Article

7min read

From Lookers to Bookers: The Small Tests Fueling Hotel Growth

Let’s be honest: your hotel’s real front door is digital. The entire guest experience now begins not in a lobby, but on a landing page. That first, make-or-break moment of hospitality has moved online, and it all kicks off with a single click.

For modern travelers, the digital experience isn’t a prelude to their stay; it’s part of it. They expect the same level of intuitive, personal service from your website that they’d expect in your lobby. They’re looking for a seamless journey, one that understands what they need before they even have to ask.

Delivering that isn’t about a massive, one-time overhaul. It’s about embracing a mindset of continuous optimization. It’s about seeing every interaction as a chance to learn, to test, and to improve. It’s about moving from “trial and error” to “trial and better.” This is the path to turning lookers into bookers and first-time visitors into lifelong guests. Let’s walk through how to build it, one step at a time.

From landing pages to lobbies

The shift is simple but profound: your website is no longer just a tool for transactions, it’s the start of the entire guest relationship. A slow-loading page, a confusing booking engine, or an offer that misses the mark doesn’t just cost you a sale, it subtly damages your brand’s promise of a stress-free, welcoming experience. The feeling a guest gets from your website is the feeling they’ll associate with your brand.

That means your digital presence needs to embody the very essence of hospitality. It should be effortless to navigate, anticipate your guests’ needs, and make them feel seen and valued from the moment they arrive. Every element, from your homepage hero image to the copy on your call-to-action buttons, contributes to this digital-first impression.

The great news is that you have more opportunities than ever to make that impression a brilliant one. While a front desk agent can only interact with one guest at a time, your website interacts with thousands. Each of those interactions is a rich source of data, a clue that can help you understand your guests on a deeper level and refine their experience. The challenge isn’t a lack of opportunity, but knowing where to start.

First, learn who’s at the door

Before you can offer a guest the perfect room, you need to know why they’re traveling. Are they on a family vacation, a solo business trip, or a romantic getaway? Just as a great concierge listens before making a recommendation, a great website must first understand user intent. Your visitors are telling you what they want through their behavior, you just have to learn how to listen.

Evolve separates its traffic sources

A fantastic example of this comes from Evolve Vacation Rental. They recognized that not all traffic is created equal when it came to attracting new homeowners to list their properties. A visitor arriving from a targeted Google search for “how to rent my vacation home” has a very different intent than someone who clicked a beautiful, brand-aware ad on Facebook. The first user is actively looking for a solution and is ready for details about fees, services, and qualifications. The second is likely in an earlier, more curious phase, just exploring the possibility.

By separating these traffic sources, Evolve was able to tailor its landing pages to match the visitor’s mindset. The high-intent Google visitor got straight to the point with clear calls-to-action and qualifying questions, while the Facebook visitor received more inspirational content. It’s a simple, powerful idea: speak to the journey your guest is on, not just the one you want them to take. Start by analyzing your traffic sources. What are your visitors’ search queries telling you? How does engagement differ between channels? Every click is a clue.

Using segmentation to deliver relevant offers

Once you have a sense of who’s at the door, you can start personalizing their welcome. A one-size-fits-all approach to offers is like a hotel restaurant with only one item on the menu, it’s bound to disappoint most of your guests. Segmentation is the key to creating a menu of experiences that feels personal to each visitor.

Best Western Rewards program

This is where we can learn from a leader in the industry, Best Western Hotels & Resorts. Their team wanted to encourage more visitors to sign up for their Best Western Rewards program. But instead of just showing the same generic pop-up to everyone, they got smart. They used data from their visitors’ search queries to create relevant, timely offers.

Here’s how it worked: a visitor searching for a one-night stay might be a business traveler with a specific need. But a visitor searching for a stay of two nights or longer is likely a leisure traveler with more flexibility. Best Western created different audience segments for these users. The leisure traveler looking for a longer stay was shown a pop-up with a special promotional offer, available only by signing up for a Rewards account. The result? A 12% increase in sign-ups from the campaign. They didn’t just shout about their loyalty program, they showed visitors exactly how it could benefit them, right when they were most receptive.

Make ‘book now’ the easiest click of their day

You’ve welcomed your visitor, understood their needs, and presented them with the perfect offer. Now comes the most critical moment: the booking. All the great work you’ve done can be undone in an instant by a clunky, confusing, or frustrating checkout process. At this stage, your one and only job is to remove friction.

Sometimes, the biggest barriers are the smallest things. The travel company Smartbox believed the “Add to Cart” CTA on their vacation packages wasn’t visible enough. They formed a simple hypothesis: a more vibrant, contrasting color would draw more attention and, therefore, more clicks. Using a simple A/B test, they changed the button color from aqua to a bright pink. This tiny change generated a 16% increase in clicks. It wasn’t a guess, it was a data-backed decision that made the user’s path clearer.

Similarly, Evolve Vacation Rental tested the copy on their call-to-action button for homeowners interested in listing their properties. The original read “See if You Qualify,” while the variation said “Start for Free.” The new phrasing, which better aligned with the user’s goal of understanding the service, resulted in a staggering 161% increase in conversions. These tests prove that you don’t need a complete redesign to see dramatic results. You need a willingness to question every element and let your users’ behavior guide you to the better option.

The journey doesn’t end at the confirmation page

What if the most hospitable digital experiences are the ones that know when to stop being purely digital? The goal isn’t just to build a self-service journey, but to create one that’s smart enough to recognize when a guest is confused or frustrated. Instead of letting that friction lead to an abandoned booking, you can proactively offer a human interaction to guide them through it. This is about augmenting the digital journey with a personal touch, right when it’s needed most.

The most forward-thinking brands understand that their website and physical properties are not separate channels, they are two parts of one holistic experience. We can draw inspiration from the travel agency Havas Voyages. Their team implemented a clever strategy for users who showed “exit intent”. Instead of just letting them go, a pop-up appeared offering them an appointment with a travel planner at their nearest physical agency. They saved a potential lost lead by seamlessly offering a human alternative.

The application for a hotel is incredibly powerful. Imagine a user struggling on a complex booking page and showing signs of leaving. What if, instead of losing them, you triggered a pop-up offering a “live chat with our concierge” or a “call back from the front desk within five minutes”? This is more than a conversion-saving tactic; it’s a brand-defining moment. It shows that your hospitality isn’t confined to your lobby. It proves your team is ready to help, across any channel, at any time. By blending your digital tools with a human touch, you build a unified brand experience that creates trust and earns loyalty.

Ready to find your better? We help teams like yours turn brave ideas into brilliant results. Let’s talk about what you want to achieve.

Article

4min read

Improved GA4 Connector Empowers Data-Driven Brands

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.

FAQs

What is the new GA4 connector from AB Tasty?

The GA4 connector is an enhanced integration that allows you to send enriched event data directly from AB Tasty’s servers to Google Analytics 4, ensuring more reliable and accurate analytics.

How does the new connector improve data accuracy?

By sending a single, enriched event from AB Tasty to GA4, the connector eliminates discrepancies and delays that can occur with separate browser-based hits. This ensures that your data in both platforms matches and is updated in near real-time.

Why is server-to-server integration important?

Server-to-server integration bypasses common issues like ad blockers, privacy settings, and browser limitations that can interfere with client-side tracking. This means your analytics are more complete and trustworthy.

Is the connector difficult to set up ?

It’s very easy! Setting up the GA4 connector is straightforward. All you need to do is add your GA4 Measurement ID and API Secret in AB Tasty—no complex engineering or code changes required.

Article

6min read

Mastering Revenue Metrics: Understand the Power and Practical Use of RevenueIQ

Revenue is the cornerstone of any e-commerce business, yet most optimization efforts focus only on improving conversion rates.

Average Order Value (AOV), an equally important driver of revenue, is often overlooked because it’s difficult to measure accurately with standard statistical tools. This gap can lead to missed opportunities and slow decision-making.

RevenueIQ addresses this challenge by providing a robust, reliable way to measure and optimize revenue directly—combining conversion and AOV into a single, actionable metric.

Here’s how RevenueIQ changes the way you approach experimentation and business growth.

Discover how to accurately measure and optimize revenue in your experiments with our patented feature.

The most important KPI in e-commerce is revenue. In an optimization context, this means focusing on two key areas:

  • Conversion Rate (CR): Turning as many visitors as possible into customers.
  • Average Order Value (AOV): Generating as much value as possible per customer.

However, Conversion Rate Optimization (CRO) often remains focused on conversion, while AOV is frequently neglected due to its statistical complexity. Accurately estimating AOV with classic tests (such as the t-test or Mann-Whitney) is challenging because purchase distributions are highly skewed and have no upper bound.

RevenueIQ offers a robust test that directly estimates the distribution of the effect on revenue (through a refined estimation of AOV), providing both the probability of gain (“chance to win”) and consistent confidence intervals.

In benchmarks, RevenueIQ maintains a correct false positive rate, has power close to Mann-Whitney, and produces confidence intervals four times narrower than the t-test. By combining the effects of AOV and CR, it delivers an RPV (Revenue Per Visitor) impact and then an actionable revenue projection.

Curious to learn more details? Please read our RevenueIQ Whitepaper for a full scientific explanation written by our Data Scientist, Hubert Wassner.

Context & Problem

In CRO, we often optimize CR due to a lack of suitable tools for revenue. Yet, Revenue = Visitors × CR × AOV; ignoring AOV distorts the view.

AOV is misleading because:

  • It is unbounded (someone can buy many items).
  • It is highly right-skewed (many small orders, a few very large ones).
  • A few “large and rare” values can dominate the average.
  • In random A/B splits, these large orders can be unevenly distributed, leading to huge variance in observed AOV.

Limitations of Classic Tests

t-test

Assumes normality (or relies on the Central Limit Theorem for the mean). On highly skewed e-commerce data, the CLT variance formula is unreliable at realistic volumes. The result: very low power (detects ~15% of true winners in the benchmark) and very wide confidence intervals, leading to slow and imprecise decisions.

Mann-Whitney (MW):

Robust to non-normality (works on ranks), so much more powerful (~80% detection in the benchmark). But it only provides a p-value (thus only trend information), not an estimate of effect size (no confidence interval), making it impossible to quantify the business case.

RevenueIQ: Principle

It uses and combines two innovative approaches:

  1. Bootstrap Technique: Studies the variability of a measure with unknown statistical behavior.
  2. Basket Difference Measurement: Instead of measuring the difference in average baskets, it measures the average of basket differences. It compares sorted order differences between variants (A and B), with weighting by density (approx. log-normal) to favor “comparable” pairs. This bypasses the problem of very large observed value differences in such data.

RevenueIQ then provides:

  • The Chance to Win (probability that the effect is > 0), which is easy for decision-makers to interpret.
  • Narrow and reliable confidence intervals on the AOV effect as well as on revenue.

Benchmarks (AOV)

  • Alpha validity (on AA tests): Good control of false positives. Using a typical 95% threshold exposes only a 5% false positive risk.
  • Statistical power measurement: 1000 AB tests with a known effect of +€5
    • MW Test: 796/1000 winners, ~80% power.
    • t-test: 146/1000, only 15% power.
    • RevenueIQ: 793/1000 (≈ equivalent to MW). ~80% power.
  • Confidence interval (CI): RevenueIQ produces CIs of €8 width, which is reasonable and functional in the context of a real effect of €5. With an average CI width of €34, the t-test is totally ineffective.
  • CI coverage: The validity of the confidence intervals was verified. A 95% CI indeed has a 95% chance of containing the true effect value (i.e., €0 for AA tests and €5 for AB tests).

From AOV KPI to Revenue

Beyond techniques and formulas, the key point is that RevenueIQ uses a Bayesian method for AOV analysis, allowing this metric to be merged with conversion. Competitors use frequentist methods, at least for AOV, making any combination of results impossible. Under the hood, RevenueIQ combines conversion and AOV results into a central metric: visitor value (RPV). With precise knowledge of RPV, revenue (in € or other currency) is then projected by multiplying by the targeted traffic for a given period.

Real Case (excerpt) Here is a textbook case for RevenueIQ:

  • Conversion gain is 92% CTW, encouraging but not “significant” by standard threshold.
  • AOV gain is at 80% CTW. Similarly, taken separately, this is not enough to declare a winner.
  • The combination of these two metrics gives a CTW of 95.9% for revenue, enabling a simple and immediate decision, where a classic approach would have required additional data collection while waiting for one of the two KPIs (CR or AOV) to become significant.
  • For an advanced business decision, RevenueIQ provides an estimated average gain of +€50k, with a confidence interval [-€6,514; +€107,027], allowing identification of minimal risk and substantial gain.

What This Changes for Experimentation

  • Without RevenueIQ: “inconclusive” results (or endless tests) lead to missed opportunities.
  • With RevenueIQ: Faster, quantified decisions (probability, effect, CI), at the revenue level (RPV then projected revenue).

Practical Recommendations

  • Stop interpreting observed AOV without safeguards: it is highly volatile.
  • Avoid filtering/Winsorizing “extreme values”: arbitrary thresholds ⇒ bias.
  • Measure CR & AOV jointly and reason in RPV to reflect business reality.
  • Use RevenueIQ to obtain chance to win + CI on AOV, RPV, and revenue projection.
  • Decide via projected revenue (average gain, lower CI bound) rather than isolated p-values.

Curious to learn more details? Please read our RevenueIQ Whitepaper for a full scientific explanation written by our Data Scientist, Hubert Wassner.

Conclusion

RevenueIQ brings a robust and quantitative statistical test to monetary metrics (AOV, RPV, revenue), where:

  • t-test is weak and imprecise on e-commerce data,
  • Mann-Whitney is powerful but not quantitative.

RevenueIQ enables faster detection, quantification of business impact, and prioritization of deployments with explicit confidence levels.

**Original information can be found by following this link to AB Tasty’s documentation, “Understanding the practical use of RevenueIQ.”

Article

6min read

Destination: Different – How Gen Z is Rewriting the Travel Playbook

Travel and hospitality is a huge industry, estimated at 955.90 billion USD in 2025. It’s also one that’s changing rapidly, with online travel bookings projected to account for 75% of all revenue by 2029. That’s why we’ve put together industry insights in our e-book, Decoding Online Shopping: Travel and Hospitality Consumer Trends for 2025.

What’s clear from our research is that Gen Z is quickly reshaping the online travel journey. They’re doing things differently to previous generations, from where they find inspiration, to why they abandon a cart, and how they view personalization. And that has big implications for travel brands.

Google flights/hotels and social media are now their go-to

A majority of Gen Z say they now start their search for travel options on Google Flights/Hotels (52%) or social media (50%). This shows a major shift from all other generations, who prefer to start their online search on a search engine or an aggregated travel site like Booking.com.

These results indicate that social media will, if anything, be an even more crucial battleground for travel brands going forward. To grab Gen Z’s attention, you’ll need to budget for targeted social media ads. Influencer content also plays an important role here, not only in making Gen Z aware of potential travel options but in providing social proof of existing ones.

Gen Z travel options

Location still leads, but reviews and visuals are more important

One thing that hasn’t changed for Gen Z when booking accommodation and transportation options online is the importance of location. Proximity to key destinations is still the most influential factor in convincing Gen Z to book, as it is with other generations.

What does change for Gen Z is their even greater reliance on authentic reviews. They want their travel choices to be validated by their peers, and well-positioned authentic reviews provide them with the reassurance they need to feel confident in their decision. Use A/B testing to determine the ideal placement of customer reviews on the relevant pages of your website.

Growing up with digital technology and social media, Gen Z also thrives on visual content. Having visually engaging, up-to-date images and videos on your website will appeal to them more than any other generation. This also holds true for hotel listings on Google Hotels and aggregated travel sites.

Gen Z influential factors

Simplify checkout and increase payment options

The number one reason that Gen Z leaves a travel website without making a purchase is that their chosen payment method is not accepted. This again is in sharp contrast to previous generations, where the top reason for doing so is that they simply aren’t ready to buy. However, that does suggest that if Gen Z gets to your website, they’ll be more ready to buy. Adding additional payment methods, like Apple Pay or Google Pay, will reduce the chances of Gen Z leaving before they’ve booked.

Gen Z also say they leave a website if there are too many steps involved in checkout. Adjusting the checkout process to make it more streamlined will reduce unnecessary friction and increase the likelihood of Gen Z making a purchase.

Gen Z and the checkout process

Adapt with personalization and AI

Above all, Gen Z wants a seamless digital experience that’s tailored to their needs. And 65% of Gen Z embrace data-driven personalization as a helpful tool. Once again, this is a higher number than generations before them. Specifically, Gen Z wants a website to remember their preferences and offer them real-time recommendations based on those preferences.

Personalization gives you a perfect opportunity to build stronger relationships with Gen Z customers. You can do this by leveraging first-party data to offer tailored recommendations, simplify the booking process, and provide faster checkout.

Gen Z are also more open than any previous generation to using AI-powered tools, like chatbots or virtual assistants. Nearly half of Gen Z (49%) say they’ve used an AI tool when booking travel options and found it helpful. And just 7% of Gen Z say they’re not interested in AI at all.

Focus on making AI interactions feel natural, efficient and genuinely helpful. Set clear expectations about what AI can and can’t do, and ensure human support is easily accessible if needed.

Use of AI and chatbots

Examples of personalization done right

While there’s no one-size-fits-all when it comes to personalization, some companies seem to do personalization consistently well:

  • Netflix uses personalization to determine customers’ interests and promote related content and suggestions in real time. This is a perfect example of how personalization can remove friction by making it easier for customers to find what they’re looking for.
  • Stitch Fix collects information customers supply about their size, shape, and personal style. It then selects outfits based on each one’s taste and personality. This shows how personalization and AI can work together to offer customers a great website experience.
  • LinkedIn is a great example of a company that knows how to balance privacy concerns with utility. By providing personalized suggestions and links based on users’ current connections, LinkedIn makes it easier to network, look for work, or catch up with former co-workers.

Conclusion

The travel and hospitality industry is rapidly evolving, and Gen Z are quickly reshaping what the online travel journey looks like. Their expectations of what makes a good digital experience are also greater than ever before. To be successful, this needs to be seamless and tailored to their needs.

Key to this is greater personalization powered by data and experimentation. By optimizing Gen Z’s experience of booking travel options, you can build trust and loyalty and keep Gen Z travelers coming back for more.

Takeaways for travel and hospitality brands

  • Work on your social media presence to influence potential customers and increase brand awareness.
  • Showcase nearby attractions and must-see sites in listings to capture attention.
  • Feature high-quality reviews and images on key pages to build trust and credibility. 
  • Simplify checkout and offer more payment options to reduce drop off.
  • Personalize with relevant real-time recommendations for a more tailored digital experience. 
  • Use AI to provide instant answers and assist travelers in real time.

Article

5min read

RevenueIQ: The New Standard for Proving ROI in Digital Experimentation

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

What is RevenueIQ?

RevenueIQ is a proprietary, patented metric developed by AB Tasty that provides a clear, unified view of how much revenue your digital experiments generate—expressed as revenue per visitor, per month.

How trustworthy are RevenueIQ’s numbers?

RevenueIQ is powered by a patented Bayesian engine, ensuring robust, transparent, and reliable calculations. All projections include confidence intervals, so you always see the full picture.

How does RevenueIQ handle complex or ambiguous test results?

RevenueIQ always provides a clear winner based on revenue impact, even in cases where traditional metrics are inconclusive (due to their lack of integration). This means fewer “stuck” campaigns and more decisive action.

How is RevenueIQ different from what competitors offer?

No competitor currently offers a metric like RevenueIQ. While others may provide “all-purpose” statistics for each individual metric, only RevenueIQ gives you a patented, unified view of revenue per visitor, per month—making it a true differentiator.

Article

8min read

Flying Through Checkout: How Experimentation Shapes Airline Consumer Behavior

The airline checkout is where booking intent becomes revenue. Yet for most airlines, it’s also where the majority of customers drop off. This high abandonment rate isn’t just a cost of doing business; it’s a direct result of a complex booking process failing to meet modern traveler expectations. Fixing this friction is one of the biggest opportunities for growth in the industry.

This drop-off isn’t just a technical problem, it’s a human one. The checkout flow is where a traveler’s excitement meets anxiety, and where price sensitivity clashes with the desire for comfort. For airline and travel professionals, understanding this interplay is the key to conversion.

The answer isn’t to guess what travelers want or to copy a competitor’s design. It’s to listen, learn, and adapt by building a system that lets you ask customers what they prefer, not with a survey, but with their clicks. As we discuss in our Travel Essentials Kit e-book, this is the world of experimentation, where every test becomes part of a continuous cycle of learning and iteration.

Why airline checkout is so complex

Unlike a simple e-commerce purchase, booking a flight is rarely a one-click affair. The complexity is baked right into the business model. You’re not just selling a seat; you’re selling a multi-faceted travel experience, and each component adds another layer to the checkout.

First, there’s the core booking. A simple round-trip flight is one thing, but multi-leg journeys with different carriers, layovers, and time zones create a significant cognitive load for the user. Then come the additional services, such as seats, bags, meals, or insurance, where each choice is a potential exit point. Finally, regulatory requirements can create long, intimidating forms.

The result of this complexity is an abandonment rate that, according to Inai, hits 90%. To put it another way, nine out of every ten potential customers that start booking a flight will leave without paying. That’s significantly higher than the already high average e-commerce site abandonment rate of 70%. And the problem is even worse on mobile.

$260 billion in lost orders across the US and EU are recoverable through better checkout

This is more than a user experience flaw; it’s a massive financial bleed. The Baymard Institute estimates that $260 billion in lost orders across the US and EU are recoverable through better checkout design alone. It’s a multi-billion dollar design challenge waiting for a solution, but the fix doesn’t require a complete and costly overhaul. A commitment to analyzing user data, testing hypotheses, and letting the results guide incremental, high-impact changes will have your customers soaring through your checkout process in no time.

Decoding consumer behavior at checkout

To optimize the checkout flow, you have to get inside the traveler’s mind. Their behavior is driven by powerful psychological factors, and your data shows exactly where the friction is.

39% of shoppers abandon cart

The single biggest culprit is cost ambiguity. The top reason for cart abandonment, cited by 39% of shoppers in research aggregated by the Baymard Institute, is discovering high extra costs at the end of the process. This points directly to the airline industry’s practice of “drip pricing.” The low base fare gets them in the door, but the steady drip of fees erodes trust. It’s not just the final price; it’s the feeling of being misled.

Next is process friction. The same research found a “too long or complicated” checkout will cause 18% of users to leave. Forcing a user to create an account is another major barrier, responsible for another 19% of abandoned carts. This accumulation of friction—multiple pages, endless form fields, and mandatory sign-ups—creates a powerful negative momentum that pushes users to exit.

Finally, there’s the trust deficit. A staggering 19% of users will abandon a purchase simply because they didn’t trust the website with their payment information. This isn’t just about SSL logos. A user who experiences a price increase through drip pricing is psychologically primed to be more skeptical when it’s time to enter their payment details, as the final cost no longer aligns with their initial expectation.

Understanding these behaviors isn’t about exploiting them. It’s about designing a smoother, more transparent, and less stressful experience that guides the traveler confidently toward their destination while also building brand credibility.

Experimentation as a window into the traveler’s mind

So, how do you solve for cost ambiguity or process friction? The answer is to ask your users, not with a survey, but by testing different approaches and measuring the results. Experimentation, through A/B and multivariate testing, is the most effective way to understand what travelers actually do.

The process starts with a data-driven hypothesis. For example, if your analytics show a high drop-off rate on the passenger details page, you could hypothesize that reducing the number of form fields will reduce friction and increase conversions. From there, you can run a simple A/B test: Version A is your current, longer form, and Version B is the new, simplified one. By showing each version to different segments of your audience, you can measure which one leads to more completed bookings. The result is no longer a guess; it’s a data-backed insight that de-risks design changes and allows you to make improvements with a measurable impact.

But this isn’t just about one-size-fits-all fixes. You can take it a step further with personalization and segmentation. A first-time booker might need more guidance and reassurance during checkout, while a frequent flyer would prefer a streamlined experience that pre-fills their preferences and key information. Experimentation allows you to test different, tailored experiences for these segments, ensuring every traveler gets the smoothest possible path to booking.

What airlines can test in checkout

Once you embrace an experimentation mindset, you’ll see test opportunities everywhere. The goal is to challenge assumptions and find what truly moves the needle. Here are a few powerful areas to start:

  • Call-to-action design: Don’t underestimate the power of a button. We worked with Smartbox to test colour variations of their “Add to cart” button, and a simple color change resulted in a 16% increase in clicks.
  • Payment options: The payment step is the final hurdle. Adding digital wallets is one of the most impactful changes you can make. An analysis by Stripe found that businesses enabling Apple Pay saw an average 22% increase in conversion. It’s a powerful antidote to checkout friction, especially on mobile. You could even explore digital boarding passes that integrate directly with mobile wallets. 
  • Form factor and flow: Is a single-page checkout less intimidating than a multi-step progress bar? Test it and see!
  • Trust-building elements: Reinforce security at the moment of payment. Test the placement of security seals and clear language around your cancellation policies. A simple statement like “Free 24-hour cancellation” can provide the reassurance a hesitant traveler needs.
  • Upsell placement: How and when you present add-ons matters. Test bundling services versus offering them a la carte. You might find users are more receptive to upsells like early check-in or seat selection via a follow-up email after the booking is confirmed, reducing friction in the initial checkout.
  • Mobile-first experiences: Your mobile checkout shouldn’t just be a shrunken version of your desktop site. Test mobile-specific designs with larger tap targets, simplified navigation, and form fields that trigger the correct mobile keyboard layout.

From insights to impact: Building a culture of experimentation

The true power of optimization isn’t found in a single winning test. It’s found in building a culture of continuous learning. When your product, marketing, and engineering teams are united by an experimentation mindset, you stop debating opinions and start making decisions based on data. You dare to go further.

Iberojet increase in clicks

Take Iberojet, for example. The online travel agency questioned whether the order of tabs on their homepage was ideal. Working with us, they ran a simple A/B test to change the order based on user browsing history. That small change increased clicks on the “Search” button by 25%, pushing more users down the conversion funnel.

Another powerful example is Ulta Beauty. Working with us, they’ve embedded experimentation into their innovation process, scaling their program from 20 tests per year to over 65. Rather than relying on assumptions, their teams use testing to get quick, data-driven answers. For example, by testing an overlay with product recommendations in the shopping cart, they drove a 9% increase in revenue and a 15% increase in “add to bag” clicks, proving the value of a nimble, “fail-fast” environment.

This is how you find your better. It’s not about finding one perfect, final version of your checkout. It’s about the restless, determined pursuit of a better experience for every traveler, on every device, every single day. The journey starts with a single question: What will you try?

Article

4min read

Progressive Rollout: The Safer, Smarter Way to Launch New Features

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.

Article

6min read

A New Era for Product Recommendations: AB Tasty’s Semantic Proximity Algorithm

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.

FAQs

Is this just another “black box” AI?

No. You control which attributes are used, can preview results, and iterate. It’s transparent and customizable.

What if the recommendations don’t make sense?

You can filter and combine attributes, set thresholds, and preview results before going live. Early feedback has led to rapid improvements.

Does it work with custom fields?

Yes! Any attribute in your catalog, including custom metafields, can be used.

Article

5min read

Why AB Tasty is the Best Digital Optimization Partner for Your Team

When it comes to digital optimization, you need more than just another tool—you need a partner who understands that every test is a step toward something bigger.

Here’s why AB Tasty stands out as the best choice for teams ready to go further.

1. Built for Everyone: Usability That Empowers Your Whole Team

Your team shouldn’t need a developer for every test.

AB Tasty’s visual editor and theme builder work for everyone—whether you’re a marketer launching your first campaign or a developer building complex experiments. Our intuitive interface means less time wrestling with code and more time testing bold ideas.

Real autonomy, real speed. Teams choose AB Tasty because their previous platform kept them dependent on developers for basic changes. With AB Tasty, they launched campaigns faster and gave their marketing team the independence they needed to iterate quickly.

Widgets that work, right out of the box. Our widget library comes from 12+ years of real-world testing. These aren’t just features—they’re battle-tested components that help teams launch more campaigns with confidence. While newer platforms struggle with bugs and limitations, our widgets deliver reliability when you need it most.

The result? Teams report launching more experiments, faster, with fewer roadblocks.

2. Honest Pricing: What You See Is What You Get

No surprise costs. No hidden fees. Just transparent value.

What starts as your solution stays your complete solution—no extra charges for essential features down the line.

Predictable partnerships. Many platforms lure teams in with low initial costs, then surprise them with steep price increases or essential features locked behind add-ons. We believe in honest pricing from day one, so you can plan your growth without budget surprises.

Long-term value that makes sense. When you calculate total cost over time—including all the features you’ll actually need—AB Tasty delivers better value. We’re smarter for the long haul.

3. Support That Actually Supports You

Customer Success Managers focused on your success—not their sales quotas.

Our CSMs are dedicated to helping you win, not upselling you. They’re your advocates, your strategic partners, and your go-to team for navigating complex challenges. No conflicts of interest, no hidden agendas—just genuine support.

Local expertise when you need it. Whether you’re based in the UK, France, or anywhere else we serve, you get local support that understands your market, your timezone, and your specific needs. Responsive, knowledgeable, and always ready to help.

Technical reliability you can count on. We handle complex environments—React, SPAs, multi-brand setups—with confidence. Teams praise our ability to navigate technical challenges that trip up other platforms. When your setup is complicated, we make the solution simple.

4. Technical Excellence: Speed, AI, and Innovation That Works

The fastest tag performance in the industry. Speed matters. Our script loads at 482ms—significantly faster than major competitors. That means better user experience, higher conversion rates, and tests that don’t slow down your site.

AI that’s transparent and ready now. Our Engagement Level and EmotionsAI aren’t black boxes or future promises—they’re transparent, advanced tools you can use today. While others demo concepts, we deliver production-ready AI that helps you understand and optimize for real user behavior.

Built for modern web experiences. Single Page Applications and dynamic content work out-of-the-box with AB Tasty. No manual workarounds, no technical debt—just seamless experimentation on the modern web.

5. Proven Reliability: Trust Built Over Time

Platform stability when it matters most. Experimentation requires trust—in your data, your results, and your platform. We deliver consistent reliability while others struggle with bugs, lost test goals, and API limitations that disrupt your work.

Recognition from the experts. Industry analysts consistently recognize AB Tasty for experiment design, pricing flexibility, community support, and market presence. But the real validation comes from our customers—teams who’ve switched to us and never looked back.

Real client wins, real results. Multiple teams have moved from other platforms to AB Tasty for better usability, superior support, and genuine value. They stay because we help them accomplish more together.

The Best Choice for Teams Ready to Go Further

What makes AB Tasty the best digital optimization partner isn’t just one thing—it’s how everything works together. Intuitive tools that empower your whole team. Transparent pricing that respects your budget. Support that genuinely cares about your success. Technical excellence that delivers results. And proven reliability you can count on.

We’re not just another platform. We’re your collaborators, your advocates, and your partners in every bold test you want to try.

Other tools might promise quick wins or flashy features. We deliver something better: a partnership that grows with you, technology that works when you need it, and a team that believes in your potential.

Try, learn, iterate—then go again. That’s how teams grow, and that’s how we help you get there.

Ready to experience the difference? Let’s build something better—together.

Article

6min read

From “What If” to “What Works”: How AB Tasty AI Transforms Experimentation

If you’ve ever wondered what to test next, struggled to get developer time, or felt overwhelmed by reporting dashboards, you’re not alone.

These are the frustrations experimentation teams face daily. That’s why we built AB Tasty AI—a suite of AI designed not to add hype to your workflow, but to genuinely help you move faster, test smarter, and get real business impact from your experimentation program.

With AB Tasty AI, those roadblocks disappear. Our AI guides you through ideation, building, personalization, and analysis—so you can focus less on the “what ifs” and more on the results that matter.

Let’s walk through how it works.

AI that crushes your “We’re guessing what to test next” problem

Step 1: Ideation generation

In many organizations, idea generation depends on gut feelings or endless whiteboard sessions that rarely produce actionable outcomes. That’s where AB Tasty AI steps in.

Our platform scans your pages and surfaces data-backed test ideas that are proven to make an impact. Instead of guessing, you get a prioritized list of opportunities aligned with your business goals. It’s like having an intelligent co-pilot who not only brainstorms with you but also brings evidence to the table.

AI that eliminates your “Our hypotheses are hunches” frustration

Step 2: Develop a hypothesis

Hypothesis Copilot by AB Tasty

A test idea is only as strong as the hypothesis behind it. Yet many teams struggle to move from fuzzy thinking to clear, structured hypotheses with measurable objectives.

AB Tasty AI eliminates the guesswork by helping you sharpen your hypotheses. You can turn casual “what if we tried this?” conversations into formal statements that define the change, predict the impact, and set up the right metrics for evaluation.

This structured approach not only improves your test quality but also boosts team confidence and stakeholder trust.

AI that annihilates your “I can’t build what I’m thinking” roadblock

Step 3: Start building

One of the biggest blockers in experimentation is the dependency on developer resources. Great ideas often languish in backlogs because the dev team is focused on other priorities.

With AB Tasty AI, you can instantly transform ideas into buildable experiments—no coding required. Whether you want to tweak a button, test a new layout, or launch a more complex variation, our AI makes it possible to build, preview, and launch without waiting weeks for a developer.

This shift not only accelerates testing velocity but also democratizes experimentation, empowering marketers, product managers, and designers to run with their ideas.

AI that ends your “Our personalization feels robotic” paralysis

Step 4: Understand your audience

10 emotional profiles with AB Tasty's EmotionsAI

Many brands struggle with personalization that feels forced, generic, or robotic. Visitors sense it, and the results often disappoint.

AB Tasty AI introduces EmotionsAI Insights, giving you a window into the emotional triggers that shape customer behavior. Instead of relying only on demographic or behavioral data, you get deeper visibility into what truly motivates your audience.

It’s personalization with empathy—designed to feel natural, human, and meaningful.

AI that solves your “I don’t know why visitors convert” mystery

Step 5: Personalize the customer journey

Understanding emotional drivers is just the start. With EmotionsAI Segments, you can act on those insights by creating experiences tailored to specific motivations.

For example, one group of visitors might be motivated by security and reassurance, while another thrives on novelty and excitement. AB Tasty AI combines emotional, behavioral, and contextual data to reveal these distinctions, allowing you to craft experiences that resonate at a deeper level.

The result? More conversions, stronger loyalty, and a customer journey that feels less like a funnel and more like a personalized conversation.

AI that crushes your “I don’t understand this report” problem

Step 6: Analyze your reports

Once experiments are running, the next challenge is often reporting. Traditional dashboards can be dense, and interpreting results takes time—especially if stakeholders want quick answers.

AB Tasty AI simplifies the process with natural language analysis. You can ask plain-English questions like “Which variation performed best with mobile visitors?” and get clear, actionable answers instantly.

This not only saves hours of manual analysis but also democratizes data, empowering non-technical teams to explore results with confidence.

Why AB Tasty AI Stands Out

The market is full of AI solutions, many of which promise more than they deliver. AB Tasty AI is different. We’ve designed it to remove the real blockers experimentation teams face every day:

  • No more guessing what to test
  • No more hunch-based hypotheses
  • No more dev backlog bottlenecks
  • No more robotic personalization
  • No more confusing reports
  • No more lost learnings

In short, AB Tasty AI moves your experiments from start to success.

FAQs about AI in digital experimentation

What type of AI does AB Tasty offer?

AB Tasty offers practical, experimentation-focused AI that supports the full testing journey. This includes AI for idea generation, hypothesis creation, no-code experiment building, emotional personalization (EmotionsAI), natural language reporting, and more.

How does AB Tasty AI help with personalization?

AB Tasty AI uses EmotionsAI to uncover visitor motivations and segment audiences based on emotional, behavioral, and contextual data. This allows businesses to create experiences that feel more human and relevant.

Can AB Tasty AI help non-technical teams run experiments?

Yes. AB Tasty AI empowers marketers, product managers, and designers to launch tests without relying on developers, thanks to its no-code experiment builder.

What makes AB Tasty AI different from other AI solutions on the market?

AB Tasty AI is designed to deliver practical, business-ready solutions. While many AI tools focus on hype, AB Tasty AI helps teams move from “what if” to “what works” by providing tangible results at every stage of the experimentation cycle.