K:AI Customer Agent is more than a support tool
K:AI Customer Agent Is More Than a Support Tool
This article, written by ECD founder and CEO Zak Cassady-Dorion and published on the Klaviyo blog, explains why most brands are leaving revenue on the table by using Klaviyo Customer Agent purely for ticket deflection. Every customer who reaches out after purchasing has already done the hard part — they found the brand, bought from it, and are now in a conversation with it. That is one of the highest-intent moments in the entire post-purchase journey, and Customer Agent has more context about that customer in that moment than most salespeople ever get.
The post walks through three custom skills ECD built using Klaviyo’s Agent Builder — order editing as an upsell engine, size guidance with a recommendation layer, and gift conversations that build first-party segmentation data — and explains how the same approach applies across the full customer journey from pre-purchase through subscription management. It also covers a browse abandonment test where replacing a discount email with a plain-text reply-prompt drove roughly a 10% lift in conversion. The main takeaway is that the skills and conversation flows built inside Customer Agent determine how it performs: out of the box it is a support tool, but with the right thinking behind it, it becomes one of the better-performing revenue channels in the stack.
- Post-purchase conversations are high-intent sales moments, not just support tickets Customer Agent knows what the customer ordered, what else they have bought, and what their loyalty tier is. Treating that interaction as a cost to minimize means ignoring context that most salespeople never have access to at all.
- Order edits, size questions, and gift conversations can each carry a revenue layer An address correction becomes a contextual product recommendation with near-zero friction. A sizing question becomes a fit consultation that ends with a pairing suggestion. A gift purchase becomes a first-party data point that flows into relationship-specific Klaviyo segments months later.
- Conversations outperform discounts on abandonment flows Sending a plain-text email that asks “what stopped you?” and letting Customer Agent handle the reply addresses the actual hesitation rather than papering over it with a coupon. A discount tells the customer their concern does not matter. A conversation resolves it.
When Klaviyo launched K:AI Customer Agent, most brands did the obvious thing: they pointed it at the support queue. They had Customer Agent answer FAQs and handle “Where is my order?” (WISMO) questions.
Freeing up the human support team to do more strategic, creative work is a reasonable first move. Ticket deflection has real value, after all.
But that’s not where the money is.
At ECD Digital Strategy, we’ve been thinking about Customer Agent differently from the start. In particular, we’re interested in what Customer Agent can do in the post-purchase stage of the customer journey, beyond resolving simple tracking queries.
Every customer who reaches out after purchasing has already done the hard part. They found you, they bought from you, and now they’re in a conversation with you. That’s one of the highest-intent moments in the entire post-purchase journey.Of course, you’re going to resolve their issue. But you shouldn’t let the interaction end there.
Reframe your AI customer service agent as a sales tool
Implementing Customer Agent purely for support treats every conversation as a cost to minimize. Someone reaches out, they get an answer, and the ticket closes. Fine.
But think about what you actually know about that customer in that moment: what they ordered, when they ordered it, what else they’ve bought, what their loyalty tier is. That’s more context than most salespeople ever get. Customer Agent has all of it, and it can act on it instantly.
That’s the shift: AI using the shopper’s answer as an opening.
3 custom skills we built with Klaviyo Agent Builder
We’ve now built and launched 10+ custom Claude skills using the Klaviyo x Claude integration for ~20 clients.These are 3 of them that move the needle the most.
1. Order editing as a sales engine
A customer who’s editing an order is already committed. They’re not browsing or comparing. They already bought. And now they have a reason to be in a conversation with you.
We built an order editing skill that handles the full range of post-purchase changes:
- Shipping address corrections
- Item swaps
- Adding forgotten products
- Gift notes
Customer Agent can handle everything a customer might need before their order ships, without getting a support rep involved.
Every one of those interactions is now an up-sell moment. The AI agent already knows exactly what’s in the order. So when someone messages in to update their shipping address, the AI agent fixes it, then recommends a complementary product based on what they ordered. Keep in mind, this is a contextual product recommendation.
The friction is nearly zero: the customer confirms, and the product gets added to their existing order. An invoice goes out from Shopify, they pay, and everything ships together.
This came from a real client problem: a blue-collar brand where workers were constantly ordering with a company credit card but using different billing and shipping addresses. The volume of requests asking to update an address was significant. Once the skill was live, Customer Agent resolved those requests automatically.
After Customer Agent updates an address, the revenue opportunity is ripe: Customer Agent can make those contextual product recommendations.
2. Size guidance with a recommendation layer
For a clothing brand, we built a sizing skill that goes beyond a standard size chart lookup. The AI agent asks relevant questions, helps the customer find their fit, and then does what a good sales associate would do: recommends products that pair well with whatever they were considering. It’s a useful experience that generates revenue.
3. Gift-giving conversations that build your segments
Every apparel brand with a gifting occasion hears some version of the same question at check-out: what should I get my dad? My wife? My best friend? For one apparel client, we built a skill that treats that question as first-party data, not just conversation.
When a shopper tells Customer Agent they’re shopping for a gift, the AI agent asks who it’s for. The answer becomes a custom property on their Klaviyo profile: gift giver, dad. From there, the brand can automatically add that shopper to the flows built for that relationship: dad-focused gift ideas ahead of Father’s Day, broader gifting content around the winter holidays, and so on.
Customer Agent pro tip: Customer Agent can’t create a brand-new custom property from scratch. The property has to already exist in Klaviyo for the skill to populate it, so this only works if you’ve built out that structure ahead of time.
It’s a smaller lift than order editing, but the principle is identical. Customer Agent is watching the words a shopper actually uses, and turning them into first-party data that shows up in a relevant email months later, not just an answer that closes out the chat.
Where Customer Agent fits across the customer journey
Here’s how we think about implementation across the full journey, not just the post-purchase window:
- Pre-purchase: Customer Agent handles discovery: size questions, product fit, “What’s right for me” conversations, etc. These are high-intent moments where a good answer converts and a non-answer loses the sale.
- Cart and check-out: Customer Agent catches the hesitations that cause abandonment. A customer who has a question and can’t get an answer quickly is probably gone. One who gets an immediate, accurate response often isn’t.
- Post-purchase: This is where we’ve focused most of our energy, because it’s where existing tools tend to drop the ball: order editing, gift notes, add-on items, WISMO, etc. Customer Agent handles all of this automatically, and all of it’s an opportunity to sell.
- Subscription management: For subscription brands, we use Customer Agent to make subscriptions stickier and to grow average order value. When someone asks when their next order ships, the AI agent answers, then checks their one-off order history and asks if they’d like to add a product they’ve bought before. That’s the kind of interaction that can turn a $50 subscription into a $65 one.
How conversations beat discounts on abandonment flows (a ~10% lift)
One of the most effective things we’ve done has nothing to do with the support queue.
We’ve been running a test on browse abandonment flows that’s outperforming the standard approach. Instead of sending an abandonment email that drops a discount code, we send a plain-text email written to get a reply. It asks a simple question: “What stopped you?”
When the customer replies, Customer Agent picks up the conversation and actually addresses their concern. Instead of a template, the customer gets a relevant response based on what they said. We’ve seen around a 10% lift in conversion on those abandonment flows compared to the standard email-plus-discount approach.
This works because a discount tells the customer their hesitation doesn’t matter. All it says is, “Here’s money off.” A conversation actually resolves the hesitation. Those are very different things, and people respond to them very differently.
A lot of the time, hesitation has nothing to do with price. Maybe someone isn’t sure about sizing, or timing, or whether the product is right for them. By defaulting to a discount, we were solving the wrong problem.
Use Klaviyo Agent Builder to build custom skills, no code required
When we first started building custom skills, it required significant technical work and close collaboration with Klaviyo’s implementation team. We were part of an early-access group of 3 agencies helping figure out what was possible.
That’s changed. Now, Agent Builder lives directly inside Customer Agent. You describe what you want it to do, and it builds the skill for you. What used to require real technical lift is now accessible to any brand willing to think creatively about what the AI agent should do.
The skills and conversation flows you build determine how Customer Agent performs. Out of the box, it’s a support tool. With the right thinking behind it, it’s one of the better-performing revenue channels in your stack.
That’s the gap we’ve been building into.
Frequently Asked Questions
Why shouldn’t brands use Klaviyo Customer Agent only for support?
Using Customer Agent purely for ticket deflection treats every conversation as a cost to minimize. But every customer who reaches out after purchasing is already in one of the highest-intent moments in the post-purchase journey — they found the brand, bought from it, and are now in a direct conversation with it. Customer Agent has access to everything about that customer in real time: what they ordered, when they ordered it, what else they have bought, and what their loyalty tier is. That is more context than most salespeople ever get, and it can be acted on instantly. Resolving the issue and ending the conversation there leaves the revenue opportunity completely unused.
How does order editing become a sales opportunity with Customer Agent?
A customer editing an order is already committed — they bought, and now they have a reason to be in a conversation. ECD built an order editing skill that handles shipping address corrections, item swaps, forgotten products, and gift notes without requiring a support rep. Once the request is resolved, Customer Agent makes a contextual product recommendation based on what is already in the order. The customer confirms, the product gets added to the existing order, an invoice goes out from Shopify, and everything ships together. The friction is nearly zero. This approach came from a real client situation where workers were regularly ordering with a company card but using different billing and shipping addresses — the volume of manual address update requests was significant, and the skill resolved them automatically while opening a revenue moment after each fix.
How can gift conversations in Customer Agent build Klaviyo segmentation data?
When a shopper tells Customer Agent they are buying a gift, a skill can ask who it is for and capture that answer as a custom property on their Klaviyo profile — for example, gift giver: dad. From there, the brand can automatically add that shopper to flows built around that relationship: dad-focused gift ideas ahead of Father’s Day, broader gifting content around the winter holidays, and so on. The conversation data becomes first-party segmentation data that shows up in a relevant email months later rather than just an answer that closes the chat. One important note: Customer Agent cannot create a brand-new custom property from scratch — the property has to already exist in Klaviyo for the skill to populate it, so that structure needs to be built out ahead of time.
How does using Customer Agent on abandonment flows improve conversion?
ECD tested replacing standard browse abandonment emails — which typically drop a discount code — with plain-text emails written to get a reply, asking a simple question like “what stopped you?” When the customer replies, Customer Agent picks up the conversation and addresses their actual concern with a relevant response rather than a template. The result was roughly a 10% lift in conversion on those abandonment flows compared to the standard email-plus-discount approach. The reason it works is that a discount tells the customer their hesitation does not matter — it just makes the uncertainty cheaper. A conversation actually resolves the hesitation. A lot of the time, the hesitation has nothing to do with price: it is sizing, timing, or product fit. Defaulting to a discount solves the wrong problem.
How difficult is it to build custom skills with Klaviyo Agent Builder?
When ECD first started building custom skills, it required significant technical work and close collaboration with Klaviyo’s implementation team as part of an early-access group. That has changed. Agent Builder now lives directly inside Customer Agent — a brand describes what it wants the skill to do, and Agent Builder builds it. What used to require real technical lift is now accessible to any brand willing to think creatively about what the AI agent should do. The skills and conversation flows built inside the tool determine how Customer Agent performs: out of the box it handles support, but with the right thinking behind it, it can become one of the better-performing revenue channels in the stack.
Written by: Zak Cassady-Dorion
For over two decades, Zak Cassady-Dorion has worked across entrepreneurship, marketing, and digital commerce, helping businesses grow in fast-moving markets. Today he is the Founder and CEO of ECD Digital Strategy, a performance-driven e-commerce marketing agency and Klaviyo Platinum Partner working closely with platforms like Shopify, Meta, and Google.