A reinforcement learning model trained to analyze sales conversations and predict conversion probability evolution turn-by-turn. Specializes in tracking how each message impacts sales success using advanced PPO and LLM-powered metrics.
Paper: SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization
Author: Nandakishor M
Published: arXiv:2503.23303
# Requires Python 3.11 for optimal compatibility
pip install deepmost[gpu] # Recommended for LLM features
from deepmost import sales
# Automatically download and use this specific model from Hugging Face
agent = sales.Agent(
model_path="https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning/resolve/main/sales_conversion_model.zip",
llm_model="unsloth/Qwen3-4B-GGUF", # For dynamic metrics
auto_download=True # Downloads and caches the model automatically
)
conversation = [
"Hello, I'm looking for information on your new AI-powered CRM",
"You've come to the right place! Our AI CRM helps increase sales efficiency. What challenges are you facing?",
"We struggle with lead prioritization and follow-up timing",
"Excellent! Our AI automatically analyzes leads and suggests optimal follow-up times. Would you like to see a demo?",
"That sounds interesting. What's the pricing like?"
]
# Get turn-by-turn analysis
results = agent.analyze_conversation_progression(conversation, print_results=True)
Output:
Turn 1 (customer): "Hello, I'm looking for information on your new AI-pow..." -> Probability: 0.1744
Turn 2 (sales_rep): "You've come to the right place! Our AI CRM helps increa..." -> Probability: 0.3292
Turn 3 (customer): "We struggle with lead prioritization and follow-up timing" -> Probability: 0.4156
Turn 4 (sales_rep): "Excellent! Our AI automatically analyzes leads and sugge..." -> Probability: 0.3908
Turn 5 (customer): "That sounds interesting. What's the pricing like?" -> Probability: 0.5234
Final Conversion Probability: 52.34%
Final Status: 🟡 Medium
Note: When using Method 1 (URL), the model is automatically downloaded and cached locally in
~/.deepmost/models/for faster subsequent usage.
git lfs install
git clone https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning
cd sales-conversion-model-reinf-learning
from deepmost import sales
# Use the local model file
agent = sales.Agent(
model_path="./sales_conversion_model.zip", # Local file path
llm_model="unsloth/Qwen3-4B-GGUF"
)
# Same analysis as above
results = agent.analyze_conversation_progression(conversation, print_results=True)
According to the research paper, this model achieves:
# Analyze what makes conversations successful
training_conversation = [
{"speaker": "customer", "message": "I'm comparing different CRM vendors"},
{"speaker": "sales_rep", "message": "Smart approach! What's most important to you?"},
{"speaker": "customer", "message": "Integration with existing tools"},
{"speaker": "sales_rep", "message": "We integrate with 200+ tools. Which do you use?"}
]
results = agent.analyze_conversation_progression(training_conversation)
# Identify turns that increased/decreased probability
for i, result in enumerate(results[1:], 1):
change = result['probability'] - results[i-1]['probability']
trend = "📈" if change > 0 else "📉" if change < 0 else "➡️"
print(f"Turn {i+1}: {trend} {change:+.3f} change")
# Compare different response strategies
script_a = ["I need pricing", "Our Pro plan is $99/month per user"]
script_b = ["I need pricing", "What's your team size? I'll get you accurate pricing"]
results_a = agent.analyze_conversation_progression(script_a, print_results=False)
results_b = agent.analyze_conversation_progression(script_b, print_results=False)
print(f"Script A: {results_a[-1]['probability']:.2%}")
print(f"Script B: {results_b[-1]['probability']:.2%}")
# Get guidance during live conversations
current_conversation = [
{"speaker": "customer", "message": "Your solution looks expensive"},
{"speaker": "sales_rep", "message": "I understand the investment concern..."}
]
results = agent.analyze_conversation_progression(current_conversation, print_results=False)
current_metrics = results[-1]['metrics']
if current_metrics['customer_engagement'] < 0.5:
print("💡 Suggestion: Customer engagement low. Ask open-ended questions.")
elif current_metrics['sales_effectiveness'] < 0.5:
print("💡 Suggestion: Refine approach. Focus on value proposition.")
# Use open-source backend (recommended)
agent = sales.Agent(
model_path="./sales_conversion_model.zip", # This model
embedding_model="BAAI/bge-m3",
llm_model="unsloth/Qwen3-4B-GGUF",
use_gpu=True
)
# Use Azure OpenAI backend (if you have Azure credentials)
agent = sales.Agent(
model_path="./sales_model.zip", # Azure-trained variant
azure_api_key="your_key",
azure_endpoint="your_endpoint",
azure_deployment="text-embedding-3-large",
llm_model="unsloth/Qwen3-4B-GGUF"
)
# Recommended models for dynamic metrics
agent = sales.Agent(
model_path="./sales_conversion_model.zip",
llm_model="unsloth/Qwen3-4B-GGUF" # Recommended
# llm_model="unsloth/Llama-3.2-3B-Instruct-GGUF" # Alternative
# llm_model="unsloth/Llama-3.1-8B-Instruct-GGUF" # Higher quality
)
import matplotlib.pyplot as plt
scenarios = {
"Successful Sale": [
"I need a CRM", "What's your team size?", "10 people",
"Perfect! Our Pro plan fits 5-20 users", "Sounds good!"
],
"Price Objection": [
"I need a CRM", "Our premium solution is $99/month",
"Too expensive", "Let me show ROI...", "Still too much"
]
}
plt.figure(figsize=(12, 6))
for scenario_name, conversation in scenarios.items():
results = agent.analyze_conversation_progression(conversation, print_results=False)
probabilities = [r['probability'] for r in results]
turns = list(range(1, len(probabilities) + 1))
plt.plot(turns, probabilities, marker='o', linewidth=2, label=scenario_name)
plt.xlabel('Conversation Turn')
plt.ylabel('Conversion Probability')
plt.title('Conversion Probability Evolution by Scenario')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()
# For NVIDIA CUDA
CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir
# For Apple Metal (M1/M2/M3)
CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python --upgrade --force-reinstall --no-cache-dir
# Install DeepMost
pip install deepmost[gpu]
import torch
from deepmost import sales
print(f"CUDA Available: {torch.cuda.is_available()}")
info = sales.get_system_info()
print(f"Supported Backends: {info['supported_backends']}")
Each turn analysis provides detailed metrics:
{
'turn': 1, # Turn number (1-indexed)
'speaker': 'customer', # Who spoke
'message': 'I need a CRM', # Actual message
'probability': 0.3456, # Conversion probability
'status': '🟠 Low', # Visual indicator
'metrics': {
'customer_engagement': 0.6, # LLM-derived score (0-1)
'sales_effectiveness': 0.4, # LLM-derived score (0-1)
'conversation_length': 3.0, # Number of messages
'progress': 0.15 # Conversation progress
}
}
This model implements the SalesRLAgent approach described in the research paper. Key innovations include:
The model identifies key conversation dynamics:
This repository contains:
sales_conversion_model.zip - Open-source trained model (recommended)sales_model.zip - Azure OpenAI trained variant# Initialize with this specific model
agent = sales.Agent(
model_path="https://huggingface.co/DeepMostInnovations/sales-conversion-model-reinf-learning/resolve/main/sales_conversion_model.zip",
llm_model="unsloth/Qwen3-4B-GGUF"
)
# Integrate into your sales tools
def analyze_sales_call(conversation_messages):
results = agent.analyze_conversation_progression(conversation_messages)
return {
'final_probability': results[-1]['probability'],
'trend': 'improving' if results[-1]['probability'] > results[0]['probability'] else 'declining',
'recommendations': generate_recommendations(results)
}
To train your own variant:
git clone https://huggingface.co/datasets/DeepMostInnovations/saas-sales-conversations
python train.py --dataset your_data.csv --model_path custom_model --timesteps 200000
MIT License - Feel free to use and modify for your needs.
If you use this model in your research or applications, please cite:
@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}
}
Focus on what matters: understanding how each conversation turn impacts your sales success. 🎯
Made with ❤️ by DeepMost Innovations