As both scenarios represent a binary choice of buy/didn't, both would best be modeled with logistic regression. Linear regression is designed for continuous outcomes that are theoretically unrestricted in values. That not the case for either scenario.
The second "selling rate" model might be interpreted in two ways. If all who bought also had watched the trailer, then its outcomes are restricted to the range [0,1] and a logistic regression makes sense. If people can buy without watching the trailer, how do you deal in a linear regression with an infinite "selling rate," as you defined it, if no one happened to watch the trailer but at least one bought? The latter interpretation of the second scenario would also best be modeled by logistic regression, with watchedTrailer
as a predictor in the model and including all visits to the site in the data set.
It's hard in general to associate linear and logistic regression coefficients, as they represent different things. For linear regression it's the change in value of the outcome per unit change in a predictor, here a change of probability. But probabilities can't be outside of [0,1]. For logistic regression it's the corresponding change in log odds of the probability of the outcome, with values covering [$-\infty,\infty$].
That said, sometimes people do formulate a linear probability model, with probability as the outcome variable in a linear regression. If you do use a linear probability model, you can interpret a regression coefficient in terms of the fractional change in probability associated with a unit change in the predictor. If probabilities are in the middle range near 50/50 that can work OK and results might be similar to a logistic regression. But with probabilities near the edges of [0,1] a linear probability model can make predictions of probabilities outside that theoretically allowed range.
So use a logistic regression or other modeling approach that takes the [0,1] limits of probability into account when you encounter these types of scenarios.