Retraining vs Fine-tuning or Transfer Learning? [D]

Hi!

I am currently working on a project that is basically an e-commerce clickstream data. We take in data, find the intent of the user(XGboost) and price sensitivity(Xgboost), segregate the user in different segments based on their purchasing intent or their research or price behaviour(Xgboost), recommend the benefit like discount or free shipping(Linucp or Thompson sampling), etc.

My question is this - when the data comes in daily to train our models, is it better to retrain the models from scratch or train our models on initial data and keep on fine-tuning everyday when the new data comes in for that day?

Retraining won't be on the whole data. I will take 100% samples from last 30 days, 50% from last 30 to 90, 10% from 90 to 180 days so to avoid the accumulation of training data and keeping the latest trends.

Also, is there any resource where I can learn this better?

Thank you for all the help.