I have a simple website with a home page that has 5 different images on it. All images have a fixed set of 'features' associated with them (size, color, position etc.,). When a visitor comes to the page, he can either click on one of the images or simply leave.
For a given time interval, since both the number of views for the page as well as the number of clicks on an image are 'counts', they can be modeled as Poisson RVs. I want to build a regression model for the click-through-rate (CTR) ie., # clicks / # views (where # views is common to all 5 images and # clicks is obviously different).
I'm using the generalized linear model (glm) in R for this:
model <- glm(log(numClicks) ~ (features) + offset(log(numViews)), family=poisson, data=mydata)
Is this the right approach? I don't know if it's correct to take the ratio of two Poissons in combination with the canonical log link function and glm.
Is there a better way to model CTR?