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Yelp has always been our favorite app to pick restaurant and dishes.But the restaurant picking experience can be exhausting because we need to make too many comparisons and judgements based on the variety and amount of information given to us.

Problem

Yelp users struggle in picking restaurant because they take too much time to digest photo reviews.

Solution

We believe by improving the consistency and readability of the photo review, users are able to pick restaurant in a more enjoyable and efficient way.

Process

Research

  • Secondary Research

  • Interview

  • Persona

  • Journey Map

Ideation

  • Competitive Analysis

  • Affinity Map

  • Feature Prioritization

Design

  • Wireframes

  • Usability Test

  • Hi-Fi Mock up

 
 
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Research

Every design targets at a specific user. The first thing we did is to specify who we design for and to know what they are struggling at. Yelp has a wide range of users from all over the world and there is no reason to design a product fit for everyone. Thus, we did some demographic research on Yelp users and found young people are the most active users group for Yelp.

Secondary Research

Who are they?

Young people who likes to explore new restaurant on Yelp

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Interview

What are they struggling at?

In order to understand how our target users think and what they are struggling at, we conducted user interview among five active Yelp users aged between 22-28.

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User Interview Findings

According to our user interviews, we highlight the following answers that show a repetitive pattern and help us frame and narrow town the problems.

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After user interview, we found that one of the real problems behind is the inconsistency and poor quality of photos rather than too much information as we assumed before.

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Persona

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Journey Map

The next thing we did is to further locate the moment that struggling occurs. To be brief, it happens when user open the photo gallery and scan it.

How to pick a satisfied restaurant on Yelp

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Ideation

Competitive Analysis

We compared some other successful restaurant searching products in the market and realized there are some common industry trends:

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Google Map gave up some inspiration:

 

Affinity Map

With all the research and finding so far, we did two rounds of affinity clustering. In the first round, we identify user’s pain and goal; in the second, we brainstormed some solutions accordingly.

 

Feature Prioritization

We then did a feature prioritization matrix to find the most essential and low effort solution.

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Design

Wireframes

 

Test and Design

 

Filter Design

 

Hi-Fi Mock Up

 
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