AI is now the frontline of retail customer service
Australian shoppers have moved faster than many brands. New data from Klaviyo's AI Shopping Index shows that 88% of Australians are open to AI handling post-purchase tasks like delivery updates and returns, and 62% expect AI to be standard by 2026. For customer support teams, the takeaway is simple: AI is no longer optional.
There are two forces pushing this forward. Consumers are using tools like ChatGPT and Google Gemini to research and compare, and brands are using AI to answer service questions and guide purchases on-site. The experiences customers get from their own tools are now the benchmark for your service.
Why shoppers leave: the unanswered question
The most expensive support failure happens before checkout. 77% of Australian consumers have abandoned a purchase because they couldn't get a fast answer on shipping, sizing, returns, or a basic product detail.
That's the final hurdle in the buyer's journey-and it's where AI shines. Instant, accurate answers stop leakage and protect the revenue you've already paid to acquire.
- Make the top four intents instant: shipping ETA, returns policy, size/fit guidance, core product specs.
- Keep answers short, linked to live data, and visible in chat, email auto-replies, and SMS.
Post-purchase is the new battleground
Support after the sale is the moment of truth. Anxiety peaks once money leaves the customer's account-especially around tracking and delivery changes. Waiting two days for a response turns small issues into brand damage.
AI flips that dynamic. Klaviyo's Customer Agent can respond instantly to tracking requests, handle delivery updates, and initiate returns. At this point, speed isn't a delight feature-it's the minimum bar. Done well, service stops being a pure cost and starts driving loyalty and repeat orders.
Trust rules: transparency and a clean human handover
Consumers are positive on AI, but trust can break fast. The two biggest risks: misuse of data and getting stuck in a bot loop. Both are solvable with the right design.
- Be clear about what data is used and why. Respect consent across channels.
- Unify data so answers reflect real orders, preferences, and history.
- Set firm limits. If emotion, risk, or complexity shows up, hand off to a human instantly.
- Pass the full conversation and context to your agent-no repeating.
- Audit responses, log decisions, and keep policies version-controlled.
Comfort with AI is already high in travel, beauty, and entertainment, and those expectations spill into broader retail. Your customers don't separate categories-they compare experiences.
The adoption gap
Here's the wake-up call: 86% of consumers have used AI tools for online shopping or product research in the past three months, yet far fewer retailers are using AI in support. That gap creates a visible experience mismatch-and it's where churn hides.
Black Friday 2025 shows the stakes
ANZ retailers saw 14% revenue growth, outpacing the global average of 11%. The surge came from repeat buyers, whose spending grew more than twice as fast as new customers.
But higher order volume means a wave of tracking, delivery, and return requests. Manual-only support models won't keep up. The only way to protect those hard-won customers is an AI-first service layer that's instant, accurate, personalised, and always on.
How to operationalise AI support in 30 days
- Map top intents: shipping status, delivery changes, returns, cancellations, size/fit, order edits, product basics.
- Build a living answer library: policies, carrier SLAs, size charts, warranty rules, return windows, fees.
- Connect systems: ecommerce, order management, shipping/tracking, returns portal, CRM, and knowledge base.
- Set routing rules: trigger human handoff for VIPs, high order value, emotion, fraud flags, or repeat contacts.
- Run in shadow mode: compare AI answers vs. agent answers for a week; fix gaps in data and wording.
- Launch in the highest-friction channels first: on-site chat, email auto-replies, and SMS. Add WhatsApp if applicable.
- Measure and iterate weekly: aim for fast time-to-first-response, high containment, and stable CSAT.
Start where latency hurts the most: post-purchase questions. Then move upstream to pre-purchase friction.
Metrics that matter
- Time to first response (TTFR) and full resolution time
- Containment rate (handled fully by AI without handoff)
- CSAT by intent, plus post-fulfilment NPS
- Repeat purchase rate and ticket-to-order ratio
- Refund-to-exchange ratio on returns
- Cost per contact and deflection from email/phone to chat
Playbooks you can ship this week
- "Where is my order?" with live carrier data, delivery change options, and proactive delay alerts.
- Returns and exchanges: policy checks, eligibility, label creation, and instant exchange suggestions.
- Size and fit: size charts + prior purchase fit feedback + low-friction recommendations.
- OOS recovery: waitlist, back-in-stock alerts, and similar-inventory recommendations.
- Proactive outreach: inform customers of delays or exceptions before they ask.
Guardrails that keep you safe
- PII protection and clear consent management across channels.
- Grounded answers only: retrieve from your knowledge base and real-time systems.
- Escalate in under two minutes if confidence is low or sentiment drops.
- Disclose that it's AI, offer a human option, and make the handoff seamless.
- Weekly quality reviews with red-team prompts and drift checks.
What this means for support teams
AI takes the repetitive, high-volume work off your queue so your agents can handle empathy-heavy, situational issues. It improves the moments that matter most: right before checkout and right after payment.
As Marcus Rossato of Klaviyo puts it, "AI has gone from being a nice to use tool to being a must use one for both consumers and brands alike." He adds, "AI is the new benchmark. Customers expect it. They trust it for the right types of tasks. And if retailers do not adapt quickly, they will lose customers to competitors who already have."
Next steps
- Review Klaviyo's approach and tooling: Klaviyo
- Upskill your team on AI for support roles: AI courses by job
Set a 30-day rollout. Prove it on post-purchase, publish the metrics, then expand. Speed and clarity win.
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