Article

14min read

Product Category Marketing: A Guide for Retailers

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

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

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

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

What is a product category?

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

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

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

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

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

Examples of product categories

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

Fashion & Apparel

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.

Check out our Product Recommendation Engine Guide → 

What are product category structures in e-commerce?

Traditional classification methods

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

Common methods include:

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

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

Hierarchical category structures

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

1

Level 1

Home

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

2

Level 2

Clothing

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

3

Level 3

Women’s Clothing

Gender-specific subcategory narrowing the browsing context significantly.

4

Level 4

Dresses

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

5

Level 5

Formal Dresses

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

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

A few practical rules of thumb:

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

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

What is product category marketing?

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

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

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

Why is product category marketing important?

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

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

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

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

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

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

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

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

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

How to categorize products for your marketing strategy

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

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

How to analyze and update your product categories

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

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

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

How to create product categories successfully for your e-commerce

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

Step 1: Identify the purpose of each page

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

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

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

Design each page type with its purpose in mind.

Step 2: Design with your customer in mind

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

A few practical moves:

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

Step 3: Give customers ways to narrow their choices

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

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

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

Product category marketing examples

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

Alltricks — Cycling & Running 

+5%

Average Order Value (AOV)

+7%

Revenue per user

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

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

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

Read the Alltricks case study here → 

Devred — Men’s Fashion

4x

more spent by recommendation-engaged users

35%

of total revenue driven by recommendations

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

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

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

Read the Devred case study here →

Maison Francis Kurkdjian (LVMH) — Luxury Fragrances 

Higher average basket value icon

Higher average basket value

Significant growth in sales index icon

Significant growth in sales index within weeks

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

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

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

Read the Maison Francis Kurkdjian case study →

Why product category marketing is indispensable for e-commerce

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

Here’s what’s really at stake.

Conversion Rate

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

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

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

SEO and Organic Traffic

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

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

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

Average Order Value (AOV) and Cross-Sells

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

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

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

Discover how to boost your Average Order Value here → 

Internal Alignment

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

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

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

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

Conclusion

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

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

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

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

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

FAQs

Article

7min read

Why Modern E-commerce Needs a Semantic-First Search Strategy

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.

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:

Data analysis icon

Natural-language queries

Test ideation icon

Broader or less precise searches

Developer dependencies icon

Discovery-oriented shopping behavior

Complex results icon

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.

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 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.

Article

4min read

Debugging Server-Side Experimentation Faster with Live Hits

When teams run server-side experiments, one of the biggest challenges is validating that everything is working correctly before and after launch.

Unlike client-side experimentation, where visual checks can often help confirm a setup, server-side experimentation depends heavily on event flows, payload quality, and implementation accuracy. If something is misconfigured, teams may not notice immediately. In many cases, they have to wait for reporting to refresh before they can confirm whether data is being collected as expected.

That delay can slow down QA, make troubleshooting harder, and reduce confidence at launch.

The server-side debugging challenge

For product, engineering, and experimentation teams, implementation validation is a critical part of the workflow. Before a campaign goes live, they often need to answer a few simple but important questions:

  • Are hits actually reaching the platform?
  • Are the right events being sent?
  • Do the payload details match what was expected?
  • Is everything working properly in production after launch?

Without real-time visibility, answering those questions can take longer than it should. Teams may need to wait for aggregated reporting or rely on manual checks across multiple tools. That creates friction in QA cycles and can make debugging more complex, especially in fast-moving release environments.

Introducing Live Hits

Live Hits is designed to make server-side QA and debugging much easier.

It provides a real-time stream of SDK events as they reach the platform, allowing teams to validate implementation immediately instead of waiting for reporting updates. This gives users direct visibility into what is being sent, helping them troubleshoot faster and launch with more confidence.

Rather than working from delayed, aggregated data, teams can inspect incoming hits as they happen.

What Live Hits helps teams do

Live Hits is especially useful during two key moments:

1. During QA before launch

When a campaign or feature is ready for validation, teams can use Live Hits to confirm that the expected events are arriving correctly. This helps verify that implementation is complete and that the right information is being sent.

2. Right after launch in production

Once a campaign is live, teams can run a second check to confirm that traffic is flowing as expected in the real environment. This helps catch issues early and adds an extra layer of confidence at go-live.

Why this matters

Real-time visibility can make a major difference for teams working on server-side experimentation.

Key benefits include:

Faster debugging

Identify issues without waiting for reporting refreshes

Smoother QA workflows

Validate implementation before launch

Better troubleshooting

Inspect detailed event information when something looks off

For teams running complex experimentation programs, these advantages can reduce back-and-forth between product, engineering, and QA while speeding up time to validation.

A more practical way to validate implementation

One of the most useful aspects of Live Hits is that it helps teams move from assumption to confirmation.

Instead of asking, “Did the event fire?” and waiting for reports, users can quickly verify:

  • the type of hit received
  • the associated identifiers
  • the event details being transmitted
  • whether the payload matches expectations

This makes it easier to investigate implementation issues, validate tracking logic, and confirm that a campaign is ready to move forward.

Built for real experimentation workflows

In practice, server-side experimentation often requires close collaboration across multiple teams. Product managers want confidence in setup, developers want to confirm implementation, and QA teams need a reliable way to validate behavior before launch.

Live Hits supports that workflow by giving teams a shared, immediate view of incoming SDK activity. It helps simplify the path from implementation to launch, especially when speed and accuracy both matter.

Why real-time validation is becoming essential

As experimentation programs mature, teams need more than reporting alone. They need tools that help them validate faster, troubleshoot earlier, and reduce uncertainty during rollout.

That is exactly where Live Hits adds value.

By giving teams real-time visibility into server-side events, it helps turn debugging and QA into a faster, more reliable process. For organizations looking to scale experimentation with confidence, that kind of visibility can be a meaningful operational advantage.

Final thoughts

Server-side experimentation offers flexibility and control, but it also raises the bar for implementation validation. Waiting for aggregated reports is not always enough when teams need to debug quickly and launch confidently.

Live Hits from AB Tasty helps close that gap by making server-side event validation immediate, practical, and easier to act on.

If your teams are looking for a better way to QA server-side campaigns and verify implementation in real time, Live Hits is built for exactly that.

Article

4min read

The Hidden Cost of Ignoring Your E-Commerce KPIs (And How to Fix It)

For e-commerce teams, the pressure to deliver results is constant. Whether you’re a merchandiser, a buyer, or a digital leader, you’re expected to show how your work translates into business value. But in the day-to-day rush, it’s easy to lose sight of the numbers that matter most.

The Real Challenge: Out of Sight, Out of Mind

Most e-commerce platforms offer a wealth of data. There are dashboards, reports, and analytics tools that can tell you almost anything about your site’s performance. But here’s the problem: these insights are often buried, hard to access, or only reviewed during quarterly business reviews. 

As a result:

  • After the initial setup, strategies can become “set and forget.”
  • Opportunities for improvement are missed because no one is regularly looking at the right KPIs.
  • When it’s time to prove ROI, it’s a scramble to pull together evidence and justify decisions.

When performance data isn’t front and center, it’s easy for teams to become reactive instead of proactive. The focus shifts from continuous improvement to simply keeping things running.

A Better Way: Make KPIs Part of the Weekly Routine

What if, instead of waiting for high-stakes quarterly reviews, you had a simple, structured way to check in every week? A routine that keeps your KPIs visible, your strategies healthy, and your team focused on improvement – not just maintenance.

That’s the thinking behind AB Tasty’s Performance Digest: a weekly loop that connects three things into one flow:

1

Step 1

Weekly Digest Email

A weekly email that highlights key performance signals and brings you back into the platform.

2

Step 2

Reporting on Business Impact

Stay up to date with KPIs designed to be buyer-friendly.

3

Step 3

Guided ideas for improvement

Think of our AI as a coach that helps you prioritize what to optimize next and gives step-by-step instructions.

This isn’t about adding more to your plate. It’s about making performance a habit, not a hurdle.

A Weekly Habit That Tells a Clear ROI Story

One of the hardest parts of e-commerce reporting isn’t collecting data — it’s telling a clear story about business impact.

AB Tasty’s Performance Digest makes that story easier by emphasizing direct contribution: the business generated when a shopper clicks a recommended or merchandised item and purchases within the same session.

Just as importantly, it builds a weekly routine around that story. Instead of hoping teams remember to log in (or only revisiting performance during quarterly reviews), Performance Digest starts in your inbox with a weekly snapshot of what moved — so KPIs stay visible and decision-making stays consistent.

Performance Digest: weekly revenue opportunities in your inbox

From “What Happened?” to “What to Do Next”

Dashboards can show you what happened, but the real bottleneck is deciding what to do next. That’s why Performance Digest will include a small set of guided improvement ideas to help you prioritize where to invest time, spot opportunities you might miss in a dashboard-only workflow, and iterate with confidence.

This weekly loop also makes ROI easier to prove. Quarterly reviews often create high-stakes, retroactive conversations. A weekly cadence creates a steady trail of decisions and outcomes — what changed, what improved, and why

Takeaway: Make Performance a Habit, Not a Hurdle

The most successful e-commerce teams don’t wait for problems to show up in quarterly reports. They build habits that keep performance and improvement part of the weekly workflow. The key is to make your most important KPIs visible, actionable, and impossible to ignore.

Because when ROI is always top of mind, better results tend to follow.

FAQs about E-commerce KPIs

Article

6min read

Conversion is a conversation: how GenAI is reshaping e-commerce and travel

Generative AI is now creating a massive shift in how online buyers discover and compare products. That’s why we’ve put together a roadmap to help you understand the 2026 consumer in our e-book, The Spontaneous Shift: Consumer E-Commerce Trends for 2025. For e-commerce and travel brands this represents a real paradigm shift in how digital marketing works; one they will need to adapt to, and fast, if they want to survive.

Meet your customer’s new shopping assistant

While Google search is still the dominant force in discovery, our research shows that other channels are rapidly gaining ground. And the fastest growing channel of all is Generative AI (like ChatGPT, Google’s Gemini, or Claude from Anthropic). Use of Gen AI tools by online shoppers in the discovery phase has grown 75% in just the last year, rising from 8% in 2025 to 18% as it quickly moves from a novelty to a utility.

But while more people are choosing to use AI, the level of uptake differs across generations. Perhaps not surprisingly, younger shoppers are more comfortable using AI tools to find what they need. 32% of Gen Z now say that they find AI helpful in their online buying journey. Millennials are not far behind, with 30% of them seeing AI as a helpful assistant, but this number falls to 13% of Baby Boomers.

What’s clear is that the online behavior of consumers is changing and at pace. Younger generations especially are no longer content with scrolling through a list of blue links. Instead, they’re asking questions – and expecting answers. They aren’t just typing keywords like “red sneakers” into their browser; they’re giving AI prompts, like “Find me a pair of red sneakers that look cool and are under $80.”

This is a profound shift and one that has big implications for e-commerce and travel brands. Firstly, it means consumers no longer need to visit multiple websites to compare different products and services. They can do all of that and more by simply asking questions to an AI interface: “Is this sustainable?” “Will this work for my specific use case?” “How does this compare to their competitor’s offer?” The transaction is quickly becoming a conversation.

What this signals is a move away from carefully crafted product detail pages (PDPs) and towards much more dynamic interactions. If your brand can’t answer these questions in the conversational spaces where they’re being asked, either an on-site AI chatbot or a third-party platform, you’re effectively invisible. You don’t exist in an online consumer’s decision-making loop.

The new frontier of optimization

That in turn presents brands with a unique challenge. When online buyers perform a Google search or search directly on your website, you can see their query. But when they ask a question to a third-party AI platform, that happens inside a metaphorical “black box”. You can’t see what question they asked, and you can’t (currently) buy an ad to influence it.

This “invisible intent” requires a new kind of optimization. Because you’re no longer just writing for humans or typical SEO algorithms; you’re also writing for the Large Language Models (LLMs) of Generative AI. Fortunately, the core SEO principles of E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) can still provide brands with a competitive edge.

This is because LLMs are designed to recommend what they interpret as being the most credible answer, not just the one with the most (or best) keywords. If you want GenAI to recommend your products or services, you’ll have to give it proof that your brand is the authority.

To stay visible in this conversational future, that means your content strategy needs to move from volume to value. And if LLMs are to place any trust in your user reviews, they’ll need to be authentic. You also need to ensure that product data is structured with schema markup so it can be “read” by these new gatekeepers. It needs to be rich enough to answer the nuanced questions of online shoppers, not just fill a spec sheet.

Changing the face of travel

Human interaction has always been at the heart of the travel industry. And travelers have traditionally been happy to receive personalized recommendations or resolve any issues they might have by speaking with a real person. But this change in online buyer behavior from keyword search to asking questions of GenAI means the travel sector is perhaps even more exposed than most to this disruption. 

Our research shows that 36% of travelers have now used AI-powered tools, chatbots, or virtual assistants to book travel or solve issues and found them helpful. Another 32% haven’t tried using these yet but are open to doing so. Again, Gen Z are the most open to AI, with 49% saying they have used AI tools and found them helpful when booking travel. And while this figure is 41% for Millennials, it falls to 21% of Baby Boomers.

Focus on quality

This is good news for travel brands looking to embrace on-site AI chatbots to help visitors plan travel. But at the same time, there’s also nothing to stop those same online buyers from doing that on a third-party platform. A prompt like “Plan a 5-day family-friendly trip to Lisbon in May, staying near the city center with a budget of $2000,” can generate a complete itinerary with flight suggestions, hotel options, and activity booking links. All without visitors ever going near a brand’s website.

That means there’s a real possibility for travel brands of being reduced to a single item in an AI-generated plan. And if they lose the ability to showcase a unique brand experience on their website, travel companies like hotels and airlines risk being compared merely on price and basic features alone. Loyalty programs and creating exceptional customer experiences are likely to be more important than ever.

Travel is also time sensitive. A third-party AI platform is unlikely to recommend a hotel or a flight if it can’t access real-time availability, accurate pricing, and clear policies. This requires travel companies to be laser-focused on the quality and accessibility of their data. Again, the best way to ensure that GenAI recommends your brand to online buyers is to have rich, well-structured information that is instantly available.

Conclusion

The world of e-commerce and online travel is undergoing a real paradigm shift in how online buyers discover and compare products. And the rules are still changing in real-time. Ensuring that your data is as authoritative as possible and easily accessible to GenAI is currently your best bet to remain visible in this new environment. The only viable way to do this is to build a culture of continuous experimentation; testing, learning, and iterating towards your “better”.

Takeaways for e-commerce and travel brands

  • Test your GenAI visibility: Go to ChatGPT or Gemini and ask questions about your product category. If your brand doesn’t show up, you have an optimization gap to fill.
  • Develop AI-friendly product data structure: Implementing comprehensive schema markup across your site is no longer optional, it’s the price of entry.
  • Create AI-friendly content: Develop clear, factual, easily digestible content that directly answers questions your customers might have. This makes it much more likely that it will be used by an LLM.
  • Track GenAI adoption monthly: Monitor the amount of traffic to your website coming from third-party Gen AI tools and how this affects sales.

Article

4min read

Real-Time Personalization in 2026: How to Meet Customer Expectations

Personalization has long been a goal for brands looking to create more relevant and engaging experiences. Traditionally, this has meant using static rules and segments – showing different content to new visitors, returning customers, or VIP shoppers.

While this approach has value, it often falls short when customer needs and interests change quickly.

Why Real-Time Personalization Matters More Than Ever

Today’s customers are unpredictable. They jump between devices, change their minds mid-session, and expect brands to keep up. It’s very common for shoppers to change their intent during a single visit.

For example: a user who starts out searching for a small purse for everyday fashion [category: bag] might begin looking for black sunglasses for an upcoming beach trip [category: accessories] – all in one session. Real-time personalization adapts to user needs in the moment.

Traditional personalization is based on fixed segments and rules.

It can’t keep up with rapid shifts in customer needs and preferences. Traditional personalization treats users the same way throughout their visit, missing crucial signals and opportunities to engage.

The Challenge: Static Personalization’s Limits

Most brands have invested in standard personalization. It’s a great foundation, using segments like “new visitor” or “VIP” to tailor experiences. But static rules are slow to react. They’re built on who a customer was, not who they are right now or how they are behaving.

The result? Missed chances to upsell, cross-sell, or simply delight your customers with relevant content. In a world where attention spans are short and competition is fierce, that’s a risk brands can’t afford.

The Solution: Real-Time Personalization with AdaptiveCX

Real-time personalization is about responding to customer intent as it happens. Instead of relying solely on historical data or static segments, real-time solutions monitor user actions (clicks, searches, time spent on pages) and adjust the experience instantly.

So, how can brands personalize in real time in 2026? The answer lies in solutions like AdaptiveCX – technology designed to sense, interpret, and respond to customer intent as it happens. Adopting real-time personalization doesn’t mean abandoning what works. Think of it as evolving your strategy – layering real-time capabilities on top of your existing efforts.

Here’s what real-time personalization looks like:

  • Continuous monitoring: Every interaction is tracked as it happens.
  • Intent recognition: The system identifies when a user’s interests or goals change, even within a single session.
  • Instant updates: Content, offers, and recommendations are refreshed on the fly to match the user’s current intent.

Example:
Let’s say there’s a user who has a typically of using one brand to rent a car for business travels and now this user wants to use the same brand to explore family vacation rental options. With real-time personalization, the website immediately shifts to highlight family-friendly deals and relevant upsells, ensuring the experience stays relevant throughout the session.

How to Personalize in Real Time in 2026

  1. Use behavioral data: Go beyond static segments by analyzing real-time actions – what users are clicking, searching, and viewing right now.
  2. Integrate across channels: Ensure your personalization engine works seamlessly on your website, mobile app, email, and even in-store.
  3. Prioritize intent: Focus on what users are doing in the moment, not just who they are or what they did in the past.
  4. Test and refine: Continuously experiment with different approaches to see what drives the best results.
  5. Respect privacy: Be transparent about how you use data and give users control over their personalization preferences.

The Impact of Real-Time Personalization

Brands that implement real-time personalization see clear benefits:

  • Higher engagement and conversion rates
  • Increased customer satisfaction and loyalty
  • More effective upselling and cross-selling
  • A stronger competitive position

Moving Forward

Personalization in 2026 is about more than just knowing your customers. It’s about understanding and responding to what they want in the moment. Real-time personalization helps brands stay relevant, capture more opportunities, and deliver experiences that truly resonate.

Ready to see how real-time personalization can work for you? Explore our resources, request a demo, or connect with our team to learn more about AdaptiveCX and how it can help you deliver what your customers want, instantly.

Article

4min read

What’s New in Feature Experimentation & Rollouts?

We’re continuously improving Feature Experimentation & Rollouts (FE&R) to make your experience smoother, faster, and more reliable. Here are the latest updates now available:

Data Explorer: Now available in FE&R

The Data Explorer, already available for Web Experimentation, is now accessible in Feature Experimentation & Rollouts. You can now explore and export your server-side data directly from the platform, without relying on external or technical tools like Postman.

With FE&R Data Explorer, you can:

  • Query and export metrics (conversion rate, revenue, custom goals, NPS, action counts, etc.)
  • Access and export raw hits (SDK events, API calls)
  • Apply filters and dimensions (campaign, environment, feature, user properties, time range, etc.)
  • Define custom date ranges and result limits
  • Generate API payloads directly from the UI

This feature was one of the most requested improvements by our clients. It increases transparency, improves trust in data collection, and brings stronger consistency between client-side and server-side capabilities within One Platform.

👉 Access Data Explorer directly from your FE&R interface.

Live Hits: Real-Time QA for Server-Side

When implementing or debugging a campaign, waiting for aggregated reports can slow you down. With Live Hits, you can now visualize incoming SDK events in real time and instantly verify that your implementation is working as expected.

No more guesswork. No more waiting.

Live Hits significantly improves QA workflows and reduces implementation friction for technical teams.

You can quickly confirm:

  • That events are properly triggered
  • That traffic is correctly allocated
  • That variations are being served as expected

👉 Access in Reporting → Button next to the filter.

Server-Side Reporting: Evolution

AND / OR filter operators

You can now switch between AND / OR operators for multiple values within the same filter type.

This gives you much more flexibility when segmenting your data and running deeper analysis directly inside your reports.

More precise segmentation means more accurate insights.

RevenueIQ*: now available for Server-Side 💰

*RevenueIQ is AB Tasty’s patented statistical engine that transforms experiment results into clear, reliable revenue projections — helping teams move from CRO to true Revenue Optimization by confidently quantifying uplift and ROI for every variation.

RevenueIQ, already available and widely adopted on client-side experimentation, is now live for server-side reports.

You can now project the financial impact of your experiments directly from your server-side reporting.

Available in any report with a transaction goal under the “Revenue stats” tab, RevenueIQ provides:

  • Uplift per visitor: estimated revenue increase per visitor once deployed
  • Uplift per month: projected monthly revenue impact
  • Confidence intervals: lower, median, and upper revenue scenarios
  • Revenue chances to win: probability that the variation will increase revenue

This means clearer decisions, fewer inconclusive experiments, and a direct view of real business impact.

You can confidently quantify and share the ROI of every server-side experiment.

Conclusion

These latest enhancements to Feature Experimentation & Rollouts are designed to empower your teams with greater transparency, flexibility, and actionable insights. With real-time data access, advanced reporting options, and robust revenue projections, you can confidently optimize your experiments and drive measurable business impact.

Article

6min read

KPIs, Custom Metrics, and Evi: Supercharge AI Insights in AB Tasty

Our platform is above all designed to help you drive your business forward. That’s why in AB Tasty you can set up a range of metrics straight out of the box that have meaningful business impact. And our agentic AI, Evi, will help you analyze test results fast and provide you with valuable insights your team might otherwise miss.

We put the key in KPI

AB Tasty provides comprehensive reporting tools to evaluate the performance of your campaigns, focusing on the achievement of specific KPIs, called goals. These can be set up both at the account level and campaign level right out of the box. There are six different types of goals that you can chose to define as a KPI. These are:

Action tracking: Clicks, dwell time, iframe clicks, element visibility, scroll rate.

Page tracking: Visits to a particular page or group of pages.

Browsing metrics: Revisit rate, bounce rate, exit rate, pageviews per session.

Transactions: Average order value (AOV), purchase rate, total revenue.

Datalayer Goals: Tracking based on variables in your website’s data layer

Custom tracking: Trackers you’ve created with custom code on your website.

You shouldn’t get bogged down trying to define too many KPIs. And less is definitely more in this case. Try to identify 3 – 5 KPIs for each major business objective. If you Start with KPIs that are directly tied to business outcomes, they’ll help drive your business forward. 

You measure you with custom metrics

These out of the box metrics are a great starting point. But no two businesses are the same, and your customer journey is unique. Standard KPIs might not capture interactions that are critical for you. By measuring what makes your business unique, you get insights that your competitors don’t have access to.

In AB Tasty, you can create Custom Trackings that are directly linked to your website’s DataLayer. This lets you create personalized metrics based entirely on your unique data. You can also create custom trackers using JavaScript, giving you the flexibility to measure almost any interaction on your website. These custom tracking events can even be used to replicate goals from other platforms, like Google’s GA4.

Real-time reporting happens now

We know that speed is critical to your business. For optimization to be effective, the feedback loop between action and insights needs to be as short as possible. Real-time reporting empowers your team to spot problems or winners within minutes, not days, enabling them to make confident decisions quickly.

That’s why AB Tasty offers real-time reporting capabilities that activate automatically when you push a campaign live. During the most critical initial phase of a test (the first 1,000 unique visitors or the first 12 hours), data for each goal and variation is updated every five minutes. We also provide a Live Hits monitoring tool which allows you to track event data at any time. This allows you to make faster, smarter decisions based on up-to-the-minute data.

Test with Confidence

Understanding the story your statistics are telling you is obviously crucial to making the right decisions. At AB Tasty, we use a Bayesian statistical model that provides you with intuitive, actionable results you can understand. We use two key figures to help you make more confident decisions:

  1. A 95% confidence interval: This gives you a likely range for the true value of a gain. If the confidence interval for the gain is [2% 8%], we are 95% confident that the true uplift from this variation is between 2% and 8%. The remaining 5% represents the margin of error.
  2. The chance to win: This is a direct probability that tells you how likely it is that a variation is better than the original. A 98% chance to win means there’s a 98% probability that the variation is the true winner.

Move at the speed of evidence with Evi

Having data and reporting from your tests is one thing, but analyzing those is another. Until recently, this has often been a time-consuming process and sometimes involved a little guesswork. But by integrating agentic AI into the reporting process, you can analyze data fast and receive valuable insights that your team might otherwise miss. 

Evi is AB Tasty’s AI-powered marketing agent designed for evidence-based decision making. It transforms your data into clear, actionable strategies for repeatable, measurable results, ensuring every step you take is grounded in evidence.

With Evi, your team can:

  • Greatly accelerate the reporting process, enabling you to analyze campaign data within a matter of clicks not hours.
  • Extract deeper insights, all driven by actual website data using built-in AI analysis.

Evi features two separate AI agents accessible from the reporting page for each campaign in AB Tasty: Evi Analysis and Evi Explore.

Evi Analysis

Tired of spending hours sifting through data tables and colorful charts and wondering what they all mean? Evi Analysis will analyze your campaign data and deliver clear, actionable insights. It highlights winning variations and breaks down why they drive transactions so you can feel confident in your next move.

Simply enter your questions and Evi Analysis will process the underlying metrics, statistical significance, and objective performance to deliver clear, concise answers backed by campaign data. All in a matter of clicks. Use case examples might include:

  • Explaining the winning variation.
  • Challenging your hypothesis.
  • Giving you the best CRO practices based on your campaign results.

Evi Explore

Want to know if your tests will actually drive revenue? Evi Explore, powered by our own patented metric, RevenueIQ, makes it easy to interpret the results of campaigns that use a transactional goal (i.e. a goal that tracks purchases or revenue). 

Evi Explore gives you a clear, statistically sound view of the revenue impact of each test variation before you launch. Rather than simply relying on traditional metrics like conversion rate or average order value (AOV), RevenueIQ combines these into a single metric to give you a direct view of revenue per visitor and per month. 

This means no more inconclusive campaigns, no more ‘conversion rate vs AOV’ dilemmas, and a significant reduction in ‘undecidable’ tests. Teams can now project the revenue impact of a campaign before full rollout with confidence intervals for best- and worst-case scenarios. This gives you the confidence to make faster, more profitable decisions. And because none of our competitors currently offer a comparable metric, by using AB Tasty you receive insights others won’t have.

Article

5min read

From Messy to Manageable: Organizing Experiments with Folders & Buckets

If you’ve ever opened your company’s experimentation dashboard and felt overwhelmed by the sheer number of campaigns, you’re not alone. As businesses grow, so do the number of teams, projects, and experiments running at any given time. Suddenly, what started as a handful of tests can turn into a maze of overlapping campaigns, making it tough to find what you need. 

That’s where Folders & Buckets come in. These two simple features can make a world of difference in how you manage, secure, and scale your experimentation efforts. Here’s how they work, why they matter, and some tips for getting the most out of them.

Too Many Experiments, Not Enough Organization

Picture this: your marketing, product, and development teams are all running their own experiments. Maybe you’ve got a few hundred campaigns live, or maybe it’s closer to a thousand. Either way, it’s easy for things to get messy. Important tests get buried, people accidentally edit the wrong campaign, and sometimes experiments even overlap – skewing your results or causing confusion.

This isn’t just a headache for your data team. It can slow down your whole organization and make it harder to get clear, actionable insights.

Folders: Your Experiment Filing Cabinet

Folders are exactly what they sound like: a way to group and organize your experiments in a way that makes sense for your business. But they’re much more than just a visual aid – they’re a powerful tool for access control and workflow management.

How Folders Work

  • Custom Organization: Structure folders by team (e.g., Marketing, Product), by project or sprint, by product line, or even by page type (e.g., Homepage, Checkout). The choice is yours.
  • Granular Permissions: Assign users to specific folders with different roles – viewer, editor, or admin. By default, new users see nothing until they’re granted access, minimizing risk and keeping sensitive experiments secure.
  • Flexible Access: Users can be given access to multiple folders, with different roles in each. This is perfect for organizations where people wear multiple hats or collaborate across teams.

Why Folders Matter

  • Clarity: Users see only the experiments relevant to them, reducing clutter and confusion.
  • Security: Sensitive or high-impact experiments are visible only to authorized users.
  • Agility: As teams grow or projects shift, folders and permissions can be reorganized on the fly – no need to start from scratch.

Pro Tip: Many organizations use folders to mirror their internal structure, but you can also get creative – organize by campaign type, business objective, or even experiment status.

Buckets: Keeping Experiments in Their Own Lanes

While folders help you organize and control access, Buckets (sometimes called “traffic repartition”) are all about managing how user traffic is allocated across experiments. Think of buckets as traffic lanes on a highway – each experiment gets its own lane, so there’s no risk of collisions.

How Buckets Work

  • Traffic Allocation: By default, you can create up to 10 buckets, each representing 10% of your total user traffic. Assign experiments to specific buckets to ensure they don’t overlap.
  • Mutual Exclusivity: Experiments in different buckets never see the same users, so results are clean and reliable.
  • Planned Flexibility: While the default is 10 buckets, future updates will allow you to customize the number of buckets and the percentage of traffic allocated to each.

Why Buckets Matter

  • No Overlap: Run multiple experiments at the same time – on the same page or feature – without worrying about interference.
  • Reliable Results: By keeping experiments mutually exclusive, you avoid skewed data and can trust your insights.
  • Enterprise-Ready: Especially valuable for organizations with multiple teams running simultaneous experiments.

Why This Matters for Your Team

Folders & Buckets aren’t just “nice-to-have” features – they’re essential for any organization looking to scale experimentation without losing control. They help you:

  • Stay organized as your program grows.
  • Keep sensitive experiments secure and compliant.
  • Empower teams to work independently without stepping on each other’s toes.
  • Deliver reliable, actionable insights by preventing experiment overlap.

As digital experimentation becomes a core business function, tools like Folders & Buckets are what separate the leaders from the laggards.

Ready to Get Organized?

If you’re struggling with a cluttered experimentation environment or worried about experiment overlap, it’s time to explore what Folders & Buckets can do for you. Customer Success Manager for more information, and see how easy it is to bring order – and results – to your experimentation program.

Experiment boldly. Organize smartly. Grow faster.

Want to learn more? Check out our documentation (folders/buckets) or contact us for a personalized demo.

FAQs about experimentation organization

How can I stay organized when running lots of experiments?

AB Tasty is built for big teams running many experiments. We offer our users a clear folder structure to group experiments by team, project, product line, or page type, and apply granular permissions so people only see the campaigns relevant to them. This reduces clutter, limits mistakes, and keeps your experimentation environment manageable as you scale.

What are folders in an A/B testing or experimentation platform?

Folders act like a filing cabinet for your tests: you can group experiments in ways that match your organization (e.g., by team, sprint, product, or page type) and assign viewer, editor, or admin roles per folder to control who can see and edit each campaign.

What are buckets in an experimentation platform?

Buckets (or “traffic repartition”) are a way to divide user traffic into separate lanes. Each bucket gets a portion of traffic (e.g., 10%) and experiments assigned to different buckets don’t share users, which keeps tests mutually exclusive.

Which A/B testing solutions help teams stay organized at scale?

The most effective solutions offer both structural tools (like folders with role‑based access) and traffic management features (like buckets for mutual exclusivity), like AB Tasty. Together, these help large organizations keep experiments secure, organized, and analytically sound as their programs grow.

Article

6min read

Agentic AI for Experimentation: Hype vs. Reality

The use of agentic AI for A/B testing is literally a game-changer for marketing teams, making it much easier to scale testing and experimentation programs. But with some companies making bold claims about what their AI can do, it can be hard to know just what to believe. So let’s look at how the competition’s AI really stacks up against ours.

Jump to comparison table

Move at the speed of evidence with Evi

First, let’s look at our agentic AI. Launched in November 2025, Evi is AB Tasty’s AI-powered marketing agent designed for evidence-based decision making. It transforms complex data into clear, actionable strategies for repeatable, measurable results and ensures every step you take is grounded in evidence.

But Evi isn’t just one tool. Evi is a suite of intelligent AI agents integrated throughout the entire AB Tasty platform, all optimized for specific tasks.

AgentFunctionDescription
Evi IdeasIdeationScans pages and generates data-backed ideas for new tests based on visual and contextual input. It uses AB Tasty’s proprietary data and UX principles.
Evi HypothesizeHypothesis creationUses a checklist of essential elements to help you create well-structured hypotheses with clear objectives. Assigns quality scores, highlights gaps and suggests edits.
Evi ContentVisual editorTurns natural language prompts into precise on-page edits (HTML/CSS/JS) with no coding required.
Evi AnalysisPost-test analysisAnalyzes campaign data, delivers clear, actionable insights. Highlights winning variations and breaks down why they drive transactions.
Evi FeedbackQualitative analysisAnalyses Net Promoter Score (NPS) and Customer Satisfaction (CSAT) feedback. Quickly identifies key themes and provides actionable insights from customer comments.
Evi ExploreRevenue insightsPowered by our patented metric, RevenueIQ, provides real revenue projections per visitor / per month with confidence intervals. Let’s you see what each test is worth before you launch.
Evi FormulaCatalog attributes (R&M)Self-serve tool for creating catalog attributes using natural language prompts.

So how does Evi compare to the AI used by our main competitors?

Evi vs. Optimizely Opal

Opal is the name of Optimizely’s suite of AI tools integrated throughout their platform. It’s not a standalone product, but rather a collection of different AI agents threaded across their entire product suite, which, along with Experimentation, also includes CMS, CDP, and Commerce.

Indeed, most of Opal’s AI agents are actually focused on CMS, CDP, and Commerce rather than Experimentation. One potential drawback for customers is that many of these AI features are tied to using the entire Optimizely tech stack. Rather than talk about Opal’s features for other areas, let’s look at what AI features Opal does have specifically for Experimentation:

FeatureDescription
Test ideationGenerates ideas for new experiments based on URLs and brand tone.
Variation editorAI-assisted creation of test variations based on Google Gemini.
Campaign creationCreates containers for both web and feature experimentation.
Variable suggestionsSuggests flag variables and variations in feature experimentation.
Chat-based data explorationAllows conversational exploration of test data.
Results summarizationSummarizes test results and provides directional guidance.
Experiment advisor agentsThese include a personalization advisor, experiment planner, and results summarizer.
Experiment scorecardScores experiments from the analytics interface.

Opal’s AI agents that are used specifically for testing and experimentation are very comparable to those of Evi. Both have dedicated AI agents for ideation, editing, and analysis. However, Evi also includes our proprietary RevenueIQ analysis and can leverage AB Tasty’s other AI features, EmotionsAI and Wandz for targeting and segmentation.

Some of Opal’s features also appear to be standard statistics features that have been rebranded as AI (e.g. multi-armed bandits and sequential testing).

Key differences between AB Tasty’s Evi and Optimizely Opal

  • Price: Evi’s AI features are included in all contracts at no additional cost. Opal is a paid add-on that costs around US$30,000 extra.
  • Speed: Evi’s AI editor is based on OpenAI and proven to be faster than that of Opal, based on Google Gemini.
  • AI Targeting: Evi can leverage our other AI features, EmotionsAI and predictive targeting (Wandz) for AI-based segmentation. Opal has nothing comparable.
  • Revenue Analysis: Evi Explore is based on our patented RevenueIQ metric for ROI projections. Again, Opal has no equivalent.
  • Experimentation focus: Evi is 100% focused on testing and experimentation. Most of Opals AI agents are designed for CMS/CDP/Commerce.

Evi vs. Kameleoon PBX

Kameleoon PBX (Prompt-Based Experimentation) is an AI-powered tool that allows users to generate A/B tests directly from natural language prompts. It is positioned as an all-in-one AI agent for test generation, fully integrated with Contentsquare.

Here is a list of PBX’s key AI features:

FeatureDescription
Prompt-based test generationUsers write prompts describing what they want to test, PBX then generates the necessary code/variation.
Contentsquare integrationTurns behavioral insights from Contentsquare into A/B tests.
Automatic site analysisUpon URL integration, 3 to 4 AI agents analyze the site’s structure (HTML/CSS/JS) giving prompts strong contextual awareness.
Figma integration(Currently a Beta feature) Allows the uploading of mockups from Figma, reducing errors in banner creation and saving time in implementation.
Code generationGenerates HTML, CSS, and JS code for experiments.

Unlike Evi’s suite of specialized AI agents, PBX is a single generalist AI agent. This provides you with a limited amount of control and can make it hard to iterate. Kameleoon claims that by using PBX specifically, its customers can build tests faster, more accurately, and at less cost per test. But the reality is that these improvements aren’t specific to PBX, all vendors with agentic AI see similar positive impacts for their customers.

Key differences between AB Tasty’s Evi and Kameleoon PBX

  • Price: Evi’s AI features are included in all contracts at no additional cost. Like Opal, PBX is a paid add-on.
  • Usage: All AB Tasty customers can make unlimited use of Evi, while use of PBX is based around credit quotas.
  • Architecture: Evi is a suite of different specialized AI agents. PBX is a single generalist agent.
  • Speed: Evi has a prompt response time of around 30 seconds, compared to up to 3 to 4 minutes for PBX.
  • Advanced segmentation: Evi can leverage AB Tasty’s other AI features like EmotionsAI for advanced segmentation. However, like Opal, PBX has nothing comparable.
  • Structured output: Evi supports structured output (JSON, rollback, versioning), while PBX makes no mention of whether this is the case.
  • Production quality: Evi is fully production-ready, while some customers have reported that PBX has QA issues and webperf impact.

Full comparison: Evi vs. Opal vs. PBX

Architecture, Pricing, Performance

CriteriaAB Tasty EviOptimizely OpalKameleoon PBX
AI architectureMulti-agent systemSuite of AI features across platformSingle generalist AI agent
Agent specializationTask-optimized agents (Ideas, Content, Analysis)General-purpose AI toolsOne-size-fits-all, prompt based
Structured outputJSON structure, rollback, versioningLimitedNo mention
PhilosophyEvidence-based, grounded in proprietary dataPlatform-wide AI integrationPrompt-to-test generation
Included in priceYes, all contractsPaid add-on (~US$30K)Paid add-on (+25% list price)
Usage modelUnlimitedCredit-basedCredit-based/quotas
AvailabilityAll usersRequires purchaseRequires purchase
Prompt response time~30 secondsReported as slow~3 to 4 minutes
Production readinessYesYesDemo-ready
Code qualityOptimized HTML/CSS/JSVariableQA issues reported

Feature comparison

FeatureAB Tasty EviOptimizely OpalKameleoon PBX
Test ideationEvi IdeasTest ideationLimited
Visual editorEvi ContentVariation editor (Google Gemini)Prompt-based generation
Hypothesis creationEvi HypothesizeNatural language interfaceNot mentioned
Results analysisEvi AnalysisResults summarizationNot mentioned
Revenue projectionsEvi Explore (RevenueIQ)No equivalentNo equivalent
Feedback analysisEvi Feedback (NPS/CSAT)Not mentionedNot mentioned
AI-based targetingEmotionsAI/WandzNo equivalentNo equivalent
Contentsquare integrationNot nativeNot nativeNative
Figma integrationNot mentionedNot mentionedBeta feature
SPA/Dynamic JS supportYesYesYes