique regions-of-classification for use by the discrimination algorithm. Measured aggregated NDVI values that fall within a region-of-classification (RoC) representing an unwanted plant generate a spray control signal that activates an external spray module, thus allowing for a targeted spraying operation. Dynamic outdoor evaluation of the advanced, proof-of-concept real-time plant discrimination system, using three different plant species and control data determined under static laboratory conditions, shows that the system can perform green-from-green plant detection and accomplish practical discrimination for a vehicle speed of 3. km/h