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The travel and hospitality industry operates in one of the most competitive digital landscapes. 

With customers comparing prices, experiences, and options across multiple platforms in seconds, every element of your website can make or break a booking. That’s where feature experimentation, personalization, a/b tests, and rollouts come in—giving travel brands the power to test, learn, and optimize their digital experiences with confidence.

The Power of Experimentation

Experimentation isn’t just about testing button colors or headlines. It’s about de-risking innovation, understanding your guests, and optimizing every experience—from the first website visit to post-stay engagement. With AB Tasty, travel and hospitality brands can:

• Validate ideas before full rollout
• Personalize journeys for every traveler segment
• React quickly to market changes and guest feedback
• Drive measurable business impact—fast

Let’s look at how leading brands are using AB Tasty to solve real challenges and unlock growth. In this article, we’ll explore five travel and hospitality use cases that demonstrate how experimentation and personalization strategies are driving measurable results.

1. Creating Urgency with Countdown Timers

The Challenge:
A major theme park operator had been using countdown timers on their season pass pages during promotional periods, but they had never actually tested whether these timers were driving purchases—or just taking up space.

The Experiment:
The team ran an A/B test to measure the true impact of countdown timers on their season pass sales page. The timer was designed to create urgency and encourage faster purchasing decisions during limited-time offers.

The Results:
The test confirmed what many marketers assume but rarely prove: urgency works. The variation with the countdown timer delivered a +7.2% increase in transaction rate, with particularly strong performance on desktop, where the majority of purchases occurred.

Key Takeaway:
Don’t assume your tactics are working—test them. Even widely used conversion techniques like countdown timers deserve validation through experimentation.

2. Smarter Sorting with Algorithmic Recommendations

The Challenge:
A travel booking platform was sorting their listing pages by ascending price—a logical approach, but one that didn’t always surface the most relevant offers for customers. Lower prices don’t always mean better value, and the team suspected they were leaving revenue on the table.

The Experiment:
Using feature experimentation, the team tested a new “Recommended” sorting algorithm that factored in product relevance and historical performance data, rather than price alone.

The Results:
The smarter sorting approach delivered impressive gains:

• +2.8% revenue uplift
• +4.6% increase in average order value (AOV)

Key Takeaway:
How you present options matters as much as what options you present. Algorithmic sorting that considers relevance and performance can guide users toward better choices—and better business outcomes.

3. Turning Dead Ends into Opportunities with Personalized Alternatives

The Challenge:
When travelers searched for a route with no available flights, they hit a frustrating dead end: a cold “no flights available” message. This led to drop-offs, lost revenue, and a poor user experience.

The Experiment:
Instead of showing an error message, the team implemented a personalized experience that displayed smart alternatives—nearby airports, flexible date options, or connecting routes. 

The Results:
The personalized approach transformed a point of frustration into a conversion opportunity:

• +35% increase in flight search modifications
• +14% improvement in conversion rate

Key Takeaway:
Every dead end is an opportunity in disguise. Personalization can rescue frustrated users and turn potential abandonment into completed bookings.

4. Testing Discount Framing: Percentages vs. Monetary Values

The Challenge:
A travel company was presenting discounts as percentages (e.g., “Save 15%”), but they weren’t sure if this framing was maximizing customer response. Would travelers respond better to seeing the actual monetary savings?

The Experiment:
The team tested a variation that displayed monetary savings instead (e.g., “Save up to $1,500 per couple”) across all devices.

The Results:
The monetary framing significantly outperformed the percentage version:

• +41% increase in revenue
• +18% more clicks on the homepage promotional link

Key Takeaway:
How you frame value matters. For high-ticket travel purchases, concrete monetary savings can feel more tangible and compelling than abstract percentages.

5. Elevating Trust Signals for Higher Conversions

The Challenge:
A travel operator had key trust signals—their Price Promise guarantee and 24/7 customer support—displayed in a banner on their homepage. However, the banner was positioned too low on the page, limiting its visibility and impact on user confidence.

The Experiment:
The team tested moving the trust signal banner higher on the page to make these reassurances more prominent earlier in the customer journey.

The Results:
The simple repositioning delivered remarkable results:

• +35% increase in transaction rate

Key Takeaway:
Trust is everything in travel. Make sure your credibility signals are visible early in the user journey—don’t bury them below the fold.

Why Experimentation Matters for Travel & Hospitality

These five use cases illustrate a fundamental truth: assumptions are expensive. Whether it’s the effectiveness of urgency tactics, the optimal way to sort listings, or how to frame a discount, the only way to know what works is to test it.

Experimentation gives travel and hospitality brands the ability to:

• Validate ideas before full deployment – Reduce risk by testing changes with a subset of users first
• Make data-driven decisions – Move beyond gut feelings to measurable results
• Personalize at scale – Deliver the right experience to the right user at the right time
• Iterate quickly – Learn fast, fail fast, and continuously improve

In an industry where margins are tight and competition is fierce, the brands that embrace experimentation will be the ones that thrive.

Ready to Start Experimenting?

The travel and hospitality industry is perfect for optimization. From booking flows to search results, from promotional messaging to trust signals, every touchpoint is an opportunity to improve the customer experience and drive business results.

At AB Tasty, we have dedicated CSMs specialized in travel and hospitality to help you on your experimentation journey. Looking to see which clients are already onboard? Check out our customers page! 

The question isn’t whether you should be experimenting—it’s how quickly you can start.

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No Compromise with Security: AB Tasty’s Commitment to Safe Experimentation

At AB Tasty, we believe security should never be an afterthought. That’s why we’ve taken a major step forward by removing the use of JavaScript’s eval() function from our platform.

While eval() was once a common way to execute dynamic code for A/B testing, it’s now widely recognized as a security risk—vulnerable to code injection attacks and often blocked by strict Content Security Policies (CSPs).

What does this mean for you?

• Our platform is now fully compatible with even the strictest CSPs.
• The risk of code injection and related vulnerabilities is dramatically reduced.
• You get a safer, more robust experimentation environment—no exceptions or workarounds needed.

What Is eval() and Why Is It Considered Unsafe?

eval() is a native JavaScript function that takes a string of code and executes it as if it were written directly in the script. This flexibility makes it convenient for scenarios where dynamic execution is needed—such as A/B testing, where variations are generated on the fly. However, eval() is often considered a security risk because:

• It executes code without validation, making it a potential gateway for malicious scripts.
• It is vulnerable to code injection attacks, which can compromise a website’s security.
• Many modern security policies, including Content Security Policies (CSPs), explicitly prohibit or restrict the use of eval().

Security at Every Level

Our commitment to security goes far beyond code execution. Here’s how we keep your data and your business safe:

• Data Protection: All data is encrypted in transit and stored securely on Google Cloud infrastructure.
• Access Management: Only a select, authorized team can access customer data, with all access logged and regularly reviewed.
• Authentication & Permissions: We support strong password policies, multi-factor authentication, and role-based access control.
• Continuous Monitoring: Regular security audits, penetration tests, and real-time monitoring ensure ongoing protection.
• Incident Response: We have clear procedures for managing and communicating any security incidents.

Choosing AB Tasty: A Safer Choice in A/B Testing

Security-conscious businesses now have an additional reason to choose AB Tasty over other Customer Experience Optimization providers. While some major providers still rely on eval(), our eval()-free approach offers a safer alternative without compromising performance.

By proactively adapting to modern security standards, AB Tasty ensures that our clients can run experiments without worrying about security vulnerabilities or policy restrictions.

The Future of Secure A/B Testing

Security and performance go hand in hand. At AB Tasty, we continuously evolve to meet the highest standards of safety and efficiency. Removing eval() is just one of the many steps we take to provide a secure, high-performance CRO experience.

If you’re looking for a compliant, secure, and high-performing experimentation platform, AB Tasty is the solution. Contact us today to learn more about how we can help you optimize your website—safely and effectively.

FAQs about security and privacy at AB Tasty:

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AB Tasty is thrilled to announce that Wandz.ai, the real-time adaptive customer experience platform, is officially joining our family. This partnership is more than just a handshake; it’s a leap forward for digital experience optimization everywhere, and it’s a game-changer for personalization.

Why Wandz? Because Digital Journeys Are Messy—and We’re Here for It

Everyone in the digital space knows that online journeys are rarely straightforward. 

Visitors pop in mid-funnel, juggle multiple tabs, bounce between devices, and most of them remain anonymous. In fact, 90% of your website visitors don’t log in or leave a data trail. But here’s the twist: when brands adapt to these unpredictable behaviors, conversion rates can skyrocket.

Wandz was built for this challenge. Wandz technology captures real-time intent signals—every click, scroll, and session pattern—and instantly adapts the experience for each visitor, even if they’re flying under the radar. Think of it as a digital guide that knows what your customers want before they do.  The key to personalizing for your visitors is also understanding your users’ intent. That’s how you’ll know what to offer them next.

What’s in It for You, Personally?

With Wandz joining AB Tasty, we’re not just adding new features—we’re unlocking a whole new level of personalization and engagement for your brand.

• Real-Time, Adaptive Personalization: Wandz’s AI engine processes 3.7 trillion data points every month, serving 1.5 billion users worldwide, and generates predictions in just 20 milliseconds. That’s not just fast—it’s lightning fast.
• Proven Results: Leading brands in the digital e-commerce space have already seen up to 15% revenue lift, 13.6% higher conversion rates, and 65% more content engagement thanks to Wandz’s adaptive approach.
• Full Transparency: No black-box mysteries here. Every data point is transparent, accessible, and actionable—so your teams can make informed decisions with confidence.

The Power of Predictive and Generative AI—Now in Your Hands

By bringing Wandz’s AI into the AB Tasty platform, we’re giving you the tools to move from reactive to proactive. Imagine forecasting behaviors, segmenting intelligently, and building models tailored to your unique customer journeys—all while optimizing every touchpoint in real time.

With generative AI, you can create and refine personalized experiences at scale, ensuring every interaction feels relevant and unique. Predictive AI keeps fine-tuning performance and outcomes, so you’re always ahead of the curve.

What’s Next? Smarter, More Adaptive Experiences for All

This isn’t just an acquisition—it’s a partnership for the future. By combining Wandz’s talent and technology with AB Tasty’s global reach and culture of innovation, we’re setting a new standard for adaptive personalization and customer experience.

Soon we’ll be rolling out these new AI-powered capabilities across all our clients. Whether you’re in marketing, product, or development, you’ll have the tools to analyze, strategize, and optimize like never before—transforming simple interactions into adaptive experiences that drive real business results.

Welcome to the Next Chapter of Digital Experience

So, what’s new at AB Tasty? Only the most advanced, adaptive, and engaging digital experiences on the market. We can’t wait to see what you’ll create with the combined power of Wandz and AB Tasty.

Stay tuned—this is just the beginning. The future of customer experience is adaptive, and we’re excited to help you lead the way.

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At AB Tasty, we know that data is the foundation of every great digital experience.

Getting accurate analytics shouldn’t feel like a guessing game. But with privacy changes, ad blockers, and browser updates, even the best teams have struggled to get clean, reliable data from their websites. We’ve seen it firsthand, and we knew something had to change.

That’s why we revamped our Google Analytics 4 (GA4) connector. It’s designed to make your data more trustworthy—without adding complexity for your team.

Designed for Today’s Data Challenges

Modern websites face a host of obstacles when it comes to data collection: privacy settings, ad blockers, and evolving browser behaviors can all impact the reliability of analytics. Our new GA4 connector is built to address these realities head-on, ensuring your data remains consistent and actionable.

Server-to-Server Integration for Unmatched Reliability

By sending events directly from AB Tasty’s servers to Google Analytics, we bypass the common pitfalls of browser-based tracking. This means your analytics are less susceptible to blockers and delays, and you can trust the numbers you see.

What’s Different?

The real shift is under the hood. Instead of sending data from the browser (where it can get blocked or delayed), our connector sends events straight from AB Tasty’s servers to GA4. This cuts out the usual noise and means your numbers in AB Tasty and GA4 finally match up.

We’ve made a significant enhancement to how data flows between AB Tasty and GA4. Instead of sending two separate hits—one to AB Tasty and another to GA4— to each platform, we now collect events once in AB Tasty, enrich them, and seamlessly send that unified data to GA4. 

This streamlined process ensures that what you see in AB Tasty is exactly what appears in GA4—delivering near-instant updates and even greater accuracy across your reports.

Easy Set Up

You don’t need to be an engineer to get started. With our connector, setup is straightforward—just add your GA4 Measurement ID and API Secret in AB Tasty, and you’re good to go. No need for complex engineering or code changes, so your teams can focus on what matters: driving results.

Built for Scale, Backed by Experts

This connector is more than a technical upgrade—it’s a reflection of AB Tasty’s commitment to innovation and customer success. Our Data and DevOps teams have prioritized both reliability and scalability, with a target of less than 7% data discrepancy between AB Tasty and GA4. We’re setting a new benchmark for the industry.

Competitive Advantage: Why AB Tasty Leads the Way

AB Tasty is the first to address client-side tracking while following Google best practices with server-to-server hits sending.

AB Tasty’s early investment means clients benefit from a proven, reliable solution today. Whether you’re looking to solve existing analytics challenges or simply want the peace of mind that comes with trustworthy data, AB Tasty’s GA4 connector is here to help you succeed.

The Bottom Line

For brands that rely on data accuracy, the stakes are high. Data accuracy drives smarter decisions, better customer experiences, and stronger business outcomes.

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Let’s be honest: proving ROI in digital experimentation has often felt like trying to solve a Rubik’s Cube, blindfolded.

For years, teams have been stuck in the same old debate: “This test boosted conversion rate, but AOV dropped!” or “AOV is up, but conversions are down!” Which is better? Who’s right? And most importantly, how do you convince your CFO that your work is actually making money?

The Problem: Conflicting Metrics and Stalled Decisions

Picture a scenario familiar to many: one campaign variation boosts conversion rate but lowers AOV, while another does the opposite. Which is the real winner? Without a unifying metric, teams get stuck in endless debates, unable to confidently declare a result or justify next steps. As a result, a significant share of tests end up in limbo, and existing ROI dashboards suffer from low trust and adoption.

In fact, last year, 8% of A/B test campaigns with a transaction goal ended up in the dreaded “no decision” zone.

The Solution: RevenueIQ

Now, imagine a world where you don’t have to choose between conversion rate and AOV. Where you don’t have to explain why one number went up while the other went down. Where you can walk into any meeting and say, “Here’s a monthly revenue projection of exactly how much revenue this campaign is generating.”

That’s the world RevenueIQ is building.

RevenueIQ is AB Tasty’s new, patented metric—a result of years of R&D and statistical expertise. It takes all the messy, contradictory data and boils it down to one simple, business-focused number: monthly revenue projection. No more “unclear winners.” No more “conversion vs. AOV” dilemmas; just a clear, actionable view of financial impact.

Key Benefits of RevenueIQ:

• Always a Clear Revenue View: RevenueIQ provides a direct view of revenue per visitor and per month, making it easy to see the financial impact of every campaign.
• Eliminates “Unclear Winners”: Even in complex scenarios, RevenueIQ provides a definitive view of which variation delivers the most revenue. This means no more inconclusive campaigns and a significant reduction in “undecidable” tests.
• Delivers faster results: Combining the two key metrics into a single revenue metric gives actionable insights more quickly than analyzing them separately
• ROI Projections Before Going Live: Teams can now project the revenue impact of a campaign before full rollout, complete with confidence intervals for best- and worst-case scenarios. This transparency helps avoid surprises and supports more strategic decision-making.
• Strong Differentiation: RevenueIQ is powered by a unique, patented Bayesian engine, ensuring robust, trustworthy results. It’s not just another metric—it’s the business truth.

Revenue IQ is able to give a confidence interval around the revenue prediction, allowing for better business decisions. With a clear best-case and worst-case scenario, business decisions are easier than just having one number without knowing its accuracy.

The best thing? No competitor currently offers an equivalent.

How RevenueIQ Works

RevenueIQ is deeply integrated into the AB Tasty platform. When a campaign runs, users see not just conversion rates or AOV, but a clear revenue uplift per visitor and per month. The system visually identifies the winning variation, quantifies the potential monthly revenue gain, and provides confidence intervals to show the range of possible outcomes.

For example, if a variation is projected to generate an additional €4,000 per month, RevenueIQ will also display the probability of this outcome and the range (e.g., €2,700–€6,000). This approach is both rigorous and transparent, helping teams make decisions with confidence.

More RevenueIQ resources:

• Curious to know more details? Please read our RevenueIQ Whitepaper for a full scientific explanation written by our Data Scientist, Hubert Wassner.
• Not a data scientist, but still want to know more? Read our RevenueIQ article that goes into more detail about the practical use of RevenueIQ.

The Impact: Confidence, Adoption, and Differentiation

With RevenueIQ, AB Tasty is setting a new standard for ROI proof in digital experimentation. The result is a simple, credible, and actionable narrative for QBRs and renewal discussions—one that restores confidence, drives adoption, and differentiates AB Tasty in a crowded market.

Revenue IQ lowers the risk of making business mistakes by focusing on revenue.

For teams tired of inconclusive debates and complex metrics, RevenueIQ offers a new way forward: clear, credible, and actionable proof of business impact.

FAQs about RevenueIQ

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Let’s face it: launching a new feature can feel a bit like walking a tightrope. You want to wow your users with something fresh, but you also know that even the best-tested releases can have surprises lurking in the shadows.

What if you could take the nerves—and the guesswork—out of your next launch? That’s exactly what Progressive Rollout is here to do.

The Problem: Risky Feature Releases and Manual Workarounds

Picture this: your team has spent weeks (maybe months!) building a new payment system, a revamped booking flow, or a shiny loyalty program. You’re excited. But you’re also worried. What if something breaks? What if a bug slips through and impacts thousands of users at once?

This is the reality for most product and engineering teams. The stakes are high, and the pressure to “get it right” is real. That’s why so many teams look for ways to release new features gradually—starting with a small group, then expanding as confidence grows.

But here’s the catch: most teams don’t have a dedicated tool for this. Instead, they put together workarounds using feature toggles or A/B tests. These methods can work, but they’re clunky, manual, and often lack the visibility and reassurance everyone craves during a launch.

The Solution: Progressive Rollout

Progressive Rollout is our answer to this all-too-common problem. It’s a feature designed not just for the tech wizards, but for everyone involved in a product launch—product managers, developers, and even business stakeholders.

How does it work?
With Manual Progressive Delivery, you can schedule your feature release in stages. Maybe you want to start with 10% of your users, then move to 20%, 40%, and so on. You decide the pace and the audience.

Our platform handles the rest, automatically exposing more users to your new feature at each step. And at every stage, you get clear notifications and a visual overview, so you always know exactly what’s happening.

What Makes Progressive Rollout a Game-Changer?

1. It’s Actually Easy to Use
Let’s be honest: many “enterprise” tools are intimidating. Progressive Rollout is different. The interface is clean, intuitive, and designed so that anyone can set up a rollout in just a few clicks. No advanced segmentation or manual math required. Whether you’re a seasoned developer or a product manager new to experimentation, you’ll feel right at home.

2. Full Control, Full Reassurance
One of the biggest anxieties during a rollout is not knowing what’s happening. With Progressive Rollout, you get a crystal-clear view of your rollout plan: who’s getting the feature, when, and how much of your audience is included at each step. Email notifications keep you in the loop, so you’re never caught off guard. This transparency isn’t just a nice-to-have—it’s a must for teams who want to move fast and stay safe.

3. Flexible for Any Scenario
Want to give early access to your VIPs or most loyal users? Easy. Need to roll out to everyone, but in controlled increments? No problem. You can import user lists, target specific segments, or just roll out to “all users” in stages. Progressive Rollout adapts to your needs, not the other way around.

Fun Fact: Most Teams Aren’t Doing This—Yet

Here’s something surprising: despite the clear benefits, most teams aren’t using dedicated progressive rollout tools. They’re still relying on toggles and A/B tests, or even manual processes. Why? Because until now, the tools have been too complex or not user-friendly enough. Progressive Rollout changes that, making safe, staged launches accessible to everyone.

The Bottom Line: Launch With Confidence

Progressive Rollout isn’t just another feature—it’s peace of mind for your next big launch. By making gradual releases easy, transparent, and accessible, we help you reduce risk, improve user experience, and focus on what matters: delivering value to your customers.

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Picture this: You’ve just launched a new product line, or maybe you’re gearing up for a themed campaign–think “Back to School” or a limited-edition collection. You want your customers to discover the right products, right away. But traditional recommendation engines are stuck waiting for data to trickle in, leaving you with generic suggestions and little control over what’s shown. For merchandisers, that’s not just frustrating – it’s a missed opportunity.

That’s exactly why we built AB Tasty’s Semantic Proximity Algorithm. Instead of relying on yesterday’s sales numbers, this new approach lets you craft relevant, business-driven product recommendations from day one. Whether you’re working with a fresh catalog or pivoting to a new campaign, you get the flexibility and control you need –  no waiting, no guesswork, just smarter recommendations tailored to your goals.

From Algorithm to Merchandiser Mindset

Most recommendation engines are just that – algorithms. But AB Tasty’s Semantic Proximity Algorithm is a paradigm shift: it allows your catalog to think like a merchandiser. Instead of passively waiting for data, it actively understands your products, your campaigns, and your business goals – giving your catalog a brain and putting you in the driver’s seat from day one.

Why Rethink Product Recommendations?

Traditional recommendation algorithms are built on analytics data – think Google Analytics or similar tools. These models can be effective, but only if you have enough historical data. What happens when you launch a new product line, a new brand, or want to activate a campaign around a specific theme (“Back to School,” “Harry Potter,” etc.)? Merchandisers are often left with little control, unable to quickly tailor recommendations to their business needs or campaign goals.

This is the challenge that inspired us to create the Semantic Proximity Algorithm: a tool that empowers merchandisers to launch relevant, business-driven recommendations instantly, even with zero historical data.

The Semantic Proximity Algorithm: A New Approach

AB Tasty’s Semantic Proximity Algorithm takes a fundamentally different approach. Instead of relying on analytics data, it leverages advanced Natural Language Processing (NLP) to analyze the attributes of your product catalog – such as product name, description, category, price, and even custom metafields. This allows the algorithm to identify products that are semantically related, regardless of whether they have ever been purchased together.

Key benefits include:

• Fast ROI: Campaign launches, upsell, cross-sell
• Instant setup: No need to wait for analytics data to accumulate. Recommendations are ready as soon as your catalog is integrated.
• Total flexibility: Merchandisers can select and combine any catalog attributes to build strategies and adapt recommendations on the fly for seasonal events or business needs.
• Full control and transparency: Preview and iterate on recommendations before going live, ensuring relevance and quality.
• Adaptable for all expertise levels: The algorithm is as simple or as advanced as you need. SMBs can start with just product names, while advanced users can leverage dozens or even hundreds of attributes for highly customized strategies.

Previously, recommendation engines were blind – waiting for clicks, sales, and data to slowly trickle in before making generic suggestions.

AB Tasty’s Semantic Proximity Algorithm delivers instant, intelligent recommendations. As soon as your catalog is integrated, the algorithm “thinks” like a merchandiser – making smart, relevant suggestions based on product meaning, not just past behavior. No more waiting, no more guesswork -just instant, business-driven recommendations that adapt as quickly as you do

Unique on the Market

No direct competitor offers this level of semantic attribute selection and flexibility. While some platforms provide basic attribute filtering, none allow merchandisers to select and combine multiple catalog attributes to fine-tune recommendations. Most competitors still rely mainly on analytics and sales data, with only limited semantic analysis capabilities.

This is a true differentiator for AB Tasty, empowering clients to adapt their recommendation strategies to their unique business challenges – without being held back by data limitations.

How Does It Work in Practice?

The Semantic Proximity Algorithm is designed to be both powerful and user-friendly. Merchandisers can choose which attributes to use for each recommendation strategy  – whether that’s product name, description, category, price, or even custom fields like Shopify metafields. This means you can tailor recommendations for specific campaigns, themes, or business objectives.

For example, during a seasonal campaign, you might want to recommend products that share a common theme in their description or category, even if they’ve never been purchased together before. Or, you might want to upsell higher-value editions of a product by prioritizing price as an attribute. The algorithm allows you to preview and iterate on these strategies instantly, making it easy to adapt to changing business needs.

Upsell, Cross-sell, and Beyond with Product Recommendations

The flexibility of the Semantic Proximity Algorithm opens up new possibilities for both upsell and cross-sell strategies. For upsell, you can recommend alternative products that are not only similar but also more profitable. For cross-sell, you can suggest complementary items that enhance the customer’s purchase – think of the classic “chewing gum at the checkout” scenario, but tailored to your specific catalog and business logic.

This approach is especially valuable for businesses with large or complex catalogs, or those looking to launch new products and campaigns quickly. It’s also ideal for expert merchandisers who want granular control over their recommendation logic, as well as for SMBs seeking a fast, easy-to-implement solution.

Fun Facts & Unique Highlights

• Did you know? This is the first AB Tasty algorithm that works directly from your product catalog–no analytics setup required.
• Unique on the market: No competitor allows merchandisers to select and combine multiple catalog attributes (including custom metafields) to fine-tune recommendations.
• Instant preview: You can see and iterate on your recommendation strategies before going live – perfect for adapting to seasonal campaigns or special events.
• Scalable: The algorithm can handle catalogs with hundreds or even thousands of attributes per product.

Conclusion

AB Tasty’s Semantic Proximity Algorithm ushers in a new era for product recommendations: faster, more flexible, and more intelligent. Whether you’re an SMB looking for simplicity or an enterprise seeking advanced personalization, this innovation lets you transform the customer experience and maximize revenue from day one.

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