What Is a Personalization Engine: Benefits & Tools

Personalization used to mean adding a first name to an email. Now, your customers expect product recommendations that make sense, offers that reflect their habits, and a website that adapts in real time—without them having to lift a finger.

The problem? Most teams are stuck juggling disconnected tools, rigid templates, and guesswork that slows everything down. You know your audience. You just need a way to turn that knowledge into results, fast.

This article breaks down what personalization engines actually do, how they work, and what separates a good one from the noise. We’ll also walk through the best tools on the market in 2025 (spoiler: some do a lot more than you’d think). By the end, you’ll know exactly what to look for—and where to start if you want more conversions, better engagement, and a customer experience that scales.

What is a personalization engine?

Let’s say two people walk into the same clothing store. One heads straight for the minimalist neutrals; the other, neon rave gear. But the store greets them both with identical displays. That’s a missed opportunity.

Now swap the physical store for a website. That’s where personalization engines step in.

A personalization engine is a system that customizes what each visitor sees, clicks, or reads based on their preferences, actions, and context. It analyzes user data (clicks, page views, purchase history, traffic source, device, location) and uses that to deliver dynamic content that feels relevant to the individual.

As a result, you provide a sharper, more adaptive customer experience that reacts in real time.

ASOS remembers the customer’s size and suggests fit recommendations across product pages

These engines map behavioral data to customer journey stages, adjusting everything from homepage layouts to CTAs and product recommendations. For marketing professionals, that means less guessing and more precision, without manually slicing audiences into a hundred different customer segments.

At its core, the personalization engine transforms static websites into smart, responsive experiences that convert better, retain longer, and actually resonate.

What is the difference between personalization and recommendation engine?

People often mix up personalization engines and recommendation engines, but they’re not the same. Not even close!

A recommendation engine has a narrow job: suggest items a user might like based on past behavior, similar users, or purchase history. Think Amazon nudging you with “Customers also bought…” or Netflix offering your next binge. It’s focused, transactional, and typically limited to product recommendations.

Amazon personalizes user experience with a highly tailored recommendation engine, “Customers who viewed items in your browsing history also viewed” section

A personalization engine, on the other hand, controls the entire experience. Beyond just recommending products, it shapes what users see, when they see it, and how it’s delivered across every touchpoint. It uses behavioral data, user data, and real-time context to adapt dynamic content throughout the site and beyond: web, email, mobile, even ads.

While a recommendation engine focuses on items, a personalization engine focuses on individuals. It aligns messaging with customer journey stages, tailors website personalization, and gives marketing professionals the tools to create smarter, more relevant experiences at scale.

So yes, a personalization engine may include a recommendation engine, but it's operating on an entirely different level.

Types of personalization engines

Not all personalization engines work the same way. Depending on how they process customer data and respond to user intent, they fall into a few distinct categories.

The method each one uses affects how they personalize on-site experiences, structure marketing campaigns, and adapt to real-time customer data.

Here’s what that actually looks like…

1. Content-based filtering engines

These engines rely on individual behavior. They build user profiles based on a person’s browsing history, previous interactions, and preferences. If someone spends time reading articles about lead generation, the system may surface content related to sales funnels or customer relationship management systems.

It doesn’t care what other users did; it cares about what you did. That’s the core of content-based filtering: matching new content to past choices using recognizable behavioral patterns.

2. Collaborative filtering engines

This model runs on collective behavior. It groups users with similar interests and recommends what others in that group found useful. If people who interacted with the same webinar also downloaded a particular guide, that guide becomes a suggestion for the next user who fits the pattern.

Collaborative filtering doesn’t require content to be similar, it only needs user behaviors to overlap.

Tip: It works well when you have large volumes of data to pull from, but it's less reliable with new users or niche products.

3. Hybrid engines

Hybrid engines combine both methods to improve accuracy and fill in data gaps. They analyze both individual and group behavior at once, creating a more complete personalization model.

This dual approach supports stronger conversion optimization by tailoring recommendations at multiple levels. It also helps with customer data segregation, making it easier to serve different segments with precision, even across complex marketing campaigns.

4. Rule-based personalization engines

Rather than relying on predictive algorithms, rule-based personalization engines use predefined conditions to dynamically adapt content based on real-time user attributes and behavior. These engines evaluate factors like traffic source, device type, location, browsing history, or session activity to decide what a visitor sees the moment they land on a site.

This method doesn’t require prior user data or large datasets. It’s built for speed, flexibility, and full control, letting marketers fine-tune experiences instantly without needing engineering resources or training complex models.

Tip: Rule-based engines are especially effective for on-site personalization, A/B testing, and targeting anonymous visitors with highly relevant content from the first click.

How do personalization engines work?

Personalization engines operate by collecting user data, interpreting intent, and delivering tailored digital experiences across touchpoints in real time. The process is multi-layered, with each stage designed to drive relevance, efficiency, and customer engagement at scale.

1. Data collection

The engine begins by gathering behavioral signals across all relevant sources: browsing history, content views, purchases, email interactions, location, device, and more.

A Customer Data Platform (CDP) consolidates this information, ensuring all customer data is unified and accessible. This centralized dataset forms the foundation for precise targeting and personalization across marketing channels.

2. Data analysis and intent recognition

With data in place, the system applies predictive analytics and machine learning to identify trends, map behavioral patterns, and infer user intent.

For example, repeat visits to a pricing page may indicate buying consideration, while product comparison activity could signal decision-stage behavior. An AI intent engine processes these signals in real time to determine the most relevant content or action for each user.

3. Decisioning and experience mapping

The engine uses predefined logic, machine learning models, or both to determine what content or offer to present, and when. These decisions are based on individual profiles, contextual data, and segmentation rules.

The system accounts for factors like time of day, device type, geolocation, and session activity to optimize each interaction without manual intervention.

4. Real-time experience delivery

Once the decision is made, the engine activates personalized assets, including content recommendations, promotional messages, product offers, or UI variations.

These updates are deployed instantly, ensuring users receive relevant experiences as they move through different marketing channels and platforms.

5. Continuous learning and optimization

Each interaction feeds new data back into the system. This feedback loop allows the engine to refine its targeting models, improve accuracy, and adapt to changing user behavior. Over time, this results in more precise personalization with reduced reliance on manual configuration.

6. Embedded recommendation systems

Advanced personalization engines integrate recommendation systems directly into the experience flow. These systems use real-time behavior and historical context to surface the most relevant products, services, or content, enhancing both usability and customer service outcomes.

Effective personalization engines work by unifying data, recognizing patterns, and delivering dynamic responses at scale.

When built and deployed correctly, they enable high-performing personalization strategies that are responsive, data-driven, and aligned with key goals such as engagement, retention, and conversion optimization.

What data do personalization engines use?

Personalization engines rely on a wide range of user data to shape relevant, real-time experiences. Not guesswork, it’s data-driven decision-making based on how users behave, who they are, and what they need in the moment.

To deliver effective personalization across touchpoints, engines analyze a combination of:

• Browsing history: Pages viewed, time on site, product interest, abandoned carts
• Behavioral cues: Clicks, scroll depth, session duration, exit points
• Demographic data: Age, gender, language, location, and device type
• Transactional history: Previous purchases, returns, frequency, and order value
• Lifecycle and customer loyalty status: Where the user is in their journey and how engaged they are
• Multichannel signals: Interactions across multichannel marketing hubs, including email, mobile, paid ads, and social
• Contextual data: Real-time signals like device, time of day, or referral source
• CRM and support insights: Customer value tiers, feedback, and service history

More advanced personalization engines use artificial intelligence to combine these data points in real time.

This enables them to respond to intent, rather than just past behavior, and optimize the customer experience as it unfolds.

As a result, you get more relevant content, better-timed messaging, and higher-impact personalization that feels seamless across every channel.

Benefits of utilizing a personalization engine: Does your business need one?

Today, personalization is no longer a bonus but the baseline. According to McKinsey, 71% of consumers now expect tailored digital experiences, and 76% report frustration when brands fail to deliver them. That gap directly affects performance across engagement, retention, and revenue.

What’s more, personalization isn’t just good for consumers; it pays off for businesses. Google found that 90% of marketers believe personalization improves profitability. That’s one reason 89% of companies are actively investing in it this year.

So, what do personalization engines actually deliver for your business?

• Stronger customer engagement: Relevant experiences drive deeper interaction. Personalized pages, messaging, and offers hold attention longer and reduce drop-off. The result is higher click-through, lower bounce, and greater time-on-site.
• Better return on content marketing: Personalized distribution increases the reach and performance of existing content. Engines match assets to user intent, ensuring your content meets the right audience instead of getting lost in the noise.
• Smarter use of marketing spend: By aligning messages with real-time interest, personalization cuts waste. Campaigns reach higher-performing segments, and channels perform more efficiently without constant manual intervention.
• Repeat visits and customer loyalty: Twilio reports that 56% of consumers are more likely to return after a positive personalized experience. That return behavior fuels long-term growth and builds brand relationships that don’t rely on discounts to retain.
• Stronger brand loyalty insights: Personalization engines help teams uncover what actually drives retention and repeat purchases. Over time, these patterns surface preferences that marketers can act on—beyond demographics or assumptions.
• Real-time decision-making: Integrated reporting makes it easier to visualize data and adapt quickly. Instead of guessing, marketing teams can fine-tune campaigns and content based on how users behave now, not last quarter.
• Cross-channel consistency: With integrations across web, email, mobile, and paid media, personalization engines serve as multichannel marketing hubs. This keeps messaging aligned across all stages of the journey.

For companies focused on performance, relevance, and retention, the value is clear. Without personalization, you’re not only missing opportunities but blatantly ignoring what customers already expect.

What to look for in a personalization engine: Key elements of personalization engines

The effectiveness of a personalization engine depends entirely on how well its parts work together. Each layer of the system plays a specific role: from collecting raw signals to delivering personalized experiences at scale.

Below are the essential components that power high-performing personalization engines and enable teams to move from data to results, fast.

1. Customer data aggregation

Everything starts with data. A capable engine must be able to collect and unify customer data from multiple sources, including on-site behavior, CRM systems, past purchases, and email interactions.

Without clean, centralized data, nothing downstream works. This layer acts as the foundation for all real-time personalization.

2. Real-time segmentation

Once data is collected, the engine needs to slice it into actionable segments and do it instantly.

Whether based on location, device type, browsing behavior, or product interest, real-time segmentation allows the system to adapt on the fly as the user interacts with your content.

The faster the segmentation, the sharper the personalization.

3. Content and product decisioning

A strong engine needs logic that decides what to show and to whom. That includes dynamic content recommendations, product suggestions, messages, or offers. These decisions may be driven by rule-based systems, predictive algorithms, or a combination of both.

4. Experience delivery across channels

The engine must be able to deliver personalized content across web, mobile, email, and other channels in sync.

This includes updating page elements in real time, triggering emails based on user activity, or customizing app interfaces.

Consistency across environments is critical for cohesive digital journeys.

5. Testing and experimentation

No personalization strategy works perfectly out of the box. Built-in support for A/B testing allows teams to test different variations of content, design, and messaging.

Engines should support holdout groups, audience split testing, and statistical confidence reporting; all without requiring a separate tool.

6. Analytics and performance tracking

You can’t improve what you can’t measure. Built-in analytics tools help teams understand how different personalized elements perform, track KPIs, and optimize based on real outcomes, not assumptions. This is where you find out what’s driving engagement, and what’s just background noise.

Beyond making recommendations, a well-rounded personalization engine connects customer data aggregation with real-time logic, personalized delivery, and measurement, all in one continuous flow.

The more seamless these layers, the more scalable and effective your personalization efforts will be.

What should you look for when choosing a personalization engine?

Choosing a personalization engine stretches beyond choosing features. It's all about making sure the features you choose are the right fit for your business.

The right tool should align with your data, team structure, goals, and budget, while giving you room to grow.

Here’s what to evaluate before making the call:

1. Data compatibility

The engine must integrate cleanly with your existing stack. That includes your CRM, analytics platform, CMS, and marketing automation tools.

Seamless customer data aggregation is non-negotiable. Look for open APIs, real-time sync, and support for both structured and unstructured data.

2. Real-time segmentation capabilities

Avoid platforms that rely on static lists or batch processing. You need real-time segmentation that reacts to user behavior instantly. Whether it’s product browsing, campaign interactions, or cart abandonment, the system should update segments dynamically without manual workarounds.

3. Content recommendation logic

Evaluate how it delivers content recommendations (and how flexible it is).

Can it tailor experiences for anonymous users? Does it allow for rule-based personalization and predictive targeting?

You’ll want a system that supports multiple approaches and adapts as your needs evolve.

4. Built-in A/B testing and experimentation

Testing should be native, not bolted on. If a tool can’t run A/B tests with clear confidence metrics, holdouts, and variant control, it’ll be harder to prove ROI or improve performance over time.

Tip: Prioritize platforms with digital experimentation built into the workflow.

5. Robust analytics tools

Look for engines with transparent, real-time reporting. You should be able to track conversions, lift, engagement by segment, and overall campaign effectiveness, without exporting spreadsheets or relying on a third-party BI tool. Strong analytics tools are essential for accountability and optimization!

6. Scalability and governance

As your traffic and team grow, the platform should keep pace. Check how well it handles large datasets, high-traffic environments, and campaign complexity.

Bonus if it supports version control, user permissions, and audit trails (especially important for cross-functional teams).

7. Omnichannel support

The engine should work across all major digital experiences, including web, mobile apps, email, and paid media. Siloed personalization fragments the experience.

Look for tools that act as true multichannel marketing hubs, not just web-only solutions.

8. SaaS support and onboarding

Even the most advanced tools fall flat without proper implementation. Review what onboarding looks like, how fast your team can realistically launch, and whether support is included or costs extra.

Tip: You’ll want access to responsive, knowledgeable customer support, especially in the early stages.

9. Pricing model and transparency

Some tools charge based on traffic, others by users, data volume, or modules.

Make sure you understand the pricing structure, where the cost scales, and whether features you need are locked behind premium tiers. Hidden costs kill velocity.

10. User experience and ease of use

Your marketing team shouldn’t need an engineer to launch basic personalization. Prioritize platforms with intuitive interfaces, clear workflows, and documentation that actually helps.

Tip: Time-to-value matters. If it takes six months to get a campaign live, that’s six months of missed revenue.

A good personalization engine should be a growth driver, not a burden. Focus on tools that make your team faster, not more dependent. The right engine will support your goals, scale with your business, and remove friction across every stage of the customer journey.

6 best personalization engines in 2025: Features, pros and cons, prices

You’ve seen the value: personalization drives customer engagement, improves ROI, and turns digital experiences into competitive advantages. But to actually unlock those benefits, you need something bigger than just theory. You need the right tool powering it all.

And no, the best personalization engine isn’t the one that checks the most boxes on a comparison chart. It’s the one that fits your stack, matches your goals, and scales with your team’s workflow.

To help you make that decision, we’ve rounded up six of the top-performing personalization engines in 2025. Each comes with a detailed look at its core features, strengths, drawbacks, and pricing, so you can find the one that’s actually worth your budget and bandwidth.

1. Personizely

More than just a personalization engine, Personizely is an all-in-one conversion rate optimization (CRO) platform. It's designed to help businesses boost engagement, improve website performance, and drive more sales — all from one place.

Personalization is a key part of the package, but it's only one piece of a broader toolkit that includes A/B testing, on-site widgets, behavioral targeting, and seamless integration with other tools from your stack. Everything is designed to work together from a single, user-friendly interface.

What really sets Personizely apart is its focus on real-time, on-site personalization. Instead of using predictive algorithms or recommendation engines, it responds instantly to actual visitor behaviour, like where someone came from, what device they’re using, or what they’ve browsed.

Thanks to this approach, your website visitors see the most relevant version of your site from the moment they land, and you don't need to write a single line of code to make it happen!

Personizely is trusted by over 2,600 brands, including names like Davines, Mavi, and Corona. With 5/5 ratings on Wix and Shopify, and 4.9/5 on Capterra, it’s clear users value how simple and effective the platform is.

For marketers looking for a powerful, accessible personalization solution that fits into a bigger CRO strategy, Personizely stands out as a top choice.

Key features of Personizely:

• No-code visual editor for real-time content changes: swap CTAs, change copy, hide elements, update images, and more
• Unlimited campaign variations per page, targeted by segment
• Advanced targeting rules based on geolocation, traffic source, device, session history, cart content, form data, and even weather
• Behavior-based triggers for showing personalized messages, offers, and widgets
• In-depth performance analytics for each personalization campaign
• Built-in A/B testing to optimize variations without external tools
• Powerful widget builder and a gallery of ready-made customizable widget templates
• Seamless integration with email marketing platforms, digital commerce platforms, analytics tools, and more

Pros and cons of Personizely:

Personizely pricing:

Personizely offers a 14-day free trial with full access to all features, no credit card required.

Website personalization is included by default in both the Premium and Business plans. There's no need to purchase it as an add-on or unlock it through extra modules.

• Premium: Starts at $47/month, billed annually
• Business: Starts at $311/month, billed annually

2. Adobe Target

Adobe Target is part of the Adobe for Business suite and closely integrated with Marketing Cloud and the Adobe Experience Platform. Unlike standalone personalization tools, Target operates as a dedicated layer within a larger ecosystem. It's built for enterprise teams that need advanced campaign orchestration and unified data across channels.

Adobe Target is designed to support complex personalization and testing programs at scale. With a mix of rule-based targeting, AI algorithms, and machine learning technology, it personalizes content in real time using behavioral, contextual, and historical data. Its flexible setup supports everything from basic A/B tests to dynamic, multi-channel experiences across web, mobile, and email.

What makes Target stand out is its ability to manage detailed audience logic, update profiles in real time, and sync directly with tools like Adobe Analytics and Real-Time CDP. This allows teams to activate insights quickly and keep campaigns aligned across the full marketing stack.

Key features of Adobe Target:

• Real-time decisioning based on session behavior, device, location, and cross-channel activity
• AI-powered Auto-Target to automatically select and serve the most relevant experience variation for each visitor
• Offer pairing and content ranking based on user profiles, recent activity, and purchase intent
• Cross-audience personalization within a single campaign
• Centralized campaign orchestration across web, mobile, and other Adobe Experience Cloud tools
• Built-in AI assistant tools for content suggestions, use case planning, and testing strategy recommendations
• Advanced A/B and multivariate testing with auto-allocation and statistical modeling

Pros and cons of Adobe Target:

Adobe Target pricing:

Adobe Target does not offer public pricing or pre-set plans. Access is quote-based, and pricing varies depending on traffic volume, use case complexity, and integration needs. It’s best suited for enterprise organizations already using Adobe’s Marketing Cloud and related products.

3. Optimizely

A veteran in the CRO space, Optimizely has been shaping the experimentation and digital experience landscape since 2010. While best known for its web and feature testing tools, Optimizely now offers a modular suite that covers everything from personalization to content management, commerce, and analytics. Each module is purpose-built and designed to plug into a larger optimization system, allowing teams to scale at their own pace, based on their needs.

The Personalization module is a dedicated solution for marketers who want to deliver 1:1 experiences at speed and scale. What makes it stand out is its strong focus on marketer usability: the visual editor, pre-built templates, and real-time segmentation make it easy to build and launch targeted experiences without bottlenecks.

Combined with fast delivery and detailed performance reporting, Optimizely’s personalization engine helps teams move quickly while keeping ROI front and center.

Key features of Optimizely:

• Real-time audience segmentation that updates instantly as visitors interact
• Event-based triggers for personalized experiences based on user actions like clicks or form submissions
• Visual WYSIWYG editor to build, preview, and launch experiences with zero code
• Pre-built templates and design elements for banners, timers, and dynamic UI blocks
• Behavior-based delivery that ensures experiences load quickly and without flicker
• Flexible collaboration tools to manage workflows, approvals, and version control in shared workspaces
• Integrated experimentation tools including Stats Engine for high-accuracy testing
• Deep analytics capabilities and ROI tracking with breakdowns by device, traffic source, and user segment

Pros and cons of Optimizely:

Optimizely pricing:

Optimizely does not provide public pricing, and each module (including personalization) is priced separately. To understand the full cost structure, we recommend reviewing our detailed breakdown in the article: Optimizely Pricing Explained.

4. Dynamic Yield

Dynamic Yield has built its reputation on helping enterprise brands deliver consistent, personalized experiences across every digital touchpoint. Now operating under Mastercard, the platform continues to lead in the personalization space with a product that’s flexible, fast, and built to scale.

At the heart of Dynamic Yield is its Experience OS, a modular system designed to support real-time targeting, AI-driven content delivery, and cross-channel optimization. Unlike tools that require workarounds to sync with your stack, Dynamic Yield connects directly with your CMS, CRM, data layer, and analytics tools. Everything from recommendations to journey orchestration is handled in one place, with enough flexibility to support industries as diverse as finance, eCommerce, travel, and B2B.

Key features of Dynamic Yield:

• AffinityML engine to surface content and product recommendations based on evolving user preferences
• Behavioral and predictive segmentation for real-time audience targeting
• Event-based triggering to personalize experiences based on live customer actions
• Omnichannel delivery across web, mobile, email, kiosks, and more
• A/B and multivariate testing built directly into the platform with streamlined setup
• Experience templates and playbooks for fast deployment of proven strategies
• Shared workspace environment for managing approvals, version history, and cross-team collaboration
• Open architecture with integrations across CMS, CRM, ESP, analytics, and data platforms

Pros and cons of Dynamic Yield:

Dynamic Yield pricing:

Dynamic Yield does not publish pricing online. Each plan is tailored based on business size, traffic volume, and technical requirements. To learn more about costs and available packages, businesses must request a custom quote from the sales team.

5. Insider

Insider positions itself as a growth management platform built for marketers who want real-time personalisation at scale, and across every channel.

Where it really shines is in the blend of real-time decisioning, cross-channel orchestration, and rapid execution. With over 100 pre-built templates, 20+ recommendation strategies, and a growing set of generative AI tools under the Sirius AI™ umbrella, Insider gives marketers the ability to launch personalized campaigns in minutes and refine them automatically based on performance.

Its interface is marketer-first (no code required) but backed by serious technical muscle. Insider’s segmentation capabilities are powered by 120+ rules and attributes, real-time triggers, and predictive models. Combined with one of the fastest time-to-value scores in the market, the platform is designed to move fast and scale without breaking.

Key features of Insider:

• AI-powered real-time segmentation and triggers based on 120+ attributes
• Sirius AI™ for predictive audience building, journey orchestration, and auto-generated copy
• Cross-channel personalization across web, app, email, push, and messaging platforms
• Dynamic recommendations using product attributes, user affinity, and pre-configured strategies
• Cookieless personalization based on contextual data for users who decline tracking
• Built-in A/B testing with AI-powered winner auto-selection
• Visual campaign editor with pre-built templates and rapid deployment workflows

Pros and cons of Insider:

Insider pricing:

Insider does not publish its pricing publicly. Plans are customized based on business size, usage, and feature requirements.

While exact costs vary, reported pricing suggests entry points around $4,800/month, with enterprise-level access averaging $48,000+ annually.

6. Nosto

Nosto is a commerce experience platform (CXP) built for modern retailers looking to unify search, merchandising, and personalization into a single, AI-driven layer. It specializes in helping ecommerce brands deliver deeply relevant, high-converting shopping journeys without requiring heavy development resources.

Nosto makes personalization the connective tissue across content, recommendations, and search. It uses predictive, semantic, and generative AI to dynamically adjust experiences based on real-time behavior, segment data, and business objectives.

It’s flexible enough for custom builds, fast enough for growing teams, and advanced enough to meet enterprise demands. And unlike many alternatives, Nosto puts intuitive tooling in marketers' hands, removing technical blockers while still offering deep configurability when needed.

Key features of Nosto:

• Predictive product recommendations based on real-time behavior, product affinity, and performance goals
• AI-powered search personalization using vector search, semantic AI, and intent-based merchandising rules
• Generative AI tools for auto-writing campaign headlines and optimizing synonyms for better search outcomes
• Built-in A/B testing for content, search results, and recommendation strategies
• Visual campaign builder with customizable templates and scheduling features
• Score insights and analytics to understand the business impact of every personalized element

Pros and cons of Nosto:

Nosto pricing:

Nosto’s pricing model isn’t publicly standardized, as it’s tailored to each business. However, most merchants can expect to pay between 2.5–4% of the revenue generated through Nosto.

Ready to elevate your customer experience?

Personalization has become essential for brands that want to increase engagement, drive conversions, and build loyalty. It’s no longer just about showing the right product—it’s about delivering timely, relevant experiences across every website or app interaction.

The right engine helps you use your data with precision, respond to behavior in real time, and streamline how campaigns are built and deployed.

But effectiveness depends on more than features. You need a platform that aligns with your strategy, adapts to your stack, and keeps pace with your goals.

Personizely makes that possible. With real-time targeting, flexible campaign control, and powerful CRO tools in one place, it gives marketers full control without added complexity. There’s no waiting on dev teams or jumping between platforms. Just fast, focused personalization that gets results.

Start your free 14-day trial today and see how Personizely helps you turn traffic into outcomes.

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