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OKO-GHO
A two-sided marketplace for finding — and being found by — a tailor you can trust
READ THE STORY
VOL. III
03
ROLE
UX Researcher
Solo Product/UX Designer
DURATION
12 Weeks
PLATFORM
iOS & Android
Web & Mobile
CATEGORY
Product Design
Brand Identity
ROLE
UX Researcher
Solo Product/UX Designer
CATEGORY
Product Design
Brand Identity
DURATION
12 Weeks
PLATFORM
iOS & Android
Web & Mobile
ROLE
UX Researcher
Solo Product/UX Designer
CATEGORY
Product Design
Brand Identity
DURATION
12 Weeks
PLATFORM
iOS & Android
Web & Mobile
Finding a good tailor is still mostly word-of-mouth — Instagram DMs, a cousin's recommendation, a shop you walked past once. There's no shared space to browse work, compare shops, or know if a tailor is actually good before you commit your fabric and your money to them.
Finding a good tailor is still mostly word-of-mouth — Instagram DMs, a cousin's recommendation, a shop you walked past once. There's no shared space to browse work, compare shops, or know if a tailor is actually good before you commit your fabric and your money to them.
Finding a good tailor is still mostly word-of-mouth — Instagram DMs, a cousin's recommendation, a shop you walked past once. There's no shared space to browse work, compare shops, or know if a tailor is actually good before you commit your fabric and your money to them.
Oko-Gho is a platform designed to fix that: a social, discovery-first marketplace where tailors showcase their work and build reputation, and users find, vet, and communicate with tailors — all in one place.
Oko-Gho is a platform designed to fix that: a social, discovery-first marketplace where tailors showcase their work and build reputation, and users find, vet, and communicate with tailors — all in one place.
Oko-Gho is a platform designed to fix that: a social, discovery-first marketplace where tailors showcase their work and build reputation, and users find, vet, and communicate with tailors — all in one place.
Overview
Overview
Overview
The Problem
Instagram/social media
No structure for comparing shops, no reliable way to filter by style or specialty, portfolios buried in a feed.
Word of mouth
Works, but doesn't scale beyond your immediate network, and says nothing about a tailor you haven't heard of.
Walk-ins
Highest risk, no way to preview quality or fit before committing.
Tailoring is a trust-based, taste-based service — and neither trust nor taste travels well through the channels people currently use to find a tailor.
Right now, discovery happens through:
The Problem
Instagram/social media
No structure for comparing shops, no reliable way to filter by style or specialty, portfolios buried in a feed.
Word of mouth
Works, but doesn't scale beyond your immediate network, and says nothing about a tailor you haven't heard of.
Walk-ins
Highest risk, no way to preview quality or fit before committing.
Tailoring is a trust-based, taste-based service — and neither trust nor taste travels well through the channels people currently use to find a tailor.
Right now, discovery happens through:
The Problem
Instagram/social media
No structure for comparing shops, no reliable way to filter by style or specialty, portfolios buried in a feed.
Word of mouth
Works, but doesn't scale beyond your immediate network, and says nothing about a tailor you haven't heard of.
Walk-ins
Highest risk, no way to preview quality or fit before committing.
Tailoring is a trust-based, taste-based service — and neither trust nor taste travels well through the channels people currently use to find a tailor.
Right now, discovery happens through:
The gap isn't "an app doesn't exist" — it's that no existing channel lets a user evaluate a tailor's craft, style fit, and reliability before they commit, and no channel lets a tailor's reputation compound the way it should.
The gap isn't "an app doesn't exist" — it's that no existing channel lets a user evaluate a tailor's craft, style fit, and reliability before they commit, and no channel lets a tailor's reputation compound the way it should.
The gap isn't "an app doesn't exist" — it's that no existing channel lets a user evaluate a tailor's craft, style fit, and reliability before they commit, and no channel lets a tailor's reputation compound the way it should.
Research & Validation
Given the academic timeline, this wasn't a fully resourced field study, but the design decisions weren't made in a vacuum either. I grounded the work through a mix of direct and secondary input such as;
Practitioner input, where I spoke with a local tailor about her actual workflow: how she currently finds clients, what she wishes clients understood about the process (measurements, timelines, fabric choices), and where trust breaks down with new customers
Peer review cycles, where I had structured critique sessions with classmates, testing whether the navigation model (For You / Following, shop structure, dual signup flows) was intuitive to someone encountering it cold
Academic supervision, through the guidance from my supervisor, used to pressure-test assumptions and scope decisions throughout
Secondary research, through the desk research I conducted, into existing marketplace and social-commerce patterns, to understand how trust and discovery are typically solved in adjacent products
Research & Validation
Given the academic timeline, this wasn't a fully resourced field study, but the design decisions weren't made in a vacuum either. I grounded the work through a mix of direct and secondary input such as;
Practitioner input, where I spoke with a local tailor about her actual workflow: how she currently finds clients, what she wishes clients understood about the process (measurements, timelines, fabric choices), and where trust breaks down with new customers
Peer review cycles, where I had structured critique sessions with classmates, testing whether the navigation model (For You / Following, shop structure, dual signup flows) was intuitive to someone encountering it cold
Academic supervision, through the guidance from my supervisor, used to pressure-test assumptions and scope decisions throughout
Secondary research, through the desk research I conducted, into existing marketplace and social-commerce patterns, to understand how trust and discovery are typically solved in adjacent products
Research & Validation
Given the academic timeline, this wasn't a fully resourced field study, but the design decisions weren't made in a vacuum either. I grounded the work through a mix of direct and secondary input such as;
Practitioner input, where I spoke with a local tailor about her actual workflow: how she currently finds clients, what she wishes clients understood about the process (measurements, timelines, fabric choices), and where trust breaks down with new customers
Peer review cycles, where I had structured critique sessions with classmates, testing whether the navigation model (For You / Following, shop structure, dual signup flows) was intuitive to someone encountering it cold
Academic supervision, through the guidance from my supervisor, used to pressure-test assumptions and scope decisions throughout
Secondary research, through the desk research I conducted, into existing marketplace and social-commerce patterns, to understand how trust and discovery are typically solved in adjacent products
Discovery needs to feel like browsing, not searching. Most people don't know what they want until they see it, a well-made agbada, an unusual colour pairing, a silhouette they hadn't considered. A feed surfaces that kind of serendipity, while a search bar assumes you already know what to look for.
Discovery needs to feel like browsing, not searching. Most people don't know what they want until they see it, a well-made agbada, an unusual colour pairing, a silhouette they hadn't considered. A feed surfaces that kind of serendipity, while a search bar assumes you already know what to look for.
Structural Decisions
Structural Decisions
Structural Decisions
Why a Twitter-like Structure?
Why a Twitter-like Structure?
The biggest structural bet in Oko-Gho is borrowing a familiar social feed pattern, For You / Following, rather than building a conventional marketplace layout (search bar, category grid, filters).
The biggest structural bet in Oko-Gho is borrowing a familiar social feed pattern, For You / Following, rather than building a conventional marketplace layout (search bar, category grid, filters).
The biggest structural bet in Oko-Gho is borrowing a familiar social feed pattern, For You / Following, rather than building a conventional marketplace layout (search bar, category grid, filters).
Saving solves for the in-between moments
Saving solves for the in-between moments
Saving solves for the in-between moments
Not every discovery leads to an immediate decision, someone might see a tailor's work while browsing and not be ready to reach out yet, or want to hold onto a specific post as a reference for a style they want replicated later. Saved posts and saved tailors give users a private holding space between "I like this" and "I'm following this shop" or "I'm starting a conversation." Without it, users lose anything they don't act on immediately, which, for a considered purchase like tailoring, is most of what they see.
Not every discovery leads to an immediate decision, someone might see a tailor's work while browsing and not be ready to reach out yet, or want to hold onto a specific post as a reference for a style they want replicated later. Saved posts and saved tailors give users a private holding space between "I like this" and "I'm following this shop" or "I'm starting a conversation." Without it, users lose anything they don't act on immediately, which, for a considered purchase like tailoring, is most of what they see.
Not every discovery leads to an immediate decision, someone might see a tailor's work while browsing and not be ready to reach out yet, or want to hold onto a specific post as a reference for a style they want replicated later. Saved posts and saved tailors give users a private holding space between "I like this" and "I'm following this shop" or "I'm starting a conversation." Without it, users lose anything they don't act on immediately, which, for a considered purchase like tailoring, is most of what they see.
Two front doors...
Two front doors...
...not one form
...not one form
Signup starts with a single choice — Customer or Tailor — rather than a generic account creation flow with a role toggle buried inside it. The two groups want fundamentally different things from the platform (one is browsing and vetting, the other is showcasing and getting discovered), so their onboarding needs different questions, different first screens, and arguably a different tone.
Making that split the very first decision, before anything else, means each path can be built for what that person actually came to do, instead of forcing both through a shared flow that serves neither well.
Signup starts with a single choice — Customer or Tailor — rather than a generic account creation flow with a role toggle buried inside it. The two groups want fundamentally different things from the platform (one is browsing and vetting, the other is showcasing and getting discovered), so their onboarding needs different questions, different first screens, and arguably a different tone.
Making that split the very first decision, before anything else, means each path can be built for what that person actually came to do, instead of forcing both through a shared flow that serves neither well.
Signup starts with a single choice — Customer or Tailor — rather than a generic account creation flow with a role toggle buried inside it. The two groups want fundamentally different things from the platform (one is browsing and vetting, the other is showcasing and getting discovered), so their onboarding needs different questions, different first screens, and arguably a different tone.
Making that split the very first decision, before anything else, means each path can be built for what that person actually came to do, instead of forcing both through a shared flow that serves neither well.
Onboarding & Personalization
Preference selection, Pinterest-style
Rather than dropping users straight into an empty feed, onboarding asks them to select from visual style categories, women's fashion, bespoke, casual, and similar, just the same way Pinterest front-loads taste before showing content. This matters more here than it would for a generic content app.
Tailoring is entirely about personal style, so a feed with no signal about taste is close to useless on day one. Front-loading preference turns the very first feed a user sees into something relevant, rather than asking them to train the algorithm through weeks of scrolling and skipping.
Onboarding & Personalization
Preference selection, Pinterest-style
Rather than dropping users straight into an empty feed, onboarding asks them to select from visual style categories, women's fashion, bespoke, casual, and similar, just the same way Pinterest front-loads taste before showing content. This matters more here than it would for a generic content app.
Tailoring is entirely about personal style, so a feed with no signal about taste is close to useless on day one. Front-loading preference turns the very first feed a user sees into something relevant, rather than asking them to train the algorithm through weeks of scrolling and skipping.
Onboarding & Personalization
Preference selection, Pinterest-style
Rather than dropping users straight into an empty feed, onboarding asks them to select from visual style categories, women's fashion, bespoke, casual, and similar, just the same way Pinterest front-loads taste before showing content. This matters more here than it would for a generic content app.
Tailoring is entirely about personal style, so a feed with no signal about taste is close to useless on day one. Front-loading preference turns the very first feed a user sees into something relevant, rather than asking them to train the algorithm through weeks of scrolling and skipping.
Measurements, captured early rather than deferred
Users can input their measurements as part of onboarding, not just when they're ready to message a tailor. This was a deliberate bet on reducing friction later because measurements are the single most tedious, easy-to-postpone step in getting clothes made, and if it's held back until the moment of committing to a tailor, it becomes one more reason to abandon the flow at the point of highest intent. Capturing it early when the user is already in a setup mindset means it's simply there when a conversation with a tailor starts, instead of being a new ask that interrupts momentum.
Measurements, captured early rather than deferred
Users can input their measurements as part of onboarding, not just when they're ready to message a tailor. This was a deliberate bet on reducing friction later because measurements are the single most tedious, easy-to-postpone step in getting clothes made, and if it's held back until the moment of committing to a tailor, it becomes one more reason to abandon the flow at the point of highest intent. Capturing it early when the user is already in a setup mindset means it's simply there when a conversation with a tailor starts, instead of being a new ask that interrupts momentum.
Measurements, captured early rather than deferred
Users can input their measurements as part of onboarding, not just when they're ready to message a tailor. This was a deliberate bet on reducing friction later because measurements are the single most tedious, easy-to-postpone step in getting clothes made, and if it's held back until the moment of committing to a tailor, it becomes one more reason to abandon the flow at the point of highest intent. Capturing it early when the user is already in a setup mindset means it's simply there when a conversation with a tailor starts, instead of being a new ask that interrupts momentum.
Every earlier decision — the feed, saving, following, ratings, preference capture all exist to get a user to one moment, a real conversation with a tailor they trust. Built-in messaging makes that possible without leaving the platform.
That's the real function of messaging here, not just adding chat, but removing the re-explaining that normally happens at the start of every new tailor relationship.
Conversations happen with the shop, not an individual, keeping the trust model consistent from discovery through to transaction.
Every earlier decision — the feed, saving, following, ratings, preference capture all exist to get a user to one moment, a real conversation with a tailor they trust. Built-in messaging makes that possible without leaving the platform.
That's the real function of messaging here, not just adding chat, but removing the re-explaining that normally happens at the start of every new tailor relationship.
Conversations happen with the shop, not an individual, keeping the trust model consistent from discovery through to transaction.
Every earlier decision — the feed, saving, following, ratings, preference capture all exist to get a user to one moment, a real conversation with a tailor they trust. Built-in messaging makes that possible without leaving the platform.
That's the real function of messaging here, not just adding chat, but removing the re-explaining that normally happens at the start of every new tailor relationship.
Conversations happen with the shop, not an individual, keeping the trust model consistent from discovery through to transaction.
Messaging
Messaging
Messaging
Closing the Distance Between Discovery and a Booked Tailor
Closing the Distance Between Discovery and a Booked Tailor
What I'd Test Next
Would users actually complete measurement input during onboarding, or does it need to move closer to the point of messaging a tailor?
Early capture reduces friction later, but it's also an early ask before trust is established, I'd want usage data to confirm the trade-off pays off rather than causing early drop-off.
Feed vs. lookup, in practice
The For You/Following model favours discovery over search. I'd want to test whether users who already know which tailor they're looking for can find them fast enough, or whether the platform needs a stronger direct-search path alongside the feed.
Whether preference selection actually improves feed relevance...
...or whether tailoring content is varied enough that broad categories like "bespoke" or "casual" don't meaningfully narrow what's useful to see.
This design was shaped by practitioner input, peer review, and supervisor critique not by real usage data. If Oko-Gho moved toward development, here's what I'd want to validate first:
Right now, discovery happens through:
What I'd Test Next
Would users actually complete measurement input during onboarding, or does it need to move closer to the point of messaging a tailor?
Early capture reduces friction later, but it's also an early ask before trust is established, I'd want usage data to confirm the trade-off pays off rather than causing early drop-off.
Feed vs. lookup, in practice
The For You/Following model favours discovery over search. I'd want to test whether users who already know which tailor they're looking for can find them fast enough, or whether the platform needs a stronger direct-search path alongside the feed.
Whether preference selection actually improves feed relevance...
...or whether tailoring content is varied enough that broad categories like "bespoke" or "casual" don't meaningfully narrow what's useful to see.
This design was shaped by practitioner input, peer review, and supervisor critique not by real usage data. If Oko-Gho moved toward development, here's what I'd want to validate first:
Right now, discovery happens through:
What I'd Test Next
Would users actually complete measurement input during onboarding, or does it need to move closer to the point of messaging a tailor?
Early capture reduces friction later, but it's also an early ask before trust is established, I'd want usage data to confirm the trade-off pays off rather than causing early drop-off.
Feed vs. lookup, in practice
The For You/Following model favours discovery over search. I'd want to test whether users who already know which tailor they're looking for can find them fast enough, or whether the platform needs a stronger direct-search path alongside the feed.
Whether preference selection actually improves feed relevance...
...or whether tailoring content is varied enough that broad categories like "bespoke" or "casual" don't meaningfully narrow what's useful to see.
This design was shaped by practitioner input, peer review, and supervisor critique not by real usage data. If Oko-Gho moved toward development, here's what I'd want to validate first:
Right now, discovery happens through:
Reflection
Reflection
Reflection
Oko-Gho was the first time I designed a full two-sided marketplace on my own with no team to pressure-test decisions with in real time, just a practicing tailor's input, peer critique, and my supervisor pushing back where it mattered. Every structural bet had to be reasoned through and defended on its own.
Oko-Gho was the first time I designed a full two-sided marketplace on my own with no team to pressure-test decisions with in real time, just a practicing tailor's input, peer critique, and my supervisor pushing back where it mattered. Every structural bet had to be reasoned through and defended on its own.
Oko-Gho was the first time I designed a full two-sided marketplace on my own with no team to pressure-test decisions with in real time, just a practicing tailor's input, peer critique, and my supervisor pushing back where it mattered. Every structural bet had to be reasoned through and defended on its own.
The biggest lesson wasn't a feature rather, it was that trust is layered, not a single mechanism. Oko-Gho asks for commitment gradually. Each step earning the next rather than assuming it. That's a pattern I'd carry into any product built around reputation or a considered decision.
The biggest lesson wasn't a feature rather, it was that trust is layered, not a single mechanism. Oko-Gho asks for commitment gradually. Each step earning the next rather than assuming it. That's a pattern I'd carry into any product built around reputation or a considered decision.
The biggest lesson wasn't a feature rather, it was that trust is layered, not a single mechanism. Oko-Gho asks for commitment gradually. Each step earning the next rather than assuming it. That's a pattern I'd carry into any product built around reputation or a considered decision.
If I revisited this today, the first gap I'd close is validation moving from directional signal to real usage data on the assumptions in the previous section. The structure held up under peer review and expert critique; the real test is whether it holds up with actual tailors and customers.
If I revisited this today, the first gap I'd close is validation moving from directional signal to real usage data on the assumptions in the previous section. The structure held up under peer review and expert critique; the real test is whether it holds up with actual tailors and customers.
If I revisited this today, the first gap I'd close is validation moving from directional signal to real usage data on the assumptions in the previous section. The structure held up under peer review and expert critique; the real test is whether it holds up with actual tailors and customers.
Currently based in Abuja, Nigeria
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