With viral tools like Sora2 and ChatGPT empowering brands to tailor content in real time, generative AI has unlocked an exciting new world of omnichannel hyperpersonalization. Think billboards that show customers sitting in a new vehicle in real time and audio ads delivered based on a listener’s local weather. And consumers aren’t just hoping for this level of personalization; they demand it. Seventy-two percent that they only interact with messaging tailored to their interests.

Read on to learn more about AI personalization marketing, best practices, and how to adopt it to create personalized experiences for millions of users simultaneously. 

What is AI personalization marketing? 

AI personalization marketing uses AI technology to analyze customer behavior and historical data in order to create customized marketing experiences—like ads, emails, and subscriber content—for individual customers. AI-powered personalization advances customer segmentation, which groups consumers into broad categories based on demographic and behavioral data. Today’s AI technology uses machine learning to analyze patterns across multiple data points, including:

• Browsing history. This tracks not just what categories users view, but the specific sequence, timing, and context of their interactions.

• Purchase patterns. AI personalization can identify each user’s unique price sensitivity, brand preferences, and buying triggers.

• Social media interactions. AI can track and understand specific interests based on the influencers a user follows and the content they engage with. 

• Demographic information. Using age and location as a baseline, demographic details are tracked and further refined by individual behavior.

From there, marketers can use generative AI to create an individualized experience for the consumer.

Applications of AI personalization tools

• Content personalization
• Personalized pricing and discount offers
• Chatbots
• Cross selling

Marketers can apply AI to personalization efforts in several ways: 

Content personalization

Content personalization entails using generative AI to create versions of content tailored to individual consumers. By feeding datasets—either from individual users or segments—into LLM or GenAI models, marketers can personalize content for digital ads, website experiences, email campaigns, and social media posts.

Loftie, a wellness company that designs sleep products, used AI to develop and launch a digital product—the Loftie Rest app— that complements its signature alarm clock. The app both created a new revenue stream and broadened Loftie’s reach as a brand, with a one-month free trial available to get new prospective customers into the Loftie universe.

AI-personalized content has become the crux of Loftie’s growth. “We wouldn’t have released this product without AI,” founder and CEO Matthew Hassett says. “We couldn’t have started our membership if we hadn’t come up with this idea for personalized content. It was the initial seed of what our subscription app became—and now we have around 15,000 subscribers.”

The app uses AI to offer personalized recommendations for better sleep habits. “We use AI to pull in screentime data and Apple Health data, and layer that into data we have about your alarm-setting habits and your self-reported sleep quality and feedback,” Matthew explains. “Over time, we help people improve their sleep by weighing in on certain correlations with AI.”

Here’s how it works: AI can detect whether you’re doomscrolling from 11 p.m. to 1 a.m.—resulting in only five hours of sleep. Loftie’s Night School coach then suggests app-blocking settings or a new sleep routine, which may include Loftie’s signature personalized content: custom bedtime stories.

Described as “a favorite use of AI” by Wired, Loftie’s Storymaker invites users to share preferences about their dream bedtime story via a simple Typeform survey. Users then receive a personalized story featuring friends and family of their choice, which they can listen to via the Loftie app or through their Loftie clock hardware.

“We don’t want to just create new content that just pulls your attention—we want to unglue you from your screen,” Matthew says. “There’s so much data we can gather and our goal is to create useful things with all of it.”

Personalized pricing and discount offers

Dynamic pricing, powered by AI, analyzes consumer buying patterns, competitor pricing, and individual customer value to optimize pricing strategies in real time. This approach helps you increase revenue while offering customers incentives like discounts and seasonal sales that align with their price sensitivity.

AI can also personalize discount offers based on purchase history and predicted behavior. This can incentivize loyalty: 56% of customers will repurchase from a company if they receive personalized loyalty discounts and rewards.

Chatbots

Chatbots like Home Depot’s Magic Apron have evolved far beyond simple FAQ responses. AI-powered chatbots use machine learning capabilities to comprehend context, recall previous conversations, and offer personalized recommendations across multiple channels and customer touchpoints.

Ikea’s AI assistant, available on OpenAI’s GPT store, provides personalized furniture and décor recommendations by considering the user’s dwelling size, personal style, sustainability preferences, and budget. Users can describe their specific needs (such as requesting a cozy layout for a 500-square-foot studio apartment with sustainable materials) and receive customized product suggestions.

The assistant can even identify furniture from uploaded photos, helping customers find complementary pieces from Ikea’s ecommerce store. The retailer’s chief data and analytics officer, Francesco Marzoni, reported to Digiday that, within the first few months of launching the AI assistant, 20% of interactions with it led to visits to Ikea’s storefront. 

Cross-selling

AI personalization enables smarter cross-selling by predicting what additional products each customer is likely to want based on their individual purchase history and preferences. Rather than showing generic “customers also bought” suggestions, AI analyzes a customer’s specific behavior patterns to surface relevant add-ons before they express their want for them—or even realize it.

Millions of consumers encounter effective cross-selling in action when they order Starbucks via the mobile app. When placing an order, AI suggests specific food pairings or seasonal drinks based on previous orders, time of day, and even local weather conditions.

Best practices for AI personalization marketing

1. Start with quality data collection
2. Enhance customer experiences
3. Prioritize privacy
4. Test and improve continuously

Implementing AI personalization marketing requires care and human oversight. Here are four best practices: 

1. Start with quality data collection

Successful personalization efforts depend on comprehensive data collection through an integrated technology stack. Use a customer data platform (CDP) like Segment or Adobe Real-Time CDP to unify data from all sources into individual customer profiles. Choose a CDP with robust API connections to feed cleaned, structured data directly into your AI personalization engine.

Real-time tracking infrastructure should capture behavioral data, like:

• Website behavior. Deploy tag management systems like Google Tag Manager alongside session replay tools to track clicks, scrolling patterns, time spent on each page, and complete user journeys across your site. 

• Customer interactions. Use CRM systems like Salesforce or HubSpot, along with chat platforms, email service providers, and support ticket systems, to capture and consolidate every customer touchpoint into a unified record.

• Purchase history. Connect your ecommerce platforms and point-of-sale (POS) systems to collect transaction data—including product details, purchase frequency, average order values, and cart abandonment signals. 

• Customer feedback. Implement survey tools like Qualtrics or Typeform, review platforms, and sentiment analysis systems that can process both structured ratings and unstructured feedback into actionable insights.

2. Enhance customer experiences 

Customers are more likely to share information when they understand how it enriches their experience: 90% of consumers are happy to share data with companies if the result is a smoother, more personalized experience.

The key is demonstrating immediate, tangible value in exchange for a customer’s data. For example, when asking users to set preferences or share additional details, explain precisely what they’ll get in return—such as size preferences in exchange for items that suit them best. Create a value exchange that feels fair: users provide data, and in return, they save time discovering products they want, avoid irrelevant content, and enjoy experiences tailored to their needs.

Loftie makes it clear how the data app subscribers share will be used to create customized audio tracks. Matthew says these experiences bring customers delight. “It’s such a surprise for kids to hear their dog, their mom’s name, their blanket being worked into their custom bedtime stories—they’re mesmerized by it.”

3. Prioritize privacy

Personalized marketing can’t come at the expense of user trust and privacy. Address data privacy concerns by adopting robust security measures and being transparent about how your business might use personal customer data. Provide clear opt-in mechanisms and let customers control their data preferences. Ensure your data is clean, organized, and complies with privacy regulations.

4. Test and improve continuously

AI-powered personalization requires continuous testing and refinement to produce the most effective content:

• Implementing structured A/B testing. Use experimentation platforms like Optimizely to test personalized versus non-personalized experiences, or to compare different personalization approaches against each other. 

• Deploy comprehensive analytics and reporting tools. Integrate your personalization system with analytics platforms to track performance metrics. Tools like Google Analytics can measure how a personalized experience affects metrics like conversion rates and engagement time. 

• Create feedback loops. Your feedback loops should combine quantitative and qualitative data to track not just what’s happening, but why. Track metrics like recommendation acceptance rates, personalization opt-out rates, and content interaction patterns to identify what’s working. Lean on customer feedback to understand why it’s working. 

When it comes to harnessing the power of AI personalization marketing, customer feedback is key. Loftie incorporates human oversight into its feedback loop, with support team members poring over customer feedback provided via an email survey.

“Early on, it was essential to hear what people liked,” Matthew said. As a result, Loftie’s AI features have been honed tightly, rarely needing tweaks in recent updates. By prioritizing customer feedback early on in the process, Loftie has been able to create customer-pleasing, personalized AI content well ahead of many competitors.