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Using GPT for Sales Automation: A Step by Step Guide

  • Writer: Jag S.
    Jag S.
  • Feb 3
  • 4 min read

Updated: Mar 5


Fine tuning LLM
Fine tuning LLM


Sales automation with the help of AI is changing the ways and means of businesses to target, nurture, and close leads. With the help of customer Large Language Models (LLM), companies can perform routine sales activities like lead scoring, qualification, and follow-up communication. However, to achieve optimal results, a general AI model may require tuning to match your specific sales approaches, language, and customer interactions.


In this guide, you will learn how refining a GPT model enhances sales automation, how to create your own model training process, and what to do if you don’t need to fine-tune the model fully.


Why Choose GPT for Sales Automation?


ChatGPT is a very useful tool in the context of conversational AI, but when it comes to sales, corporations require a model that:


  • Knows the jargon that applies to a particular industry.

  • Responds in a manner that is consistent with the brand’s sales culture.

  • Understands and avoids customer objections while leading the customer through the sales process.

  • Writes personal emails that sound natural and encourage the recipient to take action.


Fine-tuning helps the AI model capture previous sales interactions, thus performing better in converting leads to customers.


Step 1: Determine What You Want Your Custom LLM to Do


Before fine-tuning, clearly define the scope of your AI sales assistant. Ask yourself:


  • Should it qualify leads based on certain criteria?

  • How should it respond to objections?

  • Does it have to create follow-up emails or sales emails?

  • Does it need to work with CRM systems?


By answering these questions, you will know what data to gather for the training.


Step 2: Get and Organize the Data for Training


To perform fine-tuning, the data must be in a specific format in JSONL, which is a format of conversational data between a salesperson (or AI) and a customer.


Example Training Data Formats


1. Lead Qualification Conversation

{"messages": [
  {"role": "system", "content": "The role of the AI sales assistant is to qualify leads according to business objectives."},
  {"role": "user", "content": "I am interested in your AI automation solution."},
  {"role": "assistant", "content": "Great! Can you give me more information about your business and what problems you are facing with respect to sales?"}
]}

2. Objection Handling

{"messages": [
  {"role": "system", "content": "You are a very convincing AI sales assistant that can overcome any objection."},
  {"role": "user", "content": "I think AI is too costly for our small business."},
  {"role": "assistant", "content": "I understand! But our AI solutions are cost-efficient because they replace some of the manual work, which in turn saves time and money. Would you like to see a cost-benefit analysis?"}
]}

3. Follow-up Email Generation

{"messages": [
  {"role": "system", "content": "You write personalized follow-up emails for sales conversations."},
  {"role": "user", "content": "The lead was interested but has not replied."},
  {"role": "assistant", "content": "Subject: Still Interested in AI Automation?\n\nHey [First Name],\n\nJust following up on our last conversation. Our AI solutions can help you increase your sales and marketing without affecting your current workflow. Let’s schedule a short telephone conversation to see how we can assist!\n\nBest,\n[Your Name]"}
]}

You need to provide many real sales conversations to train the model properly.


Step 3: Get Ready for the Data for Fine-Tuning


Once your training data is ready, put it in a single JSONL file.


Validate and Prepare Your Data


Install OpenAI’s tools:

pip install openai

Then, validate the dataset:

openai tools fine_tunes.prepare_data -f sales_data.jsonl

This checks for formatting errors that can occur before fine-tuning.


Step 4: Train the Model


To fine-tune, simply use OpenAI’s API:

openai api fine_tunes.create -t "sales_data_prepared.jsonl" -m "gpt-4"

The command line options are:


  • -t: Points to the training file.

  • -m: Determines the base model, for instance, gpt-3.5-turbo, gpt-4.


Fine-tuning can take time depending on the size of the dataset.


Step 5: Test and Assess the Fine-Tuned Model


Once fine-tuning is done, test your AI on real sales conversations.


API Call Example

import openai

response = openai.ChatCompletion.create(
  model="your-fine-tuned-model",
  messages=[{"role": "user", "content": "Can you suggest an AI tool for sales automation?"}]
)

print(response["choices"][0]["message"]["content"])

Evaluation Metrics:


  • Does the AI qualify leads correctly?

  • How successful is objection handling?

  • Are the email responses persuasive?

  • Does it follow the brand tone?


If the performance is poor, then collect more data and train the model again.


Step 6: Deploy and Integrate


Once fine-tuned, incorporate the AI agent into your sales flow.


Deployment Options:


  1. Chatbot Integration – Connection to WhatsApp, Slack, or website chat.

  2. Email Automation – It can be used to compose and send follow-ups.

  3. CRM Integration – Responses can be automated in HubSpot, Salesforce, etc.


Sample real-time API request:

openai api completions.create -m "your-fine-tuned-model" -p "Write a follow-up email for a lead who has shown interest in AI sales automation."

Step 7: Monitor and Improve


Fine-tuning is not a one-time job. It is recommended to update the sales conversations frequently, check the performance, and update the AI agent periodically.


Step 8: If You Don’t Want to Do Full Custom GPT


If you want to avoid the expense and/or complexity of full fine-tuning, OpenAI’s Custom GPT feature enables you to:


  • Provide sales scripts and response templates.

  • Change the system prompts to influence the behavior of the AI.

  • Use structured prompt engineering instead of training data.


This approach is faster and more economical for many companies.


Conclusion


Using a GPT model for sales automation can greatly enhance lead engagement, increase conversion rates, and improve the efficiency of sales teams. Whether you choose to fine-tune a model or utilize OpenAI’s Custom GPT features, leveraging AI in your sales automation can lead to effectiveness, scalability, and personalization in your outreach activities.


Are you all set to enhance your sales process with the help of AI? Fine-tune now or explore OpenAI’s Custom GPT features and see what is best for your business.

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