Toast App

My thesis in graduate school was a project called Last Eats. It was a web platform of restaurant recommendations based on radical selectivity. We asked our users, "if you only had time for one meal in a city, where would you go and why?" It was positioned as the opposite of Yelp - trusted, selective recommendations for only the best restaurants based on the knowledge of people you know.

What we learned by making Last Eats was that there was a community that craved an alternative to Yelp and the other anonymous recommendation engines. They wanted connection and they sought experiences that they couldn't find elsewhere. After taking into account all of our user feedback and the sustained appetite for a mood and experience based discovery tool, we decided to break from our earlier platform and create something new. Enter Toast.

Toast is a food recommendation app that leverages your trusted network to make the decision of where to eat the fastest, easiest and most satisfying experience possible. We’re doing this through simple, trusted, actionable recommendations from friends, driven by mood and location.

My Role: As founder and CEO of a small team, I was responsible for the entire product experience (including all UX, UI, user research, product strategy and occasionally front end development) as well as partnerships, fundraising, marketing and outreach.

What Problem Are We Trying to Solve?

In NYC (where our company launched) there are over 5,000 restaurants that serve Chinese food. But how many of those are trusted by your friends and reliable enough to cure your hangover? Finding something edible has never been easier but finding something perfect for your mood based on your location and your friends' recommendations is near impossible.

From Last Eats to Toast

Like it's predecessor Last Eats, Toast is a word of mouth recommendation platform, but with some distinct differences.

  1. It's a native iOS app
  2. Mood and location are the foundation of the platform
  3. You can add as many Toasts or recommendations as you want. There is no limit, but you can designate a current favorite or "Top Toast" 
  4. Restaurant reservations are enabled
  5. We're launching in NYC only (with an emphasis on everything south of Union Square as well as Williamsburg)

How Do We Know This is a Problem Worth Solving?

When we started Toast, we leaned heavily on the learning that we gained by building Last Eats.  We realized with all of our research into Yelp/Google and all other impersonal digital food discovery tools that mood or experience based discovery was highly unreliable or completely unavailable on other platforms.

If I'm looking for great Thai food, I can source any generic digital restaurant ranking/evaluation tool to find an approximation of this end desire. If on the other hand I want restaurant experiences that are "fun", "comforting" or "adventurous", these criteria are distinctly personal and much harder to reliably source from strangers - or people who don't know the nuance of my own personal taste (i.e. my definition of "adventurous" is likely very different than that of a random person on the internet).

As you can see below, when a user types, "Adventurous Restaurant" into Yelp, the only results that come up are extremely fancy, expensive eateries with no logic or justification as to why each is recommended or what differentiates it as adventurous. This is a prototypical example of the filter failure of high profile recommendation engines to reliably source trusted, mood based experiences.

To further validate whether there was an unmet need, we built a quick and dirty prototype to see if digital foodies who were already using Last Eats would embrace the concept. Within the first hour of launching the prototype, we had dozens of submissions and many impassioned messages to us saying how much they loved reading what their friends wrote and recommended. Furthermore, we saw that users were using the platform to create experiential recommendations such as places that were perfect for "impressing my date", "kayaking to dinner" or "focusing and get shit done."


Competitive Analysis and Market Positioning

We began our analysis of the market, and the entrenched competitors within, by systematically breaking down the food discovery leaders into relevant categories. These included: tastemakers, established tech leaders, first movers, photo based discovery, restaurant delivery/reservation apps, socially optimized food discovery and industry insiders.

We then partitioned the companies into quadrants that express their relative positioning to Toast respective of relevance and trust.

If Toast could deliver the most trusted, relevant, experiential recommendations with immediately actionable take out or reservations, we could differentiate our offering. We also wanted to be a leader in UX and deliver value to our users built on an intuitive, powerful and easy discovery experience

As we assessed the competition, we began to think about the brand and product identity that we wanted to forge for our company.

Crafting a Brand and Product Identity

When we started to think about the product we wanted to build, we began with the voice and tone of our to-be solution. We knew we had to build something that felt communal, trustworthy, actionable and fun. But it also had to feel personal enough that users would feel empowered to create content. In order to replicate the experience of recommending a beloved place to a friend, we had to create an environment where we could engender openness and community.

Additionally, when we launched Toast in 2015 we were at near peak digital food porn. Taking photos of meals, posting said photos to social media and then disseminating what was awesome or special about the experience was common behavior. In fact there were apps dedicated to this already, but none of them were built around experience, mood or trusted friends.  

Researching Successful UX Patterns

As we began to think about how we wanted to build the fastest, easiest, mood driven food discovery tool, we did an audit of popular apps that were driven by stellar user experience. These did not have to be food discovery apps, as we wanted to identify UX patterns that were easy, intuitive and fun to use while helping their intended users make quick, empowered selections

Although we ideated on dozens of different ways to select a mood and location, we ended up employing a vertical carousel pattern that was made famous by Urbanspoon, one of the App Store's first wildly popular food discovery apps. We wanted this process to be quick, elegant and fun and we feel the carousel accomplished this goal.

With regards to the content discovery process, we used a quality over quantity paradigm and wanted to make sure we gave each restaurant its proper consideration. As a result, we never show a list of options, but instead each option is presented one at a time. Thankfully the explosive popularity of Tinder normalized this and made the lateral swipe progression powerful, intuitive and easy. Unlike with Tinder, we wanted our users to have the power to browse forward or backwards. There are plenty of apps that do this effectively so we had lots of confirmation that this style of content discovery could be applied to our platform.

Since the value of our platform was directly linked to diverse, quality recommendations, we had to make the process of making a toast  as easy as possible. We leveraged a common content creation pattern of top right additions - a convention made popular by Twitter, FB Messenger and many other products.

Exploration and Iteration

Before we committed to our first build of Toast, I designed a few alternative approaches that we tested via flinto, InVision and other prototyping tools. Some of these took similar approaches and some were drastic departures.

Among our considerations were the following. We tested a home page where we showcased a user's friends who are the most active on the platform as "Toastmasters." Furthermore we gave them a place of prominence on the bottom third of the screen enabling a user to quickly see a their most recent Toasts specific to a location and mood. This ended up being a feature only potential power users would benefit from most. It also ended up complicating the first page of the app unnecessarily.

Additionally, we tested a swipe to discover experience that had a more formal, Zagat-like brand and feel, with multiple friends photos shown on the same line as if to indicate their participation in a group conversation. At one point we also hoped to integrate and partner with a few select delivery services, so we explored exposing that capability within the bottom fixed navigation bar.

We also tested several extreme outlier experiences. Although the swipe to discover pattern that Tinder established a wonderfully effective UX pattern, the giant reject/heart buttons felt cold and distant. The casual dismissal of content may work when the basis is a stranger you'll never see again, but not for content that is hand delivered from friends. Users also told us they wanted to be able to go back and forth between considerations, so a one and done, binary model was not going to work for us. These testing sessions were essential in helping us to solidify our core product experience and learn what to avoid.

After these explorations, we returned to the advisory guidelines that we had originally established for our brand and product experience. We simplified and streamlined the visual design and UX so that everything took a backseat to the photos of the restaurant, the critical pieces of information (such as distance and price point) and the Toasts written by people you care about.

Toast 1.0 - Product at Launch

First Point of Contact in App

When a user logs into Toast, we immediately have them select the mood that they are feeling and or seeking. The moods we focused on for our launch included the following: Adventurous, Boozy, Casual, Celebratory, Cheap, Comforting, Fun, Healthy, Hungover, Indulgent, Light, Romantic, Simple and Swanky. This list was culled from extensive user research sessions was past users of Last Eats and new users from our target demographic (18-45 year old restaurant goers).

Then we default to their current location, although they can easily choose a different one if they prefer. As we launched in Manhattan, our focus was hip areas that young, tech savvy foodies frequent. These included: Nolita, SoHo, Tribeca, West Village, Williamsburg, East Village, Greenwich Village, and the Lower East Side.

Once they select a mood and a location we immediately showcase a series of restaurant that fit the collective criteria, sourced from friends and defaulted to the most recommendations and or closest location. When a user finds a restaurant that looks appealing, they can easily read toasts (reviews) from friends, determine the type of cuisine, price point and walking distance (although the typical walking distance for a user was under 30 mins, the screenshots below were taken away of NYC, thus generating wild distance estimates).

A user can then click on a restaurant to see photos of the food, a map of the location, menu, directions, website and more. The Foursquare API provides much of this data for us. Our task was to take all of this data make it as consumable and immediately actionable as possible.

Our ultimate goal with Toast was to get our users to an incredibly delicious, unique dining faster and more reliably than any other food discovery platform. First, we made every restaurant on Toast tied to one click Pick Up or Reservations. This was a critical point of emphasis for us as means to enable our users to take action on a compelling option faster and easier than any other food discovery tool.

Not Just Friends, But Friends of Friends

One of the strategic differences between Toast and Last Eats was finding a way to meaningfully expand the circle of trusted reviews. As a result we followed the model that was leveraged by the popular dating app, Hinge. We prioritize your immediate friends' recommendations but also those of your friends' friends. This third tier allowed us to grow our repository of quality, mood based recommendations while maintaining trust as one of the central tenets of our value proposition.

I am great friends with Ian McCormick. Based on my proximity to and trust in Ian, I can reasonably expect his friends to have a relatively similar set of values. As a result, my trust in Ian enables my extended trust to Tal, Ian's friend. The intimacy within this dynamic is  inherently preferable to that of recommendations from strangers.

How We Determine What Restaurants We Show

Our algorithm takes the following variables into account :

  • A user's proximity to the restaurants that fit their mood
  • The number of times a restaurant is given a Toast.
  • Whether or not the restaurant was given a Top Toast designation - which was the equivalent of the singular, be all end all Last Eats declaration. We brought back a level of radical selectivity from Last Eats with the arrival of Top Toasts. These are singular designations that are tied to just one restaurant, put on a pedestal. Algorithmically these toasts carry more weight than a regular toast and are thus positioned earlier in the discovery feed. As with Last Eats, these designations can change at any time.
  • The level of social engagement a Toast has garnered - i.e. the number of likes a review for a place has received.
  • Eventually, our goal was for users to self-select their best friends on Toast so we could appropriately bias their reviews as part of our ranking algorithm.

User Research Leads to New Features

In addition to the standard form of consuming content on Toast through the swipe to discover platform, there were a number of early, committed users that demanded another way to discover content. They wanted to see all of the Toasts specific to a mood or a friend relative to their position on a map. Additionally, we added the ability for users to just see Top Toasts declarations or to just see recently added Toasts.

We also added even more social functionality through an Activity Feed where users can see bleeding edge toasts made and endorsed by people within the community.

User generated hashtags added another layer of specificity and personalization that can be created and followed as another means of discovering distinct content. Although we provided some stock hashtags that we easy for users to select and add to their Toasts, we quickly noticed users were adopting their own tags for the experiences that mattered most to them.

    Lessons Learned and Future Opportunity

    Toast validated that there is a community hungry for a discovery platform that caters to experience and intimacy over generic, impersonal rankings and recommendations.

    While we hit some major milestones and grew quickly, we had a hard time sustaining our initial growth and defining success. We should have established clear KPIs before launch as opposed to building, learning and iterating as quickly as possible.

    Food photography and content creation has thrived on instagram and snapchat in part because those platforms are natural extensions of a user's everyday social experience. They are habitual platforms that typically include the people we know and trust best. While Toast created a space dedicated to sharing tips and recommendations, the burden of creation on a new platform was a major blocker. Although users loved to read their friends recommendations, we didn't build a clear enough benefit for users to know their Toasts were making a difference. Eventually we experimented with special titles for our power contributors such as, "Toastmaster" for folks who made 20+ recommendations. This could have been much more thoroughly developed, perhaps providing discounts for restaurants, ubers etc.

    The Cost of Getting It Wrong

    Part of the challenge with our premise is that there are plenty of alternative platforms that will get you to a decent if not great meal. The absence of our product was never going to ruin your night, but it's existence could move a user from an ordinary experience into an extraordinary one.

    One of our mistakes was not targeting a smaller, even more focused demographic such as college students going on dates or impressing out of towners. This might have allowed us to build a base of evangelists that would grow with us as we expanded to even more mood based discovery opportunities.

    Eventually our dream was to take our platform of trusted recommendations for exceptional experiences and expand into other domains where a friend's recommendation for an experience, and the cost of getting it wrong is big - travel, hotels, and bachelor and bachelorette parties.