There are several popular IR metrics based on an underlying user model. Most of them are parameterized. Usually parameters of these metrics are chosen on the basis of general considerations and not validated by experiments with real users. Particularly, the parameters of the Expected Reciprocal Rank measure are the normalized parameters of the DCG metric, and the latter are chosen in an ad-hoc manner. We suggest two approaches for adjusting parameters of the ERR model by analyzing real users behaviour: one based on a controlled experiment and another relying on search log analysis. We show that our approaches generate parameters that are largely different from the commonly used parameters of the ERR model.