By 2016, Burpple had acquired an active user base of over 1 million active users who used the platform to discover the best places to eat regularly. The product direction was evolving from a food journal app to a food discovery app.
With this change in direction, the users had one problem - the app didn’t help them discover new places to eat.
Food discovery is emotional. A photo can, easily, trigger cravings and decisions. However, this has to be combined with reliable reviews to give context, trust and the word of mouth feel. A photo review is, also, Burpple's most basic form of content.
Our aim was to improve the discoverability within the Burpple app so that our users could search and explore to find inspirations and enjoy delicious food with their loved ones.
The design process that we follow at Burpple is the double diamond design process created by the The British Design Council.
I led the design exploration of improving search and explore in collaboration with one other designer.
In addition, I worked with a Product Manager and a team of Android, iOS and Backend developers led by our CTO.
We started the discovery process by breaking down the then current app and analysed potential problems for the users. Here are our findings:
Searching is when a user knows the end-result of what they are looking for (i.e. venue, guide, people). Some of the user stories for search:
“I am going to a venue that has already been decided, I would like to find out what to order”
“I would like to find out business information like opening hours, address, contact etc. about a venue”
“I know a venue and location, but can’t remember the specific name. I want to find it out so I can share it with a friend”
Exploring is when a user is seeking inspiration with or without constraints (i.e. location, cuisine, etc). Some of the user stories for explore:
“I am going to be at a neighbourhood, I want to know what are my options”
“I am craving a particular cuisine, what are my best options to satisfy this craving”
“I’m tired of eating at the same places all the time, I need new inspiration”
“I’ve got nothing to do now, going to spend some time planning future meals”
After breaking down the user stories, we defined the key problems that we wanted to solve and found quantifiable solutions to them.
After defining all the problems, we felt that the simplest solution to help our users discover would be using widgets on the explore screen. We also wanted our users to have new content to see when they land on the explore page. Based on the information we wanted to highlight to the user, these are the widgets we chose:
Creating wireframes and low fidelity mockups gave us the freedom to solve complex problems easily and test multiple iterations without creating high fidelity designs.
With our basic assumptions and low fidelity mockups, we opted for user testing. We created a questionnaire to find out what users look for when they start discovering for food places (to find the appropriate widgets for the landing area).
The survey was sent to 150 users including friends and internal team.
Some of the questions we asked:
“How often would you like to try new places and what are the sources of inspiration?”
“How often do users face the problem of - sick of eating the same food, like to know where to find food they crave, they want to know where to eat at a given location etc.”
“If users were provided with widgets like location, promotions, guides, venue etc. how likely will they use it from 1 to 5?”
Some of the findings:
Users explored new places to eat at least once week.
Most users wanted to plan for their future meals and wanted inspirations.
User also mentioned, that they wanted to know what food or venues are nearby at a certain location.
Ranking of widgets:
1.Nearest Area Suggestion
2.Promotions
3.Relevant Search Suggestions
4.Recently Saved
5.Latest Guide
6.Trending Categories
We also created a prototype on Flinto to test out interactions for the explore and search area.
After the product development, the feature was launched in April 2017. We measured the impact and learned from the data we collected on Amplitude. The results are below: