Most organisations employ customer relationship management systems to provide a strategic advantage over their competitors. One aspect of this is applying a customer lifetime value to each client which effectively forms a fine-grained ranking of every customer in their database. This is used to focus marketing and sales budgets and in turn, generate a more optimised and targeted spend. The problem is that it requires a full customer history for every client and this rarely exists. In effect, there is a large gap between the available information in application databases and the types of datasets required to calculate customer lifetime values. This gap prevents any meaningful calculation of customer lifetime values. In this research, we present a methodology to close this gap, by using a record linkage methodology to create a holistic customer record for each client. At this point, the remaining gaps in data are filled by our imputation algorithms, a process which then facilitates the calculation of values for each customer. The final step, evaluating our methodology, is achieved using a clustering approach to classify customers so that the customer lifetime value scores can be validated against the clusters in which they reside.