Leboncoin

Leboncoin Emploi

When growth and product value are deeply intrecated.

Leboncoin Emploi connects job seekers and recruiters through one of France’s largest online marketplaces. As the platform prepared to monetize its recruitment services, the challenge was to build a sufficiently large candidate database while delivering genuine value to employers. I worked on candidate acquisition and B2B experiences before uncovering a critical issue: a mechanism designed to accelerate growth was also introducing fraudulent profiles into the database. The project evolved from optimizing conversion to questioning the long-term sustainability of the product’s business model.

Live Site

Client

Leboncoin

Year

2019 - 2021

Role

UX Designer

Scope

UX Research · Product Discovery · Conversion Optimization · Quantitative Analysis · UX/UI Design

Location

Paris, France

Project Status

Acquisition initiatives launched · Risk mitigation recommendations not implemented

DESIGN PROBLEM

Job seeking ad creation. This page is one of the entry points to create a candidate profile.

How to grow without compromising value?

Leboncoin Emploi needed to reach a critical mass of 150,000 candidate profiles to support the monetization of its recruitment services.

However, the existing candidate registration funnel suffered from significant abandonment, exceeding 50%.

At the same time, the platform allowed individuals to publish job-seeking ads without creating a candidate profile.

This alternative entry point presented an acquisition opportunity: converting job-seeking ads into candidate profiles could accelerate database growth.

RESULTS

150K+ profiles acquired

Leboncoin Emploi is monetized

The critical mass required to support the monetization of Leboncoin Emploi was reached through successive acquisition initiatives.

>50% Misuse detected in sampled ads

A problem that no one could ignore

A manual review of approximately 400 job-seeking ads revealed that more than half were actually posted by employers attempting to bypass paid recruitment services.

24 months Business risk modeled

Imperfect model, real threat

I developed a projection showing how the continued creation of illegitimate candidate profiles could progressively undermine the database’s commercial value.

USE CASE

Growth is not the same as value.

What happens when a growth mechanism captures users who were never candidates in the first place? From conversion optimization to business risk analysis.

Challenge

Build a monetizable recruitment platform by accelerating candidate acquisition.

Leboncoin was expanding its employment vertical and needed to attract enough candidates to make its recruitment services valuable to professional customers.

The initial priority was straightforward: reduce friction in candidate registration, improve conversion and increase the number of available profiles.

However, candidate volume alone was not sufficient. Recruiters needed access to a database of people genuinely looking for work.

Problem

A conversion mechanism was creating profiles without guaranteeing their relevance.

Following the introduction of automatic candidate profile creation from job-seeking ads, I identified a potential vulnerability.

The platform allowed employers to publish job-seeking ads instead of using the appropriate paid recruitment services.

When these ads generated candidate profiles by default, fraudulent advertisers could enter the candidate database.

The same mechanism that increased acquisition volume could therefore compromise the quality of the product being sold to recruiters.

Approach

01 — Reducing friction in candidate acquisition

Making candidate registration easier

My initial work focused on improving the existing candidate registration funnel.

With abandonment exceeding 50%, the challenge was to identify unnecessary friction and make profile creation more accessible.

I combined qualitative and quantitative methods, including user interviews, usability testing, conversion analysis and A/B testing, to investigate the experience and identify opportunities for improvement.

These initiatives contributed to the acquisition strategy that enabled Leboncoin Emploi to reach its target of 150,000 candidate profiles.

02 — Identifying an unexpected behavior

The people publishing job-seeking ads were not always looking for jobs.

While conducting research on the B2B recruitment experience, I encountered an unexpected pattern.

Recruiters using Leboncoin’s official recruitment services were difficult to recruit for user testing.

Yet some employers were actively using the platform to recruit candidates through an alternative channel: publishing job-seeking ads.

Several openly acknowledged using this workaround to avoid paying for professional recruitment services.

This observation became particularly concerning when I discovered that publishing a job-seeking ad could automatically generate a candidate profile unless the user opted out.

An acquisition opportunity had become a potential data-quality problem.

03 — Measuring what analytics could not

Turning qualitative suspicion into quantitative evidence

The data team could not reliably identify fraudulent ads through existing tracking. Rather than leaving the problem unquantified, I designed an independent research approach.

From a population of approximately 4,800 job-seeking ads, I calculated a representative sample of roughly 400 listings, targeting a 95% confidence level and a 5% margin of error.

I manually reviewed and categorized the ads according to their actual intent:

Legitimate: an individual genuinely looking for employment.
Misused: an employer seeking to recruit through the job-seeking category.

The analysis revealed that more than half of the sampled ads were being used by employers rather than job seekers. This provided evidence of a systemic problem affecting the acquisition channel.

04 — Modeling the long-term consequences

Identifying the problem was only the first step.

I wanted to understand its potential consequences for the commercial viability of the candidate database.
Using the observed proportion of misused ads, the existing candidate database, and average monthly acquisition volumes, I built a scenario projecting the accumulation of illegitimate profiles over 24 months.

The model used deliberately simplified assumptions, including an initially clean database and a stable proportion of misuse over time.

I defined a critical threshold: if more than 50% of candidate profiles were illegitimate, a recruiter would be more likely to access an irrelevant profile than a genuine candidate. Under the modeled conditions, the database risked reaching this threshold within approximately two years.

The exercise transformed a data-quality concern into a tangible business scenario.

05 — Recommending a low-cost intervention

I proposed two complementary measures.

Make candidate profile creation an explicit choice.
Replace automatic profile creation with an opt-in mechanism, allowing users to choose whether they wanted to join the candidate database.
Clean up the existing data.
Review the profiles generated through the problematic acquisition mechanism over the previous three months to limit the accumulation of irrelevant records.

These recommendations aimed to preserve the acquisition opportunity while protecting the integrity of the database sold to professional recruiters.

The proposed changes were deliberately limited in scope and designed to avoid a major redevelopment of the existing funnel.

Approach

01 — Reducing friction in candidate acquisition

Making candidate registration easier

My initial work focused on improving the existing candidate registration funnel.

With abandonment exceeding 50%, the challenge was to identify unnecessary friction and make profile creation more accessible.

These initiatives contributed to the acquisition strategy that enabled Leboncoin Emploi to reach its target of 150,000 candidate profiles.

02 — Identifying an unexpected behavior

The people publishing job-seeking ads were not always looking for jobs.

While conducting research on the B2B recruitment experience, I encountered an unexpected pattern.

Recruiters using Leboncoin’s official recruitment services were difficult to recruit for user testing.

Yet some employers were actively using the platform to recruit candidates through an alternative channel: publishing job-seeking ads.

Several openly acknowledged using this workaround to avoid paying for professional recruitment services.

This observation became particularly concerning when I discovered that publishing a job-seeking ad could automatically generate a candidate profile unless the user opted out.

An acquisition opportunity had become a potential data-quality problem.

03 — Measuring what analytics could not

Turning qualitative suspicion into quantitative evidence

The data team could not reliably identify fraudulent ads through existing tracking. Rather than leaving the problem unquantified, I designed an independent research approach.

From a population of approximately 4,800 job-seeking ads, I calculated a representative sample of roughly 400 listings, targeting a 95% confidence level and a 5% margin of error.

I manually reviewed and categorized the ads according to their actual intent:

Legitimate: an individual genuinely looking for employment.
Misused: an employer seeking to recruit through the job-seeking category.

The analysis revealed that more than half of the sampled ads were being used by employers rather than job seekers. This provided evidence of a systemic problem affecting the acquisition channel.

04 — Modeling the long-term consequences

Identifying the problem was only the first step.

The model used deliberately simplified assumptions, including an initially clean database and a stable proportion of misuse over time.

The exercise transformed a data-quality concern into a tangible business scenario.

05 — Recommending a low-cost intervention

I proposed two complementary measures.

Make candidate profile creation an explicit choice.
Replace automatic profile creation with an opt-in mechanism, allowing users to choose whether they wanted to join the candidate database.
Clean up the existing data.
Review the profiles generated through the problematic acquisition mechanism over the previous three months to limit the accumulation of irrelevant records.

These recommendations aimed to preserve the acquisition opportunity while protecting the integrity of the database sold to professional recruiters.

The proposed changes were deliberately limited in scope and designed to avoid a major redevelopment of the existing funnel.

OUTCOME & LEARNINGS

The target was reached. The underlying risk remained.

The acquisition initiatives helped Leboncoin Emploi reach the critical mass required for its monetization strategy. However, the recommendations I made to address the newly identified data-quality risk were not implemented. The decision reflected a tension between two competing priorities: maximizing acquisition volume and protecting the long-term relevance of the candidate database. Although I could not influence the final decision, the investigation produced three concrete contributions.

A previously unmeasured issue became quantifiable.

Manual sampling provided an estimate of the scale of misuse within a key acquisition channel.

A UX concern became a business discussion.

The projection translated data-quality degradation into a scenario directly connected to the value delivered to paying customers.

A proportionate intervention was proposed.

The recommendations addressed both future profile creation and the existing data affected by the mechanism.

REFLEXION

KEY TAKEAWAY

Optimizing conversion is meaningless if the resulting growth undermines the value customers are paying for.

This project changed how I approach product metrics. A growing database can look like success. But if the data does not represent genuine users, acquisition becomes a liability rather than an asset. As a product designer, my responsibility is not only to improve the experience or optimize a funnel. It is also to question whether the behavior we encourage creates sustainable value for users and the business.