A practical guide to Gorgias field conditions for ticket fields setup (2025)

If you’re leading a customer support team, you know your team’s conversations are a goldmine of insights. But trying to turn a flood of unstructured tickets into clean, actionable data? That’s a massive headache. Without a proper system, spotting trends like a recurring product defect or a common shipping complaint can feel like looking for a needle in a haystack.

This is exactly the problem that Gorgias Ticket Fields and Field Conditions are designed to solve. They give you a structured way to categorize every ticket, making sure your agents collect consistent and valuable data. While the feature itself is powerful, getting it set up and keeping it updated can get complicated and eat up a lot of your time.

This guide will walk you through everything you need to know about the Gorgias field conditions for ticket fields setup. We’ll cover what they are, why they’re so important, and how to configure them. We’ll also show you how to take this process a step further with AI, moving beyond manual rules to a more intelligent, automated workflow.

Understanding Gorgias field conditions: What are ticket fields and field conditions?

Before we jump into the setup, let's quickly break down the two key components. Think of them as the building blocks for creating a helpdesk that’s organized and rich with data.

Gorgias ticket fields

Ticket Fields are just custom data fields you can add to your tickets. Instead of wrestling with a chaotic mess of tags, you can create specific fields like "Contact Reason," "Product SKU," or "Issue Type." Your agents can then pick from predefined options in a dropdown menu or fill in specific details before closing a ticket. This simple step standardizes how data is collected across your entire team.

According to the official Gorgias documentation, you’ve got four main types to work with: a Dropdown menu with predefined options, a Number field for things like refund amounts, a free-form Text box for unique notes, and a simple Yes/No choice.

Gorgias field conditions

Field Conditions are the logic that makes your ticket fields smart. They’re basically "if-then" rules that decide when a specific ticket field should pop up. For example, if an agent selects "Return" as the "Contact Reason," then you can make the "Return Reason" and "Product Condition" fields appear and even be required.

This is great because it stops agents from being overwhelmed by a long list of irrelevant fields. It keeps the ticket interface clean and helps them focus on gathering only the information that matters for that specific type of ticket, a benefit Gorgias highlighted when they launched the feature.

A view of the if-then command interface in Gorgias, which is central to the Gorgias field conditions for ticket fields setup.

Why a Gorgias field conditions setup is a big deal for structured data

Setting up ticket fields isn't just a tidying-up exercise; it's a strategic move that can ripple across your whole business. When your data is clean and consistent, you can finally shift from just putting out fires to proactively making your business better.

Here are a few ways that well-structured data can help, drawing on some of Gorgias's own best practices:

• Spot product quality issues early. Imagine you create a "Defect" field that only shows up when a customer reports a problem. If you suddenly see a spike in tickets with the "Zipper Defect" field filled out for a particular backpack, you can give your product team a heads-up to check on the manufacturing.

• Figure out what's going on with shipping. Is one of your shipping carriers constantly dropping the ball? A "Shipping Carrier" field combined with a "Shipping Issue" field can quickly show you which partners are underperforming. That gives you solid data to take into contract negotiations or to justify switching providers.

• Understand and reduce customer churn. If you run a subscription business, a "Cancellation Reason" field is pure gold. Knowing whether customers are leaving because of price, a poor product fit, or a bad service experience helps you fix the root causes and improve retention.

• Make your agents' lives easier. Conditional fields create a much smoother workflow. Agents don’t have to guess what information they need to collect or scroll through dozens of optional fields. The system guides them, which reduces their mental load and makes their job less stressful.

Getting to this point, though, depends on a thoughtful setup. The more complex your business, the more detailed your rules become, and that can be a real challenge to manage by hand.

How to set up Gorgias field conditions

Gorgias gives you a pretty solid interface for creating and managing these rules. The process boils down to defining your fields first, then building the conditional logic that controls when they appear. Here’s a quick look at the steps, based on their documentation.

Step 1: Create and configure your ticket fields

You have to create the fields before you can build rules for them.

1. Head over to Settings > Ticket Fields in your Gorgias admin panel.

2. Click Create Field and give it a name, description, and type (like Dropdown or Text).

3. Under Field visibility, you absolutely must select Conditionally visible. This is the most important step. If you set a field to "Always optional" or "Always required," Gorgias will just ignore any conditions you try to create for it.

Step 2: Build the conditional logic

With your fields ready to go, it’s time to build the conditional logic.

1. Go to Settings > Field Conditions.

2. Click Create Condition and give it a clear name you’ll remember, like "Show Refund Fields."

3. Under Condition Requirements, you'll set up your "if" statement. You can base this on the value of another ticket field. For example:

   • IF "Contact Reason" is one of "Refund Request"

4. Under Then display the following fields, you'll add your "then" statement. This is where you pick the ticket field(s) that should show up when the condition is met. You can also mark them as Required, which forces the agent to fill them out before closing the ticket.

   • THEN display "Refund Amount" (Required) and "Reason for Refund" (Required).

This system is great for creating structured workflows. You can even use Ticket Fields as conditions in your main Rules engine to automate things, like assigning all tickets with a "VIP Customer" field to a senior support team.

graph TD;  

    A[Start] --> B{Step 1: Create Fields};  

    B --> C[Go to Settings > Ticket Fields];  

    C --> D[Click 'Create Field'];  

    D --> E[Define Name, Type];  

    E --> F[Select 'Conditionally visible'];  

    F --> G{Step 2: Build Conditions};  

    G --> H[Go to Settings > Field Conditions];  

    H --> I[Click 'Create Condition'];  

    I --> J[Set 'IF' Requirement e.g., 'Contact Reason is Refund'];  

    J --> K[Set 'THEN' Action e.g., 'Display Refund Amount Field'];  

    K --> L[End];  

The catch with a manual setup

While this approach works, it isn't without its own set of problems:

• It doesn't scale well. As your company adds more products, issue types, and support channels, your list of conditions can get incredibly long and messy. A tiny change to your process might mean you have to go in and update a dozen different rules.

• It's rigid. The rules are static. They can't adapt on the fly to new or weird customer issues. If a customer describes a problem in a way that doesn't trigger one of your preset rules, the right fields won't appear, and you’ll miss out on that data.

• It's prone to human error. At the end of the day, you're still relying on an agent to correctly select the initial "Contact Reason" or whatever other field kicks off the workflow. One wrong click can make the whole conditional sequence fail.

This is where bringing in AI can offer a much smarter and more scalable way forward.

Going beyond a manual setup with AI

Building rules by hand is a decent start, but real efficiency comes from a system that can understand the context of a conversation on its own. Instead of waiting for an agent to pick the right category to trigger a workflow, an AI can analyze the customer's message and handle the whole process automatically.

That's exactly what eesel AI does. By integrating with your Gorgias helpdesk, eesel AI works with your existing setup, so you don't have to switch platforms.

Let AI handle tedious classification and data entry

Using products like AI Triage and AI Agent, eesel AI reads incoming tickets, figures out what the customer wants, and takes action automatically.

• Automated field population: Forget about agents having to manually select "Contact Reason: Shipping Issue." eesel AI reads a message like "Where is my order??" and automatically sets the ticket field for you. This gets rid of human error and makes your data 100% consistent.

• Workflows that adapt: eesel AI isn't stuck with rigid "if-then" rules. It learns from thousands of your past tickets to understand the nuances of customer language. It can tell the difference between a pre-sale question about shipping costs and a post-sale complaint about a delayed package, applying different fields and tags for each.

• Get started in minutes, not months: Building a complete set of conditional rules in Gorgias can take days, if not weeks. With eesel AI, you just connect your Gorgias account with one click. The AI starts learning from your historical ticket data right away, and you can be up and running in minutes.

This image shows an AI automatically resolving a customer query, enhancing the Gorgias field conditions for ticket fields setup by removing manual classification.

Test your setup with confidence using simulation

One of the scariest parts of setting up manual rules is that you can't really test them at scale before they go live. How do you know for sure that your new "Product Defect" condition will actually work as intended?

eesel AI has a neat solution for this: a powerful simulation mode. You can run the AI over thousands of your past tickets to see exactly how it would have categorized and handled them. This gives you a clear forecast of its performance and lets you tweak its behavior in a totally risk-free environment before you turn it on for live customers. It's a level of confidence you just can't get with manual setups.

Eesel AI's simulation mode allows you to test your Gorgias field conditions for ticket fields setup on historical data before going live.

Build a smarter foundation for your support data with Gorgias field conditions

Gorgias Ticket Fields and Conditions are fantastic tools for any support team that wants to be more data-driven. They give you the structure you need to turn messy conversations into clean, useful insights. By following the setup process, you can create a much more organized and efficient workflow for your agents.

But to truly grow your support operations and find deeper efficiencies, adding a layer of AI automation is the next logical move. Tools like eesel AI go beyond fixed rules, offering an intelligent system that learns on its own, automates data entry, cuts down on human error, and gives you the confidence to automate more of your support process.

Ready to see how AI can transform your Gorgias setup? Book a demo of eesel AI today.