Hi Everyone, Im looking for some assistance on a problem that I have been working on for the last few weeks. I would like to optimise the performance of an accounts receivable department. In this dept there are several tools available to obtain a commitment to pay such as letters, phone calls, sms and others. I would like to run an experiment to determine the optimal use of these tools and I suspect that there may be some interactions between these factors. I have researching how I can run a simple design of experiments to determine the optimal treatment. For example I could run a simple 2 level 2 factorial experiment: Factors: Response: Letter Call Account Y Y Paid Y N ? N Y ? N N ? The issue that I have is that not only are my factors discrete (Y/N), my response is discrete as well (Paid/Not Paid). I am having difficulty finding an example of how to conduct a DOE with a discrete (binomial) response. After some researching I am starting to believe I need to run multiple trials for each treatment. For example running multiple instances of: Letter Yes / Call Yes treatment so that I can obtain a resulting proportion (example: 4 of 7 customers treated in this manner paid - 57%), I have seen some places that there are suggestions to run np>5 number of treatments. If in this instance if I expect 70% proportion of people to pay, then I believe I may need to run n=5/.7=(7 to 8) trials of each treatment. Can anyone advise if I am on the right track? I have searched numerous six sigma and DOE references and haven't been able to reach a definitive conclusion. Is there anyone out there that could advise me or provide a similar example or book that may assist me with a discrete response DOE?