Case Study

Case Study

Case Study

AI Onboarding Agents & AI Customer Re-Activation Agents Implementation at Apna Jobs

AI Onboarding Agents & AI Customer Re-Activation Agents Implementation at Apna Jobs

AI Onboarding Agents & AI Customer Re-Activation Agents Implementation at Apna Jobs

image showcasing enterprise to show our target audience
image showcasing enterprise to show our target audience
image showcasing enterprise to show our target audience

About

About

About

Company Overview

Company Overview

Company Overview

Apna Jobs is a leading hiring platform that connects job seekers with employers. To enhance their onboarding and customer re-activation, Apna Jobs implemented AI Calling Agents developed by RevRag.AI, an AI solutions provider for revenue teams.

Apna Jobs is a leading hiring platform that connects job seekers with employers. To enhance their onboarding and customer re-activation, Apna Jobs implemented AI Calling Agents developed by RevRag.AI, an AI solutions provider for revenue teams.

Apna Jobs is a leading hiring platform that connects job seekers with employers. To enhance their onboarding and customer re-activation, Apna Jobs implemented AI Calling Agents developed by RevRag.AI, an AI solutions provider for revenue teams.

Business Challenges

Business Challenges

Business Challenges

Before AI Implementation

Before AI Implementation

Before AI Implementation

Apna Jobs struggled with several operational inefficiencies:

  • Low Conversion Rates: Human agents achieved a conversion rate of just 4%.

  • Slow Reactivation: Reactivating a churned customer took an average of 8 minutes.

  • High Operational Costs: The manual reactivation process was costly and inefficient.

  • Onboarding Drop-offs: Inadequate engagement led to high customer attrition.

Apna Jobs struggled with several operational inefficiencies:

  • Low Conversion Rates: Human agents achieved a conversion rate of just 4%.

  • Slow Reactivation: Reactivating a churned customer took an average of 8 minutes.

  • High Operational Costs: The manual reactivation process was costly and inefficient.

  • Onboarding Drop-offs: Inadequate engagement led to high customer attrition.

Apna Jobs struggled with several operational inefficiencies:

  • Low Conversion Rates: Human agents achieved a conversion rate of just 4%.

  • Slow Reactivation: Reactivating a churned customer took an average of 8 minutes.

  • High Operational Costs: The manual reactivation process was costly and inefficient.

  • Onboarding Drop-offs: Inadequate engagement led to high customer attrition.

image showcasing enterprise to show our target audience
image showcasing enterprise to show our target audience
image showcasing enterprise to show our target audience

AI Solution by RevRag.AI

RevRag.AI deployed two AI Calling Agents:

These AI agents leveraged advanced natural language processing (NLP) to simulate human-like conversations, ensuring an enhanced customer experience.

Key Results & Impact

Improved Conversion Rates

AI agents achieved a 7% conversion rate, outperforming human agents' 4%

Faster
Customer Reactivation

The average customer reactivation time improved from 8 minutes to 5 minutes.

Cost Efficiency

4x reduction in operational costs achieved through AI-driven automation

Enhanced Productivity

Human agents were shifted from repetitive tasks to strategic activities.

Seamless
Customer Experience

AI agents delivered consistent, & personalized engagement, enhancing user satisfaction.

Proven Records

Increasing the customer top of the funnel by 3X

3

x

top of the funnel

Reactivating churned customers by 350%

350

%

Customer Reactivation

Inbound customer calls, resolving 75% automatically

75

%

Query Resolution

Early payment reminders, leading to higher collections by 110%.

110

%

Higher Collections

image showcasing enterprise to show our target audience
image showcasing enterprise to show our target audience
image showcasing enterprise to show our target audience

Future

Future

Future

Conclusion & Future Outlook

Conclusion & Future Outlook

Conclusion & Future Outlook

The integration of RevRag.AI’s AI Agents significantly enhanced Apna Jobs' efficiency, leading to:

  1. Higher conversion rates

  2. Faster customer reactivation

  3. Reduced costs

  4. Improved productivity

Moving forward, Apna Jobs plans to expand AI-driven solutions to further optimize their customer interactions and streamline operational workflows. This case study highlights the transformative scope of AI in scaling and improving enterprise performance.

The integration of RevRag.AI’s AI Agents significantly enhanced Apna Jobs' efficiency, leading to:

  1. Higher conversion rates

  2. Faster customer reactivation

  3. Reduced costs

  4. Improved productivity

Moving forward, Apna Jobs plans to expand AI-driven solutions to further optimize their customer interactions and streamline operational workflows. This case study highlights the transformative scope of AI in scaling and improving enterprise performance.

The integration of RevRag.AI’s AI Agents significantly enhanced Apna Jobs' efficiency, leading to:

  1. Higher conversion rates

  2. Faster customer reactivation

  3. Reduced costs

  4. Improved productivity

Moving forward, Apna Jobs plans to expand AI-driven solutions to further optimize their customer interactions and streamline operational workflows. This case study highlights the transformative scope of AI in scaling and improving enterprise performance.

Revolutionize Your Revenue Teams with a
Futuristic AI Workforce

Our AI Agents enable AI Calling and seamless multi-channel engagement, ensuring efficient and intelligent customer experience.

Book a Demo

Revolutionize Your Revenue Teams with a
Futuristic AI Workforce

Our AI Agents enable AI Calling and seamless multi-channel engagement, ensuring efficient and intelligent customer experience.

Book a Demo

Revolutionize Your Revenue Teams with AI Agents

Our AI Agents enable AI Calling and seamless multi-channel engagement, ensuring efficient and intelligent customer experience.

Book a Demo

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