Case Study

To Eat List Restaurant Discovery

Connecting foodie friends and influencers to get the perfect restaurant recommendation.
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To Eat List is a social network built around the most essential IRL social thing in human culture: food. To Eat List is for sharing your favorite restaurants with all your friends and followers, and getting restaurant recommendations from people whose taste you trust.

The Need

Traditional "restaurant review" apps are plagued with bots, trolls, paid reviews, and angry rants from strangers. To Eat List needed to be as much the antidote to this as possible.

To Eat List is based on a simple premise–the best recommendations are from other users you know and trust, not randos on the internet. To Eat List would focus first on floating recommendations from people you follow, then using overall popularity as a fall-back in places where your friends haven't added favorites yet.

It would also do away with reviews in favor of a binary recommendation structure. Would you recommend this place to a friend, yes or no? If yes, onto your favorites list it goes! If no, that's cool – no need to spread negativity, just remove it from your list and move on. This structure would help minimize "gaming" the results, like you see with traditional reviews and average star ratings systems.

  • A complete U.S. restaurant database was needed from day 1, so a first-party database was out of the question
  • Sought to send push notifications based on location data, like a reminder to favorite or remove a restaurant from your list just after you've left
  • Needed alternative discovery methods besides just your friends
  • Required an economical, yet robust and accurate location database
  • Sharing To Eat List restaurant profiles off-app needed to encourage app downloads without a hard gate to see content
Our Solution

After painstakingly reviewing and testing location databases and APIs from Google, Facebook, FourSquare, and more, the most reliable economical solution for an API-enabled, third-party location database for To Eat List was Google Places. This provided a robust, nationwide, accurate restaurant listings from which to build a restaurant data model.

We constructed an abstract first-party data model for restaurants in such a way that it doesn't matter where the metadata To Eat List requires would come from, so long as the source had similar metadata. We simply match up the data points and feed them into the database, and the To Eat List interface displays it beautifully. This enables maximum flexibility should the data sources change in the future.

To enable perfectly timed push notifications, we used passive polling of users' device locations based only on major location changes, then increasing in frequency and accuracy if the user comes close to the location of a restaurant on your list. This enables the app to know with reasonable confidence when you've just spent time at a restaurant on your To Eat List without being invasive nor draining device battery in the process. When you leave a defined "geofence" area around the restaurant's location, we trigger a push notification asking what you thought of the restaurant, and if you'd recommend it to a friend.

This intelligent notification system drives engagement at the perfect time–just after someone has tried a new restaurant–and encourages them to either remove the listing or add it to their favorites, so that their followers know it's worth checking out!

Table Stakes Social Features

To Eat List covers the core features you expect of any platform based on social engagement. We implemented a following/follower structure to connect users and surface an activity feed of restaurants added to "to try" and "favorite" lists, with comments, likes, shares, and replies–table stakes features for a social app!

Because we wanted content from shared links to be visible to non-users, we constructed a web template for restaurant profiles, which would allow the same great content from the app to be visible to non-users, with a call-to-action to download the app to start their own "To Eat List." When users with the app already downloaded tap To Eat List links in other places, such as on other social platforms, the deep-links take users straight to the target restaurant profile inside the To Eat List app.

Since To Eat List wanted to go all-in on the idea of surfacing user-generated content only from people you follow, you'll never see favorites and comments from people you don't. However, because most people won't have friends who have favorited content in every possible city, there is a "Popular" category which you can use as a fall-back to seed your list in a new city. To Eat List also created a number of accounts that anyone can follow with recommendations in a bunch of popular cities as yet another starter list of restaurants curated from the favorite establishments of local tastemakers.

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