Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing

In direct marketing, customers are usually asked to take a specific action, and their responses are recorded over time and stored in a database. Based on the response data, we can estimate the number of customers who will ultimately respond, the number of responses anticipated to receive by a certain period of time, and the like. The goal of this article is to derive and propose several estimation methods and compare their performances in a Monte Carlo simulation. The response patterns can be described by a simple geometric function, which relates the number of responses to elapsed time. The “maximum likelihood” estimator appears to be the most effective method of estimating the parameters of this function. As we have more sample observations, the maximum likelihood estimates also converge to the true parameter values rapidly.

► We propose a decision model of predicting customer response patterns in direct marketing.
► The performances of several estimation methods are compared in a Monte Carlo simulation.
► The simulation result shows that the method of maximum likelihood has the smallest RMSE.
► Its convergence to the true parameter value is also shown to be very fast.