Alt-spec CBC designs should lead to significant parameters. I'm assuming you're using aggregate logit to test the significance of parameters? Perhaps using the t-test for each parameter? As these are typically effects-coded, the parameters are zero-centered within attributes and thus the t-test considers the null hypothesis that the utility equal to zero.
How large are the aggregate logit standard errors for the principal attribute and the alternative-specific attributes for your n=725 choice experiment? The rule of thumb is that the standard errors for levels of the principal attribute should be about 0.05 or less. The standard errors for levels of the alt-spec attributes should be about 0.10 or less. Did you design achieve at least this degree of precision?