Foodora Ratings & Reviews

 

 

Situation

Foodora has a good selection of local eateries in your are and had already built a reputation of being a "curated" platform for restaurants. As much as people have this trust in our platform, we still want to give the opportunity for them to evaluate their order experience, figure out what went wrong or get their positive words from them.

Another challenge is to build the same flow for foodpanda, which is the other global service we own for emerging markets. The real challenge is that foodpanda and foodora users have different expectations of the platform in various aspects. The solution needs to work for all of them.

After various interviews, user testing sessions and iteration, we defined the final solution that potentially make an impact on rating motivation and better feedback. Due to amount of resources and priorities in Post-Order team, the final solution is on hold at the moment. However, we managed to improve the existing floe for foodpanda with some UX changes and rolled out globally.

 

Impact

I am excited to say that our MVP for the new rating flow has increased the conversion of ratings by 260% for foodpanda.

I am looking forward to measure the impact after the final solution is implemented, which will eventually make the better use out of this handful information for our customers. 

Challenge

How can we motivate the users to rate and review an order?

How could we get handful information about our user's ordering experience?

How should we display this information in our platforms?

 

Role

Product Design (Research, Strategy, Implementation)

Team

1 Product Designer, Engineers (TBD), 1 Product Manager

Platforms

Responsive Web, IOS, Android

 
ratings_app_overview_web.png
 

 

INSPIRATION

Key Findings & Pain points

After various interviews with internal and external users and some results from usability testing sessions, we realized that many people only rate when they have extreme feelings about a product or service (both negative or positive).

People don't mind taking risks of trying out food than buying a product at Amazon, that is why they don't always make decisions based on reviews or feel entitled to rate or review.

Based on these common patterns, we kept the focus on how to solve these challenges:

How can we encourage customers to drop positive ratings and reviews for the restaurants that are performing well?

How can we know what went wrong with their order in order to escalate to corresponding teams?

How can we solve the users problem before they send a bad public review about the restaurant partner or our platform?

 
ratings_research
 

 

 

IDEATION

Initial Concepts

 
 

Binary vs Star Rating

After some initial testing, we saw that users had different opinions on what they are rating or reviewing, which led us to test binary and star rating and understand the dynamics better.

ratings & reviews_comparison_2.png
 
 
ratings_testing.png

Testing & Iterations

After several testing sessions, we combined the two big purpose of rating flow, to get restaurants be rated and reviewed, as well as get feedback from the users about what went wrong.

 

 

 

 

IMPLEMENTATION

Final Designs 

Ratings-&-Reviews-user-flow-web.png

Positive flow

Negative Flow