The subject of missing values in databases and how to handle them has received very little attention in the statistics and data mining literature1, 2, 3 and even less, if any at all, in the marketing literature. The usual attitude of practitioners is the user will just have to ignore records with missing values. On the other hand, a few very advanced theoretical solutions have been developed, some of which have been applied, particularly to clinical trials data. These solutions can only be applied to small databases, not to the very large databases held by many companies on their customers. This paper describes a new method for imputing missing values in such very large databases.