People want more flexibility and more options, and AI can give it to them
Almost all consumer shopping today happens across more than one channel.
82% of global consumers use more than one channel to shop, and 86% say they at least occasionally start shopping on one channel and complete their purchase on another, according to Klaviyo’s 2026 future of consumer marketing research.
No matter where people shop, they expect brands to know their preferences: 74% of consumers expected more personalized experiences in 2025, according to Klaviyo’s 2025 Future of Consumer Marketing Report .
Shoppers want the flexibility and choice to interact with brands across physical locations, websites, mobile apps, and social media platforms. And they want their preferences and shopping history to follow them from channel to channel.
Omnichannel AI is one of the best ways to deliver a personalized, cohesive customer experience (CX).
Think of omnichannel AI as the infrastructure that automates the personalization of the customer journey across multiple channels. Real-time customer data, agentic AI, and predictive analytics built into CRMs work together to deliver relevance, speed, and consistency.
In this guide:
- What is omnichannel AI?
- Why omnichannel experiences matter
- The omnichannel AI tech stack
What is omnichannel AI?
Omnichannel AI is the practice of using integrated customer data, machine learning algorithms, deep learning models, and generative AI to deliver customer experiences that adapt to each shopper’s behavior and preferences.
With integrated AI and customer data, you can automate more of the personalization process across marketing and customer service channels. You can even predict future customer behavior, so you can send messages and serve customers more proactively.
Here are a few examples of omnichannel AI:
- An AI customer agent makes personalized product recommendations across web chat, email, and text messaging, based on someone’s past purchases.
- An AI marketing agent creates a multi-channel flow based on real-time changes in customer behavior, within your brand’s creative guardrails.
- Predictive analytics triggers win-back flows to customers most likely to churn, on the channel they’re most likely to interact with.
Omnichannel AI orchestrates all stages of the customer journey—from discovery to buying to post-purchase—for each individual shopper, without straining your existing resources.
Why an omnichannel customer experience matters
Shopping experiences are fragmented across websites, mobile apps, and brick-and-mortar stores. Now, with agentic commerce and agentic storefronts , shoppers can also use AI platforms to discover products, research and compare them, and sometimes even buy them within the same chat interface.
For brands, that means expanding existing omnichannel processes to make sure AI platforms fit cohesively into their CX. In practice, that looks like:
- Omnichannel marketing personalization: Customers shop across multiple channels, and they expect personalization to follow them across those channels. Brands with integrated customer data across platforms can deliver this experience because all of their channel systems, from email to texting to mobile push to WhatsApp, are pulling from the same data.
- Omnichannel customer service: When customer service platforms pull from the same data as marketing, support conversations improve with more centralized context about each customer. Omnichannel service also maintains conversation history across all support channels, so no customer ever has to explain their issue multiple times.
- Omnichannel attribution: Omnichannel attribution is a holistic, unified view of how all your channels are performing together. As opposed to single-channel analytics and reporting, omnichannel attribution assigns revenue and engagement to a combination of channels, so you can tweak your marketing and customer service strategies in more nuanced ways.
With more channels to juggle, brands with disconnected data can’t personalize CX when it branches to multiple channels. Omnichannel CX matters because it means showing up as a brand shoppers trust, no matter where they’re interacting with you.
4 tech stack requirements for omnichannel AI
Expanding tech stacks with new AI-powered marketing tools is the top martech priority for marketing decision-makers at B2C brands in 2026, according to our 2026 B2C marketer research.
But omnichannel AI requires more than a bolt-on solution. Scalable customer data, predictive analytics, MCP servers, and agentic AI are the infrastructure behind omnichannel AI.
1. Scalable, real-time customer data
B2C marketers say that integrating data across sources and platforms is their biggest challenge when trying to deliver a more personalized CX, according to Klaviyo’s 2025 State of B2C Marketing Report .
Many brands still rely on a fragmented set of platforms that each house their own customer data. When this data isn’t integrated, there’s no centralized source for omnichannel AI algorithms to pull from so they can deliver a personalized, cohesive experience.
Omnichannel AI platforms can only produce meaningful personalized outputs when they’re informed by real-time truths about the customer. This is why you need unified, detailed customer profiles that dynamically update as behaviors and preferences change.
A CRM with an embedded customer data platform (CDP) collects, unifies, and stores customer data from multiple sources. This is the infrastructure behind embedded omnichannel AI such as AI customer agents and personalized, AI-generated messages that are always fresh.
2. Predictive analytics
Predictive analytics helps you recognize patterns in your data so you can be more proactive about how, where, and when you send messages to customers.
While diagnostic analytics looks backwards, predictive analytics uses AI to look forward, seeing how the trends, patterns, and relationships in the data today are likely to play out in the future.
For omnichannel AI, “predictive analytics” is a category of algorithms that work together to keep brand experiences proactive, cohesive, and responsive to changing customer data across channels. These include:
- Personalized send time : This predicts when a customer is most likely to open and engage with a message. You can send campaigns with thousands of recipients who each receive a message personalized to their optimal send time.
- Predicted customer lifetime value (LTV) : This is how much money a customer is projected to spend in a given timeframe. You can build segments based on predicted LTV, then, for example, use those segments to find new customers via lookalike audiences in Meta Ads or similar audiences in Google Ads.
- Channel affinity : This is a prediction about which channels customers are most likely to engage with next. Let’s say customer A usually opens texts, while Customer B tends to engage more with email. With channel affinity, you can set up a flow to automatically send messages via text to Customer A but email to Customer B.
Ever since drinkware brand Corkcicle moved their email and SMS channels to one CRM platform, they’ve been able to segment their audience based on AI-powered channel affinity. Now, their most efficient segment is customers who have engaged in the past 90 days who prefer email.
“Channel affinity allows us to be efficient and targeted and has provided great results,” says Erica Olsen, ecommerce marketing specialist at Corkcicle. Consolidating email, SMS, and reviews in one B2C CRM has also led to cost savings and more automation and AI opportunities across channels.
3. MCP server
MCP is a standardization protocol that connects AI platforms with other software and databases. An MCP server uses this protocol to access information in your brand’s CRM, which ideally includes your customer data platform and marketing, customer service, and analytics functions.
With MCP servers, you can prompt LLMs to sort through your own data and get insights through simple conversations with AI. So, instead of looking at multiple reports or customer profiles to get information, you can take a shortcut with a few AI prompts and still get meaningful direction on strategy.
With apps like the Klaviyo connector to ChatGPT or Claude , you can:
- Summarize and compare channel performance. Prompt LLMs to summarize how any campaign or flow is performing across multiple channels. With these comparisons, you can tweak your omnichannel strategy across many different customer segments.
- Conduct more omnichannel experiments. AI can identify customers who fit a specific set of parameters, so you can conduct more omnichannel experiments before completely switching up your strategy.
- Generate more multi-step flows. With plain language prompts, you can create customer segments based on channel engagement. Then, you can generate multi-step flows that follow each subsequent channel the customer is most likely to respond to after that first message.
Let’s say you’re a fitness brand launching a new line of sports bras made of sweat-wicking fabric, and you want to offer early access to VIP customers before launching to your entire customer base. You can prompt AI to create a segment of customers who have previously purchased sports bras, live in hotter climates, have spent at least $200, and prefer text messages.
These parameters sound simple, but pulling a customer segment like this used to require a ton of manual work. With AI, it takes minutes.
4. AI agents
Agentic AI is what makes it possible to scale your omnichannel strategies without diluting your brand voice. With marketing and customer service agents trained on customer and product data, you can create truly 1:1 experiences across the entire customer journey—no matter where an interaction is happening on the shopper’s end.
- AI marketing agents can learn from your website and build a marketing strategy that’s custom to your brand, or help you build forms and cross-channel marketing campaigns and flows from a simple prompt or idea. They can even define target segments, test assets, and optimize them, all within brand guardrails you set and approve.
- AI customer agents aren’t confined to a website. They help customers wherever they interact with your brand, via email, text messaging, chat, or WhatsApp. Because they’re trained on your storefront and customer data, they can handle order updates, points redemptions, and other simple inquiries across multiple channels, so the customer never has to repeat themselves or start from scratch.
When home fragrance brand Happy Wax implemented an AI customer agent, they saw a dramatic reduction in support tickets. And over 90 days, 50% of the conversations handled by the AI agent were fully resolved without human involvement.
Transform your omnichannel AI strategy with Klaviyo
Omnichannel AI is the future of B2C customer experiences. It’s time to evaluate and rebuild your tech stack to create a unified foundation that drives 1:1 personalization at scale.
The foundation of a successful omnichannel AI strategy is an all-in-one B2C CRM that brings together marketing, customer service, and analytics, all powered by AI and a built-in CDP. With Klaviyo, you get an AI-first CRM that learns from your customer data in real time.