Beyond tickets: Omnichannel customer service that drives growth and loyalty

Enter: omnichannel customer service. Based on a foundation of unified data in a single AI-powered platform, an omnichannel approach to customer service makes it easier than ever to turn your customer data into personalized, automated experiences that delight across every interaction. Here’s what it can help your brand accomplish:

Marketers have long understood that personalization drives immediate action. Several years ago, McKinsey reported that companies growing faster drive 40% more of their revenue from personalization than their slower-growing counterparts.

That makes sense, considering Klaviyo’s recent future of consumer marketing report found that 74% of consumers expect more personalization from B2C brands in 2025. And according to our online shopping report, 36% of respondents said that seeing their name in a brand’s text message makes them want to engage with it.

But in customer service, personalization goes far beyond using someone’s name. It’s about turning support interactions into sales opportunities through contextual intelligence.

When a customer contacts support about a product issue, support agents with access to unified customer data can see their complete purchase history, browsing patterns, and preferences in real time. This visibility turns what could be a purely transactional exchange into a conversion opportunity. 

For example, if someone calls about a sizing issue with a shirt they just bought, a support agent can instantly see they’ve been browsing similar styles and offer an immediate size exchange plus a recommendation for the jacket they viewed yesterday—creating an up-sell opportunity that feels genuinely helpful rather than pushy.

The potential impact on conversion rates could be huge. When support agents can provide personalized, relevant, and timely product recommendations based on actual customer behavior rather than generic suggestions, customers may be more likely to make additional purchases during or immediately after the service interaction.

Connected service creates opportunities that extend far beyond the initial interaction. When customer service teams understand the full customer journey—from marketing touchpoints to purchase behavior—they can craft follow-up experiences that turn negative moments into loyalty-building opportunities.

For example, if a customer complains about a shipping delay, the marketing team might pause standard follow-up communications until the product is received, and then follow up with a discount that references their poor delivery experience.

This unified approach means customers who might have churned after a negative experience instead feel heard because the brand demonstrates they’re paying attention across all touchpoints, not just treating each interaction as an isolated event.

Nothing frustrates customers more than having to explain their problem multiple times to different representatives. With unified customer data, every support agent has visibility into all the information they need: the customer’s complete interaction history, previous solutions that worked or didn’t work, and context about their entire relationship with your brand.

This eliminates the dreaded “Let me transfer you to someone who can help” or “Can you explain what happened again?” scenarios that may drive customers to abandon brands entirely. When agents have complete context, they can more easily solve problems on the first try.

Connected omnichannel service doesn’t just react to problems—it prevents them. When customer interaction data flows between marketing and service teams, brands can deploy AI-powered analytics to analyze and identify patterns that signal emerging issues before they escalate into widespread complaints.

For instance, if multiple customers start complaining in their product reviews about a new product’s sizing, or if return requests spike for a particular item, AI can flag these trends early. Instead of waiting for complaints to flood in, brands can proactively update product descriptions, send clarifying emails to recent purchasers, or adjust inventory before the issue affects more customers.

This proactive approach can translate to fewer support tickets overall—not to mention happier customers who encounter fewer problems in the first place.

Our online shopping report found that 62% of respondents strongly prefer to have AI remember their preferences over explaining them to a sales associate in-person. 

Here’s where the financial impact becomes undeniable: when customers can check order status, initiate returns, and get answers to common questions without human intervention, your support team can focus on complex issues that actually require personal attention. Modern self-service tools powered by AI can handle routine inquiries instantly, while human agents focus on relationship-building and complex problem-solving.

This efficiency can translate directly into cost savings in the form of: 

• Fewer team members needed for the same volume of inquiries
• Shorter average handle times
• Higher first-contact resolution rates that prevent expensive callback loops.

Take Ministry of Supply, a performance clothing brand that wanted to reduce routine support inquiries while improving the customer experience. They implemented a Customer Hub that lets customers access order history, tracking information, and personalized product recommendations without contacting support.

The results? Over 650 self-serve interactions in less than 4 months, plus a significant reduction in escalated support tickets from customers requesting basic information.

Each communication channel serves a distinct purpose in the customer service ecosystem: 

• Web chat: This channel handles the immediate, straightforward questions—order status, return policies, basic troubleshooting. Web chat works best when live agents can access complete customer profiles to provide personalized responses.
• WhatsApp: This channel opens up possibilities for relationship-building through conversational commerce. Unlike traditional support channels that focus purely on problem resolution, WhatsApp lets brands have ongoing conversations that blend support with discovery. A customer might start by asking about shipping times, then naturally transition into asking about product recommendations based on their recent purchases. The conversational nature of WhatsApp makes these transitions feel natural rather than pushy.
• SMS: Texting excels at time-sensitive support updates where immediacy matters more than detail. When a shipment gets delayed or a return gets processed, customers want to know right away, not when they happen to check their email. SMS cuts through the noise of crowded inboxes to deliver critical updates when they matter most.
• Email: Renowned as the workhorse for detailed communications, email excels when you need to explain a complex return process, walk through troubleshooting steps, or provide comprehensive product information. The channel’s asynchronous nature also lets customers digest information at their own pace and refer back to instructions when needed.

A shared customer data foundation across marketing and service is vital for successful omnichannel customer service. With it, support agents can see a customer’s marketing engagement history, purchase patterns, and preferences before they even address their request.

Shared, contextual insights empower both support agents and marketers to take the best action for your customers. That might mean offering expedited shipping for a VIP customer, for example, or pausing marketing campaigns for someone who has had multiple support issues recently to avoid overwhelming them during a frustrating time. 

Shared data also makes it easier to spot at-risk customers who might be showing signs of churn—like someone who’s stopped opening emails, hasn’t made a purchase in months, and recently contacted support about order issues—and proactively address concerns before they escalate.

Modern AI agents don’t replace human connection—they make it more meaningful by handling routine inquiries so human agents can focus on complex relationship-building. Self-service tools that access the full customer profile can provide personalized support that feels tailored rather than generic. Instead of recommending the same products to every shopper, an AI agent can tailor recommended products to each individual, based on their specific purchase history.

AI shopping agents represent the next evolution of customer service AI, answering common questions while maintaining brand voice. These agents can help customers find products, explain features, and even make recommendations based on browsing behavior and purchase patterns. The key is to make sure they feel like a natural extension of your brand rather than an obvious automation.

The most implementations of AI-powered customer support encourage real-time handoff to human agents when necessary, while preserving full context. When an AI agent reaches the limits of what it can help with, it should seamlessly transfer the conversation to a human who can see the entire interaction history.

The customer never has to repeat information or start over—they simply get escalated to someone with more expertise while maintaining conversational flow.

This layered approach—AI for routine tasks, humans for complex problems—creates an experience that feels both efficient and personal. Customers get instant answers to simple questions, but they know they can always reach a real person when they need more nuanced help.