Customer service costs are climbing, and most e-commerce stores don't know their real cost per ticket. See how AI-powered customer service automation cut Open24's monthly costs from €12k to under €5k, with three concrete tactics, industry benchmarks, and honest math for 2026.
Most e-commerce store owners can quote their conversion rate, their average order value, and their cost of acquisition to two decimal places. Ask them what it costs to answer one customer service ticket, and you'll get a shrug.
That's a problem. According to industry research, 79% of e-commerce teams don't know what a single support ticket costs them, and the ones who guess commonly underestimate. That gap, multiplied across monthly volume, often becomes a five- or six-figure annual line item that never hits the P&L.
That blind spot is exactly why AI customer service automation is becoming impossible to ignore. Done right, it doesn't just make support faster. It can meaningfully cut the cost of running customer service, as much as 60% or more in the case study below, without hurting customer satisfaction. This article shows you how to automate customer service intelligently, with real numbers from Lithuanian and global e-commerce companies that have already used AI to automate customer support at scale.
Let's start with honest benchmarks. According to LiveChatAI's 2025 cross-industry analysis of 50 sectors, the average cost per ticket in retail and e-commerce sits at $2.70 to $5.60, among the lowest of any industry. That sounds cheap, but the math gets alarming fast at scale:
And that's the cost per contact, not per resolved issue. If a customer needs multiple touches to fully resolve an issue (which is common), your real cost per resolution is a multiple of the per-contact number.
On top of that, phone and voice channels are consistently the most expensive support channels, typically several times the cost of chat or email. Add management overhead, agent turnover (which averages 40-45% annually in customer service according to 2026 industry data), and tooling costs, and the picture gets worse. If you want to reduce customer service costs (or reduce support costs more broadly), understand this: most e-commerce operations don't have a cost problem because they hire too much. They have one because the same ticket gets answered over and over by different people, each costing the business real money.
Most articles about "AI in customer service" stay abstract. The tactics below are concrete, measurable, and used by real stores today. These are the customer support automation and customer service automation solutions that actually deliver ROI, not the vendor pitches you skim past. Each targets a specific cost driver.
The biggest lever, and what most people think of first when they hear "automated customer service." Route repetitive questions (order status, shipping, product availability, return policies) away from human agents by letting an AI chat agent resolve them directly.
The economics are unambiguous. According to Teneo's 2025 analysis of contact center costs, a basic AI-handled interaction costs $0.25 to $0.50, versus $3.00 to $6.00 for a human-handled one, roughly a 12× difference in unit economics for eligible ticket types.
Real deflection requires an AI trained on your product catalog and integrated with your order management system, not a generic bot. A well-configured AI chat agent for e-commerce can move from "answers FAQs" to "processes returns," which is where the biggest deflection gains live.
Not every ticket can be automated. Judgment calls, complex complaints, and angry customers still need humans, but AI can dramatically cut the time those tickets take.
Modern customer service automation software summarizes customer history before an agent opens the ticket, drafts suggested responses, and translates messages in real time. Agents stop copy-pasting from knowledge bases and start reviewing AI-drafted answers instead.
If AI-assisted agents drop handle time by around a third (a commonly reported outcome), effective cost per ticket drops by the same amount without automating anything away. Applied across a team, that frees meaningful capacity: for more volume without hiring, or for higher-value work like proactive outreach.
Most e-commerce customer service costs come from having customers ask the same question in the wrong channel. Someone emails asking "where's my order?" One ticket. They call to follow up: a second, more expensive ticket. Then they DM you on Instagram to complain: a third. Same question, three answers, three costs.
The fix: route every high-frequency question to a self-service path before it becomes a ticket anywhere. AI chat agents on FAQ pages, contact pages, Instagram/Facebook DMs, and behind a dedicated "AI Search" button in your top nav: every path leads to the same AI, which resolves the question in seconds.
Done well, this eliminates a substantial share of ticket volume, often a third or more over time. Because the same AI handles every channel, each new touchpoint costs almost nothing. For the specific tactics that drive the biggest gains, see our guide to AI chat agent engagement tactics.
Open24 is one of the largest footwear retailers in Lithuania, operating stores across the Baltics under the Open24, Crocs, and Keenfootwear brands. In 2025, their customer service costs were running at approximately €12,000 per month across the group: a mix of agent salaries, management overhead, tooling, and outsourcing. After deploying Parnidia's AI chat agent across their FAQ pages, contact pages, and social media DMs, that number dropped to approximately €4,800 per month.
That's a 60% reduction, but the honest breakdown matters. According to Open24's CEO Justas Mačionis, roughly half of the saving is directly attributable to Parnidia's chatbot handling inquiries that would otherwise have gone to human agents. The other half came from internal process optimization the chatbot enabled: identifying which questions drove volume, restructuring return workflows, and consolidating channels. Both halves matter, and both trace back to the same intervention.
Why this case study matters beyond the euro amount: Open24 sits squarely in the mid-market e-commerce segment: high volume, multilingual, thin margins. No 50-agent support team, no large AI transformation budget. Just one operations lead, a chatbot deployment, and a willingness to look at where the money was going. If it works at Open24's scale, it works at yours, including Shopify AI customer service deployments, since the underlying tactics don't change by platform.
If Open24 shows what's possible at Baltic e-commerce scale, Hostinger shows what happens when AI customer service automation runs at global scale. Hostinger is a Lithuanian-founded hosting company serving over 4.6 million users worldwide. Their in-house AI agent, Kodee, saves the company approximately €9 million per year in operational expenses, roughly €750,000 per month.
The numbers behind that headline are worth understanding:
Two things worth noting. Hostinger built Kodee in-house, which most stores can't and shouldn't do. The point isn't to copy the approach, but to see the direction of travel. And notably, they didn't reduce headcount. The AI handled volume that would otherwise have required doubling the support team: "handle growing demand without growing costs," not "fire agents."
The internet is full of AI vendors quoting eye-watering ROI numbers. Some are real. Many are marketing math. Before you take any of the numbers in this article and run them past your CFO, three honest caveats:
Reality check 1: Most companies don't reduce headcount. Gartner's December 2025 survey found that only 20% of customer service leaders who deployed AI actually reduced agent headcount. In most organizations, deflected tickets were replaced by growing customer demand, or the AI handled easy cases while agent-required volume held steady. This isn't a failure. It means you handle more customers without hiring more agents. But it does mean "AI saves 60% on customer service" usually shows up as growth capacity, not as a line item you can cut from the P&L.
Reality check 2: Deflection without resolution is worse than no deflection. A chatbot that gives a wrong answer or fails to solve the customer's problem creates a more expensive ticket, not a cheaper one. The customer contacts you again, often via a more expensive channel, already frustrated. Poorly implemented chatbots can drive up your cost per issue rather than down. This is why picking a chatbot trained specifically on e-commerce use cases matters.
Reality check 3: The 60% number isn't magic. It's setup. Open24's reduction required deploying the chatbot on high-traffic pages, integrating it with their order management system, and iterating for months. This is why the done-for-you approach tends to outperform DIY builders: technology is half the win; deployment is the other half.
Start with the questions your team answers most often, and move them through three stages until AI resolves them end to end. Order tracking and shipping policy questions are usually the biggest group: in a 2025 review of its e-commerce clients, Parnidia found they made up about 26% of all incoming questions.
Templates for common questions can cut response time from about two minutes to thirty seconds. But a human still has to read each message, pick the right template and send it. At hundreds of tickets a day, that human-in-the-loop step becomes the bottleneck, and every new question type means another template to write and maintain.
A detailed, up-to-date FAQ page or knowledge base deflects some tickets, but only a fraction of customers read it before contacting support. Simple "if-else" helpdesk rules that look for keywords like "order tracking" break as soon as a customer writes "I want to know where my package is" without the expected words. A human has to step in again.
AI understands intent, not keywords. "Where is my stuff?", "Has this shipped yet?" and "Can I get a shipping update?" all land in the same order-status category. A connected AI chat agent then pulls real-time tracking from your store, answers in seconds at any hour (including 3 a.m. and weekends), and walks customers through returns step by step. To keep answers accurate, its knowledge should be limited to your website, catalog and documents. Read more on how to prevent AI chatbots from giving false answers.
Chat is the biggest cost lever, but not the only one. Two adjacent Parnidia tools handle the cost drivers chat doesn't reach. The AI Voice Agent handles inbound phone calls end-to-end (answers, books appointments, transfers to a human when needed), which matters because phone is consistently the most expensive support channel. AI Call Analytics (formerly WiseAudio) automates the call quality review that traditionally requires a dedicated call center director. A single person can realistically analyze only about 19% of a team's calls, while the AI covers 100%. Together with the AI chat agent, they cover the three major cost drivers: written inquiries, phone inquiries, and quality assurance.
The honest math is simpler than most vendors make it sound. Multiply your monthly ticket volume by your current cost per ticket (use the $5 retail benchmark if you don't know yours). Estimate what percentage of tickets are repetitive. Ringly.io’s benchmarks show WISMO ("where is my order") queries alone account for 30-40% of total support volume for most e-commerce stores, and broader repetitive categories (shipping, returns, product info) push this higher. Assume a conservative share of that repetitive volume moves to AI (start at 25% and treat 45% as the upside case), and calculate the cost differential between $0.50 AI handling and $5 human handling. Then compare the result with a flat plan on the Parnidia pricing page.
The most expensive customer service cost isn't the one on your P&L. It's the questions your team answers over and over, the tickets no one counts, and the hidden cost of not knowing what a single interaction costs to resolve.
AI customer service automation done right doesn't just save money. It restructures where the money goes. Instead of paying more agents to answer the same questions, you invest in higher-value work: proactive outreach, recommendations, retention. That's a customer service function that pays for itself.
If you're serious about cutting customer service costs in 2026, the first question isn't "which AI vendor?" It's "what do we actually spend, and where does it go?" Comparing vendors next? See the best AI chatbots for e-commerce in 2026. Parnidia handles the full stack, from AI chat agent deployment to voice and QA.
Get a demo to see how it would work for your store.
Leo Mazzura
Co-founder & CEO @ Parnidia
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