The leftmost section of this table shows the variance explained by the initial solution. Only three factors in the initial solution have eigenvalues greater than 1. Together, they account for almost 65% of the variability in the original variables. This suggests that three latent influences are associated with service usage, but there remains room for a lot of unexplained variation. The second section of this table shows the variance explained by the extracted factors before rotation. The cumulative variability explained by these three factors in the extracted solution is about 55%, a difference of 10% from the initial solution. Thus, about 10% of the variation explained by the initial solution is lost due to latent factors unique to the original variables and variability that simply cannot be explained by the factor model. The rightmost section of this table shows the variance explained by the extracted factors after rotation. The rotated factor model makes some small adjustments to factors 1 and 2, but factor 3 is left virtually unchanged. Look for changes between the unrotated and rotated factor matrices to see how the rotation affects the interpretation of the first and second factors.