First Walk is a hyperlocal assistant that inspires new movers to explore their immediate surroundings through a guided and immersive narrative. 

Our team had a design challenge to improve the moving experience. We explored a typical journey of these experiences to understand aspects of the neighborhood and local communities for building unique storytelling. 

LinkedIn is a unique social and professional networking platform that helps individuals look for new opportunities and connect with other professionals.

Our desire to reshape the relationship between LinkedIn Groups and the newsfeed was born out of our interactions using the platform as job seekers during the COVID-19 pandemic. We sought to understand how LinkedIn used its unique position as a professional networking and social service to drive engagement, build community, and ultimately — help job seekers land their next role.

LinkedIn is a unique social and professional networking platform that helps individuals look for new opportunities and connect with other professionals.

Our desire to reshape the relationship between LinkedIn Groups and the newsfeed was born out of our interactions using the platform as job seekers during the COVID-19 pandemic. We sought to understand how LinkedIn used its unique position as a professional networking and social service to drive engagement, build community, and ultimately — help job seekers land their next role.

"Your client is a well-funded start-up that is focused on improving the moving experience. They’ve come to you and your team because while they believe the moving process is ripe for a redesign, they don’t know where to start or what to do. They hope you will come up with a recommendation for them. For whatever direction you recommend, they will want to understand what you learned in research that supports this idea."

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

First, we interviewed friends and family to understand the broad experience and nuances of the moving process in different contexts. We created insights around pain points, thoughts, feelings, themes, hacks, and workarounds for the moving process.

• Moving alone to a new city for work
  
• Moving with special needs
  
• Helping another person move
  
• Driving across the country to move for work
  
• Moving to a new country for school
  
• Someone moving locally
  

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

The card sorting activity with 22+ people helped to understand how the user thinks about the context where we asked, “What were you most uncertain about while moving? The top 5 responses were: 

• Moving alone
  
• Neighborhood Safety
  
• Culture
  
• Paperwork
  
• Packing

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

We found surprises and main themes based on various responses' thoughts and concerns. One of the unique discoveries was that memories uncover nostalgic experiences while moving.

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

"You even find some old memories. Photos, souvenirs, and random things you had collected over the years. It feels comforting."

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Nostalgia significantly impacts emotional safety when people uncover memories that create meaningful connections. It gives comfort to seek old memories during times of uncertainty. We conducted a second round of interviews to dig deep into our hypothesis.

• When moving to a new place, people want to bring with them not just familiar things but familiar experiences (routines, traditions, etc.) to “feel at home.”
  
• People crave closeness and connection, which they get through comfort objects, especially during times of uncertainty. 
  
• People place greater value on things once they have ownership of items with symbolic or sentimental value.
• People look for similar things in their new homes to feel comfortable by tending to the arrangement of past interiors. 

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Collecting personal stories and artifacts helped us to understand people's relationship to things, routines, rituals, and behavior around nostalgia while moving out to a new place. "Sense of belonging" and "feeling at home" were the main factors influencing decision makings by emotional behavior in an unfamiliar experience. As a result, we identify problem areas to narrow it down to those who have moved out for a big life change, such as a dream job, marriage, or study.

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Illustration created by Yutian Wu

Based on our research, we created a hypothetical user model (Shannon), who is a data scientist at Startup. Shannon has been thinking of leaving her current job and has started passively looking for other data science roles. She’s always been a passive user but has started data science groups to start reaching out to other data science professionals. She hopes that joining these groups and engaging with other users will help her find her next opportunity.

Based on our research, we created a hypothetical user model (Shannon), who is a data scientist at Startup. Shannon has been thinking of leaving her current job and has started passively looking for other data science roles. She’s always been a passive user but has started data science groups to start reaching out to other data science professionals. She hopes that joining these groups and engaging with other users will help her find her next opportunity.

Based on our interviews and stories of nostalgia, we created user personas to build representations of the key audiences. The three types of users aimed to capture unique characteristics and synthesize them to connect specific quotes for communicating interesting themes and patterns. 

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

We conducted secondary research on various topics such as How/Why people feel safe, emotional safety, essentialism, a sense of belonging, and competitive analysis. Then, we summarized the secondary research findings in one table and highlighted the pros and cons of the competitive landscape. The analogy research also helped to look at different perspectives where the others feel the unfamiliar or uncomfortable situation to create connections.

Based on our research, we created a hypothetical user model (Shannon), who is a data scientist at Startup. Shannon has been thinking of leaving her current job and has started passively looking for other data science roles. She’s always been a passive user but has started data science groups to start reaching out to other data science professionals. She hopes that joining these groups and engaging with other users will help her find her next opportunity.

Based on our research, we created a hypothetical user model (Shannon), who is a data scientist at Startup. Shannon has been thinking of leaving her current job and has started passively looking for other data science roles. She’s always been a passive user but has started data science groups to start reaching out to other data science professionals. She hopes that joining these groups and engaging with other users will help her find her next opportunity.

By using a whole range of prompts, we brainstormed potential opportunities to generate 50+ ideas. We then narrowed it down into more prominent themes to create affinity mapping for addressing the proper needs of the target audience. Synthesizing previous research insights illustrate five key concepts with storyboards and written contexts to ensure the design process.

We gathered the insights from our researches to conceptualize the following initial ideas.

• Expanding the groups container on the newsfeed.
• Adding filters to the recommendations tab.
• Allowing users to tag groups when creating posts.
• Promoting groups of interest on the newsfeed.
• Showing top interactive users in groups.
• Adding more filters to the followers section.

We gathered the insights from our researches to conceptualize the following initial ideas.

• Expanding the groups container on the newsfeed.
• Adding filters to the recommendations tab.
• Allowing users to tag groups when creating posts.
• Promoting groups of interest on the newsfeed.
• Showing top interactive users in groups.
• Adding more filters to the followers section.

The initial snap testing with three people shared their concerns and thoughts on five key concepts. We explained how each concept would work and asked if they would ever use such a thing in a scenario of the days right after they moved to a new place. Based on their feedback, we decided to explore the First Walk.

We added and removed concepts from the initial ideas to work toward delivering a straightforward and interactive experience between LinkedIn Groups and the newsfeed. We moved our work into Adobe XD to explore the following concepts in more detail.

We added and removed concepts from the initial ideas to work toward delivering a straightforward and interactive experience between LinkedIn Groups and the newsfeed. We moved our work into Adobe XD to explore the following concepts in more detail.

After moving out to a new place, people typically go outside and get their first impression of the neighborhood. Walking on the streets, buying groceries, and visiting parks are examples of exploring new areas where people may fall in love with the place. We built a user scenario of how the user registers the service, learns the neighborhood, and unlocks exciting experiences with detailed descriptions of potential features. 

We added and removed concepts from the initial ideas to work toward delivering a straightforward and interactive experience between LinkedIn Groups and the newsfeed. We moved our work into Adobe XD to explore the following concepts in more detail.

We added and removed concepts from the initial ideas to work toward delivering a straightforward and interactive experience between LinkedIn Groups and the newsfeed. We moved our work into Adobe XD to explore the following concepts in more detail.

We created wireframes to illustrate how it works to capture a realistic situation of design solutions. Lists of features helped to identify essential components for adding or refining into design structures.

We added and removed concepts from the initial ideas to work toward delivering a straightforward and interactive experience between LinkedIn Groups and the newsfeed. We moved our work into Adobe XD to explore the following concepts in more detail.

We added and removed concepts from the initial ideas to work toward delivering a straightforward and interactive experience between LinkedIn Groups and the newsfeed. We moved our work into Adobe XD to explore the following concepts in more detail.

We observed and asked how the participants expected to see and simulate the low-fidelity prototypes in the concept testing. The participants mostly understood the context of the concepts, which vailed our assumptions at the surface level. However, we also gained controversial and negative feedback for implementation. We clarified the token system to iterate the process based on their suggestions and concerns.

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Things that they were concerned about:

• Why am I not rewarded for my exploration?
• What about safety?
• Why would I continue using this after a few weeks?
• How do I know I’m not wasting my time following tokens?
• More number of tokens ≠ Better experience

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Things that they wished to add:

• Tiered token system (not all tokens are equal)
• Credibility system for tokens
• Ability to explore someone else’s neighborhood
• The neighborhood guide

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Things that they would change:

• Dashboard (what’s hot/local secrets)
• Pre-rendered tokens (no surprises)
  
• Personalized adaptive experiences

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Our design solutions focus on how the new mover inspire to explore their immediate surrounding to build familiar experiences, create meaningful connections and encourage interpersonal relationships through a hyperlocal and incentive narrative. Tokens system consisted of social, stories and everyday life:

• Everyday Life Tokens: Generated by local businesses. The tokens have a monetary value like first coffee free or a 5% discount on your groceries. It’s like your neighborhood’s gift to you.
  
• Social Tokens: Generated by your neighbors around your home. These could be a token for a yoga friend, or a puppy playdate dropped in a park. Collecting these would connect you to the token creator. 
• Stories Token: Generated by your neighbors or ex-neighbors who have nostalgia stories. It reveals different parts of the neighborhood.
  

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

Illustration created by Yutian Wu / UI created by Kalyani Rajguru

Based on our research, we created a hypothetical user model (Shannon), who is a data scientist at Startup. Shannon has been thinking of leaving her current job and has started passively looking for other data science roles. She’s always been a passive user but has started data science groups to start reaching out to other data science professionals. She hopes that joining these groups and engaging with other users will help her find her next opportunity.

Based on our research, we created a hypothetical user model (Shannon), who is a data scientist at Startup. Shannon has been thinking of leaving her current job and has started passively looking for other data science roles. She’s always been a passive user but has started data science groups to start reaching out to other data science professionals. She hopes that joining these groups and engaging with other users will help her find her next opportunity.

The main goal of IXD studio class is not just to learn design methodologies but to understand when and how to employ design research methods in the design process.

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization

After these interviews, we discovered patterns in how candidates, job seekers, and employers described their experiences with LinkedIn. Based on these insights, we generated HMW (How Might We) questions that we then grouped under the following categories:

• Engagement
• Connection
• Customization
• Information
• Job Search
• Motivation 
• Organization