Low precision on classification model
Precision gives the percentage of correctly predicted positives out of total positives predicted by the model. Therefore, low conversion rate of your model is due to low precision.
Looking at the support, this looks like a class imbalance problem. 92% of your training examples belong to class '0'.Therefore, while training, your model has more examples of class '0' to learn from.
There are a few ways to deal with this problem:

Over-sampling - This when you randomly draw samples from the minority class with replacement and use it in the training set
Under-sampling - This is when you draw random samples from the majority class with or without replacement and decrease the number of examples of the majority class given to the model while learning.
Synthetic sampling - There are also techniques like SMOTE that generate samples of minority class synthetically.

These are only few techniques I have highlighted. You can check this link for further reading https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/