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How did they achieve a 100% growth rate?

Eduployment's Customer Journey Breakthrough.

Industry: Internet Marketplaces

Use case: user churn, customer journey

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BEFORE

  • Limited Communication: Only AI-rejected candidates were notified, with no context for rejection.
  • High Churn Rates: Many AI-rejected candidates uninstalled the app out of frustration.
  • No Re-Engagement Tactics: Applicants who were rejected were not encouraged to explore other job opportunities matching their skills
  • Lower Churn Rates: 28% of AI-rejected candidates re-applied to jobs they qualified for instead of uninstalling the app.
  • Improved Retention: Candidates felt more engaged due to transparency about their status
  • Increased Engagement: AI-rejected users were directed to relevant job opportunities, reducing their frustration and increasing re-applications.
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AFTER

Table of Contents

Contact Sales Team

Eduployment is a job marketplace platform designed to connect job seekers with potential employers. To streamline the hiring process, the platform implemented an AI-powered screening tool that ranks candidates based on how well their profiles match the job requirements. However, the existing system only notified candidates if they were auto-rejected by the AI tool. Other candidates, such as top applicants or those under consideration, were not receiving any updates regarding their applications. This lack of communication led to candidate dissatisfaction, diminished trust, and app uninstalls, negatively impacting the platform’s ability to increase retention.

Email Campaign Performance- Sent Emails, Open Rates, Click-Through Rates, and Conversion Rates by Applicant Category

Emails sent across different categories of applicants with this respective interaction metrics

The Challenge

A key problem was the lack of transparency for candidates throughout their job application journey. Candidates expressed frustration about:

  • AI Rejections Without Context: Many users were left wondering why they were rejected.
    No Communication for
  • Top or Pending Applications: Even candidates with high scores or awaiting employer action received no updates.
  • Unmet Expectations: Poor communication affected user trust and engagement, leading to churn and lower customer retention on the app.

These issues impacted the platform’s ability to keep candidates engaged, reducing re-applications from AI-rejected users and increasing the chances of app uninstalls. The absence of a customer-centric approach was limiting the platform’s potential to increase retention rates.

The Solution

To address these challenges, we collaborated with the client’s tech team to modify the system and introduce AI-driven communication that provided meaningful updates to candidates at each stage of the process.

  • Customized Event Triggers:

We modified the platform’s existing event infrastructure to trigger emails based on three distinct application statuses : 

  • Top Applicant: Profiles matching more than 50% of the job requirements.
  • Matched Applicant: Profiles with a matching score between 15-50%.
  • AI Rejected Applicant: Profiles with a score below 15%.


Each status included personalized messaging with the candidate’s score and the reason for their selection or rejection.

  • Enhanced Messaging and Personalization:
  • We used customer-centric messaging to build trust and improve transparency, providing candidates with reasons for AI rejection or selection.
  • AI-rejected candidates received a list of alternative job postings with higher matching scores (above 15%) to encourage re-applications.
  • Channel Selection Strategy:
  • We prioritized email as the primary communication channel. Email offered both cost-efficiency and the ability to convey personalized content.
  • Push notifications were considered, but due to size limitations, they were deemed insufficient for this campaign. WhatsApp, though effective, was excluded due to high costs for moderate-priority updates.
  • Campaign Execution:

Our team prepared detailed messaging briefs for the content team to align the tone and structure with candidate expectations.

We launched the email campaigns using MoEngage, ensuring the right messages reached candidates based on their profile scores and job statuses.

Conclusion

This case study underscores the importance of customer retention and transparent communication in AI-powered hiring. By addressing user frustrations, we helped the client significantly improve retention. Personalized and transparent communication throughout the candidate journey positively influenced behavior, increased retention, and prevented churn. Besides this, AI-triggered updates and personalized messaging created a seamless experience, leading to higher re-applications and lower uninstall rates. 

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Propel specializes in customer lifecycle marketing, helping consumer brands boost retention & reduce churn with tailored strategies and our AI-augmented platform, AURA.

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