abtasty

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Aug 5, 2026
10min read

Meet Wandz: The AI Layer That Powers the Wingify Suite

Stephanie Safdie

Stephanie Safdie

Main Takeaways

› Wandz is Wingify’s embedded AI layer, not a standalone assistant bolted onto the suite.

› It’s built to support experimentation, personalization, feature management, customer insight, and broader optimization workflows.

› Unlike general-purpose AI tools, Wandz works with context that’s already inside the suite: experiments, audiences, metrics, and customer behavior rather than starting from a blank slate.

› The goal is to shorten the distance between noticing something in the data and acting on it, without leaving the suite to do so.

Wandz is Wingify’s embedded AI layer, the intelligence DNA built directly into experimentation, personalization, feature management, commerce, and customer insight workflows across VWO and AB Tasty. Because Wandz already has access to your experiments, audiences, metrics, and behavioral data, it can analyze, recommend, and help execute changes without anyone exporting data or re-explaining context to a general-purpose AI tool first.

Why digital experience teams need a different kind of AI

Many teams already have AI in their daily workflow. ChatGPT, Claude, and Gemini are genuinely useful for brainstorming, drafting copy, summarizing documents, or writing code. None of that is in question.

But optimization work has a different shape. A useful answer to “why did conversion drop on this page” or “which audience segment should see this next” depends on context that lives entirely inside a testing and personalization suite: which experiments are running, how traffic is being split, what a given audience segment actually contains, what a heatmap or session recording shows about user behavior, and what’s already been tried before.

A general-purpose AI assistant doesn’t have access to any of that by default. Getting useful output means exporting data, pasting it into a chat window, writing a prompt with enough context to make the answer usable, and then carrying the AI’s suggestion back into the suite to actually act on it. That round trip is where most of the friction lives, in the manual work of feeding it what it needs to know.

What Is Wandz?

Wandz is the embedded AI layer that runs inside the Wingify suite, present across experimentation, personalization, feature management, analytics, and customer insight workflows rather than existing as a separate destination. It’s built on what AB Tasty previously offered through its Evi assistant and what VWO offered through Copilot, now unified into a single AI layer as the two suites converge under Wingify.

The distinction that matters most: Wandz is built into the screens where the work actually happens, which means it already has the context a general AI tool would need you to provide manually.

Why Wandz lives inside Wingify

The typical workflow with an external AI tool looks like this: export data, copy it, paste it into a prompt, write enough context for the answer to make sense, interpret what comes back, go back to the suite, and then manually execute whatever the AI suggested.

Wandz is designed to shorten that into: analyze, recommend, build, validate, launch, all without leaving the suite. The data doesn’t need to be exported because Wandz is already looking at it. The recommendation doesn’t need to be manually translated into action because Wandz can help build the resulting experiment, segment, or rollout directly.

One intelligence layer across the entire Suite

Rather than being a feature attached to any single product area, Wandz is designed to sit underneath all of them, drawing on the same underlying data regardless of where in the suite a team is working.

The practical effect of this shared layer is continuity: an insight surfaced while reviewing a heatmap can inform a hypothesis for a new experiment, which can inform an audience used for personalization, which can inform how a related feature gets rolled out, without re-explaining context at each handoff, because the underlying data carries through.

How Wandz supports every Wingify workflow

Most AI tools assist by surfacing a suggestion, and then a human carries it forward manually translating the recommendation into configuration, setup, or action inside the actual product. Wandz is built to go further than that.

Across every Wingify workflow, Wandz operates agentically: it doesn’t just recommend the next step; it can execute multi-step tasks end-to-end: drafting an experiment, building an audience, reviewing a configuration, interpreting a result, while keeping a human in the loop at the points where approval, judgment, or sign-off actually matters. The distinction is meaningful. An AI that tells you what to do still leaves the work to you. An AI agent that does the work and asks you to confirm before anything ships is where the real time savings come from.

Because Wandz operates inside the suite with live access to your experiments, audiences, metrics, behavioral data, and rollout history, it can act on that context directly, rather than describing what someone else should do with it.

Experimentation

In the experimentation workflow, Wandz generates hypotheses grounded in actual performance data rather than starting from a blank slate. It can draft an experiment end-to-end, defining variations, selecting metrics, and suggesting the right audience, and then review that configuration for likely issues before it goes live.

Once results come in, Wandz interprets them in plain language and identifies what to test next based on what the completed test revealed. The team reviews and approves at each stage; Wandz handles the mechanics in between.

Personalization

For personalization, Wandz moves from behavioral data to an actionable audience segment without requiring manual analysis at every step. It identifies patterns in visitor behavior worth targeting, builds a segment, and suggests experience variations suited to that segment, compressing what typically takes a full analytical cycle into something a team can act on in a single workflow.

A human confirms the segment logic and approves the experience before it runs; Wandz does the work of getting there.

Feature management

In feature management, Wandz reviews rollout configurations before launch, checking that metrics, targeting rules, and traffic allocations are set up correctly, and flags the issues a team might not catch until a test has already run for a week.

Beyond pre-launch review, Wandz can help reason through how aggressively to roll a feature out based on the risk profile of the change, and surface signals during a rollout that suggest it’s time to accelerate, pause, or trigger a kill switch.

Customer insights

For behavioral research, Wandz does the first pass across heatmaps, session recordings, and survey responses that would otherwise require hours of manual qualitative review.

It surfaces friction patterns, identifies which sessions are worth watching in full, and connects what it finds across multiple insight sources into a starting point for the next hypothesis — so teams spend their time on interpretation and decision-making rather than data triage.

Analytics

On the reporting and analytics side, Wandz answers questions about performance in plain language, explains what’s behind a notable shift in a metric, and surfaces comparisons across segments or campaigns without requiring anyone to build a custom report first.

For teams that need to communicate results upward, Wandz can pull together a summary that answers the business question rather than leaving that translation to the analyst.

What makes Wandz different from general-purpose AI?

The point isn’t that general-purpose AI is worse at reasoning, but that Wandz starts several steps ahead because it isn’t working from a blank slate.

General-purpose AIWandz
Starts with generic knowledgeStarts with experimentation and optimization context
Needs data copied and pasted inAlready connected to your suite data
Doesn’t know your campaignsHas access to your actual experiments
Doesn’t know your audiencesHas access to your actual segments
Mostly suggests ideasCan help build and configure the resulting work
Lives in a separate workflowLives inside the workflow itself

How different teams use Wandz

  • Product managers use Wandz to help prioritize which experiments are worth running, understand feature adoption, and think through rollout pacing.
  • Marketers use it to optimize campaigns, build out personalized journeys, and understand what’s actually driving (or blocking) conversion.
  • Growth teams use it to generate hypotheses faster, identify which segments are responding well to a given test, and find the next opportunity once a test concludes.
  • Engineers use it to review feature flag configurations, sanity-check a rollout plan, and reduce the risk of shipping something that breaks in production.
  • E-commerce teams use it to personalize the shopping journey, identify friction in checkout, and connect behavioral insight to revenue-driving changes.

A day in the life of Wandz

Here’s what that looks like end to end. A product manager notices that checkout conversion has dropped. Working inside the suite, they use Wandz to identify which audience segments are most affected. Wandz surfaces relevant session recordings showing where users are hesitating, and summarizes the heatmap patterns on the affected page.

From there, it helps frame a hypothesis for what might be causing the drop, drafts a starting point for an experiment to test that hypothesis, and reviews the experiment’s configuration, metrics, targeting, and traffic split before it goes live. If the new variation wins, Wandz can help think through a staged rollout rather than an all-at-once release.

No step in that sequence requires leaving the suite or re-explaining context to a separate tool. That continuity, more than any individual feature, is the actual value of an embedded AI layer.

An AI layer that sits this close to live experiments and production feature flags has to be built with guardrails, not just capability. That means recommendations are presented for human review rather than executed automatically, changes Wandz helps build remain visible and editable before anything ships, and the suite’s existing role-based permissions, audit logs, and security controls apply to AI-assisted actions the same way they apply to manual ones.

For enterprise teams in particular, the question isn’t just “Can the AI do this?” It’s “Can we see what it did, and who approved it?” and that accountability is treated as a requirement rather than an afterthought.

[Embed Wandz into your optimization ecosystem.] <CTA link>

FAQs

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

Is Wandz a chatbot?

Not in the standalone sense. Wandz doesn’t live in a separate chat window disconnected from your work; it’s built into the experimentation, personalization, feature management, and analytics screens you’re already using, with a conversational interface as one way of interacting with it rather than the whole of it.

Is Wandz built into every Wingify product?

Wandz is designed to span the suite rather than being scoped to a single product. As VWO and AB Tasty continue integrating under Wingify, the pace at which Wandz reaches every individual feature will vary, but the direction is one AI layer across the combined suite rather than separate AI tools per product.

Can Wandz create experiments?

Wandz can help draft and configure experiments suggesting variations, metrics, and audiences, but the team reviews and approves the configuration before it launches.

Does Wandz replace analysts?

Wandz works with the experiment, audience, and behavioral data already collected within the suite in order to generate relevant recommendations, governed by the same data handling and security practices that apply to the rest of the suite.

Does Wandz use customer data?

Wandz works with the experiment, audience, and behavioral data already collected within the suite in order to generate relevant recommendations, governed by the same data handling and security practices that apply to the rest of the suite.

How is Wandz different from ChatGPT?

Your answer hereChatGPT and similar tools are general-purpose and start with no knowledge of your specific experiments, audiences, or campaigns unless you provide it manually. Wandz is purpose-built for experimentation and optimization workflows and already has that context because it operates inside the suite where the data lives.

Can Wandz recommend personalization strategies?

Yes. Wandz is designed to help identify behavioral patterns worth targeting and suggest segment-specific personalization based on data already in the suite.

Does Wandz support feature management?

Yes. Wandz is designed to assist with reviewing rollout configurations, flagging potential risks before launch, and helping teams think through how aggressively to release a given feature.

Is Wandz available across VWO and AB Tasty capabilities?

Wandz is intended to be the single AI layer spanning both VWO’s and AB Tasty’s capabilities as they converge under Wingify, unifying what each company previously offered separately through Copilot and Evi.

The future of AI is embedded

AI tends to deliver the most value when it’s part of the workflow a team is already in, rather than a separate application they have to context-switch into. By building intelligence directly into experimentation, personalization, feature management, customer insight, and analytics, Wandz is designed to help teams move more directly from noticing something in the data to actually doing something about it, without that work getting lost in translation between tools along the way.

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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. 

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Article

Aug 3, 2026
13min read

Between AI and Conversion: The New Customer Journey

Kate Feng

Kate Feng

The customer journey has been continuously evolving over the years, but there’s no denying that what’s happening right now feels different.

Not long ago, the path to purchase was familiar: a Google search, a few browser tabs, some product comparisons, a handful of reviews, and eventually – a decision to be made by the consumer. Research took time, and websites played a central role throughout that process.

But now, the process has changed entirely – impacting the original fundamentals of a customer’s shopping experience.

Today, a customer’s journey might begin with a question typed into ChatGPT, a product comparison run through Gemini, or a recommendation assembled in seconds from dozens of sources across the web. By the time someone lands on your website, they’re often not starting their research – but instead, finishing something they already started elsewhere. 

That changes everything about how your digital experience needs to work. 

This article will breakdown how websites can accommodate for these changes and the exact ways we help brands to assimilate to this changing world with AI.

The Journey Has Already Begun Before the First Click

Here’s the shift that matters most: the first conversation often happens before a visitor ever reaches your website.

The shift from “search and browse” to “ask and receive” is happening faster than most marketers could’ve ever imagined. Perplexity AI processes 780 million queries monthly, and ChatGPT now has 800 million weekly active users. Half of consumers now intentionally seek the use of AI-powered search engines, with a majority saying it’s the top digital source they use to filter through potential buying decisions.

The implications are significant. With 58% of consumers using generative AI for product recommendations (which used to be a mere 25% back in 2023), the traditional model where brands optimized to appear in search results to earn conversions is now being put on the backburner. 

Now, a new and more favorable environment is where AI is used to provide overviews and unified, informative answers. This is usually done without users ever visiting a brand’s website to begin with.

Early data from AI-enabled search experiences shows that people are now asking much longer, more descriptive queries than they did in the past. They’re no longer just typing keywords. They share context, constraints, preferences, urgency, and sometimes even emotion with full faith that AI will guide them to the right brand and purchase.

AI-powered search systems are increasingly able to break down complex queries into multiple search streams and synthesize the results in real time. As a result, research that once required dozens of tabs now comes together in seconds.

So, what does this mean for your website? 

Visitors who arrive with a recommendation already in hand don’t behave like someone discovering your brand for the first time. They’re not looking for information – but confirmation.

Users now might be thinking of websites looking to confirm questions like:

  • Is this really the right product?
  • Can I trust this company?
  • Is there anything I’ve missed before making my decision?

These are the questions your website now needs to answer quickly, clearly, and confidently to encourage a user to add to cart and checkout.

Your Website’s Role Has Fundamentally Changed

It’s true that the traditional digital funnel once represented a linear journey: awareness, consideration, and conversion. Visitors arrived, explored, and (hopefully) bought something. Essentially, your website was the stage that set the tone for the whole performance.

Now, with easily accessible abundant data and always-on connectivity, those linear paths no longer reflect reality. Consumers don’t move neatly from one stage to the next. They jump between devices, pause and resume journeys, and expect brands to meet them instantly wherever they are.

Conversion is no longer an end-of-journey event. It happens whenever confidence is high enough – which may be much earlier than marketers expect.

That’s the new reality your website is operating in. It’s no longer a discovery engine – but a confidence-confirming building machine.

The question isn’t just, “What information do we show?”. It’s, “What does this particular visitor still need to feel certain?”

And those two questions have very different answers depending on who’s asking.

The Signals Are Still There: You Just Have to Read Them Differently

A few years ago, tracking the customer journey was relatively straightforward. Search terms, campaign data, and referral sources gave you a reasonable picture of where someone was in their decision-making process.

Today, much of that process happens somewhere you can’t see. The comparison takes place in an AI conversation. Reviews are summarized before they’re ever read on your website. Questions are answered before they reach your support team.

But that doesn’t mean we know less about our customers. It means we need to pay closer attention to the signals they leave behind once they arrive.

Platform data reveals that AI-referred users spend roughly 25% less time on a website as opposed to traditional traffic – which on the surface suggested lower engagement. But in early 2025, studies showed that AI-referred users actually spent 2x more time on sites versus traditional traffic, likely to validate AI recommendations. As adoption grew, time on site decreased while conversion rates increased, demonstrating how consumers are now trusting AI recommendations more.

In other words: visitors arriving via AI are more informed, more intentional, and more ready to act. This should give websites incentive to be sharper about what happens once users land on their page. 

Every click, every scroll, every moment of hesitation tells you something. Where do visitors pause? What do they re-read? Where do they drop off? Together, those signals reveal where friction lives – and where confidence breaks down.

Understanding those signals is becoming far more valuable than simply collecting new ones. This is where tools like Insights capabilities become critical: transforming behavioral data into clear, actionable understanding of how visitors navigate your experience, where they get stuck, and what moves them forward.

How to Accommodate For Users That Arrive Already Informed

When someone has already done their research with AI, the bar for your on-site experience goes up. Small details become decisive, and can make-or-break a purchase.

  • How quickly can someone understand your product page?
  • Are customer reviews easy to scan and trust?
  • Does the page answer the questions still lingering in their mind?

Friction remains the enemy. High abandonment rates persist largely because of basic barriers like unclear pricing, complicated checkout, hidden fees, confusing returns, or too many steps remain intact.

These aren’t glamorous problems. But they are the ones that’ll cost you the conversion.

The better you understand what someone already knows before reaching your website, the better you can shape the experience that follows. Not every visitor arrives with the same level of knowledge. Not every visitor needs the same explanation. That’s why personalization isn’t a nice-to-have anymore, as it’s the foundation of a competitive digital experience.

Turning Data Into Experiences That Actually Convert

Here’s the good news: most of the data you need to make conversion possible already exists.

Product information, customer reviews, behavioral data, CRM insights, and purchase history can all contribute to a richer understanding of your customers. On their own, they’re just individual data points, but when connected together – they become the foundation for more relevant, more human experiences.

The challenge isn’t collecting more data. It’s making better use of what you already have.

AI-Powered Review Summaries: A Small Change, A Big Impact

Think about all the customer reviews. Most products have hundreds, sometimes thousands, of them. Everything a potential customer wants to know is already there. But very few people have the time or patience to read them all.

This is exactly where AI can make a real difference. Instead of asking visitors to filter through pages of reviews, AI can identify recurring themes, highlight what customers consistently mention, and surface those insights in seconds.

Breaking Down the Magic of AI Assistance with TRKKN

During our recent webinar with TRKKN, we shared a real example from the travel industry: AI-generated review summaries increased booking rates by 6%, booking form visits by 6%, and clicks on customer reviews by 3%. The information itself didn’t change. It simply became easier to access, easier to understand, and easier to trust.

That’s the power of meeting visitors where they are – not where you wish they were.

Check out the full webinar below:

Semantic Search: Understanding Intent, Not Just Keywords

Search behavior is evolving in the same direction. Not so long ago, people searched using a handful of keywords. Today, they describe what they’re trying to achieve.

  • “I’m looking for running shoes for a half marathon.” 
  • “Which camera is best for travel videos?”
  • “Which moisturizer is best for dry skin?”

It’s the natural language people have become accustomed to using with AI assistants – and these days, it’s the experience they expect from every website they visit.

Semantic search makes that possible. Instead of matching keywords, it understands intent. It recognizes context, connects related concepts, and delivers results that are much closer to what visitors are actually looking for. Less frustration, more confidence, and faster paths to purchase.

AI Shopping Assistants: The Knowledgeable Sales Associate Now Scaled

Shopping assistants are evolving for exactly the same reason.

Rather than guiding visitors through rigid, predetermined decision-making, modern AI assistants respond more like a knowledgeable sales associate. They answer questions, explain differences between products, and adapt recommendations based on the conversation unfolding in real time.

For products that require a little more guidance, such as complex purchases, high-consideration categories, and technical decisions – AI shopping assistants can help to create a shopping experience that feels far more natural. Plus, it takes real pressure off your support teams in the process!

The Answer on How to Keep Up with the Changing Times: Experimentation

Here’s something worth saying clearly: no brand has been crowned the king of figuring all of this out. Everyone is still in the process of learning how to accommodate their websites accordingly.

Only 16% of brands today systematically track AI search performance. The behaviors, the signals, and expectations are continuously shifting. What works today will need to adapt tomorrow. 

As these models continue to evolve, their subsequent strategies that work today will need to be adaptable for whatever comes tomorrow. Companies should aim to better understand how their customers’ needs and behavior are evolving along the discovery-to-purchase journey – and they’ll need to improve their experimentation capabilities to move in a flexible fashion.

That’s not a reason to wait. It’s a reason to test.

Experimentation is becoming more important, and not because AI demands it – but because people do. Visitors no longer arrive with the same questions, the same level of confidence, or the same intent. The only way to understand what builds trust, removes friction, and supports better decisions is to keep testing, learning, and adapting.

Try something. Learn from it. And try again.

That’s not a methodology. It’s a mindset. And it’s the one that separates teams who adapt from teams who fall behind.

What This Means for Your Team

The shift in customer behavior isn’t a crisis. It’s a signal. And signals are exactly what good teams act on.

Here’s where to focus:

On-Site Experience Icon

Audit your on-site experience

Audit your on-site experience for high-intent visitors. Are your product pages, reviews, and key information easy to scan and trust? Would someone who already knows what they want find the confidence they need – fast?

Connect Data Icon

Connect your data

Behavioral signals, CRM data, product information, and review content shouldn’t live in silos. Together, they power more relevant, more personalized experiences.

Semantic Search Icon

Invest in semantic search

If your on-site search still relies on exact keyword matching, you’re already behind the expectations of the visitors arriving today.

Test AI Features Icon

Test your AI-powered features

Review summaries, shopping assistants, personalized recommendations – don’t assume they work. Measure them. Iterate on them.

Experimentation Culture Icon

Build a culture of experimentation

The teams winning in this environment aren’t the ones with the most data. They’re the ones who act on it fastest.

Optimize for Trust Icon

Optimize for trust

Reduce friction at key touchpoints. Clear information about delivery, returns, availability, and reviews helps high-intent visitors convert faster and with more confidence.

In a world where consumers ask richer questions and get synthesized guidance instantly, the winners won’t be the loudest brands. They’ll be the ones that are easiest to recognize as the right solution in context.

The Journey Isn’t Over: It’s Just Starting Somewhere New

AI is changing where the customer journey begins. It’s compressing the research phase, raising the bar for on-site experiences, and putting a premium on clarity, trust, and relevance.

But here’s what hasn’t changed: people are still the ones making the decision.

They still want to feel confident. They still want to feel understood. They still want an experience that meets them where they are – not where it’s convenient for the brand to have them be.

That’s what great experimentation and personalization have always been about. And it’s what they’ll continue to be about, no matter how many AI tools come into the picture.

The question isn’t whether your team is ready for the future of the customer journey. It’s whether you’re willing to keep testing until you find your better.

Let’s go further, together. 

FAQs

Have more questions on AI and the customer journey? Find the answers you need below.

How is AI changing the customer journey?

AI shifts much of the research process before visitors reach your website, meaning they often arrive with greater knowledge, clearer expectations, and stronger purchase intent.

Why is personalization becoming more important in the age of AI?

Visitors arrive with different levels of knowledge and intent. Personalization helps deliver the most relevant content and experiences based on where they are in their journey.

What role does data play in AI-powered customer experiences?

Connected data, such as product information, customer reviews, behavioral insights, and CRM data, gives AI the context needed to deliver more relevant experiences.

How can businesses prepare for AI-driven customer journeys?

By combining high-quality data with continuous experimentation, semantic search, and personalized experiences that adapt to changing customer behavior.

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About the Author

Kate Feng

Kate Feng holds a degree in International Business from SKEMA Business School, where she researched ethical AI personalization in advertising. She is Growth Marketer and Creative Strategist at AB Tasty and founded fashion brand KATE FENG PARIS company based in Paris.

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Article

Jul 27, 2026
7min read

How Wingify’s Connected Suite Turns Optimization Into a Real Advantage

Stephanie Safdie

Stephanie Safdie

Main Takeaways

› Wingify is the Digital Experience Optimization (DXO) suite that now houses both AB Tasty and VWO.

› It unites personalization, experimentation, feature management, customer insight, and AI under a single roof.

› Current AB Tasty and VWO products keep running as-is while they’re gradually folded into one Wingify experience.

› Wandz is the AI engine woven through every part of the suite.

Most digital teams have data but lack clarity. The gap between collecting information and confidently acting on it is quietly costing far more than most organizations realize.

Experiments run, but nobody can tie the results back to revenue. Personalization tools exist, but targeting the right audience with any confidence is a struggle. Features ship, but there’s no dependable way to tell whether they actually helped. AI tools are in place too, yet every session starts with re-explaining context from scratch.

None of this is a talent problem. It’s a coordination problem, the predictable outcome of stitching together an optimization strategy from disconnected point solutions that don’t share data, audiences, or even an agreed definition of success.

Wingify was built to close that gap by bringing AB Tasty and VWO, two of the most established and widely used suites in personalization, experimentation, and digital experience optimization, into a single connected platform. Wingify gives organizations something individual tools never could: one shared, complete view of the customer that every team can act on from the same place, with no translation layer in between. Each feature feeds the next automatically; insight turns into a hypothesis, a hypothesis turns into an experiment, and an experiment turns into a better experience, without anyone manually stitching the stages together.

What disconnected tools actually cost you

Before getting into what Wingify makes possible, it’s worth naming the problem clearly, because it tends to hide behind adoption numbers and productivity dashboards rather than show up as a line item on a report.

Data that doesn’t travel slows every decision

  • An insight from a heatmap has to be manually translated into a hypothesis inside a separate experimentation tool.
  • A winning experiment has to be manually rebuilt as a personalized experience inside a different personalization engine.
  • A personalization audience has to be recreated from scratch in a feature flag tool before a rollout can even target the right people.

Every one of those handoffs costs time, invites interpretation errors, and often means that by the time a promising idea becomes a live experience, the moment it was built for has already passed.

Audiences drift apart and metrics stop agreeing

When the same audience, say, “returning customers who abandoned checkout”, is defined separately across four different tools, it quietly stops meaning the same thing in each one.

Experimentation reports one number, personalization targets a slightly different group, analytics shows a third figure, and nobody can explain the gap. The net effect is less time spent optimizing and more time spent reconciling spreadsheets.

AI is only as useful as the context it has

General-purpose AI tools are genuinely helpful for brainstorming and first drafts. But they have no visibility into your live experiments, your audience segments, your rollout history, or your campaign metrics.

Getting a useful answer means exporting, copying, and pasting enough context for the response to actually apply, then manually carrying that answer back into the suite to act on it. That round trip is exactly where the value leaks out.

Governance turns into a patchwork

For organizations running optimization across multiple teams, brands, or regions, every tool without a shared permission model becomes one more security surface to configure and maintain on its own.

Audit trails fragment, compliance questions get harder to answer definitively, and the combined overhead of governing five separate tools becomes a real cost that almost never shows up in any single tool’s ROI math.

Why connection, not just capability, drives the outcome

A connected suite outperforms a stack of disconnected best-of-breed tools for a structural reason, not a marketing one.

When experimentation, personalization, feature delivery, customer insight, commerce, feedback, and analytics all draw on the same data layer, the output of one stage automatically becomes the input for the next. A heatmap that reveals checkout friction feeds directly into a hypothesis. The hypothesis becomes an experiment.

The experiment’s winning variant becomes a personalized experience for the exact segment where the friction was worst. That personalization shapes a feature decision. The feature is rolled out progressively to the segment that responded best. Everything that happens along the way updates what the team understands about customer behavior, which sets up the next test.

That cycle… understand, hypothesize, experiment, personalize, release, learn, optimize, is how compounding improvement actually happens. It doesn’t take heroic effort, just tooling that doesn’t break the chain at every single handoff.

Wingify keeps that chain unbroken, combining AB Tasty’s depth in personalization, segmentation, and AI-driven targeting with VWO’s decade-plus track record in experimentation and behavioral insight.

Why an embedded AI layer changes what’s possible

An AI layer sitting outside your suite adds a step to everything you do. One built directly into it removes steps instead.

Wandz is Wingify’s embedded AI layer, intelligence built straight into experimentation, personalization, feature management, and analytics workflows. It builds on what AB Tasty offered through Evi and what VWO offered through Copilot, merging both into one AI layer with shared access to context across the entire suite.

Wandz runs inside every feature across the platform as the intelligence layer that keeps the connected loop moving, generating hypotheses, reviewing configurations, building audiences, interpreting results, and surfacing the next opportunity, all from within the workflow where the work already happens. The loop speeds up because Wandz removes the pauses between stages.

That translates into concrete business value:

  • Faster hypothesis generation: instead of a growth team spending a week pulling data to justify a test idea, Wandz can surface experiment hypotheses grounded in what’s already inside the suite, behavioral patterns, segment performance, and historical test results, in minutes.
  • Fewer launch mistakes: before an experiment or rollout goes live, Wandz reviews the configuration, metrics, audience targeting, traffic split, and flags likely errors. A misconfigured test that quietly runs for two weeks before anyone notices is an expensive way to learn something a pre-launch check could have caught.
  • Faster result interpretation: instead of waiting on an analyst to interpret a finished test, teams can ask Wandz plain-language questions, “how did this campaign perform on mobile vs. desktop for first-time visitors”, and get an answer straight from their actual data, immediately.
  • Faster campaign execution: the AI Editor lets teams build or adjust campaigns using natural-language instructions, with every change staying visible and reversible before it ships, faster time-to-live without giving up oversight.

Each of these shortens the distance between insight and action, and across every experiment, rollout, and personalization campaign a team runs in a year, that adds up to a meaningful difference in how much optimization actually gets done.

What Wingify delivers

Wingify’s value comes from bringing two suites together, AB Tasty’s personalization and AI-driven experience capabilities and VWO’s experimentation depth, into one data layer, one workflow, and one AI system spanning all of it.

For teams, the result is a tighter loop between understanding customers and improving their experience, running continuously instead of in occasional bursts whenever someone finds time to manually reconcile five dashboards.

That’s the business case for a unified Digital Experience Optimization suite. It’s exactly what Wingify delivers.

FAQs

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

What is Wingify?

Wingify is the combined Digital Experience Optimization suite formed by AB Tasty and VWO following their 2026 merger.

What’s included in Wingify?

Wingify covers web, mobile, and server-side experimentation; personalization and product recommendations; feature management (progressive rollouts and kill switches included); behavioral insight tools like heatmaps, session recordings, and surveys; and Wandz, the suite’s shared AI layer.

Does Wingify support feature flags?

Yes. Feature management, including progressive rollouts and kill switches, is built into the suite, giving engineering and product teams a way to roll features out gradually and shut them off instantly if something goes wrong.

Does Wingify handle personalization?

Yes, it’s one of the suite’s core pillars, drawing heavily on AB Tasty’s strength in adaptive, behavior-based targeting, including Emotions AI for reading user emotional state and Adaptive CX for adjusting the experience as a visitor spends more time on-site.

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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. 

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Jul 24, 2026
12min read

What Does a Customer Success Manager Do: Your Expert Guide to Growth and Innovation

Stephanie Safdie

Stephanie Safdie

Main Takeaways

› Customer Success Managers are a key component to managing clientele

› Discover how a good customer relationship can completely change your business

› Ways to implement the best customer strategy

› Discover why our customers rave about AB Tasty’s CSMs

The hardest part of A/B testing isn’t the statistics – it’s the human psychology behind it all, and that’s exactly where the commonly asked question of “what does a customer success manager do” steps into the picture and changes the game. 

While most people think customer success is about dashboards and monthly reports, the reality is that Enterprise SaaS runs on stakeholder management disguised as software.

We’ve learned at AB Tasty that Customer Success Managers (CSMs) can be the secret weapon to empowering your team to accomplish more. Between their exceptional knowledge on how to optimize websites and savvy communication skills – CSMs could be the bridge between seeing the rainbow from afar and collecting the pot of gold at the end of it.

What is a CSM? A Strategic Partner for Your Boldest Ideas

In the world of Enterprise SaaS, a Customer Success Manager (CSM) is often viewed as a project manager or support liaison. But at AB Tasty, we see it differently. A CSM isn’t just there to give you cold advice, but be a dedicated partner in strategic guidance who’s here to help your team move faster, test bolder, and see real results.

CSMs bring deep expertise in website optimization and user experience to the table. Beyond just explaining features, our CSMs are there to educate and empower your team by helping to translate your high-level business goals into a concrete experimentation roadmap. This ensures that every test you run is a step toward meaningful progress – demonstrating how CSMs work with brands to monitor quality over quantity in testing.

From Go-Lives to Guidance

A CSM’s true value is their ability to align your entire organization around a culture of experimentation. They act as the connector between your vision and the technical execution. Whether they’re helping a stakeholder see the long-term value of a CRO program or sharing best practices on how to optimize a checkout flow (can link to PDP to checkout article here), they’re focused on one thing: making sure you have the support you need to turn every idea into an insight.

By pairing their technical knowledge with a deep understanding of your unique business needs, our CSMs work hard to make sure you aren’t just using a tool – but that your brand is building a repeatable engine for growth.

Optimization cro snap l’occitane maybe here?

Building a Culture of Confidence, Together

Investing in an experimentation platform is a bold first step, but the real magic happens when that tool becomes a core part of your team’s culture. We’ve seen that the most successful brands don’t just “use” software: but embrace a culture of continuous learning and curiosity.

This is where our CSMs shine. They know that while a dashboard shows you the “what,” the human side of the partnership defines the “how.” They aren’t just here to monitor product adoption; they’re here to be your team’s biggest advocates and expert coaches.

More Than Metrics: A Partnership for Progress

SaaS companies often get caught up in cold operational terms like “retention” or “onboarding.” But at AB Tasty, we know that true success is much more human. Our CSMs focus on building deep relationships across your entire organization, ensuring every stakeholder, from analytical to executive teams, are all aligned and excited about the journey ahead.

It’s about more than account management. it’s about organizational alignment. Our CSMs are experts at:

  • Building Bridges: Connecting different teams to ensure everyone is moving toward the same goal.
  • Fostering Trust: Creating a safe space for teams to test bold ideas and learn from every result.
  • Sharing the Wins: Making sure your internal successes are celebrated and visible across your company.

Ultimately, our CSMs are here to help brands build an  experimentation program that isn’t just a project – but a thriving, repeatable engine for growth that your whole team believes in.

More Than Onboarding: What Customer Success Really Looks Like

The conventional understanding of a Customer Success Manager is a clinical one. 

Most people think CSMs exist to onboard the customer, drive adoption, reduce churn, and prove ROI. While these are the end goals, the process of reaching them is anything but linear. 

For a high-performing CSM, rapport-building isn’t a “nice-to-have” – it’s essential to the account’s survival.

“We apply that custom approach, whether it be in onboarding, whether it be maintaining a relationship, whether it be a CRO programme – we’re always looking to apply that unique approach because not one personality, not one business is the same”.  Scarlett Tuplin, CSM at AB Tasty. 

This means a one-size-fits-all onboarding deck usually renders useless. Every customer needs a different approach based on their personality types, which matter just as much as their technical knowledge. If you land a client who is naturally skeptical of testing, a CSM must pivot their entire communication style to match that energy.

This is the invisible work associated with the role. Becoming a CSM requires high emotional intelligence, radical adaptability, and a deep focus on relationship management. Great CSMs don’t just manage software adoption – they manage human alignment. They ensure that all stakeholders, from the data person to the product owner, are moving in the same direction before a single test even goes live.

More Than Onboarding: What Customer Success Really Looks Like

The conventional understanding of a Customer Success Manager is a clinical one – but in reality, it’s deeply human.

Our CSMs have a remarkable ability to engage people who know little to nothing about experimentation – and get them genuinely excited about it. That’s not a small thing. It takes patience, skill, and a real commitment to meeting people where they are – because not one personality or business is the same.

That’s why our CSMs always apply a customized approach. Whether it’s onboarding, maintaining a long-term relationship, or building out a full CRO program – every client gets something personally tailored to them. And before we ever get into the nitty gritty of optimization and experimentation, we prioritize one thing above all else: building the relationship.

We listen. When a client tells us they need more information or that something isn’t clicking, our CSMs dig deeper to understand what they actually need – and then we find the right solution together. That happens on both a personal and professional level. And yes, even though we’re a web optimization company, we still love being face to face. Some of the best breakthroughs happen in a room together.

From Training Wheels to Riding Solo

One of the most rewarding parts of the CSM role is watching a client’s mindset evolve.

We’ve seen clients come in completely focused on conversion rates and revenue metrics – and as we move through the onboarding process together, we challenge them to think bigger. What do you actually want out of your optimization experience? What does success look like for your team?

It’s a bit like learning to ride a bike. In the beginning, you need the training wheels – the guidance, the structure, the reassurance. But over time, as confidence builds, clients start creating their own creative testing ideas and executing them independently. They go from chasing a 3% conversion rate to building a personalized, high-impact experimentation program they’re genuinely proud of. That’s the transformation we’re here for.

When Clients Take the Wheel

One of our favorite things to see? When clients take the momentum and run with it themselves.

A lot of our clients are doing it now – the end user will build out a newsletter internally, or we’ll help support that, but usually they take it upon themselves. They’ll send it to the wider team, to the wider business, and showcase the results of each test they’re running. It gets people motivated. Others start weighing in on what to work on next. It becomes an inspirational piece that gets more people involved – and we love it when that kind of process takes hold with clients.

Because when that happens, the culture of experimentation isn’t just alive – it’s self-sustaining. And that’s exactly where we want every team to be.

The Real Work of Customer Success Happens Between the Lines

Getting to the heart of what a customer success manager does requires reading between the lines. 

An average day in the life of a CSM is more multifaceted than many of us are led to believe. It usually includes breaking the ice by asking clients about their weekend, reading emotional cues, sensing shifts in motivation, diffusing tension, and carefully reframing disagreements. 

Every Enterprise Account Has a Group Chat, a Power Map, and a Politics Problem

Long-term success requires embedding your product into the cross-functional fabric of an organization. This is where the CSM’s skill in stakeholder mapping becomes vital.

Without executive alignment, even the most successful experimentation program is at risk if a leadership disconnect exists. A major part of the CSM role is fostering internal visibility for the end-user’s wins. One of the most effective ways to do this is through internal marketing. This helps clients build newsletters that showcase test results to the wider team.

This isn’t just about reporting, but about building an internal alliance and aiming to secure business buy-in from the top down. When you turn a single advocate into a multi-departmental network of supporters, you reduce the risk of having only one person championing the cause. 

Every enterprise account is a trust building exercise, and the CSM is the lead strategist making sure the program doesn’t lose its vote when internal politics shift.

Momentum Starts with the Energy in the Room

While dashboards show you what’s happening, they don’t always capture the why. At AB Tasty, we believe that true success starts with the “energy” of the partnership. Our CSMs look beyond the metrics to the true pulse of your team’s engagement. We’ve found that the boldest breakthroughs happen when a team is curious, engaged, and ready to move fast.

Our CSMs are experts at identifying these “growth signals” – those moments of high curiosity and collaborative excitement that indicate a team is ready to go further. 

We look for:

  • Proactive Curiosity: When teams start asking “what if” and “why not,” it’s a sign that they’re fully engrained in the experimentation mindset.
  • Collaborative Brainstorming: A vibrant exchange of ideas is the ultimate indicator of a healthy, forward-moving program.
  • Shared Wins: When clients are eager to showcase their results to the wider business, we know we’ve built more than just a workflow – we’ve built a culture.

By keeping the momentum high and the human connection strong, our CSMs ensure your optimization journey is always moving forward, fueled by a team that’s truly excited to see what’s possible

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From Vision to Value: The Moment Your Strategy Becomes Reality

The transition from a final sales pitch to the first setup call is an exciting moment – it’s where “possibility” starts to become “progress.” At AB Tasty, we believe our CSMs are the architects who help build the bridge between your high-level goals and a living, breathing digital strategy.

Instead of sitting in the middle of a “gap,” our CSMs act as your strategic facilitators. They take the big ideas discussed during the sales cycle and translate them into a concrete, actionable plan that works for your teams’ exact environment and internal structure. Their goal is to ensure you feel supported every step of the way as you move from a pitch deck to a real-world experimentation program. 

By building deep rapport with your internal engineers and product leads, they provide the clarity and collaborative problem-solving needed to turn your digital ambitions into measurable results.

The Most Important Thing a Customer Success Manager Manages Isn’t Software

Excellent customer success looks technical from the outside, but in reality – its main methodologies are to be emotional, political, and rational at an enterprise level. 

It’s true that software, metrics, and experimentation matter – but the real success factor is whether people inside the organization trust one another enough to adopt change.

The most important metric in enterprise SaaS may not be conversion rate. It may be whether people still care enough to show up. Behind every ‘data-driven’ company is a group of people trying to convince one another to believe in change.

That’s exactly why the hidden job of a customer success manager isn’t managing software. It’s managing belief.

The Takeaway: Why Human Success Matters

At the end of the day, AB Tasty is a powerful tool, but it’s the CSMs who turn that tool into an organizational culture. By daring to take the next best test and initiating the next personal conversation, our CSMs ensure that enterprise SaaS remains human-led.

Looking to improve your customer perception and drive meaningful CRO outcomes?

Connect with an AB Tasty expert to see how our highly trained CSMs can support your experimentation journey.

Let’s go further, together.

FAQs

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

What does a Customer Success Manager (CSM) do?

At its core, a CSM is responsible for ensuring that a customer achieves their desired outcomes while using a product. In the world of Enterprise SaaS, this goes beyond simple support – becoming a CSM involves acting as an “organizational therapist” who bridges the gap between sales promises and technical reality, manages onboarding, and fosters a culture of experimentation within the client’s business.

What is the difference between a CSM and a CRM?

A CSM (Customer Success Manager) is a person – a strategic partner who advocates for the client and drives long-term value. A CRM (Customer Relationship Management), on the other hand, is typically a software tool (like Salesforce) used to store data, track interactions, and manage the sales pipeline. While the CRM tracks the data of a relationship, the CSM manages the energy and success of that relationship.

What skills do you need to be a successful Customer Success Manager?

A successful CSM needs a blend of hard and soft skills. These can include the ability to connect with clients on a personal level to break down barriers, identifying “emotional triggers” of churn, such as a lack of curiosity or radio silence, before they show up in the data and the ability to map stakeholders and build buy-in across different levels of a client’s organization.

What degree do you need to become a Customer Success Manager?

Most CSMs hold degrees in Business, Marketing, Communications, or Psychology, but there is no “standard” degree for the role. Because the job is so heavily focused on intuition and relationship management, many successful CSMs come from diverse backgrounds – what matters most is a track record of driving value and the ability to navigate complex human and organizational dynamics

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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. 

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Jul 22, 2026
6min read

AdaptiveCX Predictions: From Visitors Intent to Smarter Optimization

Alexandre Marquis

Alexandre Marquis

Main Takeaways

› Breaking down the importance of personalization in optimization

› The evolution of Customer Experience Optimization (CXO)

› How to prioritize optimizing for the present instead of according to past user activity

› Benefits for businesses who adapt real-time personalization.

You know what your visitors did. The next competitive advantage is understanding what they’re likely to do — and acting on it while the session is still happening.

Most optimization today is built on the past. Historical analytics. Static segments. Rules written weeks ago for visitors who showed up today. It works — until it doesn’t. Because by the time the data tells you something, the moment has already passed.

That’s the shift worth making: from knowing who visited, to understanding what they’re about to do — and responding in real time, before the opportunity slips away.

Customer Experience Optimization (CXO) has evolved beyond measuring what visitors did previously when visiting your site. The next competitive advantage is understanding what they are likely to do and acting on it while the session is still happening.

That is the role of AdaptiveCX Predictions.

Optimize for the present, not the past

Instead of relying only on historical analytics or static audience segmentation, AdaptiveCX enables brands to predict visitors intent and probability of completing a target in real time, then activate the most relevant experience accordingly.

This is going from what we call deterministic segmentation to predictive segmentation.

For CRO leaders, e-commerce managers, and digital executives, this shifts optimization from observation to action much faster.

Optimize for unique visitors, not the broad audiences

Traditional optimization often treats visitors based on broad segments made out of past signals. But two similarly categorized visitors may have completely different intent.

AdaptiveCX Predictions continuously evaluates live behavioral signals during the session and assigns a prediction score (0–100) representing the likelihood that a visitor will complete a defined goal. This can include:

  • Completing a purchase
  • Returning within a defined period
  • Showing affinity for a product or category
  • Reaching a business-specific objective

These predictions update in real time, creating an opportunity to adapt the experience before the outcome happens.

Conceptually, this changes the role of CXO: instead of optimizing journeys for the average visitor and static cohorts, teams optimize for individual probability and intent.

Turn Insights Into Immediate Business Impact

Predictions only create value if they can be activated.

With AdaptiveCX, prediction scores become usable signals across customer experiences. Teams can create audiences and trigger personalized interactions without waiting for post-session analysis or late signals.

Examples include:

  • Showing reassurance content to visitors with low conversion probability
  • Preserving margin by avoiding unnecessary promotions for high-intent users
  • Surfacing more relevant products based on predicted affinities
  • Delivering retention-focused experiences to visitors likely to disengage

The practical value is simple: better allocation of experiences, more relevant journeys, more relevant insights and stronger business outcomes.

Predictive models Without Data Science Complexity

A common barrier to predictive optimization is execution: long implementation cycles, complex models, and heavy technical dependencies.

AdaptiveCX is designed to make prediction operational.

Teams can create prediction models from pre-built templates, industry-trained models, or configure their own targets depending on business needs. Predictions can be attached to purchases, events, visits, or custom objectives and activated directly in optimization workflows.

As long as you’ve identified a Target worth predicting intent on,  the possibilities are wide open!

That means teams can move quickly – from defining an objective to testing and activating experiences – without building internal AI expertise.

The Next Step for CXO

Customer Experience Optimization is becoming less about reporting performance and more about influencing ongoing journeys and outcomes.

The competitive advantage now lies in identifying and leveraging intent as it emerges. This is how we can influence our visitors way sooner on their journey.

AdaptiveCX Predictions helps teams detect intent earlier, personalize more intelligently, and prioritize actions where they create the most impact.

  • For e-commerce teams, that means more efficient conversion strategies.
  • For CRO leaders, more precise experimentation and activation.
  • For executives, a clearer path from behavioral signals to measurable growth.

This is the move from reactive to proactive optimization. Instead of analyzing what went wrong after the fact, you’re shaping the experience while it’s still unfolding — with the right message, at the right moment, for the right person.

Ready to go further, together?

FAQs

What is the difference between adaptive and static personalization?

Static personalization relies on predisposed rules set by marketers. For example, a website might show a specific banner to users from a certain location or display different content for returning visitors. These rules remain unchanged until someone manually updates them.

Adaptive personalization, on the other hand, continuously adjusts experiences in real time based on user behavior, context, and data. Instead of relying on rigid rules, it learns and adapts dynamically to deliver the most relevant content or experience.

Adaptive personalization is also often referred to as AI-driven personalization, dynamic personalization, or real-time personalization – as it uses data and algorithms to automatically optimize the customer experience.

What kind of software can I use to implement adaptive personalization?

One option for software catered to adaptive personalization is AdaptiveCX, an in-house solution developed within the AB Tasty platform.

Effectively implementing an adaptive personalization effectively requires excellent software that can analyze user behavior, run experiments, and automatically optimize experiences. With AdaptiveCX, businesses can move beyond static rule-based personalization by using experimentation and adaptive decisioning to tailor digital experiences for each user segment in real time.

What does AdaptiveCX do?

AdaptiveCX is a tool within the AB Tasty platform designed to deliver adaptive, data-driven personalization in real-time.

It accomplishes this by continuously analyzing user interactions and testing results to determine which experience performs best for different audiences. Instead of relying on fixed segments or assumptions, AdaptiveCX dynamically adjusts content, layouts, and messaging based on what actually drives engagement and conversions.

This approach allows teams to:

  • Automatically optimize customer journeys
  • Deliver more relevant experiences in real-time
  • Reduce manual rule management
  • Continuously improve performance through experimentation

This real-time, user data allows for a more personalized digital experiences – which can help boost conversion rates and brand loyalty long-term.

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About the Author

Alexandre Marquis

Alexandre is a product management professional with a background in international relations and political science. He holds a Master’s degree from the University of Geneva, specializing in Russia and Eastern Europe, and has further developed his expertise in product management and agile product development. At AB Tasty, he brings his multidisciplinary experience to building products and driving impactful solutions.

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Jul 16, 2026
5min read

What Happens When Two Competitors Put Their Egos Aside?

Angelique de Taddeo

Angelique de Taddeo

For years, the digital experience industry has been driven by competition. Vendors compare features, publish competitive battlecards, launch migration campaigns, and work hard to differentiate themselves. That competition has played an important role in pushing the industry forward.

But it also led us to ask a different question:

What if the next breakthrough didn’t come from competing harder, but from building together?

A different way of thinking

When VWO and AB Tasty joined forces under Wingify, the question everyone asked was: why would two competitors team up? The answer is simpler than you’d think. Both companies had built products people trusted, teams they were proud of, and thousands of customers who relied on them. Neither needed the other to survive. But both shared the same belief: customer value matters more than company ego.

So instead of fighting over features or pricing, we asked a better question — how do we build the best possible platform for the people who use it? The answer was a partnership. Two teams, complementary strengths, and decades of shared expertise — coming together to build something neither could have created alone. That’s not competition. That’s progress.

Performance isn’t a feature. It’s an engineering philosophy.

There’s another reason this merger made sense.

Long before Wingify existed, both engineering teams had already spent years optimizing something customers rarely see—but benefit from every day: Performance.

The independent Third Party Web project analyzes JavaScript execution using Lighthouse data collected across approximately four million websites. Its goal is to measure how much work third-party scripts ask browsers to perform.

One detail is particularly important.

These measurements don’t represent the cost of a single feature.

They represent the execution cost of the entire platform delivered through a single tag.

For VWO, that includes capabilities such as experimentation, personalization, behavioral insights, feature rollouts, and more.

In other words, these aren’t the numbers of a lightweight A/B testing library. They’re the numbers of a complete digital experience platform.

The latest benchmark reports:

PlatformAverage JavaScript execution impact
VWO465 ms
AB Tasty524 ms
Hotjar732 ms
Optimizely898 ms
FullStory911 ms
Convert1,045 ms
ContentSquare1,504 ms
Kameleoon1,542 ms

Lower is better.

According to this independent benchmark, VWO delivers the lowest average JavaScript execution impact among the major digital experience platforms listed.

AB Tasty is closely behind, performing better than several well-known competitors, including Optimizely, ContentSquare and Kameleoon.

These aren’t marketing claims. They’re independent measurements.

Better engineering, not fewer capabilities

What makes these numbers meaningful is the context behind them.

Delivering experimentation, personalization, feature management, behavioral insights, and AI capabilities — while maintaining a low execution footprint — is genuinely hard. 

Performance at this level doesn’t happen by accident. It’s the result of thousands of engineering decisions made over many years.

Looking at this benchmark, one thing becomes clear: both VWO and AB Tasty had already developed that engineering culture independently.

Better together

This merger between AB Tasty and VWO is about multiplying strengths.

Over the years, both companies have built deep expertise in different but highly complementary areas of digital experience optimization. VWO stands out with a powerful platform spanning experimentation, feature management, behavioral insights, and platform engineering. While, AB Tasty shines as a leader in personalization, merchandising, recommendations, and enterprise optimization.

Bringing these capabilities together wasn’t simply about expanding a product portfolio. It was about creating a unified platform where these technologies work seamlessly together, powered by a shared AI layer, a single tag and one engineering vision.

data driven experimentation cycle infographic vwo ab tasty

In many industries, companies spend years protecting their territory and trying to outperform one another. We chose a different path. We believed our customers would benefit far more from collaboration than from another decade of vendor rivalry.

Competition helped both companies become stronger. Collaboration is what we believe will take the industry further. Because ultimately, our ambition isn’t to win against competitors—it’s to build the platform our customers will still choose five years from now.

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About the Author

Angélique de Taddeo

Angélique de Taddeo is Chief of Staff at AB Tasty with a focus on web experience and conversion strategy. She has a background in user experience design and digital marketing, having led the revamp of the company’s blog with a strong conversion-focused approach.

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Jul 15, 2026
12min read

Inside Wingify: The Digital Experience Optimization Suite Driven by AB Tasty and VWO

Stephanie Safdie

Stephanie Safdie

Main Takeaways

› Wingify is the Digital Experience Optimization (DXO) suite that now houses both AB Tasty and VWO.

› It unites personalization, experimentation, feature management, customer insight, and AI under a single roof.

› Current AB Tasty and VWO products keep running as-is while they’re gradually folded into one Wingify experience.

› Wandz is the AI engine woven through every part of the suite.

Wingify is a Digital Experience Optimization (DXO) suite built for teams that need to build, test, personalize, and refine digital experiences across web, mobile, and backend environments. With the 2026 merger of AB Tasty and VWO, Wingify has become the shared suite brand for both platforms, and the tools you’re already using are steadily coming together into a single ecosystem, sitting on one data and AI foundation.

Below, we break down what the suite actually contains, how the different pieces work together, and what changes (and what doesn’t) if you’re currently on AB Tasty or VWO.

What is Wingify?

Wingify brings two established, independently built platforms, AB Tasty and VWO, together under one roof, reachable at app.wingify.com. For now, in mid-2026, each still functions as its own suite while the underlying engineering, data models, and workflows are merged step by step over the coming quarters. Nothing about your current contract, pricing, or support changes in the meantime.

In practical terms, Wingify is built around seven interlocking capability areas: personalization, experimentation, feature management, behavioral insight, user surveys, commerce, and AI, topped off with an enterprise governance layer so larger organizations can run everything at scale across teams and geographies.

What you get inside Wingify

Wingify folds everything AB Tasty and VWO used to offer separately into one DXO suite. Rather than juggling several point tools, teams can now move through every stage of optimization from a single place.

Personalize every customer touchpoint

This is where AB Tasty’s DNA is most visible. It covers:

  • Experience personalization: delivering different content, page layouts, or flows to different visitor groups, without maintaining separate page versions.
  • Behavioral targeting: firing personalized experiences based on a visitor’s on-site actions, such as pages browsed, dwell time, cart activity, or how far they scroll.
  • Dynamic content and product recommendations: showing tailored content or product suggestions to each visitor, widely used across e-commerce and media sites.
  • Audience segmentation: assembling reusable visitor segments from behavioral, demographic, or CRM data, shared across both personalization campaigns and experiments.
  • AI-driven personalization: tools such as Emotions AI, which reads on-site behavior to gauge a visitor’s emotional state, and Adaptive CX, which evolves the experience the longer someone stays on a page.

The key difference from a standard A/B test is timing: personalization doesn’t sit around waiting for a statistically significant winner, it continuously serves each segment its best-known experience, while experimentation keeps refining what “best” even means.

Run experiments with confidence

This is the territory most closely tied to VWO’s roots. It spans:

  • Web A/B testing: pitting two or more page variants against live traffic to see which one moves a target metric, such as signups, purchases, or add-to-cart rate.
  • Split URL testing: comparing entirely separate URLs, useful when a redesign is too extensive to represent as a single-page variant.
  • Multivariate testing (MVT): testing several page elements in combination (say, headline, image, and CTA together) to find the strongest overall combination instead of isolating one variable at a time.
  • Server-side experimentation: running tests within backend logic, essential for anything client-side JavaScript can’t touch, like pricing logic, algorithms, or checkout flows.
  • Mobile app experimentation: running controlled tests inside native iOS and Android apps through an SDK, including feature testing that doesn’t depend on app-store review cycles.

This matters because real product decisions rarely live in just one layer, a checkout overhaul, for example, might need a front-end A/B test, a server-side pricing experiment, and a mobile counterpart, all tracked against the same success metric.

Power commerce performance

For retail and e-commerce teams, Wingify offers AB Tasty Commerce, bringing product search, recommendations, and merchandising together in one system instead of three separate tools.

It packages AI-driven product recommendations (frequently bought together, recently viewed, trending, and similar items, among other models), on-site search that understands typos and natural-language queries without manually built synonym lists, and merchandising controls that let business teams reorder products, promote or suppress specific items, and launch campaigns without pulling in a developer. Because search, recommendations, and merchandising draw on the same rules and data, a change made in one place is reflected consistently everywhere, with reporting that covers all three together.

This layer is aimed squarely at lifting conversion rate and average order value on product-heavy sites, working alongside the broader experimentation and personalization layers rather than overlapping with them.

Ship features without the risk

This is where product engineering meets experimentation, giving teams a way to release code without staking the entire rollout on launch day:

  • Feature flags: switches that turn functionality on or off without a fresh deployment.
  • Progressive rollouts: rolling a feature out to a growing share of users (1% → 10% → 50% → 100%) instead of releasing to everyone simultaneously.
  • Kill switches: a way to shut off a misbehaving feature in production instantly, with no code changes required.
  • Canary releases: trying a new feature out on a small, often internal or low-risk group before a broader release.
  • Feature experimentation: running a genuine A/B test on what a new feature actually does to key metrics, rather than shipping it and hoping for the best.

Understand what customers are actually doing

Every other layer depends on the insight this one provides:

  • Heatmaps show where visitors click, scroll, and move across a page.
  • Session recordings replay individual visitor sessions so you can see precisely what happened and where someone got stuck.
  • Funnel analysis pinpoints where visitors abandon a multi-step process, like checkout or onboarding.
  • Visitor behavior analytics roll up how traffic engages with a site or app over time.
  • AI-enhanced research, surveys, and feedback, powered by Blitzllama’s technology (acquired by Wingify in 2025) and branded Pulse, which layers AI-assisted analysis on top of qualitative input like open-text survey answers and user feedback.

This layer is what tells a team what to test next; it’s the diagnostic step ahead of a hypothesis, not simply a dashboard you glance at after the fact.

Optimize everything with Wandz

Wandz is Wingify’s AI layer, and it lives inside the suite itself rather than sitting off to the side as a bolt-on assistant. That distinction is important: a generic AI tool knows nothing about your actual experiment setup, variations, traffic split, success metrics, or audience definitions, so every question starts with exporting data and re-explaining context from scratch. Wandz skips that step because it already operates inside the same environment where the experiment lives.

Wandz merges what AB Tasty previously delivered through its Evi assistant with what VWO offered through Copilot. In practice, it can:

  • Explain results in plain language, answering things like “how did this campaign perform on mobile versus desktop” or “show me every test we’ve run on the checkout page,” without anyone touching a CSV export first.
  • Suggest hypotheses and experiment ideas grounded in real campaign performance and behavioral data, and able to factor in outside material like a competitor screenshot or a project brief.
  • Check configurations before launch, scanning metrics, audience targeting, and traffic allocation for likely errors before a test or rollout goes live, essentially a pre-launch QA check.
  • Build and modify campaigns via the AI Editor, letting teams set up or tweak an experiment through natural-language instructions, with every change staying visible and reversible before the team ships it.

Wandz is built to cover the full experimentation journey: setup, targeting, analysis, and iteration, rather than being confined to a single feature.

Govern optimization at enterprise scale

For organizations running optimization programs across several teams, brands, or regions, Wingify provides:

  • Role-based permissions that govern who can launch, edit, or view experiments and campaigns.
  • Audit logs that track changes for compliance and accountability purposes.
  • Collaboration workflows, approval chains, and shared workspaces, so marketing, product, and engineering can work off the same experiment without colliding.
  • APIs and integrations linking the suite to analytics platforms, CDPs, CMSs, and internal data warehouses.
  • Security, compliance, and multi-team governance controls are the safeguards larger or regulated organizations need to run experimentation at scale.

How it all fits together

Bringing these layers together only pays off because they run on the same underlying data, letting a team move through the entire loop without re-exporting or re-explaining context at each handoff:

Customer behavior → behavioral insight → hypothesis generation → experimentation → personalization → commerce → feature rollout → AI-powered analysis → back to customer behavior.

A heatmap flags friction on a checkout page.

That observation turns into a hypothesis.

The hypothesis becomes an A/B test.

A winning variation is rolled out as a personalized experience for a specific segment or shipped as a feature behind a progressive rollout.

Wandz analyzes the outcome and proposes the next test.

The same audience definitions, metrics, and historical data flow through every stage, illustrated below:

data driven experimentation cycle infographic vwo ab tasty

Who is Wingify built for?

Marketers

Test campaigns, personalize content, and lift conversion rates.

Product managers

Validate product ideas, oversee releases, and improve feature adoption.

Ecommerce teams

Deliver personalized shopping journeys and optimize every step of the customer path.

Growth teams

Run continuous experiments and surface the highest-impact optimization opportunities.

CX teams

Understand user behavior through a mix of qualitative and quantitative insight.

Engineers

Release features safely using feature flags and progressive rollouts.

What current AB Tasty and VWO customers need to know

Is AB Tasty becoming Wingify?

Yes. AB Tasty is moving under the shared Wingify brand, and over time, both AB Tasty and VWO are expected to fully converge under that umbrella. This is a branding shift, not a functional one.

Is VWO becoming Wingify too?

Yes, in the same way. VWO has also moved under the unified Wingify brand: the application moved from app.vwo.com to app.wingify.com (effective June 13, 2026), and VWO product names are being renamed accordingly (VWO Insights, for instance, is now Wingify Insights). Your existing plans, features, data, and access are unaffected.

Will the products themselves change?

Not right away. Your live website campaigns, mobile SDK campaigns, server-side experiments, tracking snippets, feature flags, and data collection keep working normally through the transition; only the dashboard changes. Over the medium term, expect AB Tasty’s strengths in personalization and AI-driven targeting to be woven more closely with VWO’s experimentation, analytics, and behavioral insight tools into one shared roadmap.

What happens to existing contracts?

Your current contracts, pricing, and service levels carry over unchanged as a result of the merger, and the team supporting you today keeps doing so.

What happens to logins?

If you’re a VWO customer, your account, settings, and credentials transfer over to app.wingify.com, though active sessions end the moment the domain switches, so you’ll need to sign back in at the new address. Single Sign-On (SSO) and SAML setups keep working automatically, since the SSO callback URL redirects to the new domain without any action needed on your side. Passkeys are the exception, since they’re tied to a specific domain for security reasons. A passkey created for app.vwo.com won’t transfer, so you’ll need to create a new one after logging in at the new domain. System emails, password resets, reports, and notifications now come from @wingify.com instead of @vwo.com, so it’s worth having your IT team allowlist that domain to keep them out of spam.

What happens to integrations?

Your integrations stay intact. VWO’s REST APIs are now served on the Wingify domain, and existing integrations keep working with no changes needed. The VWO SmartCode already installed on your site doesn’t need replacing either. IT teams should whitelist wingify.com and its subdomains in company firewalls to prevent disruption to features like session recording playback that run on those domains.

What stays exactly the same?

Your historical data, reports, settings, integrations, audiences, and configurations remain fully intact and accessible from the new domain. Contract terms, pricing, your support team, and service levels don’t change. Tracking snippets and live campaigns run uninterrupted throughout the switch. In short: what’s changing is the brand, and eventually, how deeply AB Tasty and VWO’s capabilities are integrated, not what you’re already paying for or how it works today.

Where digital experience optimization is headed

The next phase of digital optimization is about bringing personalization, experimentation, feature management, customer insights, and AI into a single suite, where each capability builds directly on the last.

That’s precisely the role Wingify is built to play.

Wingify gives teams one foundation for continuous digital growth, from understanding customer behavior through to launching new features and refining every interaction. Start a free trial or book a demo to see both platforms working as one unified suite.

FAQs

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

What is Wingify?

Wingify is the combined Digital Experience Optimization suite formed by AB Tasty and VWO following their 2026 merger.

What’s included in Wingify?

Wingify covers web, mobile, and server-side experimentation; personalization and product recommendations; feature management (progressive rollouts and kill switches included); behavioral insight tools like heatmaps, session recordings, and surveys; and Wandz, the suite’s shared AI layer.

Does Wingify support feature flags?

Yes. Feature management, including progressive rollouts and kill switches, is built into the suite, giving engineering and product teams a way to roll features out gradually and shut them off instantly if something goes wrong.

Does Wingify handle personalization?

Yes, it’s one of the suite’s core pillars, drawing heavily on AB Tasty’s strength in adaptive, behavior-based targeting, including Emotions AI for reading user emotional state and Adaptive CX for adjusting the experience as a visitor spends more time on-site.

What exactly is Wandz?

Wandz is Wingify’s shared AI layer, built directly into the experimentation and personalization workflow rather than bolted on as a separate tool. It combines what AB Tasty previously offered through Evi with what VWO offered through Copilot, so teams can ask plain-language questions about campaign performance, generate experiment ideas from real data, check campaign configurations before launch, and build or adjust campaigns through an AI Editor using natural language.

Does Wandz work across every Wingify product?

Wandz is designed to span the full experimentation journey, from campaign setup and audience targeting through to results analysis and hypothesis generation, rather than being limited to one feature. As AB Tasty and VWO’s underlying suites continue merging, Wandz is intended to extend across that combined surface, though how quickly it reaches every individual feature will vary as integration work continues.

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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. 

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Article

Jul 13, 2026
11min read

The Secret to Boosting Summer Travel Bookings: It’s Simpler Than You Think 

John Hughes

John Hughes

Main Takeaways

› How major airlines optimize their travel bookings

› The secret to boosting summer travel bookings

› Areas where airlines struggle the most throughout the booking process

› Our tried-and-true best experimentation methods for travel brands

The Peak-Season Illusion: Why Airline Optimization Happens Behind the Scenes Before Summer Travel

Summer is in full swing: long vacations under the sunshine accompanied by outdoor lunches and dips in the pool are no longer a part of our “middle of winter” fantasies. 

When this time of year rolls around, travel brands are in “all hands on deck” execution mode. The beaches are packed, flights are full, and marketing budgets are maxed out. 

But airlines and OTAs alike don’t rake it in the minute the sun comes out of hiding. Trying to fix a leaky booking funnel during peak season is like trying to change a plane engine mid-flight.

Most travel brands, like airlines and hotels, mistakenly think that prime-time summer is where the goldmine for conversions are – but in reality, the best of summer travel bookings happen during the quiet, off-shoulder travel season months of February, March, and April.

In fact, 80% of monthly airline traffic is now on mobile, but conversion rates lag behind desktop. This is exactly where airline brands looking to increase their summer travel conversion rates have the opportunity window to bridge the gap.

From our findings in our recent Airline Mobile Booking Benchmark Report, we’re going to break down the best ways that airlines can prepare for summer travel to maximize their earnings by optimizing their mobile booking funnel before the peak season begins –  achieving a steady conversion rate and getting people under the summer sun sooner than later. 

Why the “Shoulder Season” (Feb–Apr) is the Ultimate Optimization Window

February to April is one of the most critical planning phases for travelers. It’s when they are able to align everyone’s schedules and start dipping their toes in the water to see what kind of vacation is even possible.

In turn, this time of year is just as important for airlines – as low season means lower risk for running high-impact experiments.

Testing the Tide: The Ultimate Summer Travel Booking Playground

Running experiments during high-traffic, high-stakes periods can be stressful – especially when it comes to something as momentous as summer travel. 

Doing these tests during shoulder season, such as during February to April, allows airlines to: 

  • Collect ample search traffic to gather statistically sound data
  • Allow room to fail and iterate without risking peak summer revenue
  • Create a head start to ship validated winners before the summer rush

Industry Insight: The narrowing mobile-to-desktop conversion gap (shrunk from 60% to 24% in just two years).

The Mobile Booking Reality Check: Where Travel Funnels Leak 

Knowledge is key to creating change. This is especially true when trying to find where summer travel funnels leak the most, and how to optimize them accordingly during shoulder season to ensure next season’s sales go through the roof.

This is exactly where the information we uncovered in our 2026 Mobile Booking Friction Report. Across the board, we broke down six different systemic failures across 6 major European airlines (Air Europa, Lufthansa, SWISS, TAP Air Portugal, Iberia, Ryanair).

We discovered that despite mobile driving over 80% of monthly airline traffic, many airlines are unknowingly driving customers away at the most critical moments. Our latest benchmark report reveals a stark industry benchmark: not a single major airline scored above 80/100 on the friction scale. Even the industry’s top-performing digital teams are leaving massive amounts of peak-season revenue to sink to the bottom of the ocean instead of soaking up the sun – which happens as a result of avoidable, systemic hurdles.

To secure your summer bookings, these four critical friction points must be addressed before the shoulder season ends:

1. The “Price Surprise” & Tax Transparency

Trust is built on transparency, and yet – zero out of the six major airlines we tested display tax-inclusive pricing on their initial search results page. 

Waiting for the vacation itself is hard enough, let alone trying to be patient to wait and know how big the bill for the bookings will be. Forcing travelers to proceed multiple steps into the checkout funnel just to see the actual cost of their ticket is the ultimate cart-abandonment trigger.

2. The Trip Summary Bottleneck

The “Trip Summary” phase represents the most critical drop-off zone, averaging a mere 5.7/10 across the industry. When passengers arrive at the payment screen without a clear, consolidated view of their itinerary, baggage allowances, or selected extras – they hesitate. This lack of visual reassurance forces users to back-pedal, often resulting in abandoned sessions.

3. Obstacle-Ridden Search Entries

When people book for summer travel, they want it to be breezy just like the beach they plan to be suntanning on. That’s why forced sign-ins and intrusive app download pop-ups disrupt the natural browsing flow. 

For example, Lufthansa and SWISS aggressively push registration prompts mid-search, while Ryanair enforces a rigid login wall right before the payment screen. Every unnecessary barrier introduced before payment lowers the chances of conversion.

4. Overwhelming Ancillary Fatigue

While upselling is essential for profitability, how it’s presented can make a huge difference. Ryanair forces users through up to six consecutive, mandatory ancillary screens – such as for bags, seats, and travel insurance. This can contribute to intense user fatigue, as the sheer amount of options could prove overwhelming to the user – causing them to leave the page altogether. 

There’s a good way to kick this obstacle to the curb. Leaders like SWISS and Lufthansa reduce this friction by consolidating these options into a single, high-impact screen – which can encourage users to move forward with the summer travel booking process.

plane landing orange background

Proven Experimentation Tactics to Step-Up Before Summer

The period between February and April is the ultimate “testing playground” for travel brands. With lower seasonal risk but sufficient traffic to reach statistical significance, this is the perfect time to solidify validated winners that will carry your revenue through August. 

Here are four high-impact experimentation tactics you can implement right now.

Tactic 1: Clear the Path with Upfront Pricing & Reassurance

A major driver of mobile abandonment is “price shock” late in the funnel. Instead of hiding taxes and fees, airlines should experiment with transparency.

  • The Experiment: Test tax-inclusive labels or calendar-based pricing that surfaces the lowest fares directly inside the date picker – a tactic effectively used by TAP Portugal to set expectations before the user even starts selecting anything from the search button.
  • The Metric: Watch for an increase in the downstream conversion rate and a significant reduction in payment-page abandonment.

Tactic 2: Reorganize and Simplify Ancillaries

Travelers suffer from decision fatigue when faced with endless screens for seats, bags, and travel insurance.

  • The Experiment: Airlines and travel brands should move toward dynamic fare bundling. This can be done by grouping ancillaries into targeted “Trip Packs” for specific segments (i.e., a “Business Pro” pack including a seat, bag, and priority boarding) rather than bombarding them with individual choices.
  • Real-World Impact: When Iberojet used AB Tasty to reorganize their search tabs based on historical user intent, they saw a 25% surge in searches and a 22% reduction in friction caused by unnecessary tab-switching.

Tactic 3: Optimize the Flight Summary & Passenger Form Flow

The transition from selecting a flight to entering passenger details is a high-friction zone when booking travel on mobile devices.

  • Real-World Impact: Air Europa used AB Tasty to optimize their mobile flight summary and multi-step passenger forms. By refining the layout and reducing field clutter, they achieved a +9% conversion rate increase from the flight summary to the passenger details page, a +5% increase in form completion, and a jump in CSAT from 81% to 87%.

Tactic 4: Algorithm-Driven Sorting Over Static Price Sorting

Price is a deciding factor, but it isn’t the only factor. Airlines that limit themselves by sorting by “cheapest” could end up hiding high-value options that better suit a traveler’s needs.

  • The Experiment: Surface a “Recommended” listing at the top of search results based on a dynamic relevance algorithm (considering time of day, duration, and baggage) rather than a static price list.
  • Real-World Impact: In controlled tests, switching to dynamic algorithm-driven sorting resulted in a +2.8% revenue uplift and a +4.6% increase in Average Order Value (AOV).

The Shared Platform Opportunity: Fix Once, Win Multiple Times

When operating a multi-brand travel group, optimization is rarely an isolated effort – but rather an opportunity for exponential impact. Our latest mobile booking friction report highlights this beautifully through what we call “The Lufthansa Group Paradox.”

During our assessment, Lufthansa and SWISS scored identical results across all seven evaluation stages of the booking funnel. This happened because both carriers shared the same friction points across their respective booking journeys. 

This shared predicament presents a massive Group Multiplier Effect. For airlines like these, resolving a single friction point on a shared platform is a “solve it once, scale the win” strategy. A single optimized checkout flow, a streamlined ancillary screen, or a clearer trip summary can instantly lift performance and conversion rates across your entire portfolio of brands at the exact same time.

This systemic approach to optimization is crucial as the competitive landscape shifts. Online Travel Agencies (OTAs) are rapidly closing the digital experience gap. Unlike many carrier apps, OTAs rarely interrupt users with forced registration walls mid-booking and are highly disciplined at keeping trip summaries transparent and distraction-free.

To win back direct customer relationships from OTAs, travel brands looking to boost summer travel must leverage their proprietary data. By implementing continuous, platform-wide experimentation, travel groups can transform their shared digital assets into highly optimized conversion engines just in time for the summer rush.

How AB Tasty Helps You Navigate the Optimization Flight Path 

Identifying friction points in your booking funnel is only the first step to securing summer travel “checkouts” from travelers.

To capture peak seasonal demand, travel brands must move beyond “we think” to “we know”. 

Heuristic reviews and guesswork can point to potential problems, but only live, statistically sound experiments can quantify exactly what a fix is worth to your bottom line. Instead of rolling out unproven changes during high-traffic periods, experimentation replaces assumptions with data, letting you validate every pixel of your mobile experience before the summer rush begins.

How To Empower Your Summer Travel Booking Journey

AB Tasty provides the specialized capabilities travel brands need to systematically eliminate booking friction and maximize conversion rates:

  • Low-Latency Server-Side Testing: Run complex feature experiments on critical backend systems, search algorithms, and pricing structures directly from your servers. This ensures dynamic changes are delivered instantly without the dreaded “flicker” effect that hurts mobile user experience.
  • Dynamic Segmentation & Personalization: Instantly adapt the native app or web interface based on real-time, in-session behaviors. Tailor banners, baggage promotions, or fare summaries to specific passenger profiles without relying on third-party cookies.
  • Unified Tooling: Break down organizational silos by bringing product managers, engineers, and marketers onto a single platform. This unified environment dramatically increases testing velocity, letting teams ship and validate winners at scale.

The Bottom Line: Better Optimization Leads to Better Summer Bookings 

Remember, peak-season revenue isn’t earned during the summer peak – it’s secured during the quiet winter and spring months before summer travel is in full bloom. 

By tackling booking friction before the annual summer rush, travel brands can ensure their summer bookings take flight with ease.

Don’t leave your summer conversions on the runway. 

Download the full 2026 Mobile Booking Friction Report to map out your summer travel booking optimization strategy today →

FAQs

Still have questions about boosting summer travel bookings? Here are the answers you need.

Why should travel brands prioritize testing during the shoulder season (February to April) instead of peak summer?

Testing during the shoulder season minimizes revenue risk while leveraging ample traffic to address the industry-wide reality that no major airline currently scores above 80/100 on the friction scale. This critical window allows teams to bridge the 24% mobile-to-desktop conversion gap and ship validated winners before high-stakes peak volume arrives.

What specific mobile friction benchmarks should airlines target to improve summer bookings?

With 80% of monthly airline booking traffic now on mobile, brands should target the “Trip Summary” phase, which currently averages a failing score of just 5.7/10 across the industry. Optimizing these high-friction zones during the off-season can lead to proven results such as Air Europa’s +9% increase in conversion from flight summary to passenger details.

What are the best A/B tests for hotels and travel brands before the summer booking season?

The best pre-summer A/B tests are the ones that reduce hesitation and decision fatigue, such as showing clearer total pricing earlier, bundling add-ons more intelligently, and streamlining booking forms on mobile. These types of experiments are especially valuable before summer because validated winners can be rolled out ahead of peak demand and carry performance gains through the busy season.

Why should travel companies optimize booking journeys before summer instead of during peak season?

Travel companies should optimize before summer because peak season is the worst time to introduce unproven changes into a high-stakes booking journey. Running experiments earlier gives teams time to validate ideas, reduce mobile friction, and launch proven improvements before the highest-volume summer traffic arrives.

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. He is passionate about creativity and global growth, contributing to the blog with insights on AI, optimization, and travel industry marketing applications strategies tools.

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Jul 10, 2026
10min read

Gen Z vs Millennials: How to Optimize for Each Generation

Stephanie Safdie

Stephanie Safdie

The Myth of the “Universal User”: Why Generational Intelligence is Your Best Conversion Tool

As brands race to expand their reach across an increasingly crowded social media landscape, many have lost sight of an important reality: not every customer is the same, and a one-size-fits-all approach is no longer enough.

From a young adult  scrolling on TikTok in comparison to older age groups on Facebook could have entirely different needs or interests, meaning each of them deserves an experience tailored to their intent, context, and generational perspective.

One way to meet the high expectations and personalized needs of younger generations, like Gen Z and Millenials, is with Audience Segmentation. When we make assumptions about user needs according to age, we don’t have enough concrete data to act accordingly. That’s why at AB Tasty, we believe it’s important to be brave and test bold: as all assumptions are simply hypotheses until they are validated through experimentation.

This article will break down the differences between Gen Z and Millennials when shopping online and the ways that brands can optimize their pages accordingly. 

Defining the Gap: From the Great Recession to the Digital Natives 

Although some Gen Z and Millennials aren’t even 20 years apart, the two age groups couldn’t be more different. 

According to the Census Bureau, we can define generational boundaries between Millennials and Gen Z:

Millennials:Gen Z:
People born between 1982 and 2000 People born between 1997 and 2012
prefer emailsmore social media savvy
opt for face-to-face communicationwere born with the digital world and are accustomed to instantaneous, screen-based communication
overall more skeptical practice positivity and optimism

How are Gen Z and Millennials Different? 

Gen Z are known to be far more tech savvy than their Millennial counterparts – who witnessed the birth of technology and the transition from analog to digital, but didn’t grow up with it. As a result, Millennials are more bound to do this “the old school way” – such as searching for reviews on Google or looking for more information about a brand online. 

It’s also important to note that Millennials were conditioned to be more skeptical due to the Great Recession – which can apply to their shopping habits, too. 

Ultimately, Millennials serve as the bridge between previous generations and the hyper-connected future.

Gen Z, on the other hand, is far more likely to use social media apps to vet if a potential product or service is worth considering. They are true digital natives who grew up with a mobile phone as an extension of their identity.

This contrast in expectations highlights a massive shift, and one that is worth learning about if brands want to optimize for each generation accordingly. 

iphone face down on coral background

The Search Revolution: Social Media as the Modern Google 

The thing that brands should be most aware of today is that social media is no longer just for connection; it’s a search engine where many people are more likely than not going to find your brand.

This is increasingly relevant to understand when it comes to optimizing your webpages for Gen Z. In fact, 67% use Instagram and 62% use TikTok as their primary search tools for discovery. This makes it all the more important to ensure that you personalize the experience for them after they find you on their respective social media apps.

The Impact on Web Optimization

When a user discovers your brand through a peer recommendation or influencer campaign, they expect a seamless transition to your website. If they click a link from an interactive, highly visual social app and land on a static, generic homepage, the disconnect is jarring.

For younger generations, this immediate friction is a primary driver of high bounce rates. They have developed a distinct digital intuition shaped by endless scroll feeds. To keep them engaged, your website must maintain that “social aesthetic.” This means prioritizing highly visual layouts, integrating user-generated content directly onto product landing pages, and presenting a flow that feels native to the interactive platforms they use every single day.

Solving Problems Together: The AB Tasty Solution

This is where real-time personalization becomes your most valuable tool. 

Using AB Tasty, you can dynamically tailor your landing pages based on referral sources. For instance, if a visitor arrives from social media, such as Instagram or TikTok – the platform automatically recognizes the source and instantly swaps out generic banners for video-first content, personalized product recommendations, or curated lookbooks that mirror the exact “vibe” of the ad or post they just came from.

By aligning the on-site experience with the context of their arrival, you ensure the web journey feels as fluid and engaging as their favorite social feed. This level of responsiveness is no longer just an advantage – it is a baseline requirement to capture and convert younger audiences

Optimizing for the Millennial Pragmatist (The Efficiency Seekers) 

Attracting and keeping Millennials attention is an entirely different ball game. This is because Millennials are inherently much more pragmatic than Gen Z, meaning it’s important to cater to their needs accordingly. 

How to Optimize Your Web Pages for Millennials with AB Tasty

Trust Icon

Trust Signal Testing

Using our Visual Editor to test the placement of reviews and security badges to satisfy their need for credibility. This is because Millennials still compare and contrast reviews, more likely to read the written comments on say, a Google review or website page than Gen Z.

Cross-Device Icon

Cross-Device Optimization

Ensuring a seamless flow between mobile phone research and desktop checkout. Millennials shop on their computers more than Gen Z does, meaning it’s important to ensure that desktop setups remain consistent and approachable.

Loyalty Icon

The Loyalty Edge

Personalizing homepages based on repeat-purchase behavior, as Millennials prioritize brand loyalty.

Millennials Value: Convenience and speed. They remember the slow web and have no patience for it.

Optimizing for the Gen Z Purist (The Authenticity Seekers) 

Gen Z is infamous for being attached to their phones and one-with-the-digital-world. While it may come off as a con to older generations, there are a lot of upsides to using their digital fluency to your brand’s advantage. 

How to Optimize Your Web Pages for Gen Z with AB Tasty

Mobile Icon

Mobile-First Personalization

If your mobile site isn’t perfect, they’re gone. This is because Gen Z has a short attention span for electronic layouts that feel sterile or visually unappealing – especially when a site appears outdated.

Visual Merchandising Icon

Visual Merchandising

Using our Commerce suite to prioritize short-form video content over static text. This is because Gen Z is much more likely to research a product or service via video content over written text.

The Vibe Test Icon

The “Vibe” Test

Using A/B testing to find the right balance of user-generated content (UGC) vs. professional photography. Gen Z gravitates more towards user-generated content, so it’s important to attract them with this in your web pages.

Gen Z Values: Authenticity and real-experiences. These post millennials can spot “corporate fake” a mile away. They want visual-first, high-vibe, and personalized.

The AB Tasty Advantage: Turning Insight into Conversion Uplift 

Granular Segmentation: Tailoring the Journey for Every Generation

Creating segments catered to different age groups to test unique flows for each doesn’t have to be a mystery with AB Tasty. 

To move from broad generalizations to active conversion gains, you need the ability to slice your audience based on real-time behavior and digital context. 

Instead of relying on outdated static profiles, you can set up advanced parameters that dynamically filter your traffic. 

Here’s a breakdown of how audience segmentation would work with AB Tasty: 

  1. Build Your Segment: You can build a segment for your younger generations by targeting visitors who arrive via mobile-specific social referrers (like TikTok or Instagram) and exhibit rapid scroll speeds. 
  2. Establish Separate Segments for Gen Z vs. Millennials: Simultaneously, you can maintain a separate segment for older generations who land on your desktop site via organic search or email campaigns.
  3. Develop Unique Tests for Each Age Group: Once these custom segments are established, it’s easier to then directly test unique digital journeys for each group. This could include serving Gen Z users a highly visual, swipe-friendly product layout designed for quick decision-making, or presenting Millennials with a structured page that prominently features detailed product specs, verified customer reviews, and clear return policies.

This dynamic approach allows your team to stop guessing what works and starts acting on experiences that directly match the distinct browsing habits of every generation.

Conclusion: The Progress is Personal

Ultimately, decoding the shifting preferences between Millennials and Gen Z isn’t about slotting your audience into rigid boxes. It’s about realizing that every single visitor arriving on your site carries unique intents, expectations, and digital preferences. 

True optimization goes far beyond generic age brackets. It’s about putting the user first and using real-time behavioral cues to make every individual feel seen, understood, and valued.

Whether your visitor is a desktop-loyal shopper or a mobile-first social browser, their journey should feel tailor-made for them. Bridging this generational divide doesn’t require guesswork – but the right toolkit. 

Optimizing according to each generation doesn’t have to be a mystery. By using AB Tasty’s comprehensive experimentation and personalization platform to bridge the generational divide and remove digital friction – your brand can successfully boost your conversion rates today.

Let’s dare to go further, together. 

FAQs

Still have questions about optimizing for Gen Z vs. Millenails Here are the answers you need.

How do Gen Z and Millennials shop differently?

Gen Z is more likely to come across future products and purchases through social media, influencers, and short videos – as they often value brand authenticity and social impact. Millennials rely more on online reviews, comparison shopping, and brand loyalty when making purchases.

How can brands optimize for Gen Z’s preference for authenticity?

As Gen Z values brand authenticity, the post-click experience should prioritize User-Generated Content (UGC) and visual consistency. AB Tasty helps brands test various user-generated content, such as galleries and multi-angle imagery on webpages, which provide the immediate, unfiltered social proof this generation needs to feel they “know” a product before buying.

How does message alignment cater to the Millennial need for detailed information?

Millennials are driven by online reviews and brand loyalty. By using AB Tasty to ensure that a specific social ad’s message is depicted the same way on a dedicated landing page, brands build a consistent, transparent narrative. This alignment satisfies the Millennial need for reliable info while avoiding the high costs of manual PDP personalization.

Why is closing the ‘Expectation Gap’ critical for both generations?

Whether a visitor is a Gen Z influencer-follower or a Millennial comparison-shopper, a landing page that doesn’t match the energy of the social post leads to instant drop-off. AB Tasty allows brands to bridge this gap by curating the funnel and showing users the exact information and visuals they expect based on where they came from.

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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. 

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Jul 8, 2026
11min read

The Optimization Loop: How Behavioral Data Fuels Continuous Progress

Stephanie Safdie

Stephanie Safdie

Main Takeaways

› The importance of behavioral data in experimentation

› Examples of behavioral data such as heatmaps, scroll depth, and funnel analysis

› Clarifying how optimization should be viewed as a continuous process

› How Wingify’s AI tools can help brands develop faster and smarter experimentation

Brands at a crossroads don’t need to look any further to find the answers they’re looking for: behavioral data doesn’t lie.

Every optimization team has been there. You land on a 3% dip in conversion on your checkout page. Your dashboard tells you exactly what happened, but it doesn’t say a word about why.

So, you starting throwing out random guesses – is the CTA color off? Shipping info too far down on the page? 

You launch an A/B test based on a hunch just like these, wait two weeks, and… nothing new happens. No uplift. No insight. Just a flat line.

Testing based on a gut feeling is like trying to fix a car engine by looking at the dashboard. You can see the “check engine” light is on, but you won’t know if it’s a spark plug or a fuel line until you pop-up the hood and get to the underlying problem.

In the world of optimization, behavioral analytics is exactly how you pop the hood – helping brands to stop guessing, and start acting.

But here’s the thing: you don’t just pop the hood once. True optimization isn’t a one-way destination from data to test – but a continuous loop where every insight feeds the next test, and every experiment uncovers a new layer of user behavior.

The Upside of Knowing Where Friction Lives

Teams that start with observed behavior don’t just run better tests: they run fewer bad ones. 

That means increased cost savings, less impact on tight budgets, fewer inconclusive results, and more resources to implement the experiments that actually move the needle on conversion, engagement, and revenue.

The difference between a team that guesses and a team that watches is compounding. Every revelation sharpens the next hypothesis. Every test result feeds back into the behavioral picture. Over time, that loop becomes your biggest competitive advantage – as your brand won’t just be moving towards its better once, but over and over again.

“What” vs. “Why”: The Gap Behavioral Analytics Closes

Traditional analytics tools are great at counting… but not at telling.

They can keep track of clicks, sessions, and revenue – but they’re terrible at showing the full story behind those user behaviors. It’s kind of like showing you the finish line, but not showing you the journey it took to get there. 

Behavioral analytics tools, like heatmaps, session recordings, and funnel analysis – fill that gap. It shifts the focus from what users are doing to how they’re experiencing your site.

Additional ways that these tools can help brands better pinpoint friction include:

  • Scrollmaps: A visual layer that shows how far down a page users end up scrolling. The darker areas reveal the main point people reached, whereas the lighter areas represent more significant drop-off. This can help brands determine whether a CTA, a value proposition, or a product detail is even being seen, and if not – how to adjust the page accordingly to make sure that it is. 
  • Form Analytics: A field-by-field breakdown of how users interact with your forms. This will demonstrate which fields take the longest to fill, which ones get skipped, which ones cause people to abandon entirely, and where the drop-off happens before someone hits submit. In turn, form analytics are the difference between knowing your form has a low completion rate and knowing exactly which field is causing people to not fill it out until the end.
  • Experience Scoring: An automatic friction-detection layer that scores sessions based on signals like rage clicks, dead clicks, and error clicks – which then surfaces the highest-friction sessions and segments for your team. Instead of manually hunting through recordings to find problems, experience scoring flags them for you. Think of it as your early warning system for UX issues that haven’t hit your support queue yet.

When you stop looking at users as numbers on a chart and start watching them move through your site as people, your perspective shifts. This is because you’ll finally see the hesitation, confusion, and rage clicks that are working against you – shedding on light on the experiments you could do to finally make them work for you. 

That’s where real progress starts.

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From Observation to Hypothesis

The goal of watching a session recording or looking at a heatmap isn’t just to find your main points of friction – but to gather enough evidence to build a better hypothesis. Basically, we’re not just trying to find the root of the problem – but create innovative ways to solve it.

Type of hypothesis Example
Weak hypothesis“We think changing the button color will increase clicks because it’s more visible.”
Strong, evidence-backed hypothesisWe noticed in session recordings that 40% of mobile users are rage-clicking the non-interactive header icon instead of the ‘Buy Now’ button. By clarifying the visual hierarchy, we expect to reduce confusion and increase conversion.”

That’s the main difference right there: one is a shot in the dark – and the other is a targeted, surgical strike.

Six Dimensions of Behavior: What Each One Tells You

To build those sharper hypotheses, you need the right set of diagnostic tools working together. 

Here’s how Wingify’s Analyze suite breaks it down:

1. Heatmaps and Scrollmaps

Think of these as your “attention” maps. They show you where people are looking, and just as importantly – where they aren’t. If your key value proposition is passed where the user usually scrolls, you’ve found your first test idea. Scrollmaps are especially powerful for long-form pieces of content where page placements can make or break engagement.

2. Session Recordings

Session recordings are the next best thing to sitting right next to your customer in real life. By filtering for friction signals like dead clicks, rage clicks, or hesitations, you can go straight to the moments that matter – without watching hours of footage. This is considered as the most powerful way to build empathy for your users.

3. Funnel Analysis

Break your user journey into measurable steps. By breaking down the shakiest parts of the customer journey and identifying the main drop-off points, you can jump directly into the session recordings of those specific users to see exactly what pushed them away from your website and ultimately – from converting.

4. Form Analytics

Forms are often one of the most stressful parts of a user’s journey. Shedding light on the fields that they skip, whether it be phone number, date of birth, address, or needing to keep refilling their password – form analytics will help pinpoint the exact moment where the momentum dies before someone ever hits submit.

5. Segment Analysis

Not all friction is universal. Segment analysis lets you filter any insight by device, geography, traffic source, or behavior. What frustrates a mobile user from paid search may be completely invisible to a desktop user arriving organically. Knowing the difference changes everything.

6. Experience Scoring

Experience scoring serves as your brand’s early warning system, as it automatically flags high-friction sessions. This allows your team to be free of the need to manually hunt for problems. By surfacing visitor segments the cause the most issues – brands can act before they churn, and working to avoid the problem before it even occurs.

One Suite, Endless Ways to Test

The beauty of behavioral evidence is that it doesn’t just fuel A/B tests, but several different types of experimentation.

Once you understand the why, you can choose the best test to move forward and improve your brand’s conversion rate and loyalty long-term:

A/B Testing icon

A/B Testing

For when you have two distinct ways to solve a friction point.

MVT icon

Multivariate Testing (MVT)

For when you need to understand how multiple elements on a page interact with each other.

Personalization icon

Personalization

For when a friction point affects a specific segment – like mobile users from a paid campaign.

Feature Rollouts icon

Feature Rollouts

For when you want to see how users interact with a new feature in real time before releasing it to everyone.

The same behavioral insight can power all of them. That’s what makes Analyze the foundation of a full optimization program as opposed to a simple A/B testing add-on.

Wandz: The AI Layer That Keeps the Loop Moving

We’ve built Analyze to work seamlessly in conjunction with Wingify’s experimentation and personalization tools. 

When you find a friction point in a session recording, you shouldn’t have to switch tools or rewrite a brief to start a test. That’s why we’ve made it possible to move from discovery to running an experiment in minutes.

That connection is made possible by our AI layer, by Wandz – which holds the entire Analyze suite together. Wandz powers the “Identify Friction” button, which means teams without Analyze won’t have access to it, ultimately serving as the intelligence layer that turns six tools into one coherent workflow.

In practice, Wandz helps your team:

  • Summarize session recordings: Instead of watching hundreds of videos to get inside your customer’s mind, your brand can get a clear picture of patterns across sessions in seconds.
  • Analyze heatmap data: Heatmaps allow brands to uncover the most meaningful click and scroll patterns without needing to manually interpret the data. This permits teams to dedicate their time and resources to testing new ideas instead of the grunt work associated with trying to interpret the information from scratch.
  • Generate hypotheses: Brands can move from a friction signal to a testable idea faster, with AI-backed context from your own campaign data.

Think of Wandz as the engine that keeps the loop spinning. It ensures your team spends less time digging through data and more time acting on it – all at the speed your customers actually move.

These days, teams are under immense pressure to move faster – making it tempting to pursue short-cuts like copying and pasting reports into generic AI Tools. But there’s a hidden cost to doing this. 

Generic AI doesn’t know how your traffic is split amongst differing demographics, variations, audience segmentations, and past experiments – meaning it may answer with confidence that possibly shouldn’t even exist. This can be misleading information for brands trying to pave the path for greater success in web optimization.

Wandz is different as it lives directly inside the platform. It doesn’t just summarize, but acts as an intelligent interface between you and the real user data. Since this AI tool is woven into the experimentation journey, Wandz can take analytical data and suggest exactly where to test it on your behalf – allowing you to move from lessons to actions in seconds.

Dare to Look Closer & Loop Longer

Optimization isn’t a project with a single said-and-done deadline. It’s a continuous habit of asking better questions, listening more closely, and acting in line with those observations to stay ahead of the game.

By closing the gap between behavior and experimentation, you create a cycle where every test – win or lose – makes your next move smarter.

Teaming up with Wingify means brands can stop guessing, start watching, and keep the optimization loop moving.

Ready to see where your customers are actually getting stuck?

Let’s see what Analyze does in action, together.

FAQs

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

What is Behavioral Data?

Behavioral data refers to different aspects of human behavior. In the context of the experimentation world, behavioral data refers to the various ways a user interacts with a page that analytics tools track and measure – such as scroll depth, page views, and length of time on the page.

Why is Behavioral Data Important in Experimentation?

Behavioral data is important in experimentation as it allows brands to better understand customer patterns – such as how they interact with a page and for how long. By using behavioral data tools like heatmaps, scroll depth, form analytics, and funnel analysis – brands can curate an improved user experience to better accommodate what their users are looking for, which can encourage increased engagement, conversions, and brand loyalty.

Why is it important for brands to continuously optimize their landing pages, homepages, PDPs, and PLPs?

High bounce rates from social traffic usually point to an expectation gap – the page people land on doesn’t match what the post, ad, or reel promised them. Social visitors arrive with a specific intent shaped by what they just saw. If the landing page feels generic, slow to load, or visually disconnected from the content that drove the click, they leave – meaning it’s important to optimize accordingly to keep their interest all the way to checkout. 

How will Analyze help my brand boost conversions?

Analyze shows you exactly where your visitors are struggling – the pages they abandon, the forms they give up on, and the buttons they can’t find. This allows every test you run to be grounded in real behavior, not guesswork. That means sharper hypotheses, higher-impact experiments, and more conversions from the traffic you already have.

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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. 

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