How to Zendesk track autoreply usage with triggers and tags

Let's be real, every support leader dreams of better ticket deflection. The goal is to let customers help themselves so our agents can tackle the really tough questions. But here’s the question that keeps us up at night: are your automated replies actually deflecting tickets, or are they just causing customers to furiously type "this wasn't helpful"?

The only way to know for sure is to start tracking your autoreply usage. It’s the difference between a self-service win and a customer service dead end. In this guide, I'm going to walk you through the old-school Zendesk way to Zendesk track autoreply usage with triggers and tags. We'll get into the weeds of setting up the views you need to see what's actually working.

But I'll be honest with you, the manual method can be a bit of a maze. So, we'll also talk about its limitations and look at a more modern, AI-powered way to get better insights with way less of a headache.

What you'll need to get started

Before we jump in, let's do a quick check to make sure you have what you need. Everything we're doing is inside Zendesk, so no third-party tools are required, but you’ll want to have a few things ready.

• A Zendesk Suite account: The features we'll be using, like autoreplies and triggers, are standard parts of the Zendesk platform.

• Admin access: You'll need permission to get in and create triggers, add tags, and set up new views for your agents.

• An active Zendesk Guide (Help Center): Your autoreplies have to pull answers from somewhere. Make sure your knowledge base is up and running with articles that tackle common customer problems.

• About 30-45 minutes: Seriously, grab a coffee. The steps aren’t rocket science, but they need your full attention to make sure everything is configured correctly.

How to track autoreply usage in Zendesk with triggers and tags: The manual way

Alright, ready to roll up your sleeves? This section covers the classic method for tracking autoreplies in Zendesk. It’s a solid way to use the tools you already pay for, but you'll see pretty quickly that it involves a lot of moving parts.

Step 1: Understand Zendesk's default autoreply tags

First off, you need to get a feel for how Zendesk operates. When an autoreply (what Zendesk used to call "Answer Bot" and now includes in its AI agents) suggests an article, it automatically sticks a few special tags on the ticket. Think of these tags as breadcrumbs that let you follow the bot's journey.

It helps to know what they are, though you might notice some of the names feel a little... dated. That’s just part of the fun when you’re building on a platform that has been around for a while.

Tag Name	When It's Added
"ar_suggest_true"	An autoreply found and suggested at least one article.
"ar_marked_helpful"	The customer clicked "Yes, this was helpful" on the suggestion.
"ar_marked_unhelpful"	The customer told you the suggestion was not helpful.
"ai_agent_automated_resolution"	The ticket was automatically solved by a Zendesk AI agent.

These tags give you a basic pulse check on how customers are interacting with your automated suggestions.

Step 2: Create a trigger to apply a master tag

So, those default tags are a decent start, but they don't give you the full story. To get a complete list of every single ticket that an autoreply has touched, you need your own "master" tag. It's basically a custom label that says, "Hey, automation was here."

Here’s how to set that trigger up:

1. Head over to Admin Center, and find Objects and rules > Business rules > Triggers.

2. Click the Create trigger button.

3. Give it a name you'll remember, like "Apply autoreply_fired tag".

4. Under Conditions, you need to tell the trigger when to run. You'll need at least these two:

   • "Ticket | Is | Created"
   • "Channel | Is | Email" (or any other channels you have autoreplies on).

5. Under Actions, tell it what to do:

   • "Add tags | autoreply_fired" (you can name this whatever you want, just remember it for later).

6. Click Create.

Now, every new email ticket will get your custom tag, giving you a reliable way to bucket them all together.

Step 3: Build a custom view to track all autoreplied tickets

With your master tag ready to go, you can create a dedicated view to see all the tickets your automation has interacted with. This is where you can start to actually see the numbers.

1. Go back to Admin Center, then navigate to Workspaces > Agent tools > Views.

2. Click Add view.

3. Call it something clear, like "Autoreply Performance".

4. Under Conditions, set these rules to pull in the right tickets:

   • "Status | Less than | Closed"
   • "Tags | Contains at least one of the following | autoreply_fired"

5. Choose the columns you want to see (like Subject, Requester, and Date Created) and save the view.

This view becomes your go-to spot for a high-level count of how many tickets your autoreply system is getting its hands on.

Step 4: Create advanced views for deeper insights

Just knowing how many tickets were touched isn't enough. You need to know if your bot is actually helping. This is where you have to build even more views and triggers to slice and dice the data. It's also where the manual setup starts to feel a bit heavy.

Tracking solved tickets

Let's make a view for the wins. This will show you every ticket that a customer said was solved by an autoreply.

1. Create another new view and call it "Autoreply Solved Tickets".

2. Set the Conditions to: "Tags | Contains at least one of the following | ar_marked_helpful". This view is your happy place. It shows you exactly how many issues your knowledge base is successfully knocking down.

Tracking reopened tickets (the ones that didn't stick)

What about when a customer marks a ticket as solved, but then replies again a little while later? That reopened ticket is a huge red flag that the autoreply didn't really fix their problem. Tracking these is key to finding gaps in your support content.

To catch these, you need another trigger:

11. Create a new trigger and name it "Tag Reopened Autoreply Tickets".

12. Set the Conditions to find tickets that were solved but are now open again:

    • "Status | Changed from | Solved"
    • "Status | Is not | Closed"
    • "Tags | Contains at least one of the following | ar_marked_helpful"

13. For the Action, add a new tag like "autoreply_reopened".

Now you can create one last view called "Autoreply Failed" that looks for the "autoreply_reopened" tag. This chain of logic (a trigger adding a tag so a view can see it) is a perfect example of the complex, interconnected web you have to build and maintain.

The limits of manual tracking

If you followed all of that, you’ve now built a pretty respectable system for tracking your autoreplies. But you've probably also noticed it’s a lot of clicking around. This manual approach has some real downsides that can get in your way.

• Complexity and maintenance: You're now juggling a bunch of triggers, tags, and views that all lean on each other. It’s a bit like a house of cards, if one piece breaks, the whole reporting system can come crashing down. A quick search in Zendesk's own support forums shows that getting trigger conditions right is a common headache. It’s a brittle setup that needs regular check-ups.

• Limited intelligence: This system only tracks clicks. It tells you what happened, but not why an article was unhelpful or what knowledge you're missing. It’s also stuck suggesting answers only from your formal knowledge base, but what about all the great solutions buried in your past ticket conversations?

• Always playing catch-up: You can only spot problems after they’ve already happened. By the time you notice a spike in "reopened" tickets, dozens of customers have already had a bad experience. You're always looking in the rearview mirror instead of at the road ahead.

• It doesn't learn: This system is static. When you find a failed resolution, it’s up to you to manually dig through the ticket, figure out what went wrong, write a new help center article, and cross your fingers that the bot suggests it next time. There's no learning loop.

The modern alternative: An AI agent

Instead of building a complicated web of triggers just to monitor a flawed system, what if you could just deploy a smarter system from the get-go? That’s where something like eesel AI comes in. It’s designed to solve the root problem, delivering accurate, automated support, so you can spend your time on insights, not just tracking clicks.

• Get going in minutes, not months: Forget about blocking off an afternoon for setup. With a one-click Zendesk integration, eesel AI plugs right into your helpdesk. There are no complicated business rules to build or workflows to migrate. It just works.

• Learn from all your knowledge, not just some of it: This is the really powerful part. Instead of being confined to your help center, eesel AI trains on your past tickets, macros, Google Docs, and even your Confluence pages. It learns your brand's voice and understands how your team has actually solved problems in the past. That means it can give the right answer on day one, even if that answer isn't in a formal article.

An infographic showing how eesel AI connects to various knowledge sources like past tickets, documents, and Confluence to provide comprehensive answers.

• Test with confidence using simulation: This is the best kind of tracking, the kind you can do before it affects a single customer. The simulation mode in eesel AI runs the AI agent against thousands of your past tickets. It gives you a clear forecast of your resolution rate and shows you exactly how the AI would have replied. You can instantly see which issues are great for automation and which still need a human touch, all without any risk.

A screenshot of the eesel AI simulation dashboard, which helps you test your AI agent's performance before deployment.

• Actionable reporting is built-in: Stop building custom views to hunt for problems. The eesel AI dashboard automatically shows you where your knowledge gaps are and points out trends in customer questions. It gives you a clear roadmap for what to improve next, no manual setup needed.

The eesel AI reporting dashboard, highlighting knowledge gaps and deflection rates automatically.

Common mistakes to avoid

If you decide to stick with the manual method for now, just be careful to sidestep these common pitfalls. They can easily mess up your data and make your tracking pretty useless.

• Trigger order: This is a classic Zendesk "gotcha." The order of your triggers matters a lot. If an early trigger makes a change to a ticket, it can stop a later trigger from ever running. Always double-check the full list.

• Conflicting actions: Be careful not to have triggers that fight each other. For example, creating one trigger that adds a tag and another that immediately removes it during the same update cycle will just lead to chaos.

• Vague conditions: The more specific you can be with your conditions, the better. Vague rules can make triggers fire on the wrong tickets, filling your carefully built views with junk data.

• "Set it and forget it" mentality: A trigger-based system needs a little love and care. You should periodically check in on your triggers to make sure they're still doing what you expect, especially after Zendesk rolls out platform updates.

From manual tracking to intelligent automation

It is completely possible to Zendesk track autoreply usage with triggers and tags. With some patience and careful setup, you can build a system that gives you a basic idea of how your automation is performing. But as you’ve seen, it's a manual, often fragile process that needs constant attention.

Real efficiency doesn't just come from tracking clicks. It comes from having an intelligent system that learns from your team's expertise and gives customers the right answer the first time. Instead of spending your days building and fixing brittle workflows, you could be focused on the insights that actually move the needle for your business.

Ready to see what an AI-native platform can do? Check out how eesel AI can give you powerful automation and useful insights in a fraction of the time.