Can I apply survival analysis to predict if a user will revisit the website?
If you want to use survival analysis (which can be more flexible and insightful), I'd recommend this package and this great tutorial. Speaking shortly, as a result, you'll get "probability of being alive" for each customer.

If you want to use logistic regression I think it's trickier. Why I think so - Like any other churn problem, it's hard to define it properly. The definition depends on your task and where the model outcome will be used. Let's say churn is a particular amount of inactivity, e.g. 30 days. You can do an initial analysis of how to find this number. Just pick a particular date (you can do it multiple times) and check % of people who made next transaction. Important thing is - your time period from both sides should be same for all users:


if it's a new users he's not able to be inactive for a long period, right? 
if it's a last date in the dataset (e.g. yesterday) - not all users are not able to perform transaction within 1 day. And you'll get high churn rates. So be attentive to dates.


So you need to understand from your data - which inactivity is "normal" for the average user and define it as N. After that you can label users binary like "if inactivity > N then "churned (1)" else "not churned (0)"". And you can use this label with any classification model.