Preparing Data – Collecting and Structuring Past Customer Replies #S11E2 Podcast Por arte de portada

Preparing Data – Collecting and Structuring Past Customer Replies #S11E2

Preparing Data – Collecting and Structuring Past Customer Replies #S11E2

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 This is season eleven, episode two. In this episode, we will focus on how to collect and structure past customer replies to train a custom GPT. You will learn how to gather historical email responses, identify common patterns, clean the data, and organize it into a structured format that an AI model can use. By the end of this episode, you will have a clear understanding of how to prepare your customer support data for automation. If you want your custom GPT to generate accurate and helpful responses, it needs a strong foundation of real-world data. AI learns best when it has examples to reference. If your business has been handling customer inquiries for a while, you already have valuable training material in the form of emails, chat logs, and past responses. Instead of starting from scratch, you can use this data to make your AI assistant more effective from the beginning. Let’s go through the step-by-step process of preparing this data for training a custom GPT. Step One: Collecting Past Customer Replies The first step is to gather all existing customer interactions. These could be: Emails from customers and your repliesLive chat logs from customer support systemsFrequently asked questions and answers from your websiteInternal documents with product explanations or troubleshooting guides To start, go through your email inbox and export past customer conversations. If you use a customer support system like Zendesk, Intercom, or HubSpot, download chat logs or support ticket responses. Look for conversations where the same types of questions appear repeatedly. Step Two: Identifying Common Questions and Patterns Once you have gathered the data, it is time to analyze and categorize the most frequent types of customer inquiries. Some common categories include: Product specifications – Customers asking for size, weight, features, compatibility, or technical details.Pricing and quotations – Requests for price estimates, bulk discounts, or payment terms.Product recommendations – Customers asking which product is best for a specific use case.Shipping and policies – Questions about delivery times, returns, and refunds.Troubleshooting and support – Requests for help with installation, setup, or fixing issues. Go through at least fifty past customer inquiries and group them into categories. You will start to see patterns in the way customers ask questions and how your business responds. This will help you structure your AI training data more effectively. Step Three: Cleaning and Standardizing Your Responses AI performs best when training data is clean and consistent. To make your responses useful for training, follow these steps: Remove any sensitive customer information like names, emails, or order numbers.Rephrase repetitive responses to maintain clarity. AI does not need identical responses copied multiple times.Ensure uniform tone and style so that all AI-generated replies feel professional and consistent with your brand.Simplify language where needed. AI should generate responses that are easy for customers to understand. For example, if your previous email replies vary in tone, like: One email says: "Thank you for reaching out! Our product has a battery life of ten hours and charges in ninety minutes."Another email says: "The battery lasts ten hours, and charging time is one and a half hours." Standardizing responses ensures that AI learns a clear and professional way to reply. You might rewrite both responses into one consistent format: Final training response: "Our product features a battery life of ten hours and fully charges in ninety minutes." Step Four: Structuring the Data for AI Training Once your responses are cleaned and categorized, they need to be formatted in a structured way that AI can understand. The best format depends on how you plan to use your custom GPT. One effective format is a question-answer pair system, such as: Customer Question: What are the dimensions of your product? AI Response: The dimensions of our product are 15 cm by 10 cm by 5 cm. Customer Question: Can I get a discount if I buy in bulk? AI Response: Yes, we offer discounts for bulk orders. Please contact our sales team for a custom quote. This structured format allows AI to match new customer queries with the correct response. For more complex use cases, you might store product information in a structured database, such as: Product Name: XYZ Model 2000 Battery Life: 10 hours Charging Time: 90 minutes Weight: 1.2 kg When a customer asks for details about this product, the AI pulls the information from the structured database rather than relying on pre-written answers. Step Five: Storing and Organizing Data for Future Updates Your custom GPT should always have access to up-to-date information. This means storing your training data in a centralized document or database that can be updated regularly. Here are a few ways to organize your data for long-term use: Spreadsheets – Use Google Sheets or Excel to store...
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