OfferUp: Jobs & Services

Implement a Jobs and services feature to the existing app

Just finished my final week of Ironhack, and I honestly don't know how to feel. Ultimately, it was an amazing (intense) and life-changing experience that I will always remember fondly.

For this solo project, we got to work with OfferUp’s UX Design team, which was an invigorating experience. I set out to implement a feature that would add value to the user experience of the already highly adopted app. I followed Design Council’s Double Diamond Design Process, — Discover, Define, Develop, and Deliver, which culminated in the Minimum Viable Product and its Hi-fi prototype.

Design Council’s Double Diamond Design Process


The Client

OfferUp, founded in 2011 in Seattle by Nick Huzar and Arean van Veelen,is an online mobile-first C2C marketplace with an emphasis on in-person transactions.

At its core, it’s a “marketplace on a mission to become the simplest, most trustworthy local buying and selling experience”.

Market Research

According to The New York Times, the on-demand home services industry is expected to post a compound annual growth rate of nearly 49% by 2022. Currently, the online on-demand home services sector is an estimated $600 billion industry

As per Forbes, by 2025, 75% of the workforce will be comprised of millennials who have grown up with the internet and smartphones. Per a recent survey, 65% said they rather hire a service via chat and their cellphones than call a business directly.

One main insight I covered during research was all trusted ‘task for hire’ apps have two separate apps. One for the user that will hire the service and one for the person providing the service, as it requires a totally different interface. As such in consideration of time constraints, I focused on the user that is hiring the service in order to get a more in-depth analysis.

Lean UX Canvas

With that, I was ready to direct my focus to get more specific and used the Lean UX Canvas. Which served as a framework to establish what the Business Objective would be, who is my targeted user and what benefits would they gain. This is a living document and I would come back to it many times during my process to iterate or even to refocus on the intended objective.

Mental Models and Areas of Opportunities

Competitive Feature Chart

I had a better idea of what direction to go however needed to get familiar with the competitive market I was dealing with. I looked for competitors and indirect competitors in order to determine any mental models the users may have and areas of opportunities. direct competitors I focused on were Nextdoor, TaskRabbit, Craigslist, and some indirect competitors such as Fiverr, Upwork, and People Per Hour as those provided more technical services.

Market Positioning Map

In order to analyze where the competitors’ are positioned within the market, I turned to the Market Positioning Map, which allows you to visually determine blue oceans or uncontested market space. Based on the list of features created with the Competitive Feature chart, I looked to define the axis for the Map.

Blue Ocean: a clear/informative & multifaceted experience

Quantitative Data

I now had a better understanding of the market and where there were areas of opportunity. However, I still needed to get a better understanding of the targeted user and their needs and wants. I started my user research by collecting Quantitative data in the form of surveys and received 37 responses.

Main takeaways here were:

  • 65% Main concern is the expertise of specific tasks, followed by safety/trustworthiness
  • 62% Prefer an hourly rate as opposed to a flat rate
  • 57% Rather chat and confirm a service via an app than call a business

Qualitative Data

Having now collected the quantitative data I gained insights of the statistics associated with the task for hire services. However, now needed a more in-depth understanding of the perspectives and behaviors of the user. I approached this by collecting Qualitative data in the form of 6 interviews.

Main takeaways here were:

  • 5/6 Main concern is trust & safety
  • 4/6 Prefer to seek help from the local community as opposed to larger companies
  • 6/6 Feel they lack knowledge of what certain services should cost

“I was about to purchase a used tv but then didn’t want to figure out how I was going to mount it on my wall.”

“I missed out on an interesting find on Offerup because I didn’t want to deal with the logistics of getting it home.”

“I get anxious when trying to find help for something because I never know what the price should be and if I’m getting ripped off.”


I felt I had a better grasp of the perspective of the targeted user and was ready to synthesize insights & determine opportunities for design, and moved on to the define phase.

Affinity Map

Having collected valuable data, I now needed to dissect this data to uncover insights and patterns. I started with the Affinity map, which helps to visually enhance the patterns by placing alike thoughts and perspectives together.

Main Takeaway:

  • The strong opinions of lack of knowledge when it comes to average prices
  • The recurring theme of trust & Safety

Value Proposition Canvas — Customer Profile

I moved on to the Value Proposition Canvas which consists of two parts and in its entirety is used to ensure the product or service we ultimately provide is a good market fit with the customer and adds value to their experience.

The customer profile side is intended to uncover and define the customer or user’s “job to be done” (consisting of functional, emotional, and social) and the associated pains and gains.

I went into more depth on “Jobs to be done” in my last article, E-Commerce: Helping communities support local businesses, but just in case you haven’t had time to read ALL my articles, here’s a quick recap below.

‘Jobs to be done’ as defined by Strategyn “proposes that in order to understand customer needs in a way that makes innovation predictable, companies should stop focusing on the product or the customer and instead focus on the underlying process or “job” the customer is trying to get done.”

A quick example of the jobs to be done, gains, and pains defined were:

JTBD: hire a service, build your network of helpers, compare expertise & prices (functional). Hire a service for someone else that needs assistance (emotional) Support the local community (social)

Gains: save time to be with loved ones, feel confident in their decisions, feel efficient.

Pains: overcharged, limited availability when dealing with companies, logistics planning.

User Personas

With the insights, patterns, and perspectives into the user's behaviors, uncovered from the collected data, I was ready to create a User Persona. Essentially user personas help to humanize the data that represents the targeted User group. I built off of OfferUp's existing personas. The data collected indicated the need for two separate personas.

Pat the parent who values trust and a sense of community above all else.

•Her main need is to build a trusted network.

•The main frustration of hers is having to do extensive research in order to hire a service.

Affluent Anna, who has more money than time.

  • Her main need is she wants to be able to get things done as efficiently and effectively as possible.
  • The main frustration is missing out on a great find due to not having the time to plan or execute the logistics.

As-Is Scenario

Using the personas, I brainstormed what they would do/think/ and feel during the current experience of needing and then finding help. From there I was able to find patterns that translated to phases.

Doing: endless research, checks reviews compare prices, chats to ask questions

Thinking: ‘hope they know what they are doing’, ‘I hope it’s safe’

Feeling: overwhelmed, skeptical, apprehensive, stressed

Journey Map

From the insights gathered in the As-is Map I was able to hone in on a specific user journey for both personas in order to uncover specific pain points to address. By being as thorough as possible and considering both personas I was able to combine the journeys and uncover all pain points.

By breaking it down I was able to consider the context in which a solution would have to work.

I converted the main 3 pain points into more detailed problem statements:

  1. The user is frustrated because they are unaware if the service provider has experience with the specific task & if they are trustworthy.
  2. The user feels apprehensive about prices and not getting a fair price.
  3. The user is frustrated of having to book prior to chat and then not getting timely responses.

And then converted them into actionable How Might We Statements:

  1. How might we emphasize safety verifications and expertise?
  2. How might we provide the user with price information that will make them feel reassured?
  3. How might we ensure the chat function is efficient and effective?


I was now ready to diverge again and brainstorm ideas to ultimately develop the MVP by prioritizing features.


I brainstormed solutions for the 3 pain points. Using the HMWs statements as the framework I time-boxed myself and allowed 10 minutes for each HMW and came up with 41 ideas.

MoSCoW Method

I was now ready to dissect the ideas in order to prioritize features that would ultimately form the MVP. I applied the Moscow method in order to filter these ideas into Must-have, Should have, Could have, and Won’t have. By combining the impact vs effort and Moscow method I was able to visually see the impact an idea would have and the effort it would take to implement.

Value Proposition Canvas — Products & Services

I now needed to ensure the services we would offer were a good market fit. So was ready to complete the Value Proposition Canvas by addressing the products and services side to make sure the filtered ideas would indeed offer gains and relieve pains or frustrations for the user.

  • Gain Creators: informed decision making, sense of community, price guidance, efficiency
  • Pain Relievers: reduces the number of channels needed, relieves the feeling of being overcharged, helps to make informed decisions

Job Story

To back up the findings further I used the Jobs story framework as that allows you to focus on context instead of assumptions. I focused on the specific feature story and the user’s intended outcome. By seeing a job in a very specific context it allowed me to better determine the solution.

When searching for a service provider,

User wants to have an informed and trustworthy experience,

So that they can feel confident in their decision and build a trustworthy network within their local community.


Yes, we are so very close! I feel like you might have forgotten what my HMWs were, so to recap:

How might we emphasize safety verifications and expertise?

How might we provide the user with price information that will make them feel reassured?

How might we ensure the chat function is efficient and effective?


A Jobs & Services platform integrated into the OfferUp App. The user can search for a specific task or search via listed categories.

The user is then matched with ‘helpers’ in their area that have a certified badge visible on their profile. The ‘helpers’ will be displayed on cards with all pertinent details with the option to book or start a chat.

Feature 1: Task-specific photos & reviews and number of specific tasks completed. Certified Badge visible (cleared background check)

Feature 2: Pricing Guide provides price ranges for informational purposes, so users can feel confident in the price they’re paying.

Feature 3: Chat feature that can take place prior to booking.

Market Fit

To ensure the key features would be a good market fit I looked back at the Market positioning map. By providing services for hire with detailed information on expertise and pricing, Offerup would cross into the intended blue ocean of a more informative and multifaceted experience as one would be able to also shop for local finds.

User Flow Map

Before I began to prototype I needed a flow guide and creating two separate flows.

  • User flow for Onboarding & hiring a ‘helper’
  • User flow for the pricing guide


Now for the final phase — prototyping, testing, and creating the MVP prototype!

I conducted visual research mainly to understand users' mental models and any current trends. Also what to avoid what doesn't, work and how we can differentiate ourselves.

Lo-Fi Prototype

I sketched out the lo-fi prototype, as it is quick, easy, and inexpensive. It allows you to get the product testable quickly for feedback to then improve and test.

Lo-fi Usability Testing

I submitted my sketched prototype to Maze, a rapid testing platform that allows you to get quick insights and shows you, among other things, how many clicks it took a user to perform the task requested. The overall results were based on 8 user tests and the main takeaways were that I was missing screens to complete certain flows and the ‘skip’ option for onboarding.

Mid-Fi Prototype

Taking those insights into consideration I moved on to the mid-fi prototype using Figma.

Mid-fi Usability Testing

I then conducted Usability testing. The overall results were based on 8 user tests and the main takeaways were button size, spacing issues, header sizes, and exit option for a banner.

Atomic Inventory Design

I was now ready to create the Hi-Fi and we were provided with the OfferUp’s design inventory which is crucial in creating a consistent design and allows to speed up the process by providing accelerators like components and variants. However, as I was adding a feature I needed to implement a few icons, however still staying in line with OfferUp’s design language.

Hi-Fi Prototype

Here it is, the Hi-Fi Prototype of the Minimum Viable product of the Jobs & Services Feature. Please check it out below.

User Path:

  • User will go through the onboarding of features
  • Will look for and hire a service provider (‘helper’)
  • Will look for the average price of another service

Success & Failure Metrics

In order to measure the success of the feature, you need to implement and define success and failure metrics.


  • Increase in both DAU/MAU
  • New users
  • Good reviews
  • Sessions (App Open Rate)
  • High job conversion rates


  • High Churn rate
  • High misclick rates
  • Low or decline in DAU/MAU
  • Bad reviews
  • Low app downloads
  • Slow loading
  • Low job conversion rates
  • Decrease of ‘helpers’

Next Steps

  • Usability test the hi-fi
  • Complete the design process for the service provider side
  • Conduct additional research on any knowledge gaps
  • Build out the app
  • Work on remaining Must Haves, Should haves

Key Learnings

  • Using components, variants, and other accelerators is crucial even though it takes time
  • Staying organized in Figma(no design debt)
  • The importance of research (quant. Qual., Market and visual
  • The importance of usability testing
  • Take the time to really create the atomic design inventory prior to hi-fi

THANK YOU for reading, I appreciate your time.

Any questions or comments are welcome!



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