Well there is not too much to analyze here, statistically. You just have the proportion of results from that "single case study" with one group of three people. It's only 5 data points, and 1 per story.
I don't think you can fit a frequentist hierarchical model, and even if you could, there's no way you'd be able to reject a null hypothesis test. You definitely can forget a (non-hierarchical) linear model because you have no variance in x or variance in y, so you can't compute CIs at all -- it's like fitting a line of best fit to a single point.
You could always fit a Bayesian hierarchical model, with a parameter for the family of proportions from which each story is drawn from, but with just one update (one per story, at least, five for the family-wise parameter), I'd doubt you could learn much in a Bayesian sense... meaning, I'd doubt your parameters would change much after updating with so little data. So if you start with a flat prior (like a uniform [0-1] for the proportion directed at primary addressee) then it would stay really flat.