hemanjan/csr-agent-nudge-leadscore-lora

📞 CSR Agent LoRA Adapter

🧠 Overview

This model is a fine-tuned LoRA adapter designed to assist Customer Service Representatives (CSR) in real-time during customer calls.
It provides intelligent nudges for upsell/cross-sell opportunities and generates a Lead Score (1-10) to help agents gauge customer purchase intent.

Key Features

• Real-time Nudges: Suggests upsell and cross-sell opportunities based on conversation context
• Lead Scoring: Generates a score from 1-10 indicating customer's likelihood to proceed with a sale
• Agent Assistance: Helps CSR agents make data-driven decisions during live calls

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🧩 Model Details

• Developed by: Hemanjan Reddy Pundla & Lalith kumar Gali
• Model Type: Text Generation (Causal LM)
• Base Model: meta-llama/Meta-Llama-3-8B-Instruct
• Language(s): English
• Framework: 🤗 Transformers + PEFT
• License: Llama 3 Community License
• Fine-tuned on: Custom dataset of customer service conversations

🏷️ Capabilities

The model provides real-time assistance to CSR agents:

📈 Lead Scoring

Score	Meaning
1-3	Low intent — Customer is browsing or has concerns
4-6	Medium intent — Customer is interested but needs more info
7-9	High intent — Customer is ready to proceed
10	Very high intent — Customer is eager to close the deal

💰 Upsell & Cross-sell Nudges

• Product Recommendations: Suggests complementary products based on customer needs
• Upgrade Opportunities: Identifies when to offer premium options
• Bundle Suggestions: Recommends service bundles for better value
• Timing Cues: Indicates optimal moments to introduce offers

🎯 Supported Scenarios

• Sales Calls: New customer acquisition, product inquiries
• Service Calls: Support requests with upsell potential
• Retention Calls: Win-back offers, loyalty upgrades
• Follow-up Calls: Lead nurturing, closing pending deals

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💡 Use Cases

Use Case	How It Helps
Live Call Assistance	Display real-time nudges on agent's screen during calls
Lead Prioritization	Score leads to help agents focus on high-intent customers
Revenue Optimization	Identify upsell/cross-sell opportunities in real-time
Agent Training	Help new agents learn when and how to make offers
Performance Analytics	Track conversion rates based on nudge acceptance

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🧰 Intended Uses

✅ Direct Use

• Displaying real-time nudges on CSR agent screens during customer calls.
• Generating lead scores (1-10) to indicate customer purchase intent.
• Suggesting upsell and cross-sell opportunities based on conversation context.

⚙️ Downstream Use

Can be integrated in:

• Call center agent desktop applications
• CRM platforms (Salesforce, HubSpot, etc.)
• Real-time speech-to-text pipelines
• Sales enablement tools
• Agent performance dashboards

🚫 Out-of-Scope Use

• Do not use this model for fully automated sales — it is designed for agent assistance only.
• Not suitable for multilingual support unless fine-tuned further.
• Lead scores are recommendations — final decisions should be made by human agents.

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⚖️ Bias, Risks, and Limitations

• The dataset is domain-specific (customer service context), so performance might degrade outside that domain.
• Potential bias from training data — ensure to validate responses before production use.
• English-only model.
• Responses should be reviewed by humans for critical customer interactions.

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⚠️ Note: This is a LoRA adapter and requires the base model meta-llama/Meta-Llama-3-8B-Instruct to run.

🚀 Getting Started

You can load the model in Python with:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "hemanjan/csr-agent-nudge-leadscore-lora"
base_model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

# Load base model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype="auto",
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, model_id)

# Example: Generate nudges and lead score during a call
conversation = """
Customer: Hi, I'm calling about my internet service. It's been slow lately.
Agent: I'm sorry to hear that. Let me check your account. I see you're on our Basic plan.
Customer: Yeah, I've been thinking about upgrading but I'm not sure if it's worth it.
"""

messages = [
    {"role": "system", "content": "You are a CSR assistant. Analyze the conversation and provide: 1) A lead score (1-10), 2) Upsell/cross-sell nudges for the agent."},
    {"role": "user", "content": conversation}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=256)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

# Example output:
# Lead Score: 7/10 (High intent - customer mentioned considering upgrade)
# Nudges:
# - Offer Premium plan with speed comparison
# - Mention current promotion: 20% off first 3 months
# - Suggest bundle with home security for added value

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📦 Framework Versions

• PEFT: 0.18.0
• Transformers: 4.x
• PyTorch: 2.x