Success Stories

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

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.

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.

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+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

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

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

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.”