LSTM with time series prediction

I was initially using the random forest for my churn prediction project. However, I have now decided to switch to LSTM, as my problem exhibits a time series nature with some cyclic patterns. Unfortunately, after implementing a simple sequential model with two layers of LSTM, I obtained much worse results compared to the random forest. Does anyone know the cause of this behavior?

Additionally, I have structured the input dataset on a time basis in a 3D format, where each entry represents the number of rows, the number of months, and the features for each month.