Personalization

What is Personalization?

Personalization is the practice of tailoring experiences, content, and messages to individual customers based on their unique behaviors, preferences, and characteristics.

Think of it as creating a one-to-one conversation at scale. You’re using data—browsing history, past purchases, location, demographics—to deliver the most relevant “next best experience” for each person who interacts with your brand.

This isn’t just about adding someone’s first name to an email (though that’s a start). Real personalization means:

• Showing product recommendations based on browsing behavior
• Adapting website content to match user interests
• Triggering messages at the right moment in the customer journey
• Delivering offers that actually resonate

Customers will feel understood, and when people feel understood, they engage, convert, and stick around.

Learn more about what personalization means in digital marketing →

Personalization vs. Customization

Here’s where things get interesting. Personalization and customization sound similar, but they’re fundamentally different—and understanding that difference matters.

Personalization happens for the user. 

Your brand uses data and algorithms to modify the experience without the customer lifting a finger. A streaming service recommending shows based on viewing history? That’s personalization, the brand does the work.

Customization happens by the user. 

The customer actively makes choices to tailor their own experience. Designing your own sneakers online or rearranging your dashboard layout? That’s customization. The user is in control.

The key difference: Who initiates the action. On the one hand, personalization is implicit and brand-driven, while on the other hand, customization is explicit and user-driven.

Both approaches enhance the customer experience, and the best strategies often combine them. Treat them as two sides of the same coin, and give customers control when it matters, and anticipate their needs when you can.

Why is Personalization Important?

Personalization has become a critical driver of customer behavior and business performance. Research from Epsilon shows that 80% of consumers are more likely to make a purchase when brands offer personalized experiences. When you deliver relevance, you build trust—and when you miss the mark with generic messaging, you lose attention fast.

It Drives Real Business Results

The revenue impact is equally compelling. Here’s what the data shows:

• 40% more revenue: According to McKinsey’s research on personalization, companies that excel at personalization generate 40% more revenue from those activities than average players
• Up to 60% conversion lift: Hyper-personalized campaigns have been shown to deliver up to a 60% increase in conversion rates compared to generic, one-size-fits-all campaigns
• 10-30% higher marketing ROI: Brands implementing personalization strategies see marketing ROI lift by 10% to 30%, turning insights into measurable returns
• 202% better CTA performance: Personalized calls-to-action outperform generic ones by 202%, proving that relevance drives action

It Strengthens Customer Loyalty and Lifetime Value

Personalization doesn’t just boost immediate conversions—it builds lasting relationships:

• 44% become repeat buyers: Research shows that 44% of consumers say they’re likely to become repeat buyers after a personalized shopping experience
• 68% increased brand satisfaction: Personalized experiences significantly increase brand satisfaction for 68% of consumers, turning customers into advocates
• 15% reduction in cart abandonment: Companies using personalization often see around a 15% decrease in cart abandonment, recovering revenue that would otherwise be lost

Your Customers Expect It—and They’re Frustrated Without It

Customer expectations have fundamentally shifted:

• 76% are frustrated by generic experiences: When interactions feel disconnected from their preferences, customers disengage
• 71% expect tailored interactions: Personalization is no longer a differentiator—it’s the baseline expectation
• 79% trust brands that explain data usage: Transparency about how you use customer data builds trust and willingness to engage
• 80% will share data for value: Consumers are willing to trade personal information for tailored deals or content—if brands handle it with care and transparency

What are the Different Types of Personalization?

Personalization isn’t one-size-fits-all (ironic, right?). There are multiple approaches, each with its own strengths.

1. Segmentation-Based Personalization

Divide customers into groups based on shared characteristics—demographics, interests, purchase history—then tailor messages for each segment. It’s a solid starting point that balances scale with relevance.

2. Event-Triggered Personalization

Automatically trigger messages or content based on specific customer actions. Think abandoned cart emails, price drop alerts, or welcome messages after sign-up. The timing makes all the difference.

3. Real-Time Personalization

Respond to customer behavior as it happens. Someone spending time on a product page? Trigger a chat offer or show related items. Real-time personalization creates momentum and captures intent.

4. Omnichannel Personalization

Create seamless, consistent experiences across all touchpoints—website, email, mobile app, in-store. Your customers don’t think in channels, so your personalization shouldn’t either.

Explicit vs. Implicit Personalization

Explicit personalization uses data customers directly provide—preferences they select, surveys they complete, profiles they build.

Implicit personalization observes behavior—browsing patterns, search queries, time spent on pages—to infer preferences without asking.

Adaptive Personalization

Continuously adjusts experiences based on ongoing interactions and learning. The more someone engages, the smarter the system gets.

One-to-One Personalization

The holy grail: tailoring every interaction to the individual. It’s resource-intensive but powerful when executed well. One-to-one personalization involves customizing messages, offers, and experiences for each website visitor based on collected data.

Check out these examples we gathered for you → 

AI Personalization

AI personalization is transforming what’s possible. By leveraging machine learning, natural language processing, and predictive analytics, AI can:

• Analyze vast amounts of data to identify patterns humans might miss
• Predict what customers need before they ask
• Deliver dynamic, real-time recommendations at scale
• Continuously learn and improve from every interaction

AI doesn’t just make personalization more efficient—it makes it smarter, faster, and more human. Modern AI-powered personalization goes beyond traditional methods to understand not just what customers do, but why they do it—including the emotional drivers behind their decisions.

Key Benefits of Personalization

Let’s talk about what personalization actually delivers for your business:

1. Increased Customer Engagement

Tailored content captures attention. When messages resonate, people pay attention, click through, and interact with your brand.

2. Higher Conversion Rates

Relevant recommendations and targeted offers significantly boost the likelihood of purchase. Personalized calls to action outperform generic ones every time.

3. Improved Customer Loyalty and Retention

Customers stick with brands that understand them. Personalization builds emotional connections that reduce churn and increase lifetime value.

4. Enhanced Customer Satisfaction

Meeting expectations leads to satisfaction. Exceeding them with personalized experiences? That creates advocates.

5. Better Data Collection and Insights

Implementing personalization means collecting and analyzing customer data systematically. Those insights help you refine strategies, identify trends, and make smarter decisions.

6. Greater Marketing ROI

Targeted campaigns mean efficient spending. You’re reaching the right audience with messages that matter, maximizing impact per dollar spent.

7. Competitive Advantage

Stand out by showing customers you care enough to personalize their experience. In a sea of generic marketing, relevance wins.

8. Stronger Brand Perception

Brands that deliver meaningful, personalized experiences are viewed more favorably. Personalization signals that you value your customers as individuals, not just numbers.

See these 5 examples of personalization →

How Might Brands Use Personalization to Achieve Their Goals?

Personalization isn’t theoretical—it’s practical. Here’s how brands leverage it across the customer journey:

Product Recommendations

Curate product recommendations based on browsing behavior to guide discovery and increase average order value. When recommendations feel spot-on, they drive purchases.

Retargeting Campaigns

Reach people who showed interest but didn’t convert. Personalized retargeting reminds them why they were interested and gives them a reason to come back. 

See all these behavior-driven use cases →

Email Campaigns

Send personalized vouchers and emails for special occasions like customer birthdays. Drip campaigns tailored to where someone is in the buyer’s journey nurture relationships over time.

Dynamic Website Content

Change headlines, images, offers, and calls to action based on who’s visiting. First-time visitors see different content than returning customers.

Live Chat and In-App Messaging

Trigger proactive support or offers based on behavior. Someone lingering on a pricing page? Offer help. Someone about to abandon their cart? Send a nudge.

Targeting First-Time Visitors

Reach unknown or first-time visitors with modal pop-ups and how-to guides to help them navigate your site effectively.

Personalized Onboarding

Tailor the initial user experience to specific needs and interests. Help new users find value faster by showing them what matters most to them.

Content Interest Personalization

Tailor experiences based on the types of products or services visitors show interest in, creating more relevant journeys.

Check out our complete guide on personalization →

Building Your Personalization Strategy

Here’s how to build a personalization strategy that works.

Personalization Best Practices

Before diving into tactics, let’s cover the fundamentals:

1. Gather comprehensive customer data. 

You can’t personalize without data. Collect information on demographics, preferences, behaviors, and purchase history from every touchpoint.

2. Segment your audience. 

Divide customers into meaningful groups based on shared characteristics. Segmentation makes personalization manageable at scale.

3. Define clear goals. 

• What do you want to achieve? 
• Increased conversions? 
• Better retention? 
• Higher engagement? 

Clear goals guide your strategy and help you measure success.

4. Focus on relevance and value. 

Personalization should benefit the customer, not just serve your business goals. If it doesn’t add value, don’t do it.

5. Communicate transparently. 

Be clear about data collection and usage. Build trust by respecting privacy and giving customers control.

6. Leverage technology. 

Use CDPs, AI tools, and marketing automation platforms to manage data and deliver personalized experiences efficiently.

7. Test and refine continuously. 

Implement A/B testing to measure impact and optimize for better results. Personalization is an ongoing process, not a one-time project.

8. Prioritize privacy. 

Ensure compliance with regulations like GDPR and CCPA. Obtain enthusiastic consent and protect customer data.

9. Integrate data across channels. 

Create unified customer profiles by connecting data from all touchpoints. Fragmented data leads to fragmented experiences.

How to Build an Effective Personalization Strategy: 7 Best Practices

Let’s get tactical. Here’s your roadmap:

1. Collect Customer Data and Insights

This is your foundation. Gather information on customer needs, habits, and behaviors from every available source—website analytics, CRM systems, purchase history, support interactions, surveys.

Focus on first-party data (collected directly from your audience) for accuracy and privacy compliance.

2. Create Customer Segments

Group customers with similar traits, preferences, or behaviors. Start broad, then get more granular as you learn what works.

Common segmentation criteria:

• Demographics (age, location, job title)
• Behavioral (browsing patterns, purchase frequency)
• Psychographic (interests, values, lifestyle)
• Journey stage (awareness, consideration, decision)
• Engagement level (loyal, at-risk, disengaged)

3. Define Your Personalization Goals

Clearly outline what you want to achieve. Align personalization goals with broader business objectives. Examples:

• Increase conversion rates by 20%
• Reduce cart abandonment by 15%
• Improve customer retention by 10%

4. Plan How to Capture and Use Data

Establish a clear strategy for data collection, management, and application. Determine:

• What data you need
• Where it will come from
• How it will be used for personalization

5. Confirm Your Technology Stack

Ensure you have the tools to support your strategy:

• CRM for customer relationship management
• CDP (Customer Data Platform) for unified customer profiles
• Analytics tools for tracking behavior and measuring results
• AI and machine learning for predictive personalization
• Marketing automation for executing campaigns at scale

6. Develop and Activate Personalized Experiences

Implement your planned personalized content and interactions across relevant channels. Start with high-impact, manageable initiatives, then expand.

Quick wins:

• Personalized email subject lines and content
• Product recommendations on key pages
• Targeted exit-intent popups
• Behavior-triggered messages

7. Test and Refine

Continuously monitor performance, conduct A/B tests, and iterate based on results. What works for one segment might not work for another.

Track key metrics:

• Engagement rates
• Conversion rates
• Revenue per customer
• Customer lifetime value
• Retention rates

Treat personalization as an ongoing experiment. Test boldly, learn quickly, and keep improving.

What are the Challenges of Personalization?

Personalization isn’t easy. Here are the challenges you’ll face—and how to think about them:

Challenge	Description	Solution
Data Volume and Quality	Hard to manage large amounts of data; bad data leads to bad personalization.	Define a core data schema, use CDP/ETL pipelines to clean and unify data, and assign clear data ownership.
Privacy and Regulations	Customer data but must comply with GDPR/CCPA and maintain user trust.	Implement consent management, document data usage, and apply privacy-by-design (minimize, pseudonymize, give users control).
Siloed Data and Non-Unified Profiles	Customer data is scattered across tools and teams, creating fragmented customer views.	Use a common user ID, deploy a CDP (or similar) to unify profiles, and standardize key events and attributes across systems.
Technological Requirements	Advanced personalization requires capable tools and solid integrations between systems.	Start with a minimal viable stack (analytics + experimentation + CDP), integrate via APIs/events, and scale capabilities gradually.
Scalability	Difficult to personalize effectively for large audiences and many touchpoints.	Automate with rules and models, use a segmentation hierarchy (broad → refined), and reuse standardized performant components.
Content Creation	Creating enough personalized content for multiple segments is resource-intensive.	Prioritize high-impact journeys, use modular content blocks, and leverage AI to generate, adapt, and reuse strong variants.
Finding the Right Balance	Personalization can feel either too generic or too intrusive to customers.	Define red lines on what not to personalize, start with contextual/behavioral data, A/B test intensity, and use customer feedback to fine-tune efforts.

3 Successful Personalization Examples

Let’s look at real brands achieving remarkable results with personalization:

1. Clarins: Gamified Personalization That Converts

Clarins, the multinational cosmetics brand, implemented a “Wheel of Fortune” gamification experience on their EMEA websites during the holiday season. They personalized gamification gifts based on each country’s local culture, creating a seamless, engaging experience where visitors received personalized offers (promo codes) automatically applied to their baskets.

The results? An 89% increase in conversion rate and a 145% increase in add-to-basket metrics.

Why it works: The gamification element made the experience fun and interactive, while the personalized offers created urgency and value. By tailoring the concept to local cultures across different markets, Clarins proved that personalization works best when it respects regional preferences.

Read the full case study here →

2. GANNI: Styling Brand Identity Through Experimentation

Fashion brand GANNI used experimentation and personalization to strengthen their brand identity while driving commercial results. By testing different personalized experiences and content variations, they discovered what resonated most with their audience.

The result? A 12% increase in average order value.

Why it works: GANNI combined brand authenticity with data-driven personalization. They didn’t just push products—they created experiences that aligned with their customers’ style preferences and shopping behaviors.

See the full case study here →

3. Le Slip Français: Boosting E-commerce Sales with Personalized Recommendations

Le Slip Français, the iconic French fashion brand known for its “Made in France” ethos, implemented personalized product recommendations based on user location and browsing behavior. During the holiday season, they displayed exit-intent pop-ins promoting region-specific products—for example, visitors from Brittany saw “Made in Brittany gifts” with a CTA directing them to a curated landing page.

The results? A 6% click-through rate on the pop-in and an 11% increase in post-click transactions.

Why it works: By combining geolocation data with personalized product recommendations, Le Slip Français created a sense of local relevance and urgency. The exit-intent trigger captured visitors at a critical moment, turning potential bounces into conversions.

Check out the full case here →

Personalization and AB Tasty

At AB Tasty, we’ve built a platform that makes sophisticated personalization accessible to every team. We combine powerful AI capabilities with intuitive tools—so you can deliver experiences that truly resonate.

Here’s how our solutions transform personalization:

EmotionsAI: Personalization Based on Emotional Needs

EmotionsAI uses AI to analyze and segment website visitors by their emotional needs, then builds experiences to satisfy those needs. It identifies 10 distinct audience segments based on emotional intent: Competition, Attention, Safety, Comfort, Community, Immediacy, Notoriety, Understanding, Change, and Quality.

How Does EmotionsAI Work?

EmotionsAI helps you understand the psychological aspects behind personalization, leveraging emotions throughout the digital customer journey. By understanding lasting emotional needs for conversion, you can predict the ROI of your personalization efforts.

Real applications for different emotional segments:

1. Competition-oriented visitors strive to make the best choice and are influenced by customer opinions and reviews. Personalize with social proof widgets, countdown widgets, custom recommendations, and leaderboards.
2. Attention-oriented visitors want to feel special and valued. They appreciate personalized experiences and gifts. Use simple pop-in widgets, NPS widgets, progress bar widgets, and “Wheel of Fortune” widgets.
3. Safety-oriented visitors seek reassurance during the purchasing process with clear payment systems, trust badges, and guarantees.
4. Immediacy-oriented visitors want a clear, no-frills experience for quick purchases with direct product displays that aid fast decision-making.

EmotionsAI combines emotional, behavioral, and contextual data to reveal what truly motivates your audience.

Want to read more about EmotionsAI? Click here →

AdaptiveCX: Predictive Personalization

AdaptiveCX focuses on predictive personalization, utilizing AI and machine learning to process vast amounts of session journey data and real-time behavioral insights to deliver highly tailored shopping experiences.

Our solution represents the next evolution in personalization—moving beyond reactive responses to proactive prediction. The platform continuously learns from every customer interaction, building increasingly sophisticated models of individual preferences, intent signals, and purchase patterns.

How Does AdaptiveCX Work?

AdaptiveCX analyzes multiple data layers simultaneously:

1. Session Journey Data: Tracks the complete path a visitor takes through your site—pages viewed, time spent, scroll depth, click patterns, and navigation sequences.
2. Behavioral Signals: Identifies micro-moments that reveal intent—hovering over specific products, comparing items, reading reviews, checking size guides, or abandoning filters.
3. Real-Time Context: Considers current factors like device type, time of day, referral source, weather conditions, local events, and inventory levels.
4. Historical Patterns: Draws insights from similar customer journeys and successful conversion paths to predict what will resonate.

AI processes this information in milliseconds, making split-second decisions about what experience will drive the highest engagement and conversion for each individual visitor.

Read more on AdaptiveCX here →

Evi: Your AI Agent for Experimentation Insights

Evi is AB Tasty’s AI agent designed to help you move from complex data to actionable strategies. Think of it as having an expert teammate who helps you build, optimize, and scale personalized experiences.

What Does Evi deliver?

1. Idea generation: Evi Ideas scans web pages and provides data-backed ideas for experiments.
2. Experiment building: Evi helps transform concepts into buildable experiments, regardless of coding skills.
3. Report analysis: Evi Analysis interprets complex campaign data and delivers clear, actionable answers.
4. Revenue prediction: With AB Tasty’s RevenueIQ, Evi can help predict the revenue impact of tests before they’re launched.
5. Understanding visitor motivation: Evi, in conjunction with EmotionsAI, combines emotional, behavioral, and contextual data to reveal what truly motivates your audience.

Meet Evi here →

Beyond AI: The Complete Personalization Toolkit

Our AI capabilities are powerful, but they’re just part of the story. AB Tasty provides everything you need to execute personalization at scale:

Visual Editor

Create and edit personalized content without coding. Marketing teams can build experiences fast—no developer bottleneck required.

Advanced Segmentation

Segment audiences based on engagement levels (loyal vs. disengaged), content interests, and behavioral patterns. For example, show disengaged visitors first-time buyer promotions, while inviting loyal customers to brand ambassador campaigns.

Product Recommendations

AI-powered recommendation engine that suggests products customers actually want, driving discovery and conversions.

Seamless Integration

Connect with your existing tech stack—CRMs, CDPs, analytics tools—to create unified customer profiles and deliver consistent experiences across channels.

This is why AB Tasty is not just any platform. AB Tasty is your partner in going further with personalization.

Conclusion

Personalization is more than a marketing tactic—it’s how you show customers you see them, understand them, and value them as individuals.

The benefits are clear: higher engagement, better conversions, stronger loyalty, and real revenue growth. The challenges are real too: data management, privacy concerns, technological complexity.

But here’s the thing: you don’t have to solve everything at once. Start small. Test boldly. Learn quickly. Iterate forward.

With the right strategy, the right tools, and the right mindset, you can build personalized experiences that make customers feel something. And when people feel something, they stick around.

Ready to go further? Let’s build better experiences together → 

FAQ

What is the difference between personalization and customization?

Personalization is done for the user by the brand using data and algorithms. Customization is done by the user, who actively makes choices to tailor their experience. Personalization is implicit and brand-driven; customization is explicit and user-driven.

Why is personalization so important for businesses today?

Personalization improves customer experience, increases engagement and conversion rates, builds stronger brand loyalty, enhances lead generation, and provides a competitive advantage. Customers expect personalized experiences—80% are more likely to buy from companies that offer them.

What are the types of personalization?

Common types include segmentation-based, event-triggered, real-time, omnichannel, explicit, implicit, adaptive, one-to-one, and AI-driven personalization. Each approach has unique strengths depending on your goals and resources.

How does AI contribute to personalization?

AI enables brands to process vast amounts of customer data, analyze behaviors, predict needs, and deliver highly targeted experiences at scale. Advanced AI like EmotionsAI can identify emotional drivers behind customer actions, while predictive AI like Wandz.ai anticipates needs before customers express them.

What are challenges in implementing a personalization strategy?

Key challenges include managing data volume and quality, ensuring privacy compliance, breaking down data silos, integrating technology systems, scaling personalization efforts, creating enough personalized content, and finding the right balance to avoid over-personalization.

How can I get started with personalization if I have limited resources?

Start small with high-impact initiatives like personalized email subject lines, basic product recommendations, or behavior-triggered messages. Focus on one or two customer segments initially, test what works, and expand gradually. You don’t need a perfect tech stack to begin—start with what you have and build from there.

What metrics should I track to measure personalization success?

Track engagement rates, conversion rates, revenue per customer, customer lifetime value, retention rates, cart abandonment rates, and overall ROI. Choose metrics that align with your specific personalization goals.