I like the OkCupid-esque approach to data mining in this blog post. It's a great way to leverage customer data as a marketing tool. I'd love to see more posts of this kind going forward.

As for the data itself, I'd be interested to see which Yelp profile characteristics drive the most page views to the restaurant page (i.e. 4.5 stars vs. 3 categories vs. 500+ reviews). While the number of page views on Yelp may not be available, the number of click throughs to a restaurant's home page may be good proxy.

"...a policy to ensure that no guest leaves the dining room unhappy" -- sounds like a good policy for any business to have, regardless of their Yelp presence.

Believe it or not, this requires a real change in process for most businesses. Not many hostesses are hawk-eyed enough to spot an unhappy customer leaving.

I believe it -- I can only think of one time that a server even noticed a barely-touched entree and made an effort to find out if we were unhappy. Training, and eventually tools, for restaurants to understand customer sentiment before they leave the building would be amazingly powerful at influencing satisfaction scores. Call centers have technology at their disposal to perceive callers' moods from tone of voice and loop in a supervisor when they get exasperated. I wonder if anyone is applying this technology in restaurants?

Interesting to hear that the number of categories associated with a yelp page correlates to how many reservations per month a restaurant receives.

Yes! I wasn't expecting the correlation to be quite so strong. It seems much easier to get listed in a variety of categories than to bump up your Yelp score by more than half a star.

While it's nice to see how this relates to online reservations, I'd like to see how it correlates to overall reservations.

I appreciate the feedback. I can say off the top of my head from "eyeballing it," that it does roughly correlate to overall reservations to some degree, but to dive deeper will probably require a follow-up post :)