# Driving 32% Higher Job Application Conversion for Serbia’s Leading Employment Platform

> Source: https://www.recombee.com/case-studies/poslovi-infostud

> For the complete site index, see [llms.txt](/llms.txt). To integrate Recombee, see the developer documentation at https://docs.recombee.com/llms.txt.

# [Success Stories](https://www.recombee.com/case-studies)

# Driving 32% Higher Job Application Conversion for Serbia’s Leading Employment Platform

Poslovi Infostud

[Job Boards, HR, Networking](https://www.recombee.com/domains/jobs-boards-hr-networking)

[Content Recommendations](https://www.recombee.com/content-recommendations)

![](https://www.recombee.com/img/case-studies/poslovi-infostud.png)

**Poslovi Infostud** is the leading online job search and recruitment platform in Serbia, helping job seekers discover relevant roles and enabling employers to reach qualified candidates at scale. With over 4 million monthly visits from both anonymous and registered users, delivering relevant recommendations across every stage of the job-seeking journey is central to the platform’s core mission.

In the first week of A/B testing against the existing recommender, **Recombee** exceeded the predefined +20% success threshold across all measured metrics, leading to the decision to fully migrate.

+32%

Application conversion  
on job listings

+200%

Recommendation-to-job engagement  
among non-logged-in users

+40.3%

Thank-you page  
recommendation conversion

### Situation

* Existing in-house recommender was functional but difficult to scale without significant engineering investment
* Recommendation development relied on rebuilding individual scenarios internally
* Recommendations needed to work for both logged-in users with interaction history and anonymous visitors with limited behavioral data
* High-intent locations, such as job detail pages and post-application thank-you pages, required relevant recommendations to increase engagement and continue the journey

### Objectives

* Support multiple recommendation scenarios, including item-to-item, user-to-item, and item-to-user recommendations
* Improve scalability and handle large traffic volumes across multiple website placements and communication channels
* Reduce engineering dependency with a self-serve admin interface for configuring recommendation logic, previewing results, monitoring performance, and managing scenarios
* Deliver relevant personalization for both logged-in and non-logged-in users

### Solution

Recombee supports personalized job discovery across Infostud’s employment platform. Recommendations adapt to different user contexts, supporting browsing, job search, and application journeys with relevant suggestions in real time.

**Full-Funnel Personalization**

A flexible recommendation setup supports multiple scenarios, including user-to-job, job-to-job, and candidate-to-job recommendations. This enables relevant discovery across key touchpoints, from browsing job listings to high-intent application stages.

**Cold-start & Anonymous Optimization**

The solution delivers relevant recommendations for both logged-in users and anonymous visitors by leveraging real-time behavioral signals. Even with limited user history, recommendations adapt to available context to surface relevant job opportunities.

**Dynamic Profile Enrichment**

Personalized experiences for returning users are powered by interaction history and profile data. By analyzing user behavior and preferences, recommendations align with individual interests and improve job discovery over time.

**Iterative Conversion Strategy**

A scalable recommendation infrastructure enables continuous optimization through testing, performance monitoring, and data-driven improvements. This supports ongoing refinement of recommendation logic to increase engagement and application conversion rates.

### Benefits & Results

* **+32%** Application conversion on job listings
* **+200%** Recommendation-to-job engagement among non-logged-in users
* **+40.3%** Thank-you page recommendation conversion
* Improved job discovery across both anonymous and logged-in user journeys
* Reduced engineering dependency with self-serve personalization management
* Faster iteration and optimization across multiple recommendation scenarios

### Recombee’s Solution in Action

![poslovi-infostud](https://www.recombee.com/img/case-studies/scenarios/poslovi-infostud-1.png)

### Job-Page Continuation

Similar Jobs

A key discovery touchpoint on individual job pages, appearing at the moment when candidates decide whether to continue exploring or leave the platform.

Recommendations are generated using contextual signals such as the viewed role, job attributes, and candidate interactions to surface highly relevant alternative opportunities.

By keeping candidates engaged beyond the initial job view, this scenario helps extend the discovery journey and increases the chance of finding the right match.

**Key Benefits**

* Reduce candidate drop-off on individual job pages
* Increase exploration through contextually relevant role alternatives
* Extend browsing sessions and improve overall engagement

![poslovi-infostud](https://www.recombee.com/img/case-studies/scenarios/poslovi-infostud-2.png)

### Feed & Thank You Page

Jobs for You

A personalized discovery surface across the candidate feed and post-application thank-you page, helping users continue exploring relevant opportunities throughout their journey.

Recommendations combine behavioral signals, previous interactions, and fresh job activity to deliver the most relevant roles based on each candidate’s evolving interests.

By maintaining engagement after key actions such as applying for a role, this scenario encourages continued discovery and increases opportunities for successful matches.

**Key Benefits**

* Drive continued engagement after application completion
* Improve job discovery through personalized recommendations
* Increase return visits by keeping candidates connected to relevant opportunities

![poslovi-infostud](https://www.recombee.com/img/case-studies/scenarios/poslovi-infostud-3.png)

### Candidate Profile Matching

Candidates to Job

A reverse recommendation scenario designed to help recruiters and platforms identify the most relevant candidates for open roles.

Recombee analyzes job requirements, candidate profiles, and behavioral signals to match suitable candidates with relevant opportunities beyond traditional search-based workflows.

These recommendations can be activated across multiple channels, including notifications, pop-ups, and other platform touchpoints, enabling proactive candidate discovery.

**Key Benefits**

* Improve candidate-job matching accuracy
* Reduce manual search effort for recruiters
* Enable proactive talent discovery across multiple channels

“Within the first week of A/B testing, Recombee exceeded our success criteria across all key metrics and gave us the confidence to migrate from our internal recommender.”

![Milan Tokic](https://www.recombee.com/img/case-studies/testimonials/poslovi-infostud.png)

**Milan Tokic**

AI Engineer

![Poslovi Infostud](https://www.recombee.com/img/logos/poslovi-infostud.svg)

### About Poslovi Infostud

**Poslovi Infostud** is the leading online job search and recruitment platform in Serbia, helping job seekers discover relevant roles and enabling employers to reach qualified candidates at scale. With over 4 million monthly visits from both anonymous and registered users, delivering relevant recommendations across every stage of the job-seeking journey is central to the platform’s core mission.

[Visit Poslovi Infostud](https://poslovi.infostud.com)

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