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9min read

The Power Couple of Personalization: Where Emotion Meets Real-Time Adaptation

Personalization: Beyond Just Real-Time 

Real-time personalization has become a key way for brands to create more relevant and responsive experiences to level-up your brand’s success. By adapting to what users are doing in the moment, it helps guide them toward decisions more effectively.

But behavior alone doesn’t tell the full story. A shopper might hesitate before making a purchase, and without understanding why – it’s difficult to deliver the right message, design, or interaction to move them forward.

Imagine a shopper who seeks reassurance. This refers to needing clear return policies, secure payment signals, and trustworthy reviews before making a purchase. Without recognizing this need for confidence and security when shopping, how can you deliver the right pop-ups, banners, or design elements to make them comfortable enough to convert?

This is where the perfect pairing in EmotionsAI and AdaptiveCX can take you one step further.

Reacting to a user’s in-session behavior to serve them relevant content is a powerful way to personalize the shopping experience while they’re still on the site. But what if that’s only half the story?

From Insight to Impact: The Power of Why and What

The ultimate personalization happens when you combine real-time behavioral adaptation with a deep emotional understanding. It’s the difference between knowing what a visitor is doing and understanding why they are doing it

This clever, unbeatable combination that AB Tasty’s AdaptiveCX and EmotionsAI were built to achieve can turn every interaction into a truly resonant experience.

emotionsai infographic grey

The “Why” Engine: EmotionsAI for Deeper Understanding 

Imagine being able to recognize who a visitor is the moment they arrive at your site. This means not just understanding what they click, but how they behave. Subtle signals like hesitation, repeated comparisons, or quick exits can reveal what they’re looking for. Without any additional information needed, you can identify whether they need reassurance, inspiration, urgency, or simplicity and adapt their experience instantly to align with their shopping desires. 

EmotionsAI does the same thing, but online. It picks up on behavioral signals instead of facial expressions or tone of voice, identifies what a shopper is feeling in that moment, and helps deliver the kind of experience that truly fits their emotional needs. This can range from needing to feel comforted, a sense of established trust, that’s building trust, or sparking excitement to encourage a purchase.

Brands can use EmotionsAI to turn their website from a static storefront into a perceptive sales associate that knows exactly how to respond alongside a user’s specific needs.

EmotionsAI: Decoding the “Why” Behind the Click

It can be challenging to know which campaigns or pop-ups to use when you don’t know what the user wants in the first place. This is where EmotionsAI is designed to help brands better understand their user’s needs and intent while shopping.

EmotionsAI works by grouping users into 10 different emotional categories: 

Competition

Competition

Strive to make the best possible choice to stay ahead and be the best.

Attention

Attention

Need to feel that everything is done to satisfy them.

Safety

Safety

Need to feel secure and in control of their situation, rather than uncertain.

Comfort

Comfort

Need to drift through the customer journey in a peaceful, undisturbed environment.

Community

Community

Need to feel a strong connection with the people they care about and their environment.

Immediacy

Immediacy

Need to be constantly stimulated by suggestions of actions to take.

Notoriety

Notoriety

Prefer to minimize risk by choosing options validated by a large number of people.

Understanding

Understanding

Need factual and comprehensive information to make decisions.

Change

Change

Seek new experiences, fresh adventures, and opportunities to break free from routines.

Quality

Quality

Need qualitative information to make decisions.

By collecting this valuable information, EmotionsAI goes beyond behavior to uncover the emotional drivers behind a user’s actions. These segmentations provide a layer of perception that behavioral data alone can’t, as it’s profiling users according to their live activities.

In learning why users act the way that they do, EmotionsAI can allow brands to understand a shopper’s motivation – which allows for tools like AdaptiveCX to create more appealing content and campaigns for each individual consumer. 

The “What” Engine: AdaptiveCX for In-the-Moment Action 

Understanding why your users behave the way they do is crucial to providing them the best experience possible when shopping. Once your brand can put yourself into each shopper’s shoes with EmotionsAI, you can then create the most relevant experiences for each user. This is exactly where AdaptiveCX steps in.

AdaptiveCX: The Engine of Instant Relevance

AdaptiveCX is a tool that analyzes live, in-session signals such as clicks, scrolls, mouse movements to predict intent and instantly adapt the experience. By providing insights from these various behaviors in a mere 20 milliseconds, its cookieless design makes it perfect to tackle personalization for the 90% of anonymous visitors. 

Think of it as the action-oriented part of the team. AdaptiveCX is the engine that takes the guesswork out of personalization by focusing on what the user is doing right now. It doesn’t rely on slow, historical data, but instead, continuously processes real-time signals to anticipate a visitor’s needs. This makes it possible to deliver adaptive search results, personalized recommendations, and tailored content on the fly. 

With AdaptiveCX, brands can easily ensure that every visitor, known or anonymous, receives a curated experience that maximizes both engagement and the potential for conversion.

infographic made by ab tasty explaining the benefits of adaptivecx and real time personalization

Better Together: How the Power Team Transforms Personalization 

AdaptiveCX and EmotionsAI are each powerful in their own right – but together, they form an unstoppable personalization duo.

EmotionsAI acts as the “why” engine. In just seconds, it reads the emotional signals of each visitor and identifies their predominant need – whether that’s security, understanding, or a sense of belonging.

AdaptiveCX then serves as the “what” engine. This refers to detecting and predicting individual affinities and behaviors in real time as users navigate, capturing their immediate intent at every touchpoint.

Together, they give brands everything they need to dynamically adapt each visitor’s journey on the fly — through tailored activation scenarios that are 100% personalized to the individual. 

The result is a seamless loop: emotional intelligence informs real-time action, and real-time action brings emotional insight to life.

That’s the power of the duo: delivering the right message, to the right person, at the right time, and in the right way.

yellow light bulb and god coins on yellow background

The Future is Adaptive and Empathetic

The future of personalization isn’t just about being fast. Empathy and insights combined are an indispensable duo that lead to stronger insights and bigger wins. 

By pairing the predictive power of AdaptiveCX with the emotional intelligence of EmotionsAI, brands can create experiences that are not only relevant, but resonant with the user – building trust and driving sustainable growth in a cookieless world.

Don’t Just Adapt: Connect

Brands are tasked with the complicated challenge of adapting to users’ needs while they’re still shopping. Usually, it’s a struggle to know both what they want and how to accommodate for it – but EmotionsAI and AdaptiveCX together remove that guesswork, delivering a level of personalization and impact that consistently exceeds expectations.

Are ready to see teamwork make the dream work with EmotionsAI and AdaptiveCX?

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Still have questions about experimentation maturity? Here are the answers you need.

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Article

8min read

The New Era of Emotional Personalization with EmotionsAI

Beyond Clicks and Scrolls: The Missing Layer in Personalization 

Personalization today isn’t as simple as it used to be. The limitations of static personalization are coming to light as brands discover that it’s essential to tailor digital experiences to different types of users.

This is because more traditional personalization tactics often focus on what users do (behavioral data) but misses the why –  otherwise known as their emotional incentives for shopping in the first place. 

In turn, even brands that use these older personalization strategies will find their digital experiences still feel generic as  they don’t account for the user’s in-the-moment emotional state.

This is where using emotional intelligence with tools like EmotionsAI can transform current digital experiences, serving as the missing piece to help brands avoid missing the mark with customers and conversions. 

Meet EmotionsAI: Your Insight into the Customer’s Mind

Personalization has long relied on historical user data, but the future of optimization is privacy-first, compliant, and emotionally intelligent. This means moving beyond a dependency on third-party tracking.

Our in-house, AI-powered personalization tool, EmotionsAI, was designed to solve this exact problem. 

What is EmotionsAI?

EmotionsAI is an in-house AI tool that analyzes user activity to identify their emotional needs in real-time. By doing this within the first 30 seconds of a session, brands can tailor user experience according to why the shopper is motivated to visit the site in the first place. 

What sets EmotionsAI apart from other personalization tools as it goes beyond demographics or past purchases to capture how a user is feeling during a specific interaction. This allows for an instantaneously tailored experience that adapts to what shoppers want in real-time. 

How does EmotionsAI work?

EmotionsAI works by grouping visitors into 10 different emotional profiles:

Intuitive Shoppers

These users’ shopping experience are swayed by feelings and social influence:

  1. Competition: These shoppers want to find the best prices and thoroughly read reviews for utmost comparison before purchase. 
  2. Safety: This user wants to feel safe in their shopping experience. Official URLs or vetted reviews can make them feel more comfortable to convert. 
  3. Attention: This visitor needs to feel important or an air of “exclusivity” to eventually convert.
  4. Community: This consumer craves to feel a part of the community that uses the potential product to be purchased. 
  5. Comfort: These users need a friction-free shopping experience to purchase something. 

Rational Segments

These consumers are incentivized by reason, insight, and effectiveness as opposed to sentimental feelings when shopping: 

  1. Understanding: These shoppers require detailed information to help them make a clear decision about their purchase. 
  2. Quality: These visitors look for quality products, preferring durability and strong ratings over affordable prices. 
  3. Change: Inspired by fresh ideas or products, this shopper is most intrigued by trying something new.
  4. Immediacy: These users want to find exactly what they’re looking for with next to no detours. 
  5. Notoriety: These shoppers prefer to “follow the crowd” and purchase products from reputable brands. 

In categorizing different types of shoppers according to their emotional needs, brands can better target the user experience for each individual user. 

emotionsai infographic grey

From Insight to Impact: Putting EmotionsAI to Work

The best part about personalization tools like EmotionsAI is that brands can now take the guesswork out of trying to determine what their users want out of a shopping experience.

This is because EmotionsAI allows brands to analyze A/B test results through the lens of these emotional segments, revealing that 60% of tests show a positive impact for at least one segment.

Uncovering the Emotional Alignment Gap (EAG)

An indispensable part to using EmotionsAI is helping brands to identify and bridge the gap between not knowing what your audience emotionally needs and how to provide it. This is better known as the “Emotional Alignment Gap” (EAG). This is defined as the difference between the experience your site provides and it actually should encourage users to convert. 

In turn, implementing emotional personalization techniques can reduce the risk of potential revenue loss. 

The overview cards below will reveal how using EmotionsAI can benefit your business long-term:

Integration Icon

Seamless Integration with AB Tasty

EmotionsAI is a core part of the platform. Easily access segments via the builder to target or exclude specific emotional profiles in any campaign.

Targeting Icon

Targeting Shoppers Needs

Identify “Safety” segments and display secure payment banners to provide the reassurance they need to complete their purchase and convert.

Activation Icon

Front-End and Back-End Activation

Front-End Developers access segments from local storage for instant personalization, while Back-End Developers tailor server-side results.

By activating campaigns based on these emotional needs, brands can move beyond generic A/B testing and one step closer to delivering multiple winning experiences. Tackling these different audiences simultaneously can help to diversify your chances of increased revenue, with an average uplift in conversions from 5% to 10% on targeted campaigns.

The Proof is in the Personalization: EmotionsAI in Action 

For leading brands, turning emotional insights into revenue isn’t just a goal – it’s a reality. 

Discover how some of our partners stepped into new levels of success with EmotionsAI:

Creating comfort for shoppers with Clarins (Luxury Beauty)

Challenge: Clarins wanted to boost transaction rates by understanding exactly what shoppers needed to feel reassured.

Solution: Used EmotionsAI to identify “safety” and “comfort” segments and target them accordingly by using a pop-up to promote free delivery and secure payment.

Result: +18% conversion rate for the targeted segments.

New ideas with an North American Insurance Company (Finance)

Challenge: This insurance company wanted to address low engagement and high abandonment rates on application pages.

Solution: They implemented several, multiple targeted experiments. This included banners for “safety” and “competition” segments and an FAQ section to reduce hesitation.

Result: A +341% increase in email collection and a +95.83% increase in applications started from returning users.

These examples reveal that understanding the “why” behind visitor behavior can have a significant impact on business success. With tools like EmotionsAI, brands can evolve past one-size-fits-all experiences and create targeted personalizations that deliver significant, measurable results. 

It’s clear that a deeper, emotional connection is not just a competitive advantage – but a direct path to revenue growth and customer loyalty.

The Future is Emotionally Intelligent

You don’t have to navigate the shift away from third-party data and toward a more private web without a helping hand

Create privacy-first, fulfilling user experiences with EmotionsAI

EmotionsAI is here to be your partner for your personalization needs. Built for modern, privacy-focused web – it’s easier than ever before to catch your users in the emotional state their in simultaneously while they’re still shopping. 

By operating on in-session data and not relying on third-party data for cross-site tracking, EmotionsAI builds your brand for modern, privacy-focused web. When used with proper user consent, this approach aligns with regulations like GDPR and CCPA. This helps your brand establish trust and stay ahead of future privacy regulations.

Understanding emotional intent is on its way to becoming a major fork in the road separating brands that truly connect with their audiences from those that rely solely on static approaches to personalization. 

Building better, together

By combining behavioral data with emotional insights, brands can create experiences that are not only effective but also empathetic, building trust and long-term loyalty.

Are you ready to test the limits of what you thought personalization could do with newfound emotional awareness?

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Still have questions about EmotionsAI? Here are the answers you need.

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Article

13min read

Breaking Down Experience Optimization Platforms (EOP): How to Prioritize Experiments

Redefining the Digital Customer Experience

Nailing the online shopping experience for your customer is key to success – which is exactly what Experience Optimization Platforms (EOPs) were designed to help with. 

As a whopping 86% of shoppers are willing to pay for a better customer experience, making the digital atmosphere both approachable and conducive to conversions is key to securing a competitive edge in today’s market.  

Businesses are no longer just competing on product or price, but on the quality of the digital experience they offer. This means optimizing your websites accordingly has never been more important.

Many brands may not know where to start when it comes to curating the perfect online shopping experience, but the solution is simple. Experience Optimization Platforms (EOPs) serve as a comprehensive answer to create, test, and personalize your user experiences at scale. 

In this article, we’ll break down what an EOP is, explore a core strategic framework, and provide actionable guidance on how to prioritize experiments for maximum impact.

What is an Experience Optimization Platform (EOP)?

An Experience Optimization Platform (EOP) is a software solution that helps businesses analyze, test, and personalize digital customer interactions across apps, websites, apps, and additional points of contact with the user. 

Through a unique combination of informated-based insights, experimentation (like A/B testing), and AI-driven personalization to continuously improve the overall customer experience – Experience Optimization Platforms can help brands to optimize according to their user needs. 

This can result in improved audience retention and in turn, stronger conversion rates, which creates an environment for long-term brand loyalty and success. 

The overview cards below will reveal some of the hallmarks qualities and characteristics of Experience Optimization Platforms:

A/B Testing icon

A/B & MVT Testing

Lets teams experiment with different versions of pages or features to determine what performs best.

AI Personalization icon

AI Personalization

Deliver tailored content, offers, or experiences based on user behavior or demographics.

Data Integration icon

Data Integration

Unifies data from multiple sources (CRM, analytics, web/app) to create a single customer view.

Behavioral Analytics icon

Behavioral Analytics

Tracks user actions (clicks, scrolls, journeys) to uncover insights about digital interactions.

AI Recommendations icon

AI Recommendations

Uses machine learning to predict user needs and automate content suggestions. Link

Journey Orchestration icon

Journey Orchestration

Map and optimize end-to-end customer journeys across web, mobile, email, and more.

Why is it important to use an Experience Optimization Platform?

Using an Experience Optimization Platform (EOP) can allow for a robust, integrated suite of tools that allows businesses to expand operations and revenue with ease. This is because EOPs are a “one-stop-shop” for A/B testing, personalizing user journeys, and safely rolling out new features.

Additional benefits of Experience Optimization Platforms include:

  • Data-driven Decisions: Data revealed from testing with EOPs can centralize experimentation to make informed choices.
  • Improved Customer Loyalty: EOPs can create relevant experiences that keep users coming back.
  • Increased ROI: Information from experiments done with EOPs can allow brands to focus their efforts on changes that directly impact key business metrics.
  • Increased Average Order Value (AOV): By personalizing the user journey and optimizing the experience, EOPs can lead to an increase in the average amount spent per order.

What is the main difference between an EOP vs. Single-Point Solutions? 

The main difference between EOPs and Single-Point Solutions (point solutions) is that EOPs are best for a multifaceted approach to testing and experimentation, whereas point solutions are specialized tools or software designed to solve one specific problem.

The battle cards below will further break down the differences between EOPs and point solutions: 

Unified Approach

Experience Optimization Platform

Focus: Holistic customer journey
  • Unified data and shared audiences across all tools.
  • Streamlined workflows for testing and personalization.
  • Holistic view of the end-to-end customer experience.
  • Centralized experimentation for data-driven decisions.
Bottom line

An EOP acts as a robust “one-stop-shop” that eliminates data silos and expands operations and revenue with ease through integrated suites.

VS
Disparate Tools

Single-Point Solutions

Focus: Isolated, specific functions
  • Better for use cases where only one solution is needed.
  • Operates with standalone, single-function tools.
  • Often requires manual effort to sync data between systems.
  • Can lead to a fragmented view of user behavior.
Bottom line

While useful for simple or isolated needs, single-point solutions often lack the connectivity required for sophisticated, company-wide optimization.

How EOPs Benefit 3D Framework: Drive, Delivery, and Development

One of the most beneficial parts to using Experience Optimization Platforms is how they can propel brands to achieving the “3 D’s”.

Better known as drive, delivery, and development – the “3 D’s” in business serve as a strategic model for organizing optimization efforts. 

Drive: Fueling Strategy with A/B Testing

Drive in the “3 D’s” is about using a targeted approach to validate ideas through experimentation. In laymen’s terms, drive is the “driving force” for optimization and consequential testing or experimentation.

The main tools used for “drive” include:

One of the overarching benefits of drive in business is that it can help to mitigate risks, as brands will be able to quickly learn from user behavior and build a foundation of proven ideas.

Example: A retail client who tests two different versions of their product page layout, and finds a winning version through A/B testing which leads to an increase in “add to cart” clicks.

Delivery: Crafting Customized Experiences with Personalization

The second component of the “3 D’s” is when brands refine their method on how to create and execute action plans and tailored experiences to better align with business objectives.

This is where AI-powered personalization, such as using audience segmentation with tools like Emotions AI, can help to provide more dynamic content by basing user experiences on behavior, location, and real-time activity. 

In turn, “delivery” increases engagement, conversion rates, and average order value by showing the right message to the right user at the right time.

Example: A travel website that delivers real-time promotions for last-minute hotel deals to visitors whose browsing history shows exit-intent, which results in a lift in bookings.

Development: Innovating Safely with Feature Management

The last stage of the “3 D’s” is “development”, which centers on building a community of practice and using feature management to reduce the risk of rolling out new features.

This can be done through a variety of tactics such as feature flagging, progressive rollouts, or KPI-triggered rollbacks. During the development stage, businesses can enable product and engineering teams to innovate faster, test new features with specific user segments, and prevent negative impacts on the overall user experience.

Example: A SaaS company rolling out a new dashboard feature to only 10% of its user base to first keep an eye on early results. After closely monitoring the potential feature adoption and verifying performance metrics, they could confidently release this feature to the rest of their users. 

Drive Icon

Drive

This involves driving an optimization strategy with a focused and determined approach, often through A/B testing to validate ideas quickly.

Delivery Icon

Delivery

This focuses on delivering a bespoke action plan that aligns with objectives, often using AI-powered personalization to provide targeted experiences.

Development Icon

Development

This centers on developing a community to share best practices and methodologies, while using feature management to reduce risk with progressive rollouts.

How to Prioritize Experiments: From Ideas to Impact

With all of the different types of testing and experimentation, many brands face a problem with prioritization – not knowing which test is their next best move. 

While it’s true that mature optimization programs generate more ideas than can be tested, knowing when and where to play your experimentation cards is just as valuable. In other words, brands struggle more with deciding which tests to run first as opposed to coming up with a sufficient number of testing ideas. This presents the challenge of not knowing which of your ideas to pursue first. 

To solve this, there are prioritization frameworks which can provide an objective, structured way to rank and determine the best order to test ideas. This allows brands to move beyond gut feelings and one step closer toward information-based decisions. 

Here’s a breakdown of two prioritization frameworks that are used with Experience Optimization Platforms: 

PIE Framework

PIE, which stands for potential, importance, and ease, refers to a basic, structured method for prioritizing various experiments

Here’s the meaning behind each letter in PIE:

  • Potential: How much improvement can this test deliver?
  • Importance: How valuable is the traffic on the pages you want to test? (i.e., high-traffic or high-value pages)
  • Ease: How easy is the test to implement, both technically and operationally?

How does PIE work?

PIE framework and prioritization is determined by scoring each potential test or variation according to these categories, usually on a scale between 1 and 10. The method with the highest score is typically the first test to be run. 

blue puzzle piece yellow background

ICE Framework

ICE, which stands for impact, confidence, and ease, refers to another lightweight, organized method to help teams determine the best order for their various testing initiatives.

Here’s the meaning behind each letter in ICE:

  • Impact: If this test is a winner, what will the impact be on key metrics?
  • Confidence: How confident are you that this test will produce a positive result? (According to data, user research, past tests, etc.)
  • Ease: How easy is it to launch this experiment?

How does ICE work?

ICE framework and prioritization is decided by rating tests on a scale between 1 and 10 based on the three main facets of ICE: their potential impact, how confident the brand is it will be successful, and how easy it would be to implement. These numbers are then tallied up for a final score, revealing which test should be first in line for experimentation. 

Best Practices for Bold Implementation

Regardless of which prioritization framework you use in conjunction with your Experience Optimization Platform of choice it’s important to use the best practices in experimentation to ensure optimal results. 

These are some of the most effective experimentation practices:

  • Create a Centralized Backlog: This refers to making sure that all of your ideas are organized in one place.
  • Score as a Team: This involves stakeholders from different departments such as marketing, product, UX, and dev teams to get diverse perspectives.
  • Align with Business Goals: This requires ensuring your highest-priority tests directly map to your current company-wide objectives like OKRs or KPIs.
macbook pro against yellow background

Building a Culture of Experimentation

Beyond tools and frameworks, the overarching benefit of using an Experience Optimization Platform is not only to test ideas with fluidity – but inhabiting the idea that long-term success is more sustainable when there is a shift in how companies view experimentation.

Some of the key, cultural principles that EOPs usually foster include:

  • Embracing “Failed” Tests: Working with Experience Optimization Platforms can curate an environment where every result, win or lose, is a valuable learning opportunity.
  • Democratize Idea Generation: EOPs encourage everyone in the company to submit ideas to the testing backlog.
  • Share Results Widely: Teaming up with EOPs allows brands to better communicate the outcomes and learnings from experiments across the organization. This can also build momentum and excitement for the product while also demonstrating value.

Conclusion: Your Path to Optimization Maturity

Remember, the power of partnering with an Experience Optimization Platform is boosting your strategic value through the use of the 3D framework and structuring the prioritization process for testing and experimentation.

Optimization is not a one-time adventure that stops when a test is released to the public, but a long-time project that subscribes to a continuous cycle of learning and improvement. By teaming up with your EOP of choice, your brand can create a community where the drive to experiment and evolve becomes embedded in every decision and customer interaction.

Evaluating your current optimization process and identifying even one area where you can improve your approach to optimization, whether it’s adopting a framework or simply starting a testing backlog – could be the start of creative and constructive progress.

Are you ready to dare down the path to your next bold idea?

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Still have questions about experience optimization platforms? Here are the answers you need.

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9min read

5 Stages of Experimentation Maturity: How Teams Evolve from Simple Tests to Strategic Growth

From Hunch to Hypothesis

Experimentation isn’t a simple “switch” that a brand can flip, but more like a journey that benefits from small steps to create big shifts. 

Most brands start with a single test and eventually evolve into growth engines, such as using AI-powered tools, multivariate testing, and more.

But why even evolve from smaller tests to more complex ones? The answer is simple: high-maturity teams that continue to expand their experimentation strategies don’t just “test” – but are more bound to learn, iterate, and drive increased revenue. 

In this article, we’ll break down the roadmap from the first win to becoming an industry “blazer.”

What is Experimentation Maturity? 

Experimentation maturity refers to how developed an organization’s current strategy is in running, analyzing, and learning from experiments. 

This can include their expertise in running things like A/B tests, including its tools, processes, and culture. 

Why is Experimentation Maturity Important?

Experimentation maturity is important as stronger expertise allows for more reliable, faster experiments – which can better aid decision-making.

In addition to an increase in data-informed decisions, more developed experimentation strategies also mean brands continuously improve products, outcomes, and reduce overall business risks.

Step 1: Discover – Find Your First Win

In the first stage of experimentation maturity, a single person or small group is still advocating for experimentation. This is better known as the “Ad Hoc Testing Phase”, which is where experimentation is more reactive than proactive. 

Tests in this phase are typically: 

  • Launched on a whim with less strategic planning in mind
  • Implement with a hypothesis, but without long-term goals in mind
  • Conducted without stable metrics for greater insight
  • Using ideas that haven’t been backed by data
  • Maybe you are looking for a quick win

During this first step of your brand’s experimentation journey, the platform may be live – but it hasn’t been put to its best use yet. This is similar to someone who has recently purchased a new smart device, but hasn’t set it up to sync with all of their other devices – making their new electronic device less efficient than it could be.

Oftentimes during the discovery phase, there is one “lone champion” trying to fight for greater optimization tactics. This person is challenging those around them to overcome skepticism and testing for the sake of testing, but instead as an effort to create real change. 

Goals for this stage: 

  • Achieve one clear, high-visibility win to prove the ROI of a testing program
  • Avoid mistaking excessive testing activity for experimentation maturity 
  • View it as a stepping stone to ease into the world of optimization

Step 2: Structure – Build Your Blueprint

The second stage of experimentation maturity is when the organization shifts their focus from spontaneous tests to a more reputable, repeatable “rhythm”. 

In turn, this is when a more formal optimization team is usually built and more technical tools are established – such as client-side and initial server-side tools.

This step of a brand’s experimentation maturity usually involves: 

Hypothesis Frameworks icon

Hypothesis Frameworks

Develop stronger frameworks for creating and validating your experimentation hypotheses.

Defined KPIs icon

Defined KPIs

Establish clear primary and secondary Key Performance Indicators to measure success accurately.

Clear Ownership icon

Clear Ownership

Define who owns which experiments to ensure accountability and streamline the process.

Starter Roadmap icon

Starter Roadmap

Create a foundational experimentation roadmap to guide your initial testing efforts and strategy.

Adopted Templates icon

Adopted Templates

Ensure templates for experiment design and analysis are widely adopted for consistency.

Team Expansion icon

Team Expansion

Expand usage beyond marketing and CRO teams for company-wide implementation and impact.

This stage of the experimentation journey is where the brand is no longer a complete stranger to the world of optimization, but not yet an expert either. 

Goals for this stage: 

  • Transition from “random tests” to a structured roadmap with prioritized hypotheses
  • Treat learnings as a more systematic process that can lead to better tests
  • Recognize that experimentation isn’t about singular wins, but creating a reliable formula for continuous improvement.

Bolder Tests to Your Better

We learned more about this with Marianna Stjernvall, who has conducted over 500 A/B tests and understands the value of learning from your failures to accomplish future wins. 

With her ample knowledge in CRO, A/B testing, and experimentation – we discover all of the different ways that Marianna came to understand the value in testing, learning, and trying again. 

Listen to the full podcast episode here → 

Step 3: Accelerate – Team Up. Try Something New. 

In the third step of a brand’s experimentation journey, they will start to foster stronger cross-team collaboration. This is generally when experimentation will move beyond the marketing department and start to involve Product and UX.

During this phase, an organization becomes more well-versed in cross-team collaboration – ultimately standardizing the practice as they dive deeper into future experiments and testing. 

Advancing with AB Tasty

Our work with Pion is the perfect example of teaming up to make progress. Through the use of Feature Experimentation & Rollout, Pion was able to benefit from our server-side platform. This helps brands manage various user experiments before the full release, which can allow for both precision and security throughout the testing process. 

Partnering with AB Tasty allowed Pion to:

  • Select smarter tests to prioritize experiments that could deliver most valuable information to move forward
  • Be bold in their experiments to explore inventive ideas and break routine thinking 
  • Develop stronger hypotheses to ensure tests conducted would provide meaningful insights 

Learn more about Pion’s collaboration with AB Tasty here → 

Goals for this stage: 

  • Improve upon current technical capabilities such as by leveraging more advanced data 
  • Implement the use of more complex experimentation tactics such as heat maps and user recordings 
  • Increase testing velocity and finalize a company-wide communication plan for shared learnings
  • Ignite interest in using AI and machine learning to develop predictive models and boost personalization 
blue puzzle piece yellow background

Step 4: Scale – Testing at the Speed of Business

At this stage of the experimentation journey, optimization is no longer a project – but a core business strategy. In turn, this is usually when brands will begin to develop stronger standards, and maybe even experimentation or personalization “playbooks” to establish company-wide guidelines.

Empowering Speed with PUMA

PUMA and AB Tasty teamed up to accomplish this precise goal: personalization at scale that can bring long-term growth. 

Together, PUMA was able to empower global personalization through: 

  • Gender-Based Personalization: PUMA implemented the use of our segment builder to see which products men and women preferred to purchase. This helped with metrics including page views, on-page time, and more. 
  • Led Data Lead the Way: Through the use of newfound insights, PUMA guided their strategy to become more information-led. This encouraged more effective experimentation that resulted in increased revenue long-term. 
  • Teamwork Makes the Dreamwork: Experimentation isn’t just for CRO teams, but can be used as a brand-wide method to boost optimization. PUMA learned this with AB Tasty, now fostering a company-wide culture where everyone can contribute ideas for new tests.

Read the full case study with PUMA here → 

Hallmark characteristics for teams progressing to this stage of experimentation maturity include developing a Center of Excellence (CoE) model and an overall more robust team structure for optimization.

Goals for this stage: 

  • Measuring KPIs and additional success metrics to ensure they are aligned with top-level business goals (Revenue per Visitor, CLV, etc.)
  • Encourage all departments to not only come up with ideas for tests, but to conduct them safely and independently at scale

Step 5: Blaze – Dare to Go Further

In the highest level of maturity, brands become experts in experimentation – recognizing that this is a pivotal growth and product strategy.

Once this last stage of experimentation maturity has been achieved, every employee can be considered an experimenter. This is when the “HiPPO” (Highest Paid Person’s Opinion) is replaced by data, and choices can be made from insight-led statistics as opposed to emotionally driven business decisions.

Goals for this stage: 

  • A cultural shift where failure is viewed as a high-value learning opportunity to test and try again rather than a loss
  • Continuous and automated optimization that represents the organization’s DNA
  • Dedication toward long-term development of innovative ideas and pursuing bold experimentation 

Conclusion: Your Journey to Better

Every stage of experimentation maturity is a stepping stone to finding your better.

No matter where your brand currently stands on their optimization roadmap, we believe that your next big insight is just one test away. Small steps can create big change, and it all starts with curiosity calling your company’s name. 

Want to see your brand progress through the steps of experimentation maturity, together? 

FAQs

Still have questions about experimentation maturity? Here are the answers you need.

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11min read

What CMOs Should Demand From Their Web Experimentation Teams in 2026

The New Mandate for Growth

In 2026, growth in digital marketing and web experimentation is no longer contingent on brand-standard-styled CMOs – but their now pivotal, progressive role to be a growth architect. 

CMOs today pioneer the digital direction a company takes, and how that use of marketing is perceived by potential clients and users. This is because the C-suite now expects marketing to deliver quantifiable business results, from revenue to customer lifetime value.

What Does a CMO Do? 

A CMO, otherwise known as a Chief Marketing Officer, is the head of marketing operations in an organization. In turn, other roles in the marketing team will report to the CMO – with the CMO often communicating with other C-level executives in the organization.

CMOs are responsible for developing the planning and execution of all marketing activities within the organization. This means that successful CMOs should learn to be comfortable with growing technology and understanding changing consumer behavior. 

Some of the new tasks CMOs are responsible for in web experimentation include:

AI Solutions icon

Discover new potential AI-driven marketing solutions

Value Communication icon

Communicate value of product across partners & C-suite

Efficiency icon

Improve digital marketing efficiency as digital age skyrockets toward AI

Revenue icon

Driving company revenue & building brand relationships for future income generation

The Web Experimentation Gap: How CMOs Inspire Teams 

The challenge with web optimization in 2026 is that many experimentation teams are still focused on low-impact “superficial optimization”. 

To drive meaningful growth, CMOs must reset their expectations. Accomplishing this requires demanding that their experimentation teams evolve from tactical testers into strategic partners who can answer their business’s most pressing marketing endeavors and critical questions.

Demand #1: A Shift From Tactical Uplifts to Strategic Impact 

Moving Beyond Conversion Rate Optimization (CRO)

There’s no doubt that CRO is important, but it’s only one piece of the puzzle – as the real goal of web experimentation is Business Experience Optimization (BXO).

BXO, or Business Experience Optimization, refers to the process of seeking to better understand your customers and improve their shopping experience accordingly. 

In turn, CMOs should question how they read their analytics. Experimentation teams often share  how many conversion lifts are on a single page. Instead, CMOs could pursue analytics that reveal how experiments increase their revenue per visitor, reduce customer churn, and improve lifetime value.

Connecting Experiments to Business KPIs

To get real value from experimentation, every test should be tied to a clear business objective. Without that link, teams risk running isolated experiments that generate substantial insights – but little to no meaningful impact. A strong framework ensures that each experiment contributes to a bigger picture, whether that’s driving revenue, improving retention, or increasing customer lifetime value.

This is where concepts like North Star Metrics and OKRs (Objectives and Key Results) are key. A North Star Metric defines the single most important measure of long-term success for your business – such as active users, transactions, or engagement. This provides all experimentation efforts with a unified goal, helping teams prioritize tests that move the metric that matters most.

Meanwhile, OKRs translate that high-level ambition into actionable goals. While objectives define what you want to achieve, key results reveal how success will be measured. When experimentation is aligned with OKRs, each test has an individual motive: to influence a specific outcome. This can make it easier to measure the true impact of your experimentation program.

By tying experiments to both a North Star Metric and structured OKRs, organizations shift from running tests for incremental gains to building a disciplined, outcome-driven experimentation culture.

Here’s an example of how CMOs could inspire new conversations regarding KPIs and OKRs:

Old way: “We increased the click-through rate on the homepage banner by 8%.”

New way: “Our experiment on the homepage banner drove a 4% increase in average order value for first-time visitors. As a result, our models predict we will add $1.2M in incremental revenue this quarter.”

Demand #2: Leverage AI to Answer Bigger Questions, Faster 

It is imperative that CMOs view AI agents as a strategic co-pilot as opposed to an automation tool. This is because in 2026, AI is no longer just a tool for simple tasks – but a valuable, strategic partner for insight discovery and prediction.

Here are three main points CMOs should expect from their experimentation teams with AI: 

Three Core AI-Driven Demands From CMOs

Demand for Predictive Personalization at Scale

CMOs should anticipate teams to use predictive AI to personalize experiences for the 90% of anonymous traffic, not just known customers.

In this case, CMOs could ask their teams to avoid relying on static, rule-based segments. Instead, CMOs should convey to their experimentation teams that it’s better to use AI tools to forecast user intent in real-time and adapt the experience for every visitor, whether they’re logged-in or out. 

Tools like AdaptiveCX can help web experimentation teams to easily implement this exact strategy. This is because AdaptiveCX, cookieless by design, allows brands to personalize according to user preferences on the fly – even for anonymous visitors. 

infographic made by ab tasty explaining the benefits of adaptivecx and real time personalization

Demand for Deeper Audience Understanding

As users today are more impatient than ever, it’s crucial for CMOs to employ the concept of emotional and psychological segmentation. This is because to succeed with conversions, it’s key to understand not just what users do, but why they do it.

CMO’s should challenge their teams to go beyond demographics. This includes using AI to reveal the emotional incentives of key audience segments. Tools like EmotionsAI can accomplish this, as it groups visitors into 10 different categories according to their sentimental preferences. 

Demand for AI-Powered Ideation and Analysis

Experimentation teams shouldn’t be limited by their own biases. These predisposition results could be inclusive and fail to lead brands towards their next best test. Luckily, AI can analyze data to generate high-potential hypotheses – which can pave the path for better experimentation, improved conversions, and greater brand loyalty long-term. 

CMOs should be examining AI’s current involvement in how their experimentation teams come up with test ideas. Instead, CMOs could encourage their experimentation teams to explore generating hypotheses with AI that has analyzed our site data, competitor trends, and user feedback.

Demand #3: Build a Privacy-First, Future-Proof Program

In 2026, people are increasingly concerned with third-party cookies and personal information. As the use of these cookies has come to an end, brands that continue to rely on them have hit a strategic dead end. This means brands must find a new method to ensure data privacy for their users.

The Strategic Advantage of Privacy

Privacy doesn’t have to be a constraint, but can be used as a competitive advantage – as it’s a way to build deeper trust with your users. This is paramount for brands that want to cultivate a sense of exclusivity, long-term loyalty, and returning customers with high conversion rates. 

CMOs and their experimentation teams should aim to create a cookie-less personalization strategy with their experimentation teams. Seeking to implement the use of in-session, first-party data to create relevant experiences without compromising user privacy is key to making users feel safe to convert. 

The Right Technology

To make users more comfortable in this new age of data privacy, the right technology needs to be used. Our in-house tool at AB Tasty, AdaptiveCX, can help brands focus on real-time behavior rather than stored personal data. This ensures compliance with regulations like GDPR and CCPA and builds a sustainable foundation for the future.

macbook pro against yellow background

Demand #4: Foster a Culture of Experimentation, Not Just a Team

One Team, Endless Dreams

Experimentation shouldn’t be the sole responsibility of a small, isolated team – but rather a  shared task across marketing, product, and even sales.

This cross collaboration can allow for new, innovative ideas across the board – contributing to continued growth and more robust experimentation. 

Empower Every Idea

Experimentation teams should strive to go from being the “testers” to being the “enablers” who provide the tools, frameworks, and education for others to test safely and effectively.

In this case, CMOs should be evaluating their team’s plan to increase experimentation velocity across the entire organization. This can include how teams are scaling access to testing and creating a shared repository of learnings that are approachable for everyone.

From Trial to Better

Introduce the concept of an “Experimentation Maturity Model”. 

An experimentation program shouldn’t be static, but something that re-shapes itself in real-time to accommodate for new learnings and discoveries.

 

This is why CMOs should demand a clear roadmap for this evolution by using an Experimentation Maturity Model – which is a method for organizing the efficiency of how brands run various experiments, such as A/B tests or Multivariate testing. The main goal of an Experimentation Maturity Model is to build an organized experimentation program that delivers real results. 

This framework charts the path from early testing to a fully integrated culture of continuous improvement. This involves the organization progressing from the initial Discover stage, where tests are simple and singular, to the Scale and Blaze stages. 

At these more advanced levels, experimentation becomes an integral pillar for brands. This is because high-velocity testing, cross-team collaboration, and strategic insights contribute to major business decision making. 

This is exactly why CMOs should prioritize a clear path for advancing the organization from a “Discover” stage to a “Scale” or “Blaze” stage, where experimentation is eventually embedded in the company’s core culture. 

The interactive timeline below will break down these different stages: Discover, Scale, and Blaze:

1

Stage 1

Discover

Analyze performance, identify low-hanging fruit, and set benchmarks for initial experimentation success.

2

Stage 2

Scale

Expand testing across departments, implement automated personalization, and unify your customer data stream.

3

Stage 3

Blaze

Achieve full journey orchestration and predictive AI intent to lead with a culture of constant optimization.

Conclusion: The CMO as the Chief Experimenter

CMOs could spark growth in web experimentation by encouraging their teams to make more daring decisions in testing.

This includes”

  • Valuing strategic impact over tactical lifts
  • Benefiting from AI-powered insights over automation
  • Curating a privacy first foundation
  • Creating an enabling a company-wide culture of growth. 

Teamwork makes the dream work

Working smarter instead of harder is key for efficiency. Encouraging your web experimentation teams to take new, innovative paths towards success could prove worthwhile in the end. 

By making these demands, CMOs are not just overseeing a function – but transforming marketing to be the sustainable, customer-centric business model for growth suitable for 2026 and beyond. 

FAQs

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15min read

Product Category Marketing: A Guide for Retailers

Have you ever been to a big department store that doesn’t have signs directing you where to go? Without signs pointing you in the right direction or a map with pinpointed locations, you could expect shoppers to leave.

That’s exactly what happens when an online store gets its product categories wrong. Shoppers don’t wait around to figure it out. They bounce and they don’t come back.

Product categories are the backbone of every online shop. Get them right, and you’re guiding customers straight to what they need. Get them wrong, and you’re losing conversions every single day without knowing why.

In this article, we’ll break down what product categories are, how to structure them, and how to turn them into a real marketing lever, one that drives traffic, increases conversions, and grows your average order value.

What is a product category?

Think of product categories as a road map for your store.

Shoppers need guidance, especially when your catalog is large. The clearer the signaling, the faster they reach their destination. And in e-commerce, that destination is always the same: the product they came for.

A product category is a grouping of related products that share a common purpose, audience, or attribute. It’s the organizational backbone of your store, the system that tells both shoppers and search engines what you sell and how to find it.

Categories can be broad (e.g.,”Apparel”) or specific (e.g.,”Men’s Running Shoes”), and they typically nest within one another to form a hierarchy. The goal is always the same: get the right shopper to the right product in as few steps as possible.

Whether your customers are searching for skincare, winter coats, kitchen tools, or holiday candles — intuitive product categories aren’t optional. They’re essential.

Examples of product categories

To make this concrete, here are some of the most common top-level categories in e-commerce:

Fashion & Apparel icon

Fashion & Apparel

Clothing, shoes, and accessories — sorted by gender, season, or occasion.

Electronics & Media icon

Electronics & Media

Smartphones, laptops, wearables, and streaming devices.

Health & Beauty icon

Health & Beauty

Skincare, supplements, and personal care essentials for everyday wellness.

Home & Garden icon

Home & Garden

Furniture, kitchen tools, and garden equipment for every space.

DIY & Hardware icon

DIY & Hardware

Power tools, building materials, and home improvement fixtures.

Category pages provide links to products with common themes or attributes, funneling customers toward the items they’re most interested in. A well-named, well-structured category page does two things at once: it helps shoppers navigate, and it signals relevance to search engines.

What are the types of product categories in marketing?

You can create as many product categories as there are features, locations, functions, or use cases. At its core, a category groups products that share similar properties and deliver a similar benefit to your customers.

Categories can be built hierarchically, in the form of a tree structure. In the “Clothing” category, for example, you’d typically find a split between women’s and men’s. Those categories then branch into subcategories like “Pants,” “Jackets,” and so on.

Before you start categorizing, it’s worth understanding the four major product classifications. These aren’t rigid boxes, they overlap, but they’re a useful lens for thinking about how your customers shop.

Convenience goods

Purchased regularly with low involvement. Price and quality barely differ between brands — loyalty is habitual, not emotional.

Shopping goods

Purchased less frequently at a higher price. Customers research, compare, and deliberate — your category pages need to deliver the right information.

Specialty & luxury goods

Sought after by brand-conscious customers. It’s about exclusivity and status — high consideratioan, strong brand preference, and top revenue despite lower purchase frequency.

Unsought goods

Only purchased when a specific problem arises. These categories benefit from smart search and contextual placement rather than browsing.

Check out our Product Recommendation Engine Guide → 

What are product category structures in e-commerce?

Traditional classification methods

Traditional classification organizes products based on shared characteristics, like function, demographics, or area of application. It’s the most intuitive approach for both customers and internal teams, because it mirrors how people naturally think about and search for products.

Common methods include:

  • Functional categorization — grouping by what a product does (e.g., “cleaning supplies,” “cooking tools”)
  • Demographic categorization — organizing by audience (e.g., men’s, women’s, kids’)
  • Occasion or use-case categorization — grouping by context (e.g., “back to school,” “home office setup”)

This approach also has an internal benefit: it standardizes how your team talks about products across inventory systems, marketing copy, and customer support. This way reducing errors and keeping everyone aligned.

Hierarchical category structures

A hierarchical structure moves from broad to specific, a tree with 3 to 5 levels of depth. For example:

1

Level 1

Home

The root of the site — the starting point for every shopper’s journey.

2

Level 2

Clothing

Top-level category grouping all apparel across genders and styles.

3

Level 3

Women’s Clothing

Gender-specific subcategory narrowing the browsing context significantly.

4

Level 4

Dresses

Product-type category where intent becomes much clearer and more actionable.

5

Level 5

Formal Dresses

Deep subcategory revealing high purchase intent — the ideal moment to personalize and convert.

This kind of taxonomy does two things well: first, it helps shoppers navigate intuitively, and, second, it makes SEO implementation significantly easier. This is especially true when your naming conventions include the words customers actually search for.

A few practical rules of thumb:

  • Aim for fewer than 15 top-level categories
  • Keep it to 2–3 levels of depth where possible (5 levels maximum)
  • For most stores, 8 top-level categories with 4–8 subcategories each is a clean, scalable structure

The goal isn’t complexity, it’s clarity. The simpler the structure, the faster shoppers find what they need.

What is product category marketing?

Product category marketing is the strategic use of your product taxonomy to guide customers through your catalog, influence purchasing decisions, and hit your business goals.

It goes beyond organizing products. It means actively using your category structure as a marketing lever: to attract organic traffic, personalize the shopping experience, promote specific product lines, and grow revenue per visitor.

Done well, your category structure can promote high-margin product types, highlight seasonal occasions, or surface bestsellers. All without a single paid click.

Why is product category marketing important?

A well-structured product category system is one of the most impactful decisions an e-commerce team can make. Get it right, and you reduce friction for shoppers, improve SEO rankings, and directly increase revenue. Get it wrong, and you’re losing conversions every day, quietly and consistently.

According to Nosto, 69% of online shoppers go straight to the search bar when visiting e-commerce sites, but 80% leave due to a poor experience. Category pages are critical touchpoints where structure, content quality, and technical optimization all converge. It even affects both search rankings and conversion rates.

Optimized category pages with engaging content, clear navigation, and high-quality visuals don’t just convert better. They build the kind of experience that keeps customers coming back.

Curious about how personalization fits into this? Our Everything You Need to Know About Personalization resource breaks it down → 

What are the benefits of product category marketing in e-commerce?

With product categories, you can track and evaluate how customers browse your store. You can analyze purchasing behavior, which enables you to make individually tailored product recommendations, because very few customers have any interest in your entire catalog.

The data you gather through category-level analysis provides valuable insight into how shoppers navigate, what they click, and what they buy. That’s intelligence you can act on.

You can also use categories to deliver product recommendations: surfacing relevant products on the homepage, displaying matching items from a specific category, or personalizing the order in which products appear based on individual browsing and purchase behavior.

Breaking your catalog into categories also lets you monitor which pages get the most visits and which have the highest conversion rate. This will give you a richer, more actionable picture of customer behavior than product-level data alone.

How to categorize products for your marketing strategy

As previously mentioned, a product category is created by grouping similar products that share similar features. Those shared characteristics, the ones that determine how items in your catalog are grouped, should be baked into your marketing strategy from day one.

That said, categories aren’t set-and-forget. What resonated with shoppers a few years ago may not land the same way today. Timeliness matters. Relevance matters. Your categories should evolve with your customers.

How to analyze and update your product categories

A category analysis starts with recording and evaluating product features, and identifying the factors that positively or negatively influence demand. Your own traffic data is essential here, but competitor stores can also be a valuable source of inspiration.

Beyond assigning categories, a thorough analysis digs into each product type individually. How do customers perceive these categories? How do they talk about them? What does their purchasing behavior reveal?

A comprehensive analysis  also helps you spot emerging market trends and develop new strategies for category creation. Review your category pages at least quarterly, and always when you’re adding new product lines, responding to seasonal shifts, or noticing patterns in customer support questions.

How to create product categories successfully for your e-commerce

Here are three steps to build product categories that actually work.

Step 1: Identify the purpose of each page

Your product detail pages and category pages serve different purposes, and it’s important to be clear on both.

Product category pages are conversion-focused. They’re where the decision gets made.

Category pages are navigation-focused. They’re where customers orient themselves and move through your range. Their job is to guide, not to sell.

Design each page type with its purpose in mind.

Step 2: Design with your customer in mind

Always build from the customer’s perspective. What motivates them to buy? What information do they need at this stage of their journey?

A few practical moves:

  • Summarize and describe your categories clearly. Use your groupings to define main and submenu items. Keep naming specific and searchable.
  • Personalize the menu order. A customer who browses “sneakers” regularly should see that category first. Someone else might see “jackets” at the top. Personalized navigation reduces friction and increases relevance.
  • Design attractive category pages. Show product details that help visitors differentiate items (size, type, key specs). Use clear images — visuals help shoppers orient themselves far faster than text. Aim for 10–50 products per page: enough variety, not enough to overwhelm.
  • Personalize product ranking. Surface “relevant” products at the top — based on the collective click and purchase behavior of your visitors.

Step 3: Give customers ways to narrow their choices

Reducing the number of items displayed makes it easier to choose — and increases conversion. Here’s your toolkit:

  • Subcategories — divide broad categories into more specific ones
  • Functional categorization — group by what the product does
  • Demographic categorization — organize by gender, age, or audience
  • Area of application — where or how the product is used (bathroom, kitchen, outdoor)
  • Specific attributes — size, color, material, or other product-level characteristics
  • Solution-based categorization — group by the problem the product solves (e.g., “muscle recovery,” “better sleep”)
  • Filters and faceted navigation — let shoppers narrow results themselves
  • Onsite search — for customers who know exactly what they want

See how teams use A/B testing to optimize CRO →

Product category marketing examples

The best category strategies don’t just organize products — they reflect how customers actually think, shop, and discover. Here are four brands doing it well.

Alltricks — Cycling & Running 

+5%

Average Order Value (AOV)

+7%

Revenue per user

Alltricks, a specialist cycling and running retailer, was managing product recommendations manually — which meant slow load times, irrelevant suggestions, and zero mobile coverage.

By switching to AB Tasty’s AI-powered recommendation engine, they were able to surface complementary products automatically at the right moment: when a customer adds a mountain bike to their cart, they now see helmets, gloves, and maintenance products that other customers commonly buy alongside it.

The result: more items in the basket, more revenue per visit, and a category experience that works as hard as the products themselves.

Read the Alltricks case study here → 

Devred — Men’s Fashion

4x

more spent by recommendation-engaged users

35%

of total revenue driven by recommendations

Devred, a French men’s ready-to-wear brand, had a clear challenge: a large catalog, a loyal customer base, and an e-commerce experience that wasn’t pulling its weight.

They worked with AB Tasty to combine personalized recommendations, merchandising optimization, and A/B testing — transforming their category and product discovery experience from the ground up.

The results were striking: users who engaged with recommendations spent four times more than those who didn’t, and recommendations now account for 35% of the site’s total revenue.

Read the Devred case study here →

Maison Francis Kurkdjian (LVMH) — Luxury Fragrances 

Higher average basket value icon

Higher average basket value

Significant growth in sales index icon

Significant growth in sales index within weeks

For a luxury fragrance brand, every product page is a category unto itself — and the path from browsing to buying needs to feel effortless and elevated.

Maison Francis Kurkdjian partnered with AB Tasty to implement a smart recommendation and merchandising strategy, A/B testing different approaches to surface the right products at the right moment.

Within weeks of going live, they saw a measurable increase in average basket value — particularly for new collections — and meaningful growth in their overall sales index.

Read the Maison Francis Kurkdjian case study →

Why product category marketing is indispensable for e-commerce

Product categorization is a core pillar of conversion optimization. When you understand how customers click and buy, you can design a category structure that guides them more effectively — reducing bounce rates, increasing conversions, and generating insights that feed every other part of your marketing strategy.

Here’s what’s really at stake.

Conversion Rate

Well-structured category pages reduce friction. They guide visitors toward relevant products, present the right information at the right moment, and make the path to purchase feel effortless.

The more precisely your categories are structured and named, the more likely you are to attract high-intent visitors who are ready to buy. Long-tail keywords — the kind that map to specific category names — convert at around 36%, compared to 11.5% for short-tail terms. That’s not a small gap. That’s a structural advantage.

When done right, a strong category architecture doesn’t just improve rankings — it creates a shopping experience that converts and keeps customers coming back.

SEO and Organic Traffic

Category pages are your highest-value SEO real estate. They target commercial keywords — “wireless headphones,” “running shoes for women,” “natural skincare for sensitive skin” — that attract shoppers in research and comparison mode. These are the terms that drive qualified traffic, and category pages are built to capture them.

Category pages sit at the intersection of discovery and intent, which is exactly where you want to be in organic search.

Category pages also build topical authority. Search engines treat them as hubs that signal your site’s expertise in specific product areas. And every product linked from a category page receives ranking signals — creating compound SEO benefits as your catalog grows.

Average Order Value (AOV) and Cross-Sells

Well-designed category pages don’t just convert — they expand the basket.

A shopper who lands on “Yoga Mats” is also a candidate for “Yoga Blocks,” “Water Bottles,” and “Activewear” — but only if your category structure makes those connections visible. Cross-selling is responsible for 35% of Amazon’s revenue. Studies show that upsells and cross-sells can increase AOV by 20–30%.

Effective product categorization acts as a built-in cross-sell engine. When customers see an entire category page full of relevant options, they’re more likely to add multiple items — especially when personalized recommendations surface products that match their behavior.

Discover how to boost your Average Order Value here → 

Internal Alignment

Here’s a benefit that often gets overlooked: product categories don’t just serve customers, they align your entire organization.

A clear, consistent taxonomy standardizes how your team talks about products across inventory systems, marketing copy, paid campaigns, and customer support. Inconsistent naming leads to reporting errors, misaligned campaigns, and stock management headaches.

When your category structure is solid, your marketing, merchandising, and logistics teams all work from the same framework. Campaigns are easier to build. Reports are easier to read. And new team members can get up to speed faster.

Internal alignment isn’t a soft benefit. It’s a growth driver.

Conclusion

Product category marketing is far more than a back-end organizational task. It’s a strategic pillar of e-commerce success, one that touches every part of the customer journey, from first click to final checkout.

Get your categories right, and you’re doing several things at once: attracting qualified organic traffic, reducing friction for shoppers, surfacing cross-sell opportunities, and giving your internal teams a shared language to work from.

The key is to treat your categories as living assets. Analyze them regularly. Design them from your customer’s perspective. Test, iterate, and refine. In a competitive e-commerce landscape, the brands that win are the ones that make it easiest to find what you’re looking for, or discover something you didn’t know you needed.

That’s not just good UX. That’s good marketing.

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

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14min read

Highlights From AB Tasty Experience Talks 2026: Customer Experience Strategy Examples From Our Clients

Ready, Set, Match: A Grand Slam Welcome at Roland-Garros  

At Roland-Garros Stadium, where champions are made, AB Tasty Experience Talks 2026 served up its own match-winning theme: Ready, Set, Match – bringing together experts and clients alike for a memorable day filled with high-impact customer experience strategies.

We met with our clients for another one of our “Experience Talks” on Tuesday, March 31st for a human-first environment where guests were welcomed with warm smiles, custom badges, and a community-driven spirit.

Exclusive to AB Tasty clients, part of our customer club, over 300 people attended – revealing the demand for future Experience Talks in New York and London, and soon all around the world. 

We started the day bright and early with a smooth, welcoming check-in experience for new arrivals.

The goal of our Experience Talks 2026 was to establish a community-driven foundation before diving deep into digital strategy.

The Roadmap to Better: Our Vision and Shared Momentum

Our Journey in Global Growth 

To start the afternoon, we had Alix de Sagazan (Co-founder of AB Tasty) and Julie Dumont (Chief Product Officer) recapping our recent momentum – including our recent Manchester event and our growing global community.

They also discussed our recent announcement in merging with VWO, clarifying our singular, shared vision with total continuity and alignment for our teams.

julie and alix experience talks 2026

Progress With Our Platform

Alix and Julie also provided an overview of our platform evolution. As AB Tasty was originally focused on a single capability (A/B testing), they discussed our shift from being a software with a single capability to a more comprehensive, multi-product platform. This is especially relevant in the world of optimization, where AI now goes hand in hand.

With AI features from AB Tasty, we can now dive deeper into experimentation and personalization with:

  • Easier onboarding
  • Emerging insights
  • One click activations
  • AI to control content optimization

Daring to Go Further with AI

We also touched on what AI can do in a world where it’s becoming “normal” to make use of it, and therefore – more challenging to stand out with features developed by artificial intelligence.

At AB Tasty, we reiterated our focus on building practical, everyday AI features that can be used as a co-pilot in your optimization strategy. 

Some of the current and upcoming innovative, AI-driven features highlighted at the start of the Experience Talks 2026 included:

AI Campaign Studio

Effortlessly design and launch AI-driven experiments with high-performing hypotheses.

Commerce Strategy Builder

Align your site’s commerce logic with user intent to drive higher margins and conversions.

Revenue IQ

A strategic pillar focused on connecting every digital interaction directly to your bottom line.

AdaptiveCX

Predict and react to user behavior in real-time to deliver truly unique user journeys.

With all of these projects underway, we continue to build a valuable optimization engine where AI reshapes knowledge and automation to deliver unique user experiences.


The Champion’s Mindset: Adapting for the Win with Marion Bartoli  

To go along with our tennis match theme, we invited Marion Bartoli, Wimbledon Champion and former pro-tennis player, to speak with us about how trial-and-error got her to the successful peak she stands at today.

From her legendary matches against Serena Williams to her current roles with Prime Video and the BBC, Marion shared how capacity to adapt helped her to achieve her highest hopes as a former athlete – even when the odds were stacked against her.

alix and marion experience talks 2026

Stay Ready. Shift Fast. Win.

Marion shared several stories on how her curious courageous nature helped her to beat the odds and be bold for her tennis competitions. 

For instance, she shared an anecdote about a match in Miami in 2007, and how her resilience allowed her to win when others said she couldn’t. Despite being told her physical stance wasn’t the best for sports, in the end – she became an accomplished tennis player. 

Experimenting with an Analytical Edge

Marion’s early use of data and structured analysis with her father, who was a doctor, helped her to gain greater insights into what she could do to maintain a competitive edge in sports. 

This parallels our modern AI tools used to organize performance insights, which she also mentioned is a useful tool to accomplish your goals. 

Personal Motivation to Make Progress

Marion discussed how tennis is both a high-stakes competition and personal therapy.

She then shared how routine and repetition, confidence in preparation, and finding creative ways to take one step closer toward your goals is key. This aligns with our own brand beliefs, as Marion represents how determination in daring to go further can unlock new levels of success.  

In the end, success isn’t just raw talent – but the ability to adapt continuously.

ab tasty merch

Scaling the Summit: Personalization at Peak Traffic with Groupe La Poste

Next we heard from Nicolas Vandenbulcke & Cecile Breil from Groupe La Poste. Celebrating over a decade of innovation and partnership with AB Tasty, he shared their challenge in managing massive desktop and mobile traffic to improve navigation and service discoverability.

Climbing to the Top as a Team

La Poste’s strategy started with a deep analysis, shifted to focusing on menu optimization (burger navigation), and ended with  journey-wide personalization. Long-term, their target was to move beyond isolated pages and toward a more structured model of audience segmentation and global activation.

The results were clear. After involving dozens of cross-team contributors and using personalization as an entry point to broader experimentation culture – La Poste successfully built a community ecosystem. This allowed them to connect physical and digital life moments and ultimately – a more fulfilling user experience.

Curated for You: Elevating the Digital Boutique with Soeur  

Soeur, a powerhouse digital presence with high product volume and partnered with AB Tasty since 2024, joined us at our Experience Talks with Léa Moraly and Capucine Charreyre presenting their e-commerce strategy. 

Focused on expanding their e-commerce operations including international optimization, merchandising, and the intersection of experimentation and personalization – Soeur was dedicated to discovering new ways to boost their personalization strategy. 

Brave Ideas to Bold Results

With over 500,000 Instagram followers and high product volume, Soeur teamed up with AB Tasty to go further.

Soeur illustrated their journey in improving recommendations and search for their users, before heading into personalization.

Here were some of the key pages Soeur sought out to optimize with AB Tasty:

Category icon

Optimized Category Pages

Reorder products in real-time based on in-session intent signals to ensure the most relevant items are always front and center.

Product icon

Product Pages

Tailor social proof, urgency signals, and technical details to match the visitor’s unique browsing behavior and predicted preferences.

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Innovative Search Bars

Predict what users are looking for from the first keystroke and surface result patterns that align with their real-time interests.

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“Chosen for You” Sections

Leverage predictive AI to curate personalized product carousels that automatically adapt as a visitor’s interest evolves during the session.

They also employed pop-ups to provide users with customer support if there was inactivity. This method of intervening during cart hesitation helped shoppers to feel more confident in their potential purchase, such as by helping to reduce size uncertainty before a user accesses the size guide.

In the end, with success in increasing their recommendation pages and search bar revenue, Soeur was able to use behavioral insights and targeted activation to build a better foundation for ongoing experimentation. 

The Art of the Possible: Personalization Across the Maisons with LVMH

Sunny Song shared LVMH’s story in their adventure to accomplish the 3 D’s: drive, delivery, and development. 

The overview cards below will define each of the 3 D’s often used in the world of optimization:

Drive Icon

Drive

This involves driving an optimization strategy with a focused and determined approach, often through A/B testing to validate ideas quickly.

Delivery Icon

Delivery

This focuses on delivering a bespoke action plan that aligns with objectives, often using AI-powered personalization to provide targeted experiences.

Development Icon

Development

This centers on developing a community to share best practices and methodologies, while using feature management to reduce risk with progressive rollouts.

Having partnered with AB Tasty since 2015, LVMH showcased their journey in omnichannel scale, seeking to move from multichannel to a truly cross-channel and omnichannel experience.

To work toward this goal, LVMH followed 5 main pillars:

  • Identification
  • Segmentation
  • Activation
  • Measurement
  • Technical Foundations

This isn’t just theory, but a proven model for success – and LVMH’s story shows us how.

From a North Star to a Shared Galaxy

LVMH took a leap forward in their progress with personalization using these core pillars designed for success. 

This framework came to life through tangible actions at Maisons like Acqua di Parma, Maison Francis Kurkdjian, and FRED. By focusing on critical KPIs such as increasing login rates, enhancing product discovery, and boosting add-to-cart conversions – the teams turned strategy into measurable results.

The secret to scaling this success was their operating model, such as how LVMH curates a  culture of shared learning through internal workshops and detailed “Playbooks” that act as guides for personalization. 

By aligning every “Maison” around a central “North Star” metric, they allowed each brand to maintain its unique identity while still moving in the same direction. This proves value and building an insight-driven ecosystem of excellence.

sunny song giving presenation

Democratizing Data: The Voice of the Customer at Carrefour 

Next we had Laura Duhommet (CRO) and Pauline Massart (Customer Experience Manager) from Carrefour, which is a French based retail and wholesaling company. They recounted their mission to reduce risk, measure impact, and represent the customer’s voice.

Carrefour’s main mission was to experiment with the intent to better anticipate customer needs. 

This involved a collaborative model and a 4-key role approach to involve several teams including: 

  • Business
  • Product
  • Tech/Data
  • Design

CXO & AI Teaming Up For Your Next Big Win

Carrefour used both CXO Strategy and AI together to embark on their journey from the homepage through the checkout funnel using UX analysis, A/B testing, and NPS scoring.

Their CXO strategy, which included reworking product discovery across their home page, product pages, cart journeys and checkout funnel, made use of the following tools for their methodology: 

Carrefour also used AI integration for internal insight repositories, in addition to several AI assistants to analyze results and boost efficiency. This fostered a culture of improvement, where every test, win or lose, could be viewed as a learning opportunity that feeds a continuous improvement loop.

In teaming up with AB Tasty, Carrefour was able to build a collaborative, experimentation oriented environment where AI enhances efficiency and CRO enables better decision making.

Celebrating the Bold: The Customer Experience Awards 

Nearing the end of our Experience Talks 2026, we announced the winners for this year’s award winners for:

  • Best Mobile Strategy
  • Best Merchandising
  • Most Innovative
  • Best AI Usage
  • Most Engaged Clients

Some of our winners included L’Oréal Canada, SNCF Connect, Hello bank!, and Clarins – celebrating strategies that focus on simplicity, impact, and strong experimentation frameworks.

We also honored long-term partners who have shaped their industries through the strategic use of personalization and experimentation.

Here’s a breakdown of the winners from AB Tasty’s Experience Talks in Paris 2026:

Best Mobile Strategy

  • Bronze: L’Oreal Canada & SNCF Connect
  • Silver: Wurth & La Banque Postale
  • Gold: MACIF

Best Merchandising

  • Silver: Manutan
  • Gold: Raja

Most Innovative

  • Bronze: Sandaya 
  • Silver: Clarins
  • Gold: Hello bank!

Best AI Usage

  • Bronze: Tikamoon
  • Silver: SNCF
  • Gold: Mademoiselle bio

Most Engaged Clients

  • Bronze: Maxime Donnet: E-Commerce Manager Oscaro 
  • Silver: Lea Moraly: E-Commerce Director for Soeur 
  • Gold: Mathilde Veau: CRO Manager EMEA L’Occitane
  • Gold: Sunny Song: Lead E-Commerce Optimization
auditorium chairs

Innovative Ideas For the Win

The “Most Innovative” category recognized brands that moved beyond traditional testing to build both unique and successful user journeys.

  • Gold: Through the development of personalization tactics for users who had previously abandoned the application process, Hello bank! was able to encourage users to return and significantly improve lead generation.
  • Silver: Clarins focused on maximizing the impact of their welcome pages to better engage new customers.
  • Bronze: Sandaya used a “Headless & Data-Driven” approach to place product recommendations at the heart of their digital growth strategy and accelerate conversions.

Awarding Amazing AI Usage

These awards celebrated teams that best used AI to automate workflows and bring real-time user experiences to life.

  • Gold: Mademoiselle Bio improved their testing velocity with AI by making a majority of their tests automatic. This drastically reduced test generation time, making it easier than ever to modify site elements like buttons and colors.
  • Silver: Even in our long-standing partnership of over 10 years, SNCF Connect is still ready to take on new challenges –such as with AI. Using AI for both geolocalization and dynamic content to reduce friction in user’s search experiences, SNCF Connect was able to successfully increase engagement with service pages.
  • Bronze: Tikamoon Tikamoon used AI to accelerate the development of mobile variations. This was done specifically by implementing a scroll indicator that encouraged users to explore more products and improved mobile click-through rates.

Game, Set, Match: The Networking Celebration

After the awards ceremony, we regrouped for drinks and small bites –opening the floor for a celebratory, collaborative environment where clients, speakers, and AB Tasty teams connect beyond the presentations.

During this cocktail hour, we were able to strengthen the human connections that fuel digital innovation.

Trial, Better, Repeat: Bringing the Vision Home

In the end, the best parts of A/B Tasty go beyond the tool itself – but in how our bold, powerful partnerships create a creative community that encourages everyone to take new steps towards brave experiments.

Experience Talks 2026 showed how our clients don’t just use the platform, but contribute to re-shaping the future of optimization.

Roland-Garros proved that tactics like personalization and customer experience innovation are mindsets that require more than just strategy – but teamwork to find the perfect winning serve. 

Find Your Winning Swerve

Want to join our community of courageous thinkers dedicated to continuous optimization?

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12min read

AB Tasty Cements Leadership Position in G2 Spring 2026 Reports: What Sets Us Apart 

Real Reviews. Real Results. No Filter.

Choosing the right optimization software isn’t easy. The market is crowded, the promises are loud, and everyone claims to be the best. So how do you cut through the noise?

You listen to the people who’ve actually been there.

That’s exactly what G2 does. As one of the most trusted sources for peer-driven software reviews, G2 gives buyers something rare: honest, unfiltered insights from real users — no spin, no sales pitch. Just authentic experiences from teams who’ve tested, iterated, and formed an opinion worth sharing.

And in the G2 Spring 2026 reports? AB Tasty’s users spoke — loudly.

We’re breaking down exactly how G2’s rating system works, what it takes to earn a top spot, and why AB Tasty’s results reflect something we’re genuinely proud of: a community of users who believe in what we’re building together.

Decoding G2: What Are G2 Ratings and Why Do They Matter? 

G2’s rating serves as one of the world’s largest and most trusted software marketplaces for vetted, peer-to-peer reviews for  software. Operating as a cloud-based platform with millions of verified reviews, G2 helps companies to compare and contrast various technology solutions to discover which one is right for their business needs. 

How G2 Ratings Work

G2 ratings work by comparing software across four different categories: Leader, High Performer, Contender, Niche. These are determined by two key differentiators: Market Presence and Customer Satisfaction.

In addition to this breakdown, G2 ratings are also decided through:

Reliable Data Sources

As rankings are chosen using verified user reviews and select social data, G2 scores are widely regarded as both trustworthy and comprehensive. This is because each review undergoes a validation process to ensure authenticity, helping prevent bias or manipulation. 

With insights on review quality, timeliness, and volume, this approach ensures that rankings reflect real, up-to-date customer experiences as opposed to one-off opinions or outdated feedback.

Badge Significance

In order to showcase each special feature associated with each software, G2 awards various badges. 

Here’s a breakdown of the various badges offered by G2:

Leader Badge Icon

Leader

These badges indicate products with both high customer satisfaction and strong market presence.

Best Relationship Badge Icon

Best Relationship

This badge highlights tools that deliver exceptional customer experience, including support, trust, and ease of doing business.

Most Implementable Badge Icon

Most Implementable

These badges are awarded to software solutions that are exceptionally easy to set up and get up and running quickly.

Unique Algorithm

G2 Ratings use a specific algorithm to calculate their data for customer satisfaction and market presence, allowing for the most up-to-date information at all times.

In order to ensure precision, G2 ratings are scored according to two main areas: customer satisfaction and market presence. 

Customer Satisfaction

This score is derived from verified user reviews and aims to focus on the customer’s experience with the software. 

Various key factors contribute to this component of G2’s scoring system, such as: 

  • User-Focused Scores: This includes information from review forms related to the product’s ease of use and how helpful users found customer support. 
  • Recent Reviews & Volume: If reviews are more recent, they are given more weight to boost relevancy. A high volume of reviews is also required for statistical significance.
  • Admin-Focused Scores: How user-friendly customers found admin, set-up, and overall business logistics with the software they used. 
  • Review Quality & Source: If reviews are completed on the user’s own behalf without any additional incentives, they are given more weight to calculate the customer satisfaction score. 

Market Presence

This score combines G2’s internal review data with external third-party sources to measure a company’s market presence and influence in the industry:

  • Review Count: The total number of reviews associated with a specific category are weighted depending on quality and how recent the reviews are. 
  • Company Size & Revenue: Various data from platforms like ZoomInfo, LinkedIn, and Crunchbase can help to verify employee counts and estimated B2B software revenue.
  • Web Presence: This refers to search engine rankings (Moz Authority), estimated search volume, and website traffic data. However, this isn’t as important as review counts, revenue, or company size.
  • Growth & Engagement: Any substantial trends in employee growth over time or overall social/market influence are taken into account for G2’s market presence scoring system. 

Why G2 is a Critical Benchmark for Competitors 

G2 is an aspirational goal for many competitors in software as it provides an objective reflection of customer satisfaction and real-world product performance. 

Our Users Said It Best. We’re Just Here to Share It.

We could talk about our G2 Spring 2026 results all day. But honestly? Our users already did it better. From intuitive experimentation to responsive support, the reviews speak for themselves — and they’re saying something worth reading.

Here’s a look at where we landed, and why it matters.

We’re happy to announce that AB Tasty is once again recognized by G2 in their Spring 2026 report, showcasing us as a leader across the digital experience optimization landscape.

With an average 4.4 out of 5 review score across over 400 reviews, AB Tasty has been established as a leader in web experimentation and optimization – with over 19 different badges including regional leader, high performer, and providing industry-leading user adaptation. 

Badges to Show Our Bold

Here’s a breakdown of just a few the badges AB Tasty received:

Regional Leader

Software services that acquire this badge receive strong ratings from G2 users. This demonstrates both strong customer satisfaction and market presence.

High Performer

This badge reveals that companies like AB Tasty have higher customer satisfaction scores while having a smaller market presence than competitors in the same category.

Highest User Adoption

This badge showcases AB Tasty’s exceptional performance in user adoption in comparison to its competitors.

Best Estimated ROI

Companies that receive this badge earned the best estimated ROI rating in its category. This is calculated by the length of time it took to achieve ROI and how long it needed to go live.

Momentum Leader

Products and services that obtain this badge are rated in the top 25% of products in their category by users.

Easiest To Do Business With

This badge is awarded to software services that provide exquisite customer service and make it easy for customers to partner with AB Tasty and make progress together.

AB Tasty also positioned itself as a leader in several key categories, which illustrated our comprehensive platform capabilities in:

Here are some more places where AB Tasty shines according to this year’s Spring 2026 G2 ratings:

Recognition for Excellence in Customer Experience

AB Tasty acquired several badges related to exceptional customer experience.

These are some of the special awards we received that reflect our strong, customer-centric approach:

  • Momentum Leader: Recognized for our rapid growth and positive user feedback.
  • Most Implementable: Achieved for our user-friendly setup and smooth implementation process.
  • Best Relationship: Awarded for our solidified reputation for building strong, positive relationships with clients.

Broad Market Appeal

This year, AB Tasty received high performer and leader badges badges across several categories for businesses of all sizes: small businesses, mid-market, and enterprise. In turn, this depicts our ability to provide outstanding customer service and business value for companies of all sizes. 

Speed & Efficiency in Optimization

AB Tasty is being recognized for our quick, well-structured plans to help websites implement optimization. It can be tricky to be both fast and organized. However, our recent G2 Spring 2026 badges prove we can successfully do both.

Our Fastest Implementation badge shows that our product was a leader in its category with the shortest time to go live. Furthermore, our Most Implementable badge reveals that our product had the strongest rating for implementation within its group. 

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AB Tasty vs. Competitors: How We Stand Out 

AB Tasty isn’t just known for our approachable, beneficial services – but for going one step ahead of our top competitors. This is because our optimization software offers various features not found with other A/B testing or web experimentation tools.

Here are some of the ways that AB Tasty stands out against competitors:

A Unified Platform for All Teams

Unlike other tools, AB Tasty offers a single platform for both client-side (no-code) web experimentation and server-side feature experimentation. This empowers marketing, product, and engineering teams to easily collaborate with one another.

Innovation Driven by AI & Patented Technology

At AB Tasty, we believe in innovative ideas to take you one step further – and that AI can help support your optimization journey.

Here are some of the AI-powered tools and technology we offer that make growth in experimentation even more exciting and efficacious than before:

  • Evi AI: Our AI-driven personalization tools, such as Evi AI beyond basic A/B testing with advanced AI agents and EmotionsAI for sophisticated audience segmentation.

Unwavering Performance and Speed

At AB Tasty, we put speed and precision together to create an unstoppable, unforgettable optimization experience.

AB Tasty has one of the fastest tags on the market, plus a built-in Performance Center to ensure experiments don’t slow down the user experience.This “performance-first” philosophy is what positions us as a leader in the field of web experimentation

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A True Partnership Approach

We know that successful software doesn’t just mean crunching numbers and spitting out data, but having real people pushing you towards greater progress. One of the reasons that our customers leave such overwhelming positive reviews is because people are at the heart of our core mission – combining powerful experimentation with expert guidance every step of the way.

AB Tasty provides dedicated Customer Success Managers (CSMs) who act as strategic partners. On the other hand, several of our top competitors have more expensive or less cohesive support models.

Moreover, more than 300 customer ideas were implemented into the product in the last year. This demonstrates our commitment to listening and evolving with not only client needs, but out-of-the-box ideas that could turn into exceptional experiments. 

This includes creating personalized road-maps, expanding feature capabilities inspired by user feedback, and continuously refining the platform to support more memorable experimentation.

Your Next Big Test Starts Here.

G2’s Spring 2026 reports are a good reminder of what we’re here for — helping brands turn bold ideas into real action.

The badges we received this year only further cements our leadership status in the world of optimization. This is because we’re recognized for our strong customer relationships and ease of implementation. In fact, 9.3 out of every 10 customers at AB Tasty share their satisfaction in working with us.

AB Tasty is more than just an A/B testing tool. We’re a comprehensive experience optimization platform that combines user-friendliness, powerful AI, uncompromising performance, and partnering together to make progress.

Want to see one of the leading platforms in optimization in action?

See AB Tasty in action with Maison Francis Kurkdjian.

Read the full Case Study here →

Our Customer Reviews from G2

Read some of our most recent reviews from our clients on G2:

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9min read

Sample Size Calculation in A/B Testing: 7 Best Practices

Sample Size Calculation for A/B Tests Made Simple

At its heart, the A/B testing process is designed to generate reliable results so you can make decisions based on hard data. But working out just how many visitors you need to sample to have confidence in these can depend on a number of different factors. Fortunately, online tools can now help you take the guesswork out of the process, without the need for a math’s degree.

white calculator

How Sample Size Calculation Works

The key reason for calculating the correct sample size for a given test is to ensure that this is representative of your entire audience. This in turn will ensure that your test results are reliable and help you to avoid false positives and negatives. If your sample size is too small, you could end up with wildly misleading results. If it’s too big, you could be wasting time and resources without gaining any useful insights. 

A very general rule of thumb is to have a minimum sample of 10,000 visitors per test variation and at least 300 conversions for each. However, you can calculate the correct sample size for a given A/B test variation with the aid of a standard mathematical formula which looks like this:

Here’s a breakdown of what each letter stands for in the equation:

  • n is the required sample size per test variation
  • p1 is the Baseline Conversion Rate
  • p2 is the conversion rate lifted by absolute Minimum Detectable Effect
  • Z/2 is the Z-score for Statistical Significance Level
  • Zβ is the Z-score for Statistical Power

Looks complicated? Before you start reaching for the algebra textbook, don’t panic! Instead, let’s have a look at what the above variables actually mean:

  • Baseline Conversion Rate: the current conversion rate for the specific goal that you are trying to improve. This might be something like subscription rate, transaction rate, or click though rate.
  • Minimum Detectable Effect (MDE): the smallest change in the conversion rate that you want to detect with statistical confidence. This essentially determines how sensitive your A/B test will be.
  • Statistical Significance Level: the probability that the difference in your baseline conversion rate and the conversion rate of a test variation is not caused by chance. The accepted standard for statistical significance is 95%. The Z-score for 95% significance is 1.96.
  • Statistical Power: the probability that your test will detect a real effect where one exists. Again, standard practice is to set power at 80%, meaning you have an 80% chance of catching a true winner. The Z-score for 80% power is 0.84.
Boost your conversion rates by creating personalized experiences

Fortunately, there are now a range of tools available online that will perform this somewhat intimidating calculation for you. For most of these, all you typically need to do is enter the variables above.

It’s worth noting that both the Minimum Detectable Effect (MDE) and statistical power have a direct relationship on the sample size of a test. If you want higher statistical power (i.e. more chance of catching a winner) or a smaller MDE (i.e. greater test sensitivity), your sample size will need to be bigger. That can affect the time taken for a test to run and the resources involved.

At some point, you’ll have to ask yourself: is it worth it?

Different Approaches to Calculating Sample Size

Many online platforms recommend calculating the sample size of an A/B test in the pre-test planning phase. But at AB Tasty, we think this is too late. Because If you discover that this number is too high, meaning the test would need to run too long to be practically feasible, then it is just useless to build the variant.

That’s why we’ve developed an MDE calculator specifically for the pre-test planning phase. This helps you understand the minimum uplift required and how much time you would need for an experiment to achieve statistical significance based on your actual historical data. This will ensure that you set realistic expectations before you launch a test.

Using our Minimum Detectable Effect Calculator couldn’t be easier:

1

Input

Define Your Baseline

Input your current website visitors and the conversion rate for the specific goal you intend to improve.

2

Calculate

Map the Opportunity

The calculator estimates the minimum uplift needed for significance. See exactly how many days it takes to reach your confidence threshold.

3

Launch

Eliminate Waste

Avoid wasting time and resources on tests that are unlikely to produce conclusive or statistically significant results.

We also have a Sample Size Calculator which helps you determine the required number of visitors for your test and estimate how long your test should run for to achieve the desired results. This should be used for ongoing tests, and not for pre-test planning.

To Estimate the Number of Visitors :

  • You input the current conversion rate for the goal you are trying to improve and the expected uplift between test variations.
  • Our calculator then estimates the required number of test visitors per test variation.

To Estimate the Duration of Your A/B Test:

  • In addition to the information entered in the previous step, you input the average number of daily unique visitors a tested page receives and the total number of test variations including the control version.
  • Our calculator then estimates the minimum required test duration in days to achieve the desired results. However, this number comes with a caveat, as explained below.

Best Practices and Pitfalls

Now let’s look at some of the major dos and don’ts to keep in mind when calculating test duration and sample size.

1. Run tests for a minimum of 14 days

Even if you reach your target sample size in a few days, or our test duration calculator suggests otherwise, it’s best practice to run an A/B test for a minimum of two weeks. This helps to account for variations in user behavior, such as weekday versus weekend traffic, and ensures your data is much more reliable.

2. Account for external factors like seasonality

Certain periods of the year, like Christmas, Black Friday, or Bank Holiday weekends can skew your results if you’re running a test at these times. You’ll need to take these into account if you want your sample to remain representative of your normal audience.

3. Don’t stop a test too early

You also need to avoid the temptation of checking on test results before both the test duration and sample size have been reached. Doing so dramatically increases the chances of coming to a false conclusion about the test.

Our Evi Analysis AI agent relies on statistical significance to tell you whether a particular variation is a winner. For it to do its job correctly, you should only ask Evi to interpret the results after the test has reached the number of visitors recommended by the Sample Size Calculator. That’s because Evi Analysis can’t inherently know that you planned to have a sample size of, say 100,000 visitors, but decided to stop after only 10,000.

4. Don’t overlook practical significance

Having test results that are statistically significant doesn’t automatically mean they have a practical application for your business. If it will be too costly to implement a change indicated by a test variation it might not be worth running the test in the first place.

5. Prioritize high-traffic pages

Testing should be initially focused on pages of your website that are likely to receive the most visitors. For example, the homepage, product listing pages (PLPs), or product detail pages (PDPs). The greater volume of traffic to these pages means you’ll be able to gather data more quickly and run faster tests.

6. Limit the number of variations

Testing more variations at once can seem more efficient, but it increases the risk of false positive results. If you’re testing on pages with low traffic volume, using fewer variations avoids splitting sample visitors too thinly.

7. Target broadly

When possible, run A/B tests across multiple countries or segments to increase the sample size.

Conclusion: From Guesswork to Growth

Calculating the correct sample size for your A/B tests is the key to delivering statistically significant results you can trust. But you no longer have to be a math whizz to figure out how big your sample size needs to be.

By using our MDE calculator for pre-test planning and adhering to best practices for sample size and test duration, you can ensure your A/B tests will be both more effective and more reliable.

Ready to go from calculating to converting?

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10min read

From Average to Attractive: Email Personalization That Converts

The Unopened Email & The “Close Enough” Offer 

These days, with emails incessantly filling our inboxes, it’s increasingly challenging to not only capture someone’s attention – but to get them engaged with the subject matter of attractive mail.

Between email campaigns, newsletter, and social media stories – every marketer knows the feeling of spending days crafting an “extraordinary” email campaign, only for it to be met with low open rates or even unsubscribes.

The main challenge in developing dynamic email campaigns isn’t a lack of effort, but a lack of immediacy. Traditional email personalization relies on past purchases and slow-moving segments. These slow moving factors mean that by the time the email lands in someone’s inbox, they’ve already lost interest or their reason for subscribing in the first place has changed.   This leads to “close enough” offers that feel average instead of attractive.

Luckily, there’s a way to bridge the gap between what a customer did yesterday and what they want right now – and that’s with real-time personalization through AdaptiveCX

This article will explore quick, easy ways to move beyond static segments and use real-time, in-session behavioral data to create truly attractive email personalization that converts.

The Old Playbook: Where Traditional Email Personalization Falls Short 

While the benefits of real-time personalization in email campaigns are indispensable, it’s important to remember the hallmark tricks and tips to traditional email marketing that have stood the test of time. 

Here are some of the classic email campaigns tactics that still remain relevant today:

Purchase & Demographic Segmentation

A classic strategy that groups users by past buying behavior and traits like age or location. It was revolutionary for moving beyond one-size-fits-all marketing to deliver more relevant product recommendations and messages.

Basic Engagement Campaigns

Proactively re-engaging inactive customers with messages like “We miss you!” This tactic directly addresses customer churn and was a key step in focusing on customer retention instead of only acquisition.

Limited-Time Coupons & Discounts

Using time-sensitive offers to create a sense of urgency. This classic psychological trigger encourages immediate action and is highly effective at converting hesitant buyers by tapping into the fear of missing out.

Visual Engagement Tactics

Employing animated GIFs and flashing images to capture attention in a crowded digital space. This approach makes promotions more dynamic and memorable, cutting through the noise to draw the user’s eye to key messages.

However, there are several ways to improve upon these already impactful strategies to make not only attractive email, but messages that actually convert users. 

Identify the Cracks

The first step in tailoring email campaigns to be more effective is to determine where improvements could be made. This is where stale data, based on what the user did, not what they are doing – is less useful. 

For instance, someone who bought hiking boots last month might be browsing for running shoes today. There’s no way to know what a user is shopping for today using the data for what they shopped for months, weeks, or even days ago. 

Tackle Anonymous Visitors 

One of the main downsides of traditional email personalization is that it renders useless for the 90% of visitors that browse your website while logged out. Without their personal data, there’s no way to ignite interest until they sign up – and by then, the moment to pique their interest may have already passed.

infographic made by ab tasty explaining the benefits of adaptivecx and real time personalization

Generic Campaigns

The best way to a shopper’s main motivation, and ultimately their conversion, is to spike curiosity in the exact thing they’re interested in buying. 

This means that segments such as “interested in women’s fashion” are too generic to successfully convert users. It doesn’t differentiate between users looking for a discount on last season’s dresses, or one looking for new-in luxury handbags. The result of these basic, “batch-and-blast” emails posing as personalized, attractive messaging is audience feature and missed revenue opportunities.

This is where AdaptiveCX can help to make your email personalization “pop” for readers and incentivize them to return to your website and proceed with a potential purchase.

The New Playbook: Real-Time Intent Signals

There’s an easy way to go from ordinary to outstanding email campaigns: and it’s with real-time personalization.

AdaptiveCX allows brands to gather information not just on what data they collect, but when you collect and act on it. With technology designed to track thousands of in-session “micro-behaviors”, brands can better understand their users’ intent in the moment and provide them with relevant content according to their interests in the moment.

What are Real-Time Intent Signals?

Here are some examples of how real-time intent signals collect useful information that traditional email platforms can’t see: 

Hesitation icon

Hesitation

A user pausing on a specific product image signals interest mixed with indecision—a key moment to intervene.

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Comparison Shopping

Switching between multiple product tabs is a classic sign of a user weighing their options and looking for the best deal.

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Price Sensitivity

Immediately sorting a category by “Price: Low to High” reveals a budget-conscious shopper who will be receptive to discounts.

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High Intent

Zooming in on product details or repeatedly viewing an item are strong indicators of a user close to making a purchase decision.

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Affinity

Showing a preference for a certain color, brand, or style within a session allows for immediate, relevant product recommendations.

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Contextual Awareness

Knowing if a user is on mobile vs. desktop or from a search engine vs. social media adds a crucial layer to other behavioral signals.

The magic of these moments is turning these microscopic signals into “intent profiles” for users on that same day. This tactic works for both known customers and anonymous visitors. 

Remember, boosting engagement with email personalization is contingent on who the user is in a live-session today – not yesterday or two weeks ago. 

Three Ways to Make Your Emails More Attractive with Real-Time Data 

Going from bland to brilliant email campaigns doesn’t have to be tedious with real-time data.

Here are just a few of the ways email personalization can help your mailing campaign go from generic to golden:

The Hyper-Relevant Abandoned Cart Email

Reminding users that they’ve left something in their cart is a common tactic to incentivize them to re-engage with your website. 

Imagine a user that showed price sensitivity (i.e., they used a coupon extension or sorted the products by price). An email tailored toward lower-prices, such as a small, one-time-use discount, could send them back to the site. 

Here are some examples:

Average: “You left something in your cart!” (Shows the item).

Attractive (with AdaptiveCX): The email trigger knows not just what was in the cart, but also why it might have been abandoned – ”Don’t leave your cart empty during this one-time sale!”(shows the discount) .

The “We Read Your Mind” Follow-Up Email

In our fast-paced moving world, users often see items they like – but get distracted before proceeding to checkout. A great way to encourage them is to re-prompt them to add those items to their cart.

For instance, a user might have been browsing three different black dresses but didn’t add any to the cart. The follow-up email’s subject line could be something along the lines of, “Still Thinking About the Perfect Black Dress?” with the content showcasing those exact three dresses, plus a “similar style” recommendation.

Another example is when someone browsed flights to Paris for specific dates, but never followed through with booking the trip. The “mind-reader” email to follow could include a  real-time price drop alert for that exact route to grab their attention.

Here are some examples:

  • Average: A typical “Top Picks for You” email sent 24 hours after a user visits the site.
  • Attractive (with AdaptiveCX): An email sent minutes after a session ends that reflects the user’s true intent from that same session.

The Intelligent “Back in Stock” Notification

Nothing is worse than finding exactly what you’re looking for, only for it to be out of stock. This is where email notifications reminding previous visitors that a product they view in the past is now available could do wonders for conversions.

Here are some examples:

Average: A simple notification that an item is back in stock.

Attractive (with AdaptiveCX): The email will remember details such as how many times the user looked at the specific product, which images they zoomed in on, and more. With AdaptiveCX, these users can be placed in a priority queue to receive the notification before other customers. This sense of urgency could provide a sense of exclusivity that could motivate them to convert quickly. 

shopping cart abandonment

Conclusion: Stop Predicting, Start Adapting

The future of email marketing isn’t going to be based on making better guesses from stagnant data, but about listening to what your customers are telling you right now through their behavior and adapting to their needs in a flash. 

By connecting real-time, in-session intent to your email strategy, you can move past average, forgettable messages to attractive, can’t-miss moments that drive immediate conversions and long-term loyalty.

Ready for smarter email personalization strategies that will take your conversion levels from basic to bold?

FAQs

Still have questions about email personalization? Here are the answers you need.

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