Building Product Recommendation Logic Based on Customer Needs #S11E6
This is season eleven, episode six. In this episode, we will focus on how to train a custom GPT to recommend the right products based on customer needs. You will learn how to classify products by application, teach AI how to match customer requirements with the best options, and use structured decision-making models to improve AI-driven recommendations. By the end of this episode, you will know how to create an AI assistant that helps customers choose the right product, just like an experienced salesperson.
So far, we have trained AI to handle pricing and quotations. Now, we are moving into a more advanced task—helping customers select the right product based on their needs.
Let’s go step by step on how to classify products, define product selection rules, and train AI to provide personalized recommendations.
Step One: Categorizing Products by Application and Use Case
Before AI can recommend the best product, it needs a clear understanding of how products are grouped and which ones are best suited for different applications.
Most businesses sell products that can be categorized by features, intended users, and specific applications. For example:
If you sell electronics, products may be categorized by battery life, power output, or connectivity.
If you sell medical devices, categories may include patient type, use case, and compliance with regulations.
If you sell software, categories may focus on features, subscription levels, and integrations.
By grouping products into categories, AI can match customer questions with the right product based on key attributes.
Start by reviewing common customer requests and defining which product features are most important in their decision-making process. This will serve as the foundation for AI recommendations.
Step Two: Training AI to Recognize Customer Requirements
Once products are categorized, AI needs to learn how to understand customer requirements and map them to the right product.
For example, customers might describe their needs in different ways:
One customer might ask: “Which product is best for high-speed performance?”
Another might say: “I need a product that works well in outdoor conditions.”
Even though the wording is different, both customers are asking for a specific product feature. AI must be trained to recognize key phrases and match them with the appropriate product category.
To do this, AI training should include:
Common questions customers ask about product features.
Standardized responses that guide customers to the right options.
Follow-up questions if AI needs more details before recommending a product.
For example, if a customer asks, “What is the best option for cold-weather use?”, the AI should respond with:
“To recommend the best product for cold-weather conditions, I need to confirm a few details. Will the product be used for outdoor activities, industrial applications, or personal use?”
This approach ensures AI gathers enough information before making a recommendation.
Step Three: Creating a Decision Tree Model for AI Recommendations
To improve AI-driven recommendations, you need to define a structured process for decision-making. One of the best ways to do this is by using a decision tree model.
A decision tree is a set of rules that guide AI through a series of logical steps before recommending a product.
For example, if you sell fitness equipment, the AI’s decision process might look like this:
If the customer wants cardio training equipment, recommend treadmills or stationary bikes.
If the customer prefers strength training, recommend weight sets or resistance bands.
If the customer needs compact equipment, suggest foldable or portable options.
By defining these selection rules, AI can provide more accurate and tailored product recommendations.
Step Four: Refining AI Responses to Sound More Human and Helpful
Even when AI provides correct recommendations, it should still sound like a human assistant rather than a search engine.
Here are some ways to make AI-generated responses more conversational and engaging:
Use natural phrasing. Instead of saying, “The best option based on your request is Model X.”, AI should say, “Based on what you are looking for, I would recommend Model X because it offers high performance and is designed for your specific needs.”
Offer comparisons when necessary. If multiple products fit the customer’s needs, AI should explain the key differences. Example: “Model X is great for high-speed performance, while Model Y is better for durability and long battery life.”
Encourage further engagement. AI should invite customers to ask follow-up questions or request additional details. Example: “Would you like me to compare two options side by side?”
These refinements make AI more helpful and user-friendly, leading to better customer satisfaction.
Step Five: Handling Customer Uncertainty and Alternative Suggestions
Sometimes, customers are not sure what they need, and their requests may be vague. In these cases, AI should be trained to:
Ask clarifying questions to narrow down the best recommendation.
Provide general guidance when exact preferences are unclear.
Offer alternative product suggestions if the first recommendation does not match customer expectations.
For example, if a customer asks, “I need something lightweight and portable, but I’m not sure which one to choose.”, AI could respond with:
“I can suggest a few options based on your needs. Do you prioritize battery life, durability, or price when selecting a product?”
This keeps the conversation open and helpful, allowing AI to guide customers effectively.
Key Takeaways from This Episode
Products should be categorized by key features, applications, and use cases so AI can match them with customer needs.
AI must recognize different ways customers describe their needs and translate them into product recommendations.
Decision tree models help AI provide structured recommendations rather than random suggestions.
AI responses should sound natural, engaging, and helpful to improve customer satisfaction.
When customers are unsure about their needs, AI should ask guiding questions to refine recommendations.
Your Action Step for Today
Review your product categories and common customer requests. Ask yourself:
Are my products classified clearly based on features and applications?
Do I have a structured way to determine which product is best for different customer needs?
What common questions do customers ask before making a purchase decision?
If your product recommendation process is not yet structured, start defining key attributes and decision-making rules so AI can provide more accurate suggestions.
What’s Next
In the next episode, we will focus on how to fine-tune AI-generated drafts to make responses more accurate and professional. You will learn how to review and improve AI responses before sending them to customers, use human-in-the-loop validation, and train AI to adapt based on feedback.
New Zealand