We consider two types of averaging of complex covariance matrices, a sample mean (average) and the sample Fréchet mean. We analyse the performance of these quantities as estimators for the true covariance matrix via `intrinsic' versions of bias and mean square error, a methodology which takes account of geometric structure. We derive simple expressions for the intrinsic bias in both cases, and the simple average is seen to be preferable. The same is true for the asymptotic Riemannian risk, and for the Riemannian risk itself in the scalar case. Combined with a similar preference for the simple average using non-intrinsic analysis, we conclude that the simple average is preferred overall to the sample Fréchet mean in this context.