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”.
This step of a brand’s experimentation maturity usually involves:
Hypothesis Frameworks
Develop stronger frameworks for creating and validating your experimentation hypotheses.
Defined KPIs
Establish clear primary and secondary Key Performance Indicators to measure success accurately.
Clear Ownership
Define who owns which experiments to ensure accountability and streamline the process.
Starter Roadmap
Create a foundational experimentation roadmap to guide your initial testing efforts and strategy.
Adopted Templates
Ensure templates for experiment design and analysis are widely adopted for consistency.
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.
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
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.
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.
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?
Still have questions about experimentation maturity? Here are the answers you need.
What is Experimentation Maturity?
Experimentation maturity refers to measuring how effectively your organization uses testing to make decisions and encourage company growth. This is usually determined by the level of your various processes, tools, and culture for running experiments consistently and at scale.
How can I know which stage of experimentation maturity my company is at?
You can determine how mature your brand’s experimentation process is by looking at how structured your testing process is, how many teams contribute, and how you use data to make decisions. Comparing these factors to industry benchmarks can help you identify your current stage in experimentation maturity.
How can my company boost its Experimentation Maturity?
Investing in training, tools, and clear processes can enable teams to run more tests confidently. This can also encourage collaboration across departments and use insights from experiments to guide strategic decisions, which also helps your brand progress through the stages of experimentation maturity.
About the Author
Angélique de Taddeo
Angélique is Chief of Staff at AB Tasty, where she works closely with leadership on strategic initiatives and cross-functional projects. With a strong interest in product, customer experience, and market trends, she shares insights and perspectives through her articles.
What CMOs Should Demand From Their Web Experimentation Teams in 2026
Stephanie Safdie
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:
Discover new potential AI-driven marketing solutions
Communicate value of product across partners & C-suite
Improve digital marketing efficiency as digital age skyrockets toward AI
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.
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.
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.
Still have questions about CMOs and web experimentation? Here are the answers you need.
What Does a CMO Do?
CMOs are in charge of leading digital marketing and raising brand awareness for a company. In turn, they spend a lot of their time scouting new technology to improve efficiency in digital marketing, cross-coordinating with other teams and C-suite level, and organizing meetings with other brands, both online and in-person, to further promote the company and its product or service.
How Can Web Experimentation Benefit CMOs?
Web experimentation and optimization platforms like AB Tasty can greatly benefit CMOs as it provides them direct insight into how certain catch phrases, CTAs, colors, banners, buttons, and more are performing on the site. Furthermore, they’ll gain a better understanding of what their users respond to – allowing for more efficacious digital optimization, and ultimately improved revenue long-term.
Are CMOs responsible for seeking AI-driven marketing opportunities?
Yes, CMOs are responsible for scouting out new AI-driven technologies that could help their marketing teams to run more efficiently. This is one of the new, evolving responsibilities of CMOs in the age of artificial intelligence and optimization.
About the Author
Stephanie Safdie
Stephanie Safdie holds a bachelor’s degree in English Language and Literature from the University of Maryland, specializing in multimedia studies. She has worked as a social media video creator, freelance copywriter, SEO copywriter at Greenly climate tech, and runs a travel blog Destination Dreamer Diaries.
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
Clothing, shoes, and accessories — sorted by gender, season, or occasion.
Electronics & Media
Smartphones, laptops, wearables, and streaming devices.
Health & Beauty
Skincare, supplements, and personal care essentials for everyday wellness.
Home & Garden
Furniture, kitchen tools, and garden equipment for every space.
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.
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.
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.
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.
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
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.
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.
Maison Francis Kurkdjian (LVMH) — Luxury Fragrances
Higher average basket value
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.
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.
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.
A product category is a grouping of related products that share a common purpose, audience, or attribute. It’s the organizational backbone of an e-commerce store, helping both shoppers and search engines understand what you sell and how to find it.
What are the types of product categories?
The main types are: convenience goods (frequently purchased, low involvement), shopping goods (less frequent, higher consideration), specialty and luxury goods (brand-loyal, high exclusivity), and unsought goods (only purchased when a specific need arises).
Why are product categories important for SEO?
Category pages typically have the highest search potential of any page on an e-commerce site. A clean, keyword-rich category structure drives qualified organic traffic, helps search engines index your products correctly, and builds topical authority for your domain.
How do product categories affect conversion rates?
Well-structured category pages reduce friction in the shopping journey, guide users toward relevant products, and create natural cross-sell and upsell opportunities — all of which contribute to higher conversion rates and average order value.
How often should I update my product categories?
Review your category pages at least quarterly — and whenever you’re adding new product lines, responding to seasonal shifts, or noticing patterns in customer questions or support tickets.
Highlights From AB Tasty Experience Talks 2026: Customer Experience Strategy Examples From Our Clients
Stephanie Safdie
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.
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:
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.
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.
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:
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 Pages
Tailor social proof, urgency signals, and technical details to match the visitor’s unique browsing behavior and predicted preferences.
Innovative Search Bars
Predict what users are looking for from the first keystroke and surface result patterns that align with their real-time interests.
“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
This involves driving an optimization strategy with a focused and determined approach, often through A/B testing to validate ideas quickly.
Delivery
This focuses on delivering a bespoke action plan that aligns with objectives, often using AI-powered personalization to provide targeted experiences.
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.
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
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?
Still have questions about AB Tasty’s Experience Talks? Here are the answers you need.
What are Experience Talks?
AB Tasty’s Experience Talks is an annual events where we gather our clients together to showcase their progress in the world of optimization. It also serves as a chance for networking opportunities and for everyone partnered with AB Tasty to meet face to face.
What’s the difference between Experience Talks and Experience Next?
Experience Talks refer to congregating with our local, French clients an community to discuss peer learnings and real world insights. On the other hand, Experience Next is the international version of this event, with editions held in cities like London and New York. This draws in clients from all over the world to discuss global customer experience strategies.
When are the next Experience Talks/Experience Next events?
Stephanie Safdie holds a bachelor’s degree in English Language and Literature from the University of Maryland, specializing in multimedia studies. She has worked as a social media video creator, freelance copywriter, SEO copywriter at Greenly climate tech, and runs a travel blog Destination Dreamer Diaries.
AB Tasty Cements Leadership Position in G2 Spring 2026 Reports: What Sets Us Apart
Stephanie Safdie
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
These badges indicate products with both high customer satisfaction and strong market presence.
Best Relationship
This badge highlights tools that deliver exceptional customer experience, including support, trust, and ease of doing business.
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:
Software services that acquire this badge receive strong ratings from G2 users. This demonstrates both strong customer satisfaction and market presence.
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.
This badge showcases AB Tasty’s exceptional performance in user adoption in comparison to its competitors.
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.
Products and services that obtain this badge are rated in the top 25% of products in their category by users.
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.
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.
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.
Patented RevenueIQ: Our unique technology that provides a clear projection of the revenue impact of A/B tests into a single, sharable number. This exclusive to AB Tasty feature helps brands define a more concrete view of their financial impact.
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
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 some of our most recent reviews from our clients on G2:
FAQs
Still have questions about G2 Ratings? Here are the answers you need.
What are G2 Ratings?
G2 collects peer-reviewed scores directly from verified users on its platform. Reflecting real customer experiences with a product, these reviews often cover areas like usability, support, and overall satisfaction. These ratings can help businesses to make informed decisions based on authentic user insights.
Why are G2 Ratings Important?
G2 Ratings provide businesses with clear, unbiased feedback directly from real people using a product or software, which helps teams evaluate software with confidence. Companies use these ratings to build credibility and earn trust from potential customers. Strong ratings also highlight product strengths and uncover areas for improvement.
What are G2 Badges?
G2 Badges are awards for products based on performance, customer satisfaction, and market presence within G2 reports. These badges showcase achievements like “Leader” or “High Performer,” helping users quickly identify top-rated solutions. These badges serve as a visual representation of a company’s reputation and customer trust.
About the Author
Stephanie Safdie
Stephanie Safdie holds a bachelor’s degree in English Language and Literature from the University of Maryland, specializing in multimedia studies. She has worked as a social media video creator, freelance copywriter, SEO copywriter at Greenly climate tech, and runs a travel blog Destination Dreamer Diaries.
Sample Size Calculation in A/B Testing: 7 Best Practices
Hubert Wassner
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.
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 verygeneral 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.
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.
Still have questions about sample size calculators? Here are the answers you need.
Why is sample size calculation important in A/B testing?
Sample size calculation allows your test to have enough power to detect a meaningful difference between variants while avoiding wasted resources. Too small a sample may lead to false negatives, whereas too large of a sample size may be inefficient or expose too many users unnecessarily.
Which approach should I use to calculate sample size?
Usually, you use a power analysis that considers the expected effect size, significance level (α), and desired power (1−β). Tools like online calculators or statistical software can help, working under the notion a binomial or normal approximation depending on your metric.
How do you determine the minimum detectable effect (MDE) for an A/B test?
MDE is the smallest change between your control and variant that you consider practically significant. This is calculated based on business goals, baseline conversion rates, and the statistical power you want – usually by deciding what lift in metrics (e.g., click-through rate, revenue) would justify implementing the new variant.
About the Author
Hubert Wassner
Hubert Wassner is Chief Data Scientist at AB Tasty with over thirty years of experience in AI and machine learning. He builds advanced statistical models, shares insights on the blog, and helps brands make confident, data-driven decisions. His most recent achievement is obtaining a patent for RevenueIQ.
From Average to Attractive: Email Personalization That Converts
Kate Feng
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.
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.
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
A user pausing on a specific product image signals interest mixed with indecision—a key moment to intervene.
Comparison Shopping
Switching between multiple product tabs is a classic sign of a user weighing their options and looking for the best deal.
Price Sensitivity
Immediately sorting a category by “Price: Low to High” reveals a budget-conscious shopper who will be receptive to discounts.
High Intent
Zooming in on product details or repeatedly viewing an item are strong indicators of a user close to making a purchase decision.
Affinity
Showing a preference for a certain color, brand, or style within a session allows for immediate, relevant product recommendations.
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.
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?
Still have questions about email personalization? Here are the answers you need.
What is email personalization?
Email personalization refers to the practice of tailoring emails to each individual recipient according to their personal preferences. Instead of generic messages, personalized emails deliver more relevant content recommendations or targeted offers.
How does real-time personalization improve email campaigns?
Real-time personalization helps email campaigns as it uses live data from the very moment an email is opened. This helps to curate content that is always up to date, making emails more relevant and timely – which improve engagement and conversions.
What data is used for email personalization?
Email personalization uses data such as names, purchase history, browsing behavior, and engagement patterns. Additional, advanced strategies may also include geographic location, preferences, and predictive insights to further improve content relevance.
About the Author
Kate Feng
Kate Feng holds a degree in International Business from SKEMA Business School, where she wrote her thesis, “Personalization with Principles: Ethical AI Strategies in Advertising.” Her research focused on how brands can leverage personalization responsibly — balancing performance, consumer trust, and ethical AI frameworks. Working on the AB Tasty team since 2024 as our Growth Marketer and Creative Strategist. Driven by blending her background in international business and art, Kate often links the gap between creativity and strategic thinking. Furthermore, Kate has experience developing her own business, a fashion company named KATE FENG PARIS – further demonstrating her skills in marketing, personalization, and international business. Kate often contributes to the blog to share her knowledge on growth marketing and personalization.
Why Modern E-commerce Needs a Semantic-First Search Strategy
Emily Healy
For years, e-commerce search was built around a simple principle: match the words a shopper types with the words stored in a product catalog.
That model made sense in an earlier era of online retail. Traditional search solutions were built around keywords, synonym dictionaries, and manual rule-setting. If results were poor, teams fixed them by adding more synonyms, refining product terms, or tuning search rules.
That approach can still work in some cases. But shopper behavior has changed – and many search solutions have not evolved quickly enough to keep up.
Today’s shoppers don’t search like machines. They search in natural, sometimes imprecise language. They describe what they need, the problem they want to solve, or the type of product they have in mind. And they expect search to understand them.
That’s why more brands are rethinking their search strategy – and why semantic-first search is becoming the better foundation for modern e-commerce.
The problem with keyword-first search
For brands with highly technical catalogs, structured product data, or shoppers who search using precise references, exact-term logic can be critical. In these cases, synonyms, keyword rules, and manual controls help ensure precision and consistency.
But problems arise when keyword-first search becomes the core model for every search experience.
Many established search solutions were built on that foundation. And even as they evolve, they often remain heavily reliant on manual synonym mapping, exact-term matching, and rule-based tuning to maintain relevance.
That creates real limitations.
Search quality can depend too heavily on manual upkeep
Broader or more natural-language queries can be harder to interpret
Modern shopper behavior gets forced into an older search model
Teams end up compensating for engine limitations through constant tuning
In other words, keyword logic is still useful – but for many brands, it works better as a layer of control than as the foundation of search itself.
That is why more e-commerce teams are moving toward semantic-first search: not to eliminate precision, but to build on a foundation that better matches how people search today.
Modern Approach
Semantic-based search
Starts from: meaning and intent
Understands what the user is trying to find.
Works well with more natural-language queries.
Less dependent on manual rule-building.
Best for modern, intent-driven experiences.
Bottom line
Semantic-based search is a strong modern foundation, while synonyms and keywords are still important for complex catalog environments. Newer search solutions, like AB Tasty Search, are built semantic-first, with flexibility for complex catalog needs.
VS
Traditional Approach
Keyword-based search
Starts from: exact terms and predefined rules
Matches what the user literally typed.
Works well with structured, precise product language.
More dependent on synonym lists, keyword mapping, and tuning.
Best for precision, control, and technical catalogs.
Bottom line
While keyword-based search is useful for complex catalogs, it is not adapted for modern buyer behavior. Legacy search solutions are trying to shift toward semantic-first architecture.
Shopper expectations have moved on
Modern shoppers are not thinking in taxonomy structures or exact product terms. They search in a way that feels intuitive to them.
They might type:
“comfortable black boots for winter”
“gift for a coffee lover”
“lightweight jacket for rainy weather”
“desk chair for back support”
These are not just keywords. They are expressions of intent.
A traditional keyword-based engine may interpret them literally and unevenly. A semantic-first engine is better equipped to understand the meaning behind the query and return more relevant results.
That difference matters because search is not just a navigation tool anymore. It is a core part of the customer experience. If search feels rigid or unhelpful, shoppers lose confidence quickly – and often leave.
Why semantic-first is the better foundation
Semantic-first search starts from meaning and intent, not just exact terms.
Instead of asking only, “Did the shopper type the right keyword?”, it is built to ask, “What is this shopper actually trying to find?”
That creates a stronger foundation for modern commerce because it better supports:
Natural-language queries
Broader or less precise searches
Discovery-oriented shopping behavior
Evolving shopper language over time
This does not mean keyword logic has no value. For technical catalogs, specialized products, or highly structured environments, synonyms and precision controls still matter.
But those elements should support the search experience – not carry it.
That is the key difference.
A semantic-first strategy uses intent understanding as the foundation, then adds precision where needed. A keyword-first strategy starts with rules and tries to build toward intent afterward. For brands thinking long-term, that distinction matters.
Search should not stop at the search bar
There is another reason semantic-first matters: shoppers do not only search. They also browse, compare, refine, and explore.
That means search should be part of a broader product discovery strategy:
Search helps users find more
Capture user intent, return relevant results, and reduce friction when shoppers know what they want.
Recommendations help users discover
Surface alternative and complementary products, extend the journey beyond the original query, and support inspiration and browsing behavior.
Merchandising helps brands guide discovery
Promote strategic products, balance relevance with business priorities, and give teams control where automation alone is not enough.
When those elements work together, the experience becomes more cohesive and more effective. Instead of treating search as a standalone tool, brands can create a connected discovery journey that balances shopper intent with business priorities.
This is also where many point solutions fall short. A search tool may solve part of the problem, but still leave teams managing fragmented logic across multiple systems.
Why AB Tasty’s Search approach is different
At AB Tasty, our Search solution is built around a semantic-first approach. Rather than treating semantic search as an add-on to a legacy keyword model, we designed it to better reflect how shoppers actually search today: with intent, context, and natural language.
Just as importantly, semantic-first does not mean rigidly semantic-only. AB Tasty Search still allows brands to use synonyms and precision controls where they add value – especially for complex or technical catalogs.
That gives teams a better balance:
a more modern, intent-driven foundation
flexibility for catalog complexity
less dependence on manual rule management alone
And because AB Tasty Search sits within a broader optimization and product discovery ecosystem, brands can connect Search with Recommendations, Merchandising, and experimentation strategies instead of managing search in isolation.
For teams re-evaluating legacy vendors or looking for a more future-ready approach, that is a meaningful advantage.
The brands that win will modernize search
The question for e-commerce teams is no longer simply whether their search tool functions.
The better question is whether their search strategy reflects how people shop today.
Many older search models were built for an era when exact keyword matching was enough. Today, that is no longer sufficient on its own. Shoppers expect relevance, flexibility, and a search experience that understands more than the literal terms they type.
That is why semantic-first search is becoming the new standard.
And it is why brands looking to modernize should move beyond keyword-first thinking toward a strategy built for intent, discovery, and adaptability.
Because modern shoppers do not search like machines.
And with AB Tasty Search, brands no longer need a search strategy that expects them to.
Everything You Need to Know About Multivariate Testing in Travel
John Hughes
There’s nothing like the feeling of having your suitcase packed, ready to take flight, and being on your way to take a step on new parts of the planet. But the moments before takeoff may not be as simple as you think. This is where multivariate testing in travel steps in.
These days, travelers know what they want: and they’ll bounce quickly if their booking experience doesn’t meet their expectations.
As travel remains one of the most competitive, emotionally complex purchase decisions a customer can make online – it’s extremely important to curate a satisfying digital experience. Otherwise, you don’t only risk losing a sale – but a long-term customer.
The travel experience starts long before luggage is pulled out of closets. People check the weather, potential flight cancellations, and research recommendations and reviews prior to booking flights or hotels.
In this guide, you’ll learn what multivariate testing is, how it can benefit travel, and why optimization can be the answer to making the booking journey as smooth as the trip itself.
The Travel Booking Funnel: Why It’s Uniquely Complex
The Modern Traveler’s Journey is Non-Linear
Today’s travelers don’t move in a straight line. They open up a link on their phone, move to their laptops to make comparisons, add a flight to their cart on tablet, and abandon the purchase altogether — all before booking.
The overview stat cards below will reveal how dynamic the travel booking experience can be:
94%
A whopping 94% of travelers switch between devices when planning a trip.
70%
The majority use mobile devices, with up to 70% of people researching on their smartphones. However, only 31% complete a booking there.
53%
Over 53% of Americans have made a same-day hotel booking, revealing how spur-of-the-moment travel can be.
This cross-device, multi-session behavior makes it extremely difficult to identify which types of pages provoke drop-off during the booking process.
The Emotional Complexity of Travel Purchases
Unlike buying a product online, booking a holiday involves a myriad of emotional factors:
Excitement
The primary driver of travel planning. Lean into this emotion with high-quality, aspirational imagery and headers that spark joy.
Stress
Navigating flight times, hotel locations, and budgets can be overwhelming. Simplification and clear UX are your best tools here.
Potential Disorganization
Travel involves many moving parts. Help users stay organized by providing clear summaries and easy-to-find booking details.
Trust
Booking travel is a high-cost commitment. Build trust through social proof, secure payment markers, and clear cancellation policies.
Budget Sensitivity
Most travelers are price-conscious. Highlighting value, discounts, and “best price” guarantees is critical for this group.
Fear of Regret
Reassure users with flexible booking options, easy comparisons, and real-time availability updates to mitigate the “what if” factor.
All of these factors must be taken into account when designing the travel booking experience. This is because there’s no way to measure how one user may be reacting emotionally to travel-driven content created with the intent to draw in more bookings.
For instance, a headline that creates urgency may convert one visitor and deter another. A price display that feels transparent to one user may feel overwhelming to another. This uncertainty and complexity is exactly where multivariate testing can help, as it experiments with different elements to help travel brands discover which winning combination works best for a diverse set of visitors.
The High Cost of Getting It Wrong
Across several industries, the travel industry is home to some of the highest cart abandonment rates. As booking funnels often require several steps and sensitive, financial data to secure – every part of the journey is an opportunity to either build trust or lose it.
Multivariate testing (MVT) refers to the method of testing multiple variables on a page at the same time to determine which combination of changes produces the best result.
Unlike A/B testing, which compares two versions of a single set of choices – MVT tests include several elements at the same time, providing a greater set of data to reveal how each component interacts with one other.
The goal of multivariate testing is to test a multitude of ideas on the same page, at the same time, to determine which set of variables make for the most impactful digital experience.
A Travel-Specific Example
Multivariate testing and travel can go hand in hand for several steps throughout the booking journey.
Imagine you want to optimize a flight search results page. This could include testing the headline copy, the layout of the pricing display, the CTA button text, or even the way an urgency message is phrased (i.e., “only 3 seats left – book now).
As multivariate testing automatically generates and runs every possible combination of these elements, it can identify not only the best-performing version of each individual element (button, text size, font, etc.), but the best-performing combination.
MVT vs. A/B Testing in a Travel Context
When it comes to optimizing your travel booking website, A/B testing and Multivariate testing can both be beneficial – but it’s important to understand how different the two types of testing can be in the world of travel.
Here’s a breakdown of how each type of testing works in the travel industry:
A/B Testing: Best for testing one hypothesis at a time — e.g., “Will ‘Book Now’ outperform ‘See Availability’?” Fast and accessible.
Multivariate Testing: Best for understanding how multiple trust signals, urgency cues, and CTAs work together to drive a booking. More powerful, but requires more traffic.
Does My Travel Brand Qualify for Multivariate Testing?
In general, MVT requires more traffic than A/B testing to reach statistical significance.
If your travel website wants to optimize using multivariate testing, you’ll need to have substantial traffic. As a rule of thumb, sites seeking to use MVT should aim for pages with at least 50,000 to 100,000 monthly visitors.
Lower-traffic travel pages can consider Fractional Factorial Testing, which tests a statistically representative subset of combinations.
How Does Multivariate Testing in Travel Work?
Multivariate testing works by simultaneously testing several components to discover which elements can have the most profound effect on making progress on your optimization journey.
Here’s a step-by-step guide to how multivariate testing works:
Step 1: Identify the High-Value Page
This is where brands will focus on high-traffic, high-intent pages where even small improvements could create a tangible impact on revenue.
In the travel industry, these pages often include:
Search result pages for flights & hotels
Destination landing pages
Booking & checkout pages
Loyalty or reward program sign-up pages
Mobile homepages
Step 2: Define the Elements to Test
This is when travel companies choose between 2 and 4 elements that are likely to have the greatest influence on user behavior.
In travel, these variables could be:
Urgency messaging: This refers to the message travel bookers often see as a way to incentivize a purchase. Examples of these types of text include, “only 2 rooms left” or “book before prices rise” – as it puts users under the impression that they should book before the good deal is gone.
Price display format: The way that prices are presented on a travel booking website can have an impact on potential travel purchases. This means brands must decide between showing the total price, price per-night, or even pay over time options.
CTA button copy and design: The color of a button, size of the text, or even the font could have an effect on the travel experience and incoming visitors looking to book a trip.
Trust signals: When booking a trip, a lot of anxiety is induced – as people are spending large, lump sums of money on a single website. This means that things like security badges, reviews, and payment icons are important, as they make the user feel emotionally secure and more confident to move forward with a travel purchase.
Hero image or destination photography: Have you ever been enticed to take a trip because of the gorgeous photo on a travel brand’s homepage? The images used on travel websites can play a pivotal role in encouraging users to book a vacation.
Social proof: These days, people find comfort in knowing a particular website or brand is popular – as it serves as a form of security throughout the multi-faceted booking process. Subtitles or images revealing other people are interested in a travel booking site, like “500 people viewed this trip today”, can make a difference.
Step 3: Create Variations and Launch
This is when travel brands will pick 2 to 3 variations to test at the same time. A MVT platform will automatically generate all of these possible combinations, meaning no manual setup will be required.
Once these elements have been decided, a hypothesis can be defined – and the effects of MVT can provide new, indispensable insights.
Here’s an example of how this works in practice:
2 urgency messages (e.g., “Only 3 seats left at this price” vs. “Prices will increase in 24 hours”)
× 2 price displays (e.g., total price upfront vs. per-night breakdown)
= 8 unique combinations, all tested simultaneously
The traffic is evenly split across all the different combinations, and the test runs until statistical significance is achieved.
Step 4: Analyze Results
After all the multivarious elements have been simultaneously tested, the platform determines the winning combination. This advanced analysis reveals which individual components were most successful and which parts had an unexpected effect on user interaction.
Types of Multivariate Tests for Travel
The great thing about multivariate tests is that there are several different kinds – all of which could prove beneficial for travel in different ways.
Here’s a breakdown of the various types of multivariate tests that can be used for travel:
Full Factorial Testing
A full factorial test determines how multiple factors influence a specific outcome, otherwise known as the response variable. Each of these factors are tested at different levels, and the experiment includes every possible combination of these levels across all variables.
While full factorial testing is the most comprehensive, it is also the most traffic intensive. This makes full factorial testing best for high-traffic search results or homepages for large OTA or airline sites.
Fractional Factorial Testing
A reduced version of full factoring testing, fractional factorial testing uses a smaller subset of combinations in conjunction with statistical modeling to predict performance for untested combinations.
This type of multivariate testing requires significantly less traffic, making it more suitable for mid-sized travel brands. While it’s not as precise as full factorial testing, it can still provide actionable results – such as for hotel booking pages, tour operator product pages, loyalty sign-up flows.
Taguchi Method
The taguchi method is a highly structured form of fractional testing designed to minimize the number of experiments required while maximizing directional insight. This is particularly useful for travel brands with seasonal constraints, since long test durations aren’t viable.
The Top Pages to Run Multivariate Tests in Travel
When multivariate testing for travel, there are several pages that are worth experimenting with, such as:
1. Search Results Pages
Search results pages are often the highest-traffic, highest-intent page on any travel site – and yet, they remain one of the most challenges ones to optimize.
Some of the ways search pages can be tested and optimized include default sort order, filter visibility, urgency messaging, price display, and card layout.
Real-world example: CGN increased transaction rates by +29.3% and filter clicks by +6% by making the search bar and filters sticky on scroll.
2. Product / Destination Pages
This is a key component in travel website testing and optimization. Since this is where emotional decisions and purchases are made, this page needs to build desire, trust, and urgency to encourage the consumer to make a booking.
Popular elements to test on these pages include the hero image selection, itinerary layout, pricing transparency, social proof placement, and CTA placement.
Real-world example: Club Med achieved a +2.4% uplift in conversion rate by hiding the default price until the user had selected their travel criteria, reducing sticker shock and improving perceived value.
3. The Booking & Checkout Funnel
The checkout is usually the most off-putting part of the travel booking journey. A/B testing alone typically can’t reveal the most optimal combination of trust signals, payment options, and form design. This is where multivariating testing can be vital to long-term success.
Common things to test for these travel pages include the length of forms users must fill out for purchases, progress bar visibility, where the trust badge or FAQs are placed, and payment display.
Real-world example: A North American insurance company saw a +140% increase in application submissions after repositioning an FAQ section above the quote form.
4. Mobile Homepage & Landing Pages
With 70% of travelers researching on mobile, the mobile homepage is a critical conversion opportunity – but remains difficult to optimize.
Key elements to test for these pages include the design and location of the search bar, CTA prominence, navigation layout, and what content is displayed for promotional banners.
Real-world example: Air Europa adopted a mobile-first experimentation strategy and achieved a +9% increase in overall conversions.
5. Loyalty Program Pages
Loyalty enrollment is one of the best ways for travel brands to ensure long-standing, high-value conversion rates. But it isn’t always tested as much as it could be, as the focus is usually on search results, landing, and checkout pages.
Crucial components to test for loyalty reward program pages include value proposition messaging, benefit display format, and sign-up form length.
Travel is inherently a personal choice: with people picking places to discover based on emotions, preferences, and lifestyle. This means that not only can multivariate testing help to optimize the travel booking experience, but so can personalization – especially when they work together.
Finding the Winning Combination for Every Segment
The real power of MVT in travel is not just finding the best-performing combination for the average visitor, but understanding which combination performs best for different types of travelers.
A family booking a summer holiday has different emotional needs than a solo business traveler. Traditionally, it could be hard to differentiate what each traveler needed – but multivariate testing can analyze results by audience segment and reveal personalization opportunities that would’ve otherwise gone unnoticed.
EmotionsAI: Adding an Emotional Layer to MVT
In addition to the benefits of traditional multivariate testing, personalization tactics with the use of tools like AB Tasty’s EmotionsAI classifies visitors into one of 10 emotional segments. These groups include safety-seekers, competition-driven shoppers, or people prone to impulsive purchases users.
Think of a safety-oriented visitor, who may convert best with a website layout that emphasizes secure payment icons, flexible cancellation copy, and a softer CTA. On the other hand, an immediacy-driven visitor may respond better to urgency messaging, a prominent “Book in 1 click” CTA, and real-time availability data.
By layering the use of EmotionsAI with MVT, travel brands will be able to better serve each visitors according to their emotional needs – which increases the chances of conversion.
AdaptiveCX: Solving the 90% Anonymous Visitor Problem for Travel
As 90% of travel site visitors are anonymous, traditional personalization tools can fall short – and with travel, it’s increasingly imperative to ensure the booking experience is tailored to each individual traveler.
This is where tools like AdaptiveCX can help travel brands meet their booking goals. AdaptiveCX uses in-session behavioral signals instead of traditional third-party cookies. This predicts user intent and their preferences in real-time, which can ensure that MVT insights are implemented for 100% of your traffic.
Common Mistakes to Avoid in MVT for Travel
Multivariate testing can be transformative for travel, but it’s also important to know when pushing the boundaries is pioneering, and when it’s been pushed too far. Maintaining the right data and strategy for MVT is integral to achieve the right growth.
Here are some of the most common mistakes made for multivariate testing and travel
Testing Too Many Variables at Once
Multivariate testing can make it exciting for travel brands to test several bold ideas simultaneously, but it’s also important to avoid getting carried away.
Remember, the more combinations your brand wants to test requires more traffic. It’s best to stick to 2 to 3 elements and 2 to 3 variations each to keep the length of your test comprehensive and effective. This is especially important in travel, as seasonality means tests that run for too long can risk being compromised by external factors – like school holidays or weather predicaments.
Ignoring Mobile vs. Desktop Differences
Recent research from PYMNTS suggests that mobile has become the predominant channel for travel purchases – with 59% of long-distance travel bookings now being made on mobile devices. This highlights the shift toward smartphone-first vacation planning.
It’s essential it is to ensure that winning desktop combinations could also be successful on mobile devices, and vice versa – as one could over or under perform the other.
To prevent this, aim to divide your MVT results by device type. Given the fact that most people research trips on mobile devices and switch to booking on their desktops – this is a decisive factor to keep in mind while multivariate testing.
Running Tests During Peak Periods: A Strategic Choice Seasonality
Seasonality is an influential force in the travel industry. This means that knowing when to test is just as important as knowing what to test. Unlike retail’s traditional holiday rush shopping period, travel has multiple “peak” seasons. This includes the January-to-March booking window when many travelers plan their summer vacations in addition to the traditional summer and holiday travel periods themselves.
Running tests during these high-traffic times can provide several strategic benefits.
Advantages of Peak Season Testing
When your site traffic is in the middle of a peak, tests can reach statistical significance in a fraction of the usual time. This allows for rapid iteration and quick wins on high-impact pages. If you have a strong, data-backed hypothesis for a simple change, like a new CTA on your booking page, testing during peak season can deliver reliable results at a much faster rate.
Risks of Peak Season Testing
The downside of testing during peak season is that it can present external variables that could compromise your test results. Factors like competitor fare sales, school holiday schedules, and sudden spikes in demand can influence user behavior. For instance, a winning combination during a Black Friday sale might not be as successful during a typical week in May.
It’s true peak season tests can be very effective and responsive due to high traffic. However, it’s important to remember these potential variations and account for them accordingly when interpreting results and making long-term decisions.
The Strategic Approach: A Balanced Testing Calendar
The most effective travel brands use a balanced approach:
Use Peak Seasons for Data & Speed: Implementing low-risk A/B tests during a massive influx of traffic can allow for fast answers and the opportunity to gather rich behavioral data for building future hypotheses.
Use Shoulder Seasons for Stability & Confidence: Running more complex multivariate tests during more “normal” traffic periods are great for stable results. This is because they will be more representative of your site’s baseline performance, giving you greater confidence for your next bold test.
Stopping Tests Too Early
Travel websites may be eager to implement new changes that have seen success with multivariate testing, but declaring a winner too early could lead to costly decisions.
Reaching statistical significance is crucial to effectively target leads.
Otherwise, you could be deploying tactics derived from unreliable results.
We believe that failure can lead you to new learnings for your next best test. Every MVT result, whether it be a win, loss, or inconclusive – contains valuable insights about your customers.
Building off of this newfound knowledge as base can help your brand to avoid duplicating unsuccessful tests and improve future personalization strategies.
Getting Started: A Travel Brand’s MVT Roadmap
You don’t have to be a whizz in optimization to take flight with multivariate testing for travel.
Here’s a step-by-step guide on how your travel brand can incorporate multivariate testing in its optimization strategy:
Step 1: Audit Your Booking Funnel
The first step of multivariate testing for travel is to identify which pages and steps of the booking process have the opportunity for the highest drop-off rates. Travel brands should seek to prioritize pages that are both high in traffic and user intent, as they provide the strongest opportunities for conversion. This can be done by using session recordings, heatmaps, and funnel analytics.
Step 2: Build Hypotheses Based on Real Data
Every element to be tested for travel websites should be backed by a data-informed hypothesis. This can be done by using qualitative data like exit surveys and support tickets, which provide direct feedback on the customer’s experience.
The goal is to understand why your visitors leave the site before making a booking, and amending the site’s various fonts, colors, and layouts to reduce the risk of abandonment.
Step 3: Start with a Focused Test
Multivariate testing could appear overwhelming for a travel brand dipping its toes in the water and world of optimization. To make MVT more approachable, it’s best to choose one high-value page, 2 to 3 elements to test, and 2 to 3 variations per element.
Using AB Tasty’s no-code visual editor can help your travel brand to build your variations quickly, without developer dependency. (might remove this if there isn’t a good internal link)
Step 4: Analyze Beyond the Headline Result
Looking beyond the most successful combination could inspire daring ideas that lead to even smarter wins.
After multivariate testing, travel brands should ask themselves which elements had the greatest individual impact and aim to develop new mechanisms that could take them one step closer to new, courageous experiments.
Coming up with new ways to tackle targeted customers from different devices, traffic sources, or audience segments could allow for additional optimal outcomes in multivariate testing for travel.
Step 5: Activate the Insights
Collecting the information is one thing, but actually putting it to use is another. Using your MVT learnings to improve your long-term personalization strategy could prove worthwhile over the long-term for travel companies. This is because overtime, your brand will be more knowledgeable in how to draw-in customers according to their specific travel needs.
Find what wins with your highest-intent users, and let it lead your personalization playbook.
Conclusion & Next Steps
The travel industry can be challenging to test, as stakes are high, competition is fierce, and the customer journey is cluttered.
Thankfully, multivariate testing is one of the most powerful tools available to digital teams – and can help mitigate the obstacles associated with travel website optimization. This is because MVt doesn’t just tell you what works, but what works together — and that distinction is what separates incremental gains from destination-defining results.
The most successful travel brands combine MVT with personalization, EmotionsAI, and real-time behavioral data to deliver adventure ready experiences that feel effortless for every type of traveler.
Let your booking take off without turbulence. Together, we uncover insight tactics to make travel optimization effortless, seamless, and adventurous.
Still have questions about multivariate testing in travel? Here are the answers you need.
What is Multivariate Testing?
Multivariate testing is a method of testing multiple variations of different elements on a page at the same time to see which combination performs best. It helps identify how changes in design, messaging, and layout interact to influence user behavior.
Why is Multivariate Testing Good for Travel?
Travel booking journeys are often complex and non-linear, with many decision points along the way. Multivariate testing helps optimize each touchpoint, making it easier for users to move from inspiration to booking with less friction.
Which Travel Brands have used Multi-Variate Testing?
Many leading travel brands like airlines, OTAs, and hotel platforms use multivariate testing to refine their booking flows and improve conversions. These brands continuously experiment with layouts, pricing displays, and messaging to enhance the user experience.
Can small travel brands benefit from multivariate testing?
Yes, small travel brands can gain valuable insights even with limited traffic by testing key elements strategically. Starting with high-impact areas allows them to improve performance without needing large-scale resources.
About the Author
John Huges
John Hughes is the Vice President of Marketing for AB Tasty, with over 20 years experience in tech and SaaS. Passionate about creativity and driving global growth to the company, John often seeks new and innovative ways to demonstrate the benefits of experimentation and bold solutions offered by AB Tasty. As he is often traveling for the company, John contributes to the AB Tasty blog to share his firsthand insights on how AI and optimization can benefit all areas of the travel industry in addition to various consumer patterns relative to our software and services.
What is AI-Powered Optimization Really Doing in 2026?
What can AI agents do in 2026? It’s far more than you think – as AI is now playing an integral role in reshaping how optimization works.
The noise has never been louder for AI, with industries from food, fashion, to entertainment finding new ways to push the limits of what AI can do to optimize their success. No longer a futuristic concept, AI is now a daily component for teams across multiple industries to establish success in marketing, conversion, and more.
However, many people still remain wary of AI in 2026 — as some fear it could replace them entirely. This concern is one that resonates across many industries, and the world of optimization is no exception.
But when we look past the potential fear associated with AI, we can see that AI isn’t here to replace the optimizer – but to be used as a tool to amplify already existing strategies. This is because AI can automate tedious, analytical tasks and uncover new insights that the naked human eye might miss. In turn, this can allow teams to focus more on strategy and creativity – and leave the more mundane chores in the hands of AI.
We’re going to explore the tangible benefits of AI in optimization software today, how it is continuing to evolve, and address the common concerns regarding what the future holds for AI in Experimentation Optimization Platforms (EOPs).
The World Before: A Quick Look at Optimization Without AI
Things were different just a couple of years ago. College students didn’t have Claude to help write their papers. High school students didn’t have Chat GPT to finish their homework. Colleagues didn’t have Gemini to respond to emails.
The same goes for the EOP world before AI.
The overview cards below will show what traditional optimization process consisted of:
Manual data analysis
Time-consuming test ideation
Developer dependencies for simple changes
The challenge of interpreting complex results
Before AI, optimization heavily relied on statistical models to optimize. While these were effective, it remained challenging to choose the best hypothesis to test – which is the first step to achieving conclusive results in experimentation.
Luckily, this is where AI has stepped in – helping us to develop stronger hypotheses and consequently, more robust testing and results.
When brands dare to find growth in unexpected ways, such as with AI, you can leverage opportunities that would have otherwise gone unnoticed.
This is where AI can step in and bring expertise to make experimentation easier.
How AI Benefits Experience Optimization in 2026
There are several benefits to using AI for your Experience Optimization Platform (EOP) in 2026.
Here are some of the various advantages AI brings to optimization platforms:
Manual Analysis to Proactive Insights: AI doesn’t just present data, but interprets with more conducive results. This reduces the chance of subpar results and makes testing more effective and concise. Tools like AB Tasty’s Report Copilot (Evi) analyze results, summarize key takeaways in natural language, and even suggest the next best action, turning reporting from a passive dashboard into an active “optimization engine”.
Brainstorming to Data-Driven Ideation: AI tools like Visual Editor Copilot can analyze user feedback, competitor sites, and performance data to suggest high-impact A/B test ideas. This allows brands to overcome creative blocks and prioritize their roadmap.
Code-Dependent to Code-Free Creation: Generative AI in tools like the Visual Editor allows marketers to make changes with simple text prompts, such as by suggesting to make a button blue or add a banner. This dramatically reduces reliance on developers and accelerates test velocity.
Static Segments to Dynamic, Emotional Targeting: Perhaps the most pivotal benefit of AI in optimization software, AI tools can provide real-time, intelligent targeting as opposed to basic segments.
Addressing the Elephant in the Room: Will AI Replace Me?
Understandably, there’s a lot of uncertainty that accompanies the growing use of Artificial Intelligence (AI). Many people working across various industries worried that AI could replace them entirely.
According to National University, a whopping 52% of employed people are concerned that AI will be able to do their job and render their professional skills useless.
The overview cards below will reveal some key figures about the current use and future direction of AI:
35%
of companies around the world use AI in their business models
52%
of experts think AI will both replace and create jobs
77%
of companies are exploring the use of AI
It’s understandable that the near omnipresence of AI is overwhelming, especially with the looming apprehension that AI could make optimization roles obsolete. But if you think of AI as more of an assistant instead of a replacement, it could actually make your existing work easier. This can leave more room for more innovative ideas instead of time consuming data-analysis.
The problem with AI in fields like optimization is that many people tend to think of it as simply being on autopilot. In reality, AI functions as more of a co-pilot in EOPs – handling the “how” so humans can focus on creating goals for the “why” and the “what’s next”.
The table below will break down the differences between AI being on “autopilot” (what many consumers fear) and when AI works as a “co-pilot” :
AI on “autopilot”
AI as a “co-pilot”
Uses AI to create content
Uses AI to make suggestions & spark ideas
Executes tasks end-to-end
Collaborates with humans to refine decisions
Prioritizes speed and automation
Balances speed with control and creativity
How AI Can Boost Time Efficiency in Workflows
AI serves as more of an assistant rather than a replacement. In fact, 88% of creative professionals said that they felt generative AI could help them to produce content faster – without replacing them entirely (Adobe Digital Insights).
Think of a marketer staring at a blank hypothesis backlog. By using AI as a sounding board, they can quickly generate fresh test ideas, suggest high-impact page elements to experiment on, and even draft email subject line variations to A/B test — all in a fraction of the time it would take to do manually. AI isn’t replacing the marketer’s judgment, but avoiding an incessant blank page so they can spend more time doing what they do best: thinking strategically and acting decisively.
AI can serve as a way to enhance already existing human thinking, creativity, and brand understanding. This is because the best AI strategies blend automation with human oversight and expertise.
How Tools Like Evi AI & Emotions AI Boost Optimization Strategies
Sometimes it’s hard to believe in the power of AI until you see it in action.
At AB Tasty, we want optimization and AI to work together as a team – and that’s exactly what our software accomplishes.
Here are three different AI-powered software tools used in AB Tasty optimization platform:
EmotionsAI analyzes in-session behavior to identify a visitor’s emotional needs (e.g., Need for Safety, Need for Immediacy) in just 30 seconds. This allows for personally tailored carousels, pop-ups, and more to boost conversion.
AdaptiveCX uses predictive AI to personalize experiences for anonymous visitors based on their live intent, adapting the journey in milliseconds.
Evi AI is an AI agent that transforms complex data into clear, actionable strategies for your experimentation program by automating the A/B testing journey. This includes generating data-backed ideas, hypotheses, and analyzing campaign results to help make smarter, evidence-based decisions.
AB Tasty, we want optimization and AI to work together as a team – and that’s exactly what our software accomplishes.
Our AI powered tools reveal that AI doesn’t have to work against you, but with you. When paired with daring ideas, AI can take you one step closer to the next level of success.
The Future is Collaborative: What’s Next for AI in Optimization?
The future of AI in optimization isn’t just making suggestions, but taking action. As AI agents become more autonomous, teams will be able to redirect their focus to achieving greater goals instead of spending additional time on tiresome tasks.
Here are just a few of the ways that AI could boost our collaborative efforts and make progress in personalization even more possible:
Agentic AI
Instead of just suggesting a test, an AI agent might propose an idea, build potential models, monitor performance, and even make suggestions for follow-up steps. This turns the platform into an intelligent, self-improving system – one that requires no extra work or a large team of coders.
Deeper Integration
Also, AI in optimization can help to bridge the gap between different platforms – such as between analytics, e-merchandising, and Customer Data Platforms (CDPs). This creates a unified, quick-witted optimization ecosystem that can respond to changes instantaneously.
Proactive Optimization
As AI becomes more authoritative, it will be able to not only report past activity – but actively make suggestions for the future.
This means that instead of redirecting optimization tactics toward potentially obsolete information, future optimizations can expand revenue or reduce catalog fatigue according to live data. This shows how AI can operate as an effective tool in supporting already existing personalization plans, predictive analysis, A/B testing strategies, and more.
Conclusion: Your New Teammate is an Algorithm
In 2026, AI is not a threat – but an essential teammate in your growth journey.
It makes optimization faster, smarter, and more human-centric by handling the machine-scale tasks that used to slow us down. Brands that are brave enough to try, iterate, and test the boundaries of what AI can do will be bound to be more successful than those who don’t take small steps forward with AI as their ally.
Remember, AI isn’t meant to replace raw, human talent – but amplify your power to make progress.
Let’s push past the limits and learn what AI agents can do for optimization, together.
Still have questions about AI and optimization? Here are the answers you need.
What does “AI-powered optimization” mean in 2026?
AI-powered optimization refers to using machine learning models to automatically test, learn, and improve digital experiences in real-time. This means instead of relying only on manual A/B testing, using AI in the world of optimization can allow brands to quickly analyze user behavior, predict outcomes, and create robust personalization experiences.
How is AI different from traditional A/B testing?
Traditional A/B testing compares a few variations and waits for statistical significance. AI and optimization, on the other hand, can:
Test multiple variables simultaneously (multivariate testing)
Adapt experiences in real time
Personalize content for different audience segments automatically (such as with AdaptiveCX or Emotions AI)
This makes optimization faster, dynamic, efficient – and better tailored to individual users while they’re still shopping on your website.
Is A/B testing still relevant in 2026?
Yes, A/B testing is still important in 2026 – as it’s especially useful for validating major changes, running controlled experiments, and building trust in the decision making. Remember, AI enhances A/B testing as opposed to replacing it – often working alongside it.
What data is needed for AI optimization to work effectively?
AI-powered optimization tools usually rely on behavioral data (clicks, sessions, navigation), contextual data (device, location, time), and historical performance data. The more high-quality data available, the better the AI can make accurate decisions.
How do I know if AI is being used ethically in optimization?
You can determine if AI is ethically being used in optimization software by seeing if they respect user privacy and consent, avoid biased outcomes, are transparent about data usage, and still follow regulations like GDPR.
Can small teams benefit from AI-driven optimization?
Yes, small teams can absolutely benefit from AI-driven optimization tools! This is because many modern tools are designed to be accessible, allowing smaller teams to automate testing, scale personalization tactics, and gain insights without the need for large data teams.
What are the biggest mistakes companies make with AI optimization?
Common mistakes companies in optimization make with AI include relying on poor-quality or insufficient data, over automating without a clear strategy, mitigating the initial and fundamental experiment design, and focusing on short-term wins over long-term learning.
How long does it take to see results from AI optimization?
Brands could see the impact of AI-powered optimization in as little as a few days to weeks. That being said, the more meaningful long-term gains of AI tools in optimization often aren’t seen for several months.
About the Author
Stephanie Safdie
Stephanie Safdie holds a bachelor’s degree in English Language and Literature from the University of Maryland, specializing in multimedia studies. She has worked as a social media video creator, freelance copywriter, SEO copywriter at Greenly climate tech, and runs a travel blog Destination Dreamer Diaries.
