📈 Real-Time Sales Conversion Prediction & AI Coaching
Turn-by-Turn Reinforcement Learning Analysis for Enterprise Sales Conversations
🤗 Model on Hugging Face • 📄 Research Paper (arXiv:2503.23303) • 📦 DeepMost Library
⚡ Load Pre-built Scenario
Turn-by-Turn Progression Audit
Turn | Speaker | Message | Probability | Delta | Status | Engagement | Effectiveness |
|---|---|---|---|---|---|---|---|
Run analysis to generate coaching insights.
Compare Two Sales Pitch Strategies
Test how different responses to the same customer concern impact conversion probability and rep effectiveness.
Click Compare to see A/B test results.
🎯 About SalesRLAgent
SalesRLAgent is a reinforcement learning framework trained with Proximal Policy Optimization (PPO) specifically designed for real-time sales conversation tracking and optimization.
Key Innovations:
- Turn-by-Turn Dynamic Tracking: Evaluates how individual utterances incrementally advance or hinder deal velocity.
- Multi-Modal State Representation: Combines semantic embeddings (
BAAI/bge-m3) with dynamic behavioral metrics. - Ultra-Fast Real-Time Inference: Achieves 85ms inference speed compared to 3,450ms for conventional LLM prompting, making it production-viable for live call guidance.
- High Accuracy: Demonstrated 96.7% conversion prediction accuracy and a 43.2% lift in conversion rates when deployed in rep-assistance settings.
BibTeX Citation:
@article{nandakishor2025salesrlagent,
title={SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization},
author={Nandakishor, M},
journal={arXiv preprint arXiv:2503.23303},
year={2025},
url={https://arxiv.org/abs/2503.23303}
}