You could use identical priors for each variant, with the mean equal to the empirical mean of your existing system, and the variance controlling how plausible big improvements are.

In this case your prior mean could be something as simple as an average conversion rate for all your past articles.

Remember that by using frequentist statistics your are implicitly assuming a uniform prior---I think it's quite easy to find weakly informative priors which are more plausible than saying that any conversion rate is equally likely.