Businesses typically have large amounts of data about customer transactions and other customer information which is not fully utilized. The present invention provides a means of using this information to make predictions about future customer behavior, for example by estimating the probability that a customer will leave a bank. Using these predictions the business is able to take action in order to improve its performance. Using customer data a Bayesian statistical model is generated and this model used to generate statistical estimators of customer behavior. The statistical model is formed using hidden Markov model techniques by clustering customer data and attributes (e.g. Age, sex, salary) into a finite number of states. The number of states is unobserved and considered random. Bayesian prior probability distributions are specified and combined with the data to produce Bayesian posterior probability distributions. Using these Bayesian posterior probability distributions the statistical estimators are obtained. For example, Monte Carlo sampling techniques are used or alternatively the posterior distributions are calculated numerically or analytically.