Solving the marketplace cold-start problem

A marketplace with no sellers has nothing for buyers. A marketplace with no buyers has nothing for sellers. Each side waits for the other, and an empty platform stays empty. This is the cold-start problem, and nearly every two-sided marketplace has to solve it before anything else matters.

It is tempting to treat cold start as a marketing problem: spend enough on acquisition and both sides will arrive. Sometimes that works for well-funded companies in simple categories. More often it produces a large number of sign-ups who look around, find nothing relevant and never return. The empty-room experience is expensive to buy and hard to recover from.

This article describes the approach we take to solving the cold-start problem for CastLyra, our marketplace for verified creative talent and brands, and the general principles behind it. The short version: start with supply, go narrow, and match by hand until you understand what you are automating.

Why marketplaces start empty

Network effects are the attraction of marketplaces. Every new seller makes the platform more useful for buyers, and every new buyer makes it more useful for sellers. Once they are working, they compound.

The problem is that network effects run in both directions. At small scale, each side's experience depends on a thin, unreliable other side. A brand searching for a model in a particular city finds three profiles, none suitable. A photographer who joins sees no requests for weeks. Both conclude the platform does not work, and both are right, for them, at that moment.

Two things follow. First, what matters is not the total number of users but whether a particular buyer can find a suitable seller when they need one. Second, a platform can be large overall and still empty in the corner a given user cares about. Solving cold start means making specific corners full.

Principle one: go narrow before you go wide

The most reliable way to beat cold start is to shrink the market until you can fill it. A marketplace that is thin across fifty categories and thirty countries feels empty everywhere. The same number of profiles concentrated in one category and two cities can feel busy.

For a talent marketplace, the dimensions to narrow along are:

Category. Models, creators, photographers, stylists, make-up artists, video crews. Each has different buyers, different pricing and different expectations.
Geography. Much creative work is physical: a shoot happens somewhere. Even with remote work, time zones and language matter.
Use case. E-commerce product shoots, campaign work, social content, events. A brand looking for e-commerce models needs different talent from one planning a campaign.
Buyer type. Independent brands, larger brands, agencies. They search, decide and pay differently.

Picking the first niche is a judgement, not a formula. We look for a combination where supply exists and is underserved by existing options, demand is recurring rather than one-off, the typical booking is valuable enough to justify the effort of matching, and we have some existing knowledge or relationships to start from. Oryvelon's founders have long experience in e-commerce and fashion brands, which makes product and fashion-related creative work a natural candidate for early focus. That is an input to the decision, not a guarantee.

Going narrow can feel like giving up on a global vision. It is the opposite. CastLyra is designed as a global marketplace, and every successful global marketplace we can think of started by being dense somewhere. Expanding from a full niche is far easier than filling a thin, wide one.

Principle two: start with supply

Most marketplaces are easier to start from supply. Buyers arrive with a need and a deadline. If they find nothing, they leave and rarely come back. Supply is usually more patient, especially if the platform gives it value before the first booking arrives.

That means asking: why would a talented photographer or model join an empty marketplace? "Because buyers will come eventually" is a weak answer. Stronger answers include:

A better profile than they can build elsewhere. A clean, verified portfolio page they can share with their own clients, independent of whether CastLyra sends them work.
Verification that means something. For talent who are tired of fake castings, being on a platform where businesses are checked is valuable in itself. See Verification in talent marketplaces.
Control over contact. No public phone number, no unsolicited direct messages from unknown accounts.
Help with presentation. AI-assisted profile structuring that turns a scattered set of credits and images into a clear page, without inventing anything.
Early status. Founding members of a niche can be recognised, within reason, as long as the recognition is honest.

This is sometimes called "come for the tool, stay for the network". The tool has to be genuinely useful on its own. If it only makes sense once the network exists, it will not bring supply in.

Quality before quantity

There is a trap here. It is easy to fill a marketplace with low-quality supply quickly, by importing listings, relaxing verification or recruiting anyone who will sign up. That makes the numbers look good and the product worse. A brand that finds fifty unsuitable profiles is not better off than one that finds none.

We would rather launch a niche with a smaller number of carefully verified, well-presented profiles than a large number of thin ones. Verification slows early growth. It also means that every brand who searches finds something credible, which is what brings them back.

Principle three: seed demand deliberately

Once there is a credible set of supply in a niche, demand has to be brought in on purpose. Waiting for search traffic is too slow at the start.

Ways to seed demand for a talent marketplace:

Direct outreach to the right buyers. Brands and agencies in the chosen niche, contacted individually, with a specific offer: "we have verified e-commerce models and photographers available in these cities."
Solving a real brief. Rather than asking brands to browse, asking them what they need for their next shoot and then finding it.
Existing relationships. Where the group already has trusted relationships in a sector, those can open doors, as long as the relationship is honest about what CastLyra is and nothing is shared across companies without permission. User data never crosses between Oryvelon companies; see Shared infrastructure, separate data.
Content and search visibility. Guides, rate expectations, briefs and practical information that buyers in the niche actually look for. This takes longer but compounds. See Search and AI visibility for companies.

What we avoid: paying for broad demand before supply is ready. A campaign that brings brands to a platform with no suitable talent in their city damages the brand in a way that is hard to undo.

Principle four: match by hand, then automate

In the first months, the most useful thing a marketplace team can do is make matches personally. A brand submits a brief. Someone on the team reads it, finds suitable talent, checks availability, makes the introduction and follows up after the job.

This does not scale, and that is the point. Manual matching teaches you things no dashboard can:

What a good brief looks like, and which fields actually matter.
Which talent get chosen, and why.
Where matches fail: price, availability, style, communication, trust.
How long each step takes, and which steps could be removed.
What both sides say when things go well and when they go badly.

Every one of those lessons becomes a product decision. The brief form gets the fields that predicted good matches. Search filters reflect how brands actually choose. Profile prompts ask talent for the information brands kept asking about. AI features, where they help, are shaped around the real matching process rather than an imagined one.

We treat manual matching as research with revenue attached. It is expensive per match, and we measure that cost honestly, as part of unit economics per product. But it is far cheaper than building an automated system around the wrong assumptions.

When to reduce manual work

Manual matching should shrink as patterns become clear. Signs that it is time:

The team can predict which talent a brand will choose from the brief alone.
Most briefs follow a small number of shapes.
Brands start finding and contacting talent themselves before the team intervenes.
The team is spending more time on logistics than judgement.

We do not set a fixed date. We watch for these signals and move one step at a time: first a structured brief, then suggested matches reviewed by a person, then self-serve search with support available.

Measure liquidity, not sign-ups

Sign-ups are the most misleading number in an early marketplace. They measure interest, not function. A platform can add many profiles a week and still fail every brand who searches.

The number that matters is liquidity: the likelihood that a participant who comes looking for a match finds one. For CastLyra, useful ways to express it are:

Measure	What it tells us
Share of briefs that receive suitable responses within a set time	Whether demand is being served
Share of briefs that turn into bookings	Whether matches are real
Share of verified talent with at least one enquiry in a period	Whether supply is getting value
Time from brief to booking	Whether the process is practical
Repeat booking rate among brands	Whether the platform is worth returning to

These are measured per niche, not across the whole platform. A niche with strong liquidity is ready for more investment. A niche with weak liquidity needs more supply, better matching or a narrower definition before anything else.

These measures feed CastLyra's continue, stop or scale reviews, which ask a specific question: is this niche working well enough to expand, or do we need to fix it first?

Expanding from a full niche

When one niche is working, expansion follows the same logic. Adjacent niches are easier than distant ones:

Same category, new geography. E-commerce models in a new city, using the same brief format and verification process.
Same geography, adjacent category. Photographers or stylists for the same brands already booking models.
Same buyers, new use case. Brands that booked for product shoots now looking for campaign or social content.

Each expansion is its own small cold start, but a warmer one. Existing brands can be told about new talent. Existing talent can refer colleagues. The platform's reputation travels ahead of it.

We try to expand one dimension at a time. Moving into a new category and a new country at once doubles the uncertainty and makes it hard to tell what went wrong if it does.

What never to fake

Cold start creates pressure to make a platform look busier than it is. Some marketplaces have filled early listings with invented profiles, scraped listings the owners never agreed to, or fake activity counters. We do not do any of these, and we think they are a mistake even on purely practical grounds.

Fake supply fails at the moment it matters: a buyer contacts a profile and nobody answers. Scraped listings put real people on a platform without their consent, which is exactly the harm a verified talent marketplace exists to prevent. Invented activity numbers are claims that can be checked, and a brand that catches one will not trust anything else the platform says.

The honest alternative is to be open about being early. A niche page that says, in effect, "we are starting with a carefully verified group of professionals in these two cities, and our team will help match your brief" is more attractive to the right buyer than a page pretending to be a mature marketplace. Early buyers often value the personal attention more than breadth.

Keeping early supply engaged

Supply is more patient than demand, but not endlessly so. Talent who complete verification and then hear nothing for two months drift away. Keeping them engaged is part of the job.

Practical habits that help:

Tell talent what is happening. A short, honest update on which briefs are coming in and what kinds of profiles are being chosen.
Give profile feedback. When a brand passes over a profile for a fixable reason, such as missing rates or an outdated portfolio, say so privately and constructively.
Match fairly. In manual matching, it is easy to keep sending work to the same few people. Spreading early opportunities, where quality allows, keeps more of the supply side invested.
Ask for referrals carefully. The best early talent know other good professionals. A personal invitation from a peer carries more weight than any campaign, as long as every referred profile goes through the same verification.

Common mistakes in solving cold start

Launching everywhere. Thin supply across many categories and places feels empty to everyone.
Buying demand before supply is ready. Paid traffic into an empty niche is expensive disappointment.
Lowering quality to hit numbers. Unverified or low-quality profiles make every search worse.
Treating sign-ups as success. Liquidity is what matters.
Automating too early. Building matching algorithms before understanding matches.
Charging too early or in the wrong way. A monetisation model that adds friction before value is proven can stop both sides joining. See Marketplace monetisation models.
Ignoring the supply side's patience. Talent who join and receive nothing for months will leave, and tell others.

How this fits Oryvelon's way of starting companies

The cold-start approach is a marketplace-specific version of how we start every company. We validate before we build heavily (How to validate a digital business idea). We often begin with a focused early site that states the offer clearly and collects interest (Phase-zero sites). We work through a defined protocol with written checkpoints (How we start a new company).

For a marketplace, validation means testing both sides. Will talent join and complete verification? Will brands submit real briefs? Will a manual match turn into a booking both sides are happy with? A handful of good answers to those questions in one niche is worth more than a large list of interested sign-ups across many.

A scenario: the first thirty days in a niche

Imagine CastLyra opening a niche for e-commerce product shoots in two cities.

In the first weeks, the team recruits photographers, models and stylists who already do this work, helps them build verified profiles, and explains exactly what the platform will and will not do. At the same time, they contact a small number of independent brands that regularly need product imagery and offer to help with their next shoot.

Each brief is matched by hand. After each job, both sides are asked what worked and what did not. Patterns emerge: brands care more about turnaround time than the team expected; talent want clearer information about usage rights before accepting. Both become product changes, one a brief field, the other a standard terms section.

At the thirty-day checkpoint, the question is not how many people signed up. It is how many briefs became bookings, how quickly, and whether anyone came back.

Summary

The cold-start problem is solved in small, dense pieces, not across a whole market at once. Narrow to one niche and one geography. Start with supply and give it a reason to join that does not depend on demand already being present, such as a better profile, meaningful verification and control over contact. Seed demand deliberately in that niche. Match by hand until the patterns are clear enough to automate. Measure liquidity rather than sign-ups, and expand one dimension at a time once a niche is working. It is slower than buying growth. It is also how a marketplace becomes somewhere both sides actually want to be.

Questions and answers

What is the cold-start problem in a marketplace?

It is the difficulty of launching a two-sided marketplace when neither side has a reason to join until the other side is already there. Buyers want choice, and sellers want buyers.

Should a new marketplace start with supply or demand?

Most marketplaces are easier to start from supply, because a credible set of verified sellers or talent gives buyers something to find. The right choice depends on which side is harder to attract and which side creates value for the other.

How long should a marketplace rely on manual matching?

Until the team understands which matches succeed and why well enough to design the product around it. Manual work should shrink as patterns become clear, not disappear on a fixed date.

Published by Oryvelon, a digital company builder · About the group · Our companies · More in Marketplaces & Consumer Products

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