A redesign of the Open Table app to help connect existing users with friends reviews
OpenTable is an online restaurant-reservation service that helps users discover and book restaurants as well as earn rewards. Whilst attending the Memorisely Bootcamp, together with a team-mate, we explored a redesign of the Open Table app and how it could help connect existing users with their friends.
OpenTable is an online restaurant-reservation service that helps users discover and book restaurants as well as earn rewards. Whilst attending the Memorisely Bootcamp, together with a team-mate, we explored a redesign of the Open Table app and how it could help connect existing users with their friends.
Helping Open Table connect existing users with their friends to see their latest restaurant reviews
OpenTable is an online restaurant-reservation service that helps users discover and book restaurants as well as earn rewards. Whilst attending the Memorisely Bootcamp, together with a team-mate, we explored a redesign of the Open Table app and how it could help connect existing users with their friends.
Role
User Research
Product Strategy
UI Design
Interaction Design
Usability Testing
Tools
Figjam
Notion
Maze
Figma
Timeline
5 weeks
The Problem
When faced with thousands of anonymous restaurant reviews on Open Table, whose review do you trust? Would you trust a review or recommendation made by a friend more than a stranger? Currently, the OpenTable app doesn’t have an option for their existing users to connect with friends and view their restaurant reviews.
When faced with thousands of anonymous restaurant reviews on Open Table, whose review do you trust? Would you trust a review or recommendation made by a friend more than a stranger? Currently, the OpenTable app doesn’t have an option for their existing users to connect with friends and view their restaurant reviews.
When faced with thousands of anonymous restaurant reviews on Open Table, whose review do you trust? Would you trust a review or recommendation made by a friend more than a stranger? Currently, the OpenTable app doesn’t have an option for their existing users to connect with friends and view their restaurant reviews.
The Solution
To help solve this, we wanted to seamlessly connect users with their friends so they can view their restaurant reviews and recommendations. In turn, the Open Table app could help build trust faster among its users, and enough confidence to book, ultimately leading to an increase in the number of booking conversions on the app.
To help solve this, we wanted to seamlessly connect users with their friends so they can view their restaurant reviews and recommendations. In turn, the Open Table app could help build trust faster among its users, and enough confidence to book, ultimately leading to an increase in the number of booking conversions on the app.
To help solve this, we wanted to seamlessly connect users with their friends so they can view their restaurant reviews and recommendations. In turn, the Open Table app could help build trust faster among its users, and enough confidence to book, ultimately leading to an increase in the number of booking conversions on the app.
Usability Review
To begin with, we carried out a usability review to understand the ease of use of the product, focussing on the sign-up and reviewing experience. Within Figjam, we worked as a team to carry out a usability review by highlighting and discussing the pain points (on red stickies) and wow moments (on green) as shown below.
To begin with, we carried out a usability review to understand the ease of use of the product, focussing on the sign-up and reviewing experience. Within Figjam, we worked as a team to carry out a usability review by highlighting and discussing the pain points (on red stickies) and wow moments (on green) as shown below.
To begin with, we carried out a usability review to understand the ease of use of the product, focussing on the sign-up and reviewing experience. Within Figjam, we worked as a team to carry out a usability review by highlighting and discussing the pain points (on red stickies) and wow moments (on green) as shown below.
Sign-up and Reviews on Open Table Mobile App
Sign-up and Reviews on Open Table Mobile App
Sign-up and Reviews on Open Table Mobile App
Business & User frustrations
Overall the app was easy to use with simple steps to complete a booking however the experience of searching for a restaurant could be made easier and more enjoyable
Overall the app was easy to use with simple steps to complete a booking however the experience of searching for a restaurant could be made easier and more enjoyable
Overall the app was easy to use with simple steps to complete a booking however the experience of searching for a restaurant could be made easier and more enjoyable
Primary Frustration
Primary Frustration
Primary Frustration
Users are faced with hundreds, sometimes thousands of largely anonymous reviews to read per restaurant. On the Open Table app, it seems to fall on users to try sift through them to make a decision. This can be a time-consuming and potentially boring experience.
Users are faced with hundreds, sometimes thousands of largely anonymous reviews to read per restaurant. On the Open Table app, it seems to fall on users to try sift through them to make a decision. This can be a time-consuming and potentially boring experience.
Secondary Frustration
Secondary Frustration
Secondary Frustration
As well as the quantity of reviews per restaurant, many of them were very similarly rated which could potentially lead to choice paralysis.
As well as the quantity of reviews per restaurant, many of them were very similarly rated which could potentially lead to choice paralysis.
Competitor Benchmarking
To gain inspiration for the different approaches and potential solutions currently being used, we carried out competitor benchmarking of direct and indirect competitors Tripadvisor (see below) and Amazon. Whilst I focussed on Tripadvisor's restaurant booking app experience, my team-mate reviewed Amazon's. After conducting these reviews independently, we came together to discuss our findings.
To gain inspiration for the different approaches and potential solutions currently being used, we carried out competitor benchmarking of direct and indirect competitors Tripadvisor (see below) and Amazon. Whilst I focussed on Tripadvisor's restaurant booking app experience, my team-mate reviewed Amazon's. After conducting these reviews independently, we came together to discuss our findings.
To gain inspiration for the different approaches and potential solutions currently being used, we carried out competitor benchmarking of direct and indirect competitors Tripadvisor (see below) and Amazon. Whilst I focussed on Tripadvisor's restaurant booking app experience, my team-mate reviewed Amazon's. After conducting these reviews independently, we came together to discuss our findings.
Restaurant Booking on Tripadvisor Mobile App
Restaurant Booking on Tripadvisor Mobile App
Restaurant Booking on Tripadvisor Mobile App
Tripadvisor's restaurant booking experience was found to be a more playful and enjoyable one for users overall, whilst maintaining good ease of use, although there were still a lot of largely anonymous reviews to sift through.
Meanwhile Amazon had various features that helped users get a macro understanding of how well a product was reviewed such as an infographic of each product's overall review ratings and the ability to filter reviews by tags.
Tripadvisor's restaurant booking experience was found to be a more playful and enjoyable one for users overall, whilst maintaining good ease of use, although there were still a lot of largely anonymous reviews to sift through.
Meanwhile Amazon had various features that helped users get a macro understanding of how well a product was reviewed such as an infographic of each product's overall review ratings and the ability to filter reviews by tags.
Tripadvisor's restaurant booking experience was found to be a more playful and enjoyable one for users overall, whilst maintaining good ease of use, although there were still a lot of largely anonymous reviews to sift through.
Meanwhile Amazon had various features that helped users get a macro understanding of how well a product was reviewed such as an infographic of each product's overall review ratings and the ability to filter reviews by tags.
Ideation
We then begun an ideation process with a focus on a problem statement. We did a series of messy crazy's 8's and created a mind map of what we could add or improve as shown below.
Balancing desirability, feasbility and viability is important so we then used a prioity matrix to determine which features to add and improvements to make, taking into account these sometimes opposing forces.
We then begun an ideation process with a focus on a problem statement. We did a series of messy crazy's 8's and created a mind map of what we could add or improve as shown below.
Balancing desirability, feasbility and viability is important so we then used a prioity matrix to determine which features to add and improvements to make, taking into account these sometimes opposing forces.
We then begun an ideation process with a focus on a problem statement. We did a series of messy crazy's 8's and created a mind map of what we could add or improve as shown below.
Balancing desirability, feasbility and viability is important so we then used a prioity matrix to determine which features to add and improvements to make, taking into account these sometimes opposing forces.
What can we add
A "friend" tab on the nav bar to help users connect with friends and view their reviews
A map that allows users to better visualise where friends' reviews are located as avatars on a map
What can we improve
A filter that allows users to filter reviews by friends with additional filters such as "top-rated", "5*", "4*" etc.
User Flows
Next we turned our attention to user flows and used Figjam to map out the current user flow followed by improved user flows with the additional friend filter and the
Next we turned our attention to user flows and used Figjam to map out the current user flow followed by improved user flows with the additional friend filter and the
Improved User Flow for Booking Experience
Improved User Flow for Booking Experience
Improved User Flow for Booking Experience
New User Flow for Connecting with Friends
New User Flow for Connecting with Friends
New User Flow for Connecting with Friends
Rapid Prototyping - Wireframing
Between us, we then used Figjam to wireframe these additions and improvements to the app.
Between us, we then used Figjam to wireframe these additions and improvements to the app.
Between us, we then used Figjam to wireframe these additions and improvements to the app.
Styles & Components
Ahead of building the prototype, we started by creating the colour styles and text styles within Figma, from action primary and secondary colours to text for headings, body and buttons. We made sure to reference Open Table's published brand guidelines. We then started to build components from buttons and nav bars, to cards and tabs.
Ahead of building the prototype, we started by creating the colour styles and text styles within Figma, from action primary and secondary colours to text for headings, body and buttons. We made sure to reference Open Table's published brand guidelines. We then started to build components from buttons and nav bars, to cards and tabs.
Ahead of building the prototype, we started by creating the colour styles and text styles within Figma, from action primary and secondary colours to text for headings, body and buttons. We made sure to reference Open Table's published brand guidelines. We then started to build components from buttons and nav bars, to cards and tabs.
High Fidelity Prototype
We then built a high fidelity prototype which you can view below. As well as creating a filter to help users filter their reviews by their friends' top reviews, we enabled users to connect with their friends on the platform and follow their foodie experiences. This in turn, we believed, would help to keep returning users engaged, build their trust and confidence in the app and its reviews and ultimately lead to an increase in restaurant bookings.
We then built a high fidelity prototype which you can view below. As well as creating a filter to help users filter their reviews by their friends' top reviews, we enabled users to connect with their friends on the platform and follow their foodie experiences. This in turn, we believed, would help to keep returning users engaged, build their trust and confidence in the app and its reviews and ultimately lead to an increase in restaurant bookings.
We then built a high fidelity prototype which you can view below. As well as creating a filter to help users filter their reviews by their friends' top reviews, we enabled users to connect with their friends on the platform and follow their foodie experiences. This in turn, we believed, would help to keep returning users engaged, build their trust and confidence in the app and its reviews and ultimately lead to an increase in restaurant bookings.
Usability Testing
We uploaded our prototype for testing using Maze and have included some heatmaps below. We set the following missions:
You want to book Honey & Smoke restaurant, but first you want to find out whether it has been top-rated by your friends. Go ahead and find out whether Honey & Smoke has been rated 5 stars by your friends and then book the earliest time slot available.
You're currently viewing your friends top rated restaurants within the Open Table app, and Honey & Smoke restaurant is one of them, but you want to see where it's located first before booking the next available time slot. Go ahead and find Honey & Smoke's location and then book the next time slot available.
You want to connect with your friend Marco Capelli, a friend of yours who told you the other day that he is also using the Open Table app. How would you go about finding and connecting with them?
Usability Testing
We uploaded our prototype for testing using Maze and have included some heatmaps below. We set the following missions:
You want to book Honey & Smoke restaurant, but first you want to find out whether it has been top-rated by your friends. Go ahead and find out whether Honey & Smoke has been rated 5 stars by your friends and then book the earliest time slot available.
You're currently viewing your friends top rated restaurants within the Open Table app, and Honey & Smoke restaurant is one of them, but you want to see where it's located first before booking the next available time slot. Go ahead and find Honey & Smoke's location and then book the next time slot available.
You want to connect with your friend Marco Capelli, a friend of yours who told you the other day that he is also using the Open Table app. How would you go about finding and connecting with them?
Usability Testing
We uploaded our prototype for testing using Maze and have included some heatmaps below. We set the following missions:
You want to book Honey & Smoke restaurant, but first you want to find out whether it has been top-rated by your friends. Go ahead and find out whether Honey & Smoke has been rated 5 stars by your friends and then book the earliest time slot available.
You're currently viewing your friends top rated restaurants within the Open Table app, and Honey & Smoke restaurant is one of them, but you want to see where it's located first before booking the next available time slot. Go ahead and find Honey & Smoke's location and then book the next time slot available.
You want to connect with your friend Marco Capelli, a friend of yours who told you the other day that he is also using the Open Table app. How would you go about finding and connecting with them?
Test outcomes
Having tested the prototype with 10 users, we learnt that users were able to complete all 3 missions successfully however one path proved most common and others were more indirect, as demonstrated by the heatmaps above.
We believe with some tweaks, these new features could really help returning users view others reviews and build enough trust and confidence in a restaurant to book, increasing Open Table's booking conversion rate by an estimated 15%.
Having tested the prototype with 10 users, we learnt that users were able to complete all 3 missions successfully however one path proved most common and others were more indirect, as demonstrated by the heatmaps above.
We believe with some tweaks, these new features could really help returning users view others reviews and build enough trust and confidence in a restaurant to book, increasing Open Table's booking conversion rate by an estimated 15%.
Having tested the prototype with 10 users, we learnt that users were able to complete all 3 missions successfully however one path proved most common and others were more indirect, as demonstrated by the heatmaps above.
We believe with some tweaks, these new features could really help returning users view others reviews and build enough trust and confidence in a restaurant to book, increasing Open Table's booking conversion rate by an estimated 15%.
Three key learnings
1. The A/B split testing showed that 5/10 users navigated to their friends' 5 star reviews from the homepage, whilst 5 users navigated through indirect paths.
1. The A/B split testing showed that 5/10 users navigated to their friends' 5 star reviews from the homepage, whilst 5 users navigated through indirect paths.
1. The A/B split testing showed that 5/10 users navigated to their friends' 5 star reviews from the homepage, whilst 5 users navigated through indirect paths.
2. The friend filter which allowed users to filter reviews by how well their friends had rated them, was an underutilised feature and therefore an area to improve.
2. The friend filter which allowed users to filter reviews by how well their friends had rated them, was an underutilised feature and therefore an area to improve.
2. The friend filter which allowed users to filter reviews by how well their friends had rated them, was an underutilised feature and therefore an area to improve.
3. Both the map button and finding and connecting with friend Marco Capelli had an 80% success rate.
3. Both the map button and finding and connecting with friend Marco Capelli had an 80% success rate.
3. Both the map button and finding and connecting with friend Marco Capelli had an 80% success rate.
Next steps
As next steps, we would like to review whether a new feature pop-up might improve the visibility of the friend filter and test with a wider audience before beginning to build out these new features.
As next steps, we would like to review whether a new feature pop-up might improve the visibility of the friend filter and test with a wider audience before beginning to build out these new features.
As next steps, we would like to review whether a new feature pop-up might improve the visibility of the friend filter and test with a wider audience before beginning to build out these new features.