The future of hyper-personalization in commerce: Unified, composable and AI-forward

A scene in Departure, a Canadian suspense TV series, perfectly encapsulates the opportunities and challenges of hyper-personalization. The protagonist, played by the wonderful Archie Panjabi, sits down with a shadowy billionaire in the aftermath of an airplane disaster. He offers her a drink.

“Rioja is your preference, if I’m not mistaken?” he says casually.

“Oh, I don’t know whether I should be flattered or creeped out that you know that,” Archie replies, raising an eyebrow.

She pauses, then adds with a faint smile, “But you’re mistaken. I took up whiskey a year ago.”

This brief exchange captures a central tension of hyper-personalization: Getting it almost right can feel worse than not trying at all.

There’s no denying that hyper-personalizing customer experiences remains one of the most critical factors for business success. When their experiences are personalized, 89% of leaders believe personalization is crucial to their business’s success. Consumers crave personalized approaches, as 77% feel frustrated by irrelevant promotional notifications, and 52% report higher satisfaction as experiences become more personalized.

What’s more, the Boston Consulting Group forecasts that leaders in personalization grow revenue ten points faster annually than laggards and enjoy higher customer satisfaction scores. The consulting firm also predicts that over the next five years, $2 trillion USD in revenue will shift to companies that can create personalized experiences and communications. 

The problem with hyper-personalization? To strike a balance between customer data and privacy. To deliver it at scale and in real-time. And to adapt as customers’ tastes and preferences evolve. 

It’s not easy to check all of these boxes. The good news is that brands, retailers and even B2B organizations are cracking the code of hyper-personalization and delivering experiences that feel relevant and intuitive — not intrusive.

Hyper-personalization goes beyond traditional personalization tactics like first-name emails or location-based offers. It uses real-time behavioral data, contextual signals and AI to tailor content, products, pricing and experiences to each user.

Rather than segmenting customers into static groups, hyper-personalization responds dynamically to:

• Browsing behavior (e.g., time on page, scroll depth).

• Purchase intent and patterns.

• Real-time inventory and availability.

• Geolocation and device usage.

• Customer lifetime value, loyalty status and preferences.

Take Ulta Beauty, a beauty powerhouse that leverages data across every part of the customer journey to ensure shoppers receive incredibly tailored information. Accordingly, integrating a hyper-personalization strategy requires integrating many components into your platform, such as data analytics and artificial intelligence (AI) tools.

These cutting-edge technologies empower businesses to collect, dissect and implement extensive data from diverse sources such as browsing history, previous purchases and social media interactions. By leveraging such data, brands can foresee a user’s desires, preferences and potential future behaviors with remarkable precision. 

This translates into meeting customers on their preferred channels at the right time to suggest a product that aligns with their tastes, update them on events that match their interests or deliver personalized experiences tailored to their unique profile.

Hyper-personalization in commerce requires strategic execution. The most effective brands apply the following practices:

1. Capture and update customer data 

There’s no personalization, hyper or otherwise, without data. Collecting and updating data is crucial, but even more so if your business captures this information in a way that feels intuitive for customers. After all, relying on pure demographic data, while easier and probably faster, won’t give your brand the type of data to truly personalize experiences. 

Doing so means putting King Charles III and Elton John in the same bucket, for example. While they share many demographic similarities, their profiles are worlds apart. 

Customer profiling goes beyond static segmentation: It involves ingesting and analyzing data streams such as demographics, browsing behavior, transaction history, location, channel preference, loyalty activity and sentiment from reviews or support interactions. This information fuels real-time persona creation and individualized customer profiles that adapt over time.

Short surveys on preferences, styles and tastes also go a long way in creating customer profiles that aren’t based on general information. For example, the kitchen appliance brand Breville created a “coffee quiz”  — a guided selling process that provides recommendations for coffee bean roasts and blends tailored to their taste profile — as part of the customer experience. 

2. Centralize and unify customer data

A unified view of the customer enables brands to deliver consistent personalized experiences across all channels by aggregating customer interactions into a single, dynamic profile. When every touchpoint (online, in-store, mobile, social) is connected, brands can understand customer behavior holistically and respond in real time.

For instance, a retailer could identify a customer who frequently browses via mobile on weekday evenings, but completes purchases in-store on weekends. With this insight, they can deliver hyper-relevant offers, like personalized weekend in-store discounts tied to location, when the customer is most likely to convert.

In addition, your business can:

• Integrate backend and frontend systems, so that data across systems reflects a single, accurate view of the customer.

• Leverage AI and ML to identify patterns, micro-segments and predictive behaviors at scale, enabling smarter engagement strategies.

• Empower customer-facing teams by ensuring that associates, support agents and digital concierges have real-time access to key customer data, such as purchase history, preferences and loyalty status, to deliver personalized, high-impact service.

• Drive personalization with data using AI to surface the following best actions, recommend products or proactively address potential churn. Ensure AI use is communicated transparently to build trust. 

3. Leverage AI for real-time personalization

Speaking of AI, many tools and models can help your business to dynamically adjust content, pricing, offers and recommendations at every touchpoint.

Today’s advanced models allow for real-time personalization that adapts dynamically to each customer’s context, behavior and intent. By leveraging machine learning (ML) and predictive analytics, businesses can surface relevant products, content, pricing and promotions at the right moment across channels. 

For example, a returning customer might see a personalized homepage featuring replenishment reminders, loyalty-based discounts and category-specific recommendations based on previous purchases or browsing habits — all without needing to log in.

Looking ahead, agentic AI offers even deeper personalization potential as it can proactively act on behalf of users to simplify decision-making or guide journeys. Think of a digital agent that suggests products based on prior interactions and understands broader context — like inventory, seasonal trends and intent — to initiate helpful actions, such as building a cart, booking services or negotiating personalized offers. 

This level of autonomous, contextual engagement enables a more intelligent, human-like interaction layer that’s both scalable and highly tailored, pushing personalization into a new era of customer experience.

4. Personalize the buyer’s journey where it matters most

Brands should tailor experiences across the buyer’s journey: From targeted marketing and personalized website content to thoughtful email flows and post-purchase engagement. But the key is knowing when and where to personalize. Just because you can customize every interaction doesn’t mean you should. True intelligence means recognizing customer context — if someone already bought a dishwasher, stop showing them ads for dishwashers.

Instead, focus on meaningful, timely personalization that adds value. Leading retailers can use unified data to trigger genuinely helpful actions — like VIP loyalty offers, tailored product suggestions or personal outreach from stylists (especially luxury fashion houses like Harry Rosen). These moments aren’t just about conversion; they’re about building deeper loyalty and increasing customer lifetime value. Intelligent personalization respects the buyer’s journey, avoids redundancy and makes each interaction more relevant — not more frequent.

5. Personalize across channels and devices

Customers don’t think about “channels” — they expect seamless, connected experiences whether browsing on mobile, shopping in-store or engaging through social media. That’s why it’s essential to deliver consistent messaging, offers and recommendations across every touchpoint. 

If a customer adds a product to their cart on a desktop, they should see the same cart and related suggestions on their phone or tablet. Similarly, if they’ve interacted with a product in-store or via a customer service channel, that data should inform follow-ups and online experiences.

The goal is to meet customers where they are, without creating fragmented or repetitive experiences. Unified data and centralized customer profiles help ensure personalization flows naturally across all interactions, strengthening the overall journey and increasing conversion, retention and brand trust.

6. Respect data privacy and transparency

Personalization only works when customers trust you with their data. That means being transparent about how information is collected, stored and used. Make it clear what customers can expect when they share their preferences or behavior, and offer them easy ways to control that experience. Simple tools like preference centers, opt-ins and the ability to edit or delete data can go a long way toward building long-term trust.

Just as necessary, adhere strictly to privacy regulations like GDPR, CCPA, etc. Don’t just treat compliance as a box to check — treat it as a foundation for customer loyalty. When people feel confident that their data is handled responsibly and used to improve their experience (rather than bombard them with ads), they’re more willing to engage deeply with your brand. Respectful personalization isn’t just ethical — it’s a competitive advantage.

7. Test, learn and adapt

Personalization isn’t a “set it and forget it” tactic — it’s an ongoing refinement process. What resonates with one audience today might fall flat tomorrow. That’s why continuous testing is crucial. Use A/B testing to evaluate different messaging, offers, content placements and recommendation strategies. Look at engagement metrics, conversion rates and customer feedback to understand what’s working and where friction exists.

Beyond surface-level metrics, dig into long-term trends: Are personalized experiences increasing customer lifetime value? Are they driving repeat purchases or boosting loyalty program engagement? Use those insights to fine-tune your algorithms, segmenting logic and communication strategies. 

Hyper-personalization can only thrive if data isn’t siloed. That’s where unified commerce comes in — a model where all product, customer, order and interaction data is centralized and accessible in real time across all touchpoints.

Without unified commerce:

• Store associates can’t see online purchases.

• Promotions might not sync across channels.

• Customer profiles are incomplete.

• Loyalty programs feel disconnected.

When unified commerce brings together data into a single source of truth, it enables:

• Real-time personalization across every channel.

• Consistent experiences, whether online, mobile or in-store.

• Smarter loyalty programs that reward holistic behavior.

• A foundation for advanced AI and analytics with agentic-ready commerce. 

Unified commerce unifies all data (customer, products, pricing, etc.) in one platform, ensuring that brands can maximize personalization.

AI is revolutionizing commerce by enabling businesses to deliver personalized experiences, optimize operations and enhance decision-making:

• Predictive AI forecasts customer behavior and demand patterns, helping with inventory and marketing strategies. 

• Generative AI creates personalized content and recommendations, driving engagement at scale. 

• Agentic AI autonomously makes data-driven decisions, improving efficiency by acting on real-time insights based on customer behavior and context. 

In the context of personalization, agentic AI can:

• Anticipate customer needs before they arise.

• Dynamically adapt offers and journeys in real time.

• Optimize experiences across devices and channels without manual input.

• Continuously learn from interactions to fine-tune future responses. 

For example, imagine an AI agent that not only recognizes a customer’s abandoned cart, but understands the reason (e.g., pricing hesitation), adjusts the promotion accordingly, and reaches out through the customer’s preferred channel — all autonomously.

This level of intelligence and adaptability is only possible with real-time unified data and a composable infrastructure that supports seamless orchestration.

Personalization at scale demands agility. Traditional monolithic commerce platforms simply can’t keep up — they’re too rigid, slow, and dependent on IT. 

Composable commerce, in contrast, empowers retailers to assemble a best-of-breed tech stack that can flex, scale and evolve as fast as customer expectations change. Your business can, for instance, add or replace third-party personalization tools, such as A/B testing platforms. This integration facilitates the creation of a personalized shopping journey, providing a modular approach for businesses to adapt their systems in response to evolving customer needs.

What sets composable commerce apart is its ability to transform the traditional linear customer journey into a more dynamic path, tailored to individual behaviors and preferences. Each interaction becomes an opportunity to engage and convert from personalized homepages to dynamic product recommendations.

Finally, hyper-personalization is an ongoing process. Composable commerce enables businesses to continuously adapt to customer behavior by easily integrating new technologies and iterating on experiences in real time — without disrupting core operations.

commercetools powers hyper-personalized commerce experiences across all channels — online and in-store — using its native capabilities, open data model, ready-to-use connectors and strong partnerships, such as Optimizely. 

This foundation enables brands to activate demographic, behavioral and AI-driven personalization for customer groups. Customer segments can be created directly within the commercetools’ business tooling, Merchant Center, or synced from external systems like CRMs or CDPs. A single customer can belong to up to 500 groups, allowing highly granular, overlapping segmentation; for example, targeting someone as both a loyal customer and a student located in New York City.

Product selections enable curated catalogs for specific customer groups or sales channels, showing only relevant products. The flexible pricing engine tailors prices by customer groups or channels, while the rule-based promotion engine supports targeted campaigns with conditions such as cart value, customer segments or custom attributes.

Key personalization features include:

• Extensible customer profiles with custom attributes (e.g., size preferences, loyalty status) for rule-based or AI-driven recommendations.

• Dynamic pages with content, layout and product recommendations that adapt in real-time based on customer group, location, device or campaign.

• Prebuilt frontend solutions like the B2C Store Launchpad integrate real-time event data and AI recommendations for seamless personalized shopping journeys. 

Together, these capabilities allow brands and retailers to deliver rich, dynamic, truly personalized commerce experiences at an enterprise scale.

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