Finding a restaurant that’s actually good is about to get a whole lot easier. The exclusive new Renzell restaurant rating app promises to improve the way we pick our favorite New York City dining spots by enlisting the help of reviewers who actually know their stuff — New Yorkers who love to dine out.
How does it work? Renzell has picked a highly curated group of anonymous restaurant reviewers to visit a list of 54 high-end establishments in New York City that provide a "holistic dining experience," according to app creator Bo Peabody. The hand-selected members have the next year to visit each restaurant on the list, and then must complete an exhaustive 75-question survey about their overall experience following each meal. Questions are broken down into eight categories — hospitality, service, value, food, design, vibe, cocktails and wine/sake — making this one of the most thorough review systems currently out there.
Then, a proprietary algorithm takes users’ dining preferences into consideration to come up with an accurate and balanced rating for each restaurant. The first batch of ratings will be published in September of 2016, concurrent with the inaugural issue of The Renzell Magazine. The Renzell app itself is available for download, but only members selected by the Renzell team can log in and access the review system.
Interested? Here's a step-by-step breakdown of how Renzell works:
The Hand-Selected Panel Completes A Survey
The survey is designed to first nail down aversions and preferences of the sophisticated panel of New York foodies so that each person's reviews can be adjusted for objectivity later.
Anonymous Diners Will Visit Each Restaurant
The Renzell panel, who will remain anonymous to restaurants, will have through next fall to visit each establishment on the list and submit their reviews by answering 75 questions about the atmosphere, beverage program, food, service, and value.
Renzell Will Adjust Reviews For Objectivity
Remember that survey that the panel took in the beginning? Well, now the app itself normalizes the data from each user's restaurant review based on the aforementioned preferences of the expert panel.