Analyzing conversion rates with Bayes Rule (Bayesian statistics tutorial)

So I’ve just launched my new startup, BeerBnB. It’s a hip little site matching beer drinkers with specialty microbreweries - AirBnB for drinkers, or maybe eBay for brewers. My marketer growth hacker has gotten some early publicity by advertising in the bathroom of a few bars - the result was 794 unique visitors of whom 12 created an account. Doing some division I’ve computed an empirical conversion rate of 12/794=1.5%.

To begin with, this seems promising. A 1.5% conversion rate isn’t great, but it’s certainly enough to get started. Investors have suggested that they will probably invest if the conversion rate exceeds 1%.

Now, suppose the marketer has the ability to get a lot more publicity. He can expose BeerBnB site to approximately 10,000 visitors via toilet adds at bars around the city. Suppose we make the assumption that these 10,000 visitors will convert at the same rate as the 794 early visitors. How many people can I reasonably expect to signup? This isn’t a trick question - the expectation is about 150 signups. But how confident are we that we will really see 150 signups? How confident are we that the conversion rate is higher than 1%?