A method of analyzing the pattern of decision-making units in moving from one behavior state to another. Construct a transition matrix to show the proportion of times that the behavior in one trial will change (move to another state) in the next trial. If the transition process remains stable and if the sample of actors is representative of the entire population, the matrix can be used to forecast changes. However, there is a problem. Forecasts are most useful when changes occur. But given the assumption of stability, Markov chains are risky for predicting behavior when organizations make efforts to change behavior and thus to change the transition matrix. Markov chains have been recommended for predictions in marketing when people are assumed to go through various states in using a product (e.g., trial, repeat purchase, and adoption) and for cases in which consumers purchase different brands. Early published applications of Markov chains covered problems such as predicting changes in the occupational status of workers, identifying bank loans that will go into default, and forecasting sales in the home-heating market. Despite many research publications on Markov chains, I have been unable to find accounts of research that supports their predictive validity. Armstrong and Farley (1969) compared Markov chains with simple extrapolations in forecasting store visits and Markov chains produced no gains in accuracy.