Loss functions guide machine learning models towards concentrating on the error most important to improve upon. We introduce power loss functions for neural networks and apply them on imbalanced click-through rate datasets. Power loss functions decrease the loss for confident predictions and increase the loss for error-prone predictions. They improve both AUC and F1 and produce better calibrated results. We obtain improvements in the results on four different classifiers and on two different datasets. We obtain significant improvements in AUC that reach $0.44\%$ for DeepFM on the Avazu dataset.