KPIs, Custom Metrics, and Evi: Supercharge AI Insights in AB Tasty
Emily Healy
Our platform is above all designed to help you drive your business forward. That’s why in AB Tasty you can set up a range of metrics straight out of the box that have meaningful business impact. And our agentic AI, Evi, will help you analyze test results fast and provide you with valuable insights your team might otherwise miss.
We put the key in KPI
AB Tasty provides comprehensive reporting tools to evaluate the performance of your campaigns, focusing on the achievement of specific KPIs, called goals. These can be set up both at the account level and campaign level right out of the box. There are six different types of goals that you can chose to define as a KPI. These are:
Action tracking: Clicks, dwell time, iframe clicks, element visibility, scroll rate.
Page tracking: Visits to a particular page or group of pages.
Transactions: Average order value (AOV), purchase rate, total revenue.
Datalayer Goals: Tracking based on variables in your website’s data layer
Custom tracking: Trackers you’ve created with custom code on your website.
You shouldn’t get bogged down trying to define too many KPIs. And less is definitely more in this case. Try to identify 3 – 5 KPIs for each major business objective. If you Start with KPIs that are directly tied to business outcomes, they’ll help drive your business forward.
You measure you with custom metrics
These out of the box metrics are a great starting point. But no two businesses are the same, and your customer journey is unique. Standard KPIs might not capture interactions that are critical for you. By measuring what makes your business unique, you get insights that your competitors don’t have access to.
In AB Tasty, you can create Custom Trackings that are directly linked to your website’s DataLayer. This lets you create personalized metrics based entirely on your unique data. You can also create custom trackers using JavaScript, giving you the flexibility to measure almost any interaction on your website. These custom tracking events can even be used to replicate goals from other platforms, like Google’s GA4.
Real-time reporting happens now
We know that speed is critical to your business. For optimization to be effective, the feedback loop between action and insights needs to be as short as possible. Real-time reporting empowers your team to spot problems or winners within minutes, not days, enabling them to make confident decisions quickly.
That’s why AB Tasty offers real-time reporting capabilities that activate automatically when you push a campaign live. During the most critical initial phase of a test (the first 1,000 unique visitors or the first 12 hours), data for each goal and variation is updated every five minutes. We also provide a Live Hits monitoring tool which allows you to track event data at any time. This allows you to make faster, smarter decisions based on up-to-the-minute data.
Test with Confidence
Understanding the story your statistics are telling you is obviously crucial to making the right decisions. At AB Tasty, we use a Bayesian statistical model that provides you with intuitive, actionable results you can understand. We use two key figures to help you make more confident decisions:
A 95% confidence interval: This gives you a likely range for the true value of a gain. If the confidence interval for the gain is [2% 8%], we are 95% confident that the true uplift from this variation is between 2% and 8%. The remaining 5% represents the margin of error.
The chance to win: This is a direct probability that tells you how likely it is that a variation is better than the original. A 98% chance to win means there’s a 98% probability that the variation is the true winner.
Move at the speed of evidence with Evi
Having data and reporting from your tests is one thing, but analyzing those is another. Until recently, this has often been a time-consuming process and sometimes involved a little guesswork. But by integrating agentic AI into the reporting process, you can analyze data fast and receive valuable insights that your team might otherwise miss.
Evi is AB Tasty’s AI-powered marketing agent designed for evidence-based decision making. It transforms your data into clear, actionable strategies for repeatable, measurable results, ensuring every step you take is grounded in evidence.
With Evi, your team can:
Greatly accelerate the reporting process, enabling you to analyze campaign data within a matter of clicks not hours.
Extract deeper insights, all driven by actual website data using built-in AI analysis.
Evi features two separate AI agents accessible from the reporting page for each campaign in AB Tasty: Evi Analysis and Evi Explore.
Evi Analysis
Tired of spending hours sifting through data tables and colorful charts and wondering what they all mean? Evi Analysis will analyze your campaign data and deliver clear, actionable insights. It highlights winning variations and breaks down why they drive transactions so you can feel confident in your next move.
Simply enter your questions and Evi Analysis will process the underlying metrics, statistical significance, and objective performance to deliver clear, concise answers backed by campaign data. All in a matter of clicks. Use case examples might include:
Explaining the winning variation.
Challenging your hypothesis.
Giving you the best CRO practices based on your campaign results.
Evi Explore
Want to know if your tests will actually drive revenue? Evi Explore, powered by our own patented metric, RevenueIQ, makes it easy to interpret the results of campaigns that use a transactional goal (i.e. a goal that tracks purchases or revenue).
Evi Explore gives you a clear, statistically sound view of the revenue impact of each test variation before you launch. Rather than simply relying on traditional metrics like conversion rate or average order value (AOV), RevenueIQ combines these into a single metric to give you a direct view of revenue per visitor and per month.
This means no more inconclusive campaigns, no more ‘conversion rate vs AOV’ dilemmas, and a significant reduction in ‘undecidable’ tests. Teams can now project the revenue impact of a campaign before full rollout with confidence intervals for best- and worst-case scenarios. This gives you the confidence to make faster, more profitable decisions. And because none of our competitors currently offer a comparable metric, by using AB Tasty you receive insights others won’t have.
From Messy to Manageable: Organizing Experiments with Folders & Buckets
Emily Healy
If you’ve ever opened your company’s experimentation dashboard and felt overwhelmed by the sheer number of campaigns, you’re not alone. As businesses grow, so do the number of teams, projects, and experiments running at any given time. Suddenly, what started as a handful of tests can turn into a maze of overlapping campaigns, making it tough to find what you need.
That’s where Folders & Buckets come in. These two simple features can make a world of difference in how you manage, secure, and scale your experimentation efforts. Here’s how they work, why they matter, and some tips for getting the most out of them.
Too Many Experiments, Not Enough Organization
Picture this: your marketing, product, and development teams are all running their own experiments. Maybe you’ve got a few hundred campaigns live, or maybe it’s closer to a thousand. Either way, it’s easy for things to get messy. Important tests get buried, people accidentally edit the wrong campaign, and sometimes experiments even overlap – skewing your results or causing confusion.
This isn’t just a headache for your data team. It can slow down your whole organization and make it harder to get clear, actionable insights.
Folders: Your Experiment Filing Cabinet
Folders are exactly what they sound like: a way to group and organize your experiments in a way that makes sense for your business. But they’re much more than just a visual aid – they’re a powerful tool for access control and workflow management.
How Folders Work
Custom Organization: Structure folders by team (e.g., Marketing, Product), by project or sprint, by product line, or even by page type (e.g., Homepage, Checkout). The choice is yours.
Granular Permissions: Assign users to specific folders with different roles – viewer, editor, or admin. By default, new users see nothing until they’re granted access, minimizing risk and keeping sensitive experiments secure.
Flexible Access: Users can be given access to multiple folders, with different roles in each. This is perfect for organizations where people wear multiple hats or collaborate across teams.
Why Folders Matter
Clarity: Users see only the experiments relevant to them, reducing clutter and confusion.
Security: Sensitive or high-impact experiments are visible only to authorized users.
Agility: As teams grow or projects shift, folders and permissions can be reorganized on the fly – no need to start from scratch.
Pro Tip: Many organizations use folders to mirror their internal structure, but you can also get creative – organize by campaign type, business objective, or even experiment status.
Buckets: Keeping Experiments in Their Own Lanes
While folders help you organize and control access, Buckets (sometimes called “traffic repartition”) are all about managing how user traffic is allocated across experiments. Think of buckets as traffic lanes on a highway – each experiment gets its own lane, so there’s no risk of collisions.
How Buckets Work
Traffic Allocation: By default, you can create up to 10 buckets, each representing 10% of your total user traffic. Assign experiments to specific buckets to ensure they don’t overlap.
Mutual Exclusivity: Experiments in different buckets never see the same users, so results are clean and reliable.
Planned Flexibility: While the default is 10 buckets, future updates will allow you to customize the number of buckets and the percentage of traffic allocated to each.
Why Buckets Matter
No Overlap: Run multiple experiments at the same time – on the same page or feature – without worrying about interference.
Reliable Results: By keeping experiments mutually exclusive, you avoid skewed data and can trust your insights.
Enterprise-Ready: Especially valuable for organizations with multiple teams running simultaneous experiments.
Why This Matters for Your Team
Folders & Buckets aren’t just “nice-to-have” features – they’re essential for any organization looking to scale experimentation without losing control. They help you:
Stay organized as your program grows.
Keep sensitive experiments secure and compliant.
Empower teams to work independently without stepping on each other’s toes.
Deliver reliable, actionable insights by preventing experiment overlap.
As digital experimentation becomes a core business function, tools like Folders & Buckets are what separate the leaders from the laggards.
Ready to Get Organized?
If you’re struggling with a cluttered experimentation environment or worried about experiment overlap, it’s time to explore what Folders & Buckets can do for you. Customer Success Manager for more information, and see how easy it is to bring order – and results – to your experimentation program.
Experiment boldly. Organize smartly. Grow faster.
Want to learn more? Check out our documentation (folders/buckets) or contact us for a personalized demo.
How can I stay organized when running lots of experiments?
AB Tasty is built for big teams running many experiments. We offer our users a clear folder structure to group experiments by team, project, product line, or page type, and apply granular permissions so people only see the campaigns relevant to them. This reduces clutter, limits mistakes, and keeps your experimentation environment manageable as you scale.
What are folders in an A/B testing or experimentation platform?
Folders act like a filing cabinet for your tests: you can group experiments in ways that match your organization (e.g., by team, sprint, product, or page type) and assign viewer, editor, or admin roles per folder to control who can see and edit each campaign.
What are buckets in an experimentation platform?
Buckets (or “traffic repartition”) are a way to divide user traffic into separate lanes. Each bucket gets a portion of traffic (e.g., 10%) and experiments assigned to different buckets don’t share users, which keeps tests mutually exclusive.
Which A/B testing solutions help teams stay organized at scale?
The most effective solutions offer both structural tools (like folders with role‑based access) and traffic management features (like buckets for mutual exclusivity), like AB Tasty. Together, these help large organizations keep experiments secure, organized, and analytically sound as their programs grow.
When you’re running A/B tests, you’re making a choice—whether you know it or not.
Two statistical methods power how we interpret test results: Frequentist vs Bayesian A/B testing. The debates are fierce. The stakes are real. And at AB Tasty, we’ve picked our side.
If you’re shopping for an A/B testing platform, new to experimentation, or just trying to make sense of your results, understanding these methods matters. It’s the difference between guessing and knowing. Between implementing winners and chasing false positives.
Unlike descriptive statistics—which simply describes what already happened—inferential statistics help you forecast what’s coming. They let you extrapolate results from a sample to a larger population.
Here’s the question we’re answering: Would version A or version B perform better when rolled out to your entire audience?
A Quick Example
Let’s say you’re studying Olympic swimmers. With descriptive statistics,you could calculate:
Average height of the team
Height variance across athletes
Distribution above or below average
That’s useful, but limited.
Inferential statistics let you go further. Want to know the average height of all men on the planet? You can’t measure everyone. But you can infer that average from smaller, representative samples.
That’s where Frequentist vs Bayesian methods come in. Both help you make predictions from incomplete data—but they do it differently, especially when applied to A/B testing.
What is the Frequentist Statistics Method in A/B Testing?
The Frequentist approach is the classic. You’ve probably seen it in college stats classes or in most A/B testing tools.
This is one of the main Frequentist vs Bayesian A/B testing comparisons: Frequentist statistics focus on long-run frequencies and fixed hypotheses.
Here’s how it works:
The Hypothesis
You start by assuming there is no difference between version A and version B. This is called the null hypothesis.
At the end of your test, you get a P-Value (probability value). The P-Value tells you the probability of seeing your results—or more extreme results—if there really is no difference between your variations. In other words, how likely is it that your results happened by chance?
The smaller the P-Value, the more confident you can be that there’s a real difference between your A/B testing variations.
Frequentist Pros
Frequentist Cons
Fast computation: Frequentist models run quickly and are widely available in any stats library.
No peeking allowed: You can only estimate the P-Value at the end of a test. Looking at results early—called “data peeking”—creates misleading conclusions because it turns one experiment into multiple experiments.
Widely understood: It’s the standard approach taught in most statistics courses and used in many A/B testing tools.
No gain estimation: You’ll know which version won, but not by how much. That makes A/B testing business decisions harder.
What is the Bayesian Statistics in A/B Testing?
The Bayesian approach takes a different route—and we think it’s a smarter one for many A/B testing scenarios.
The Bayesian approach allows for the inclusion of prior information (‘a prior’) intNamed after British mathematician Thomas Bayes, this method lets you incorporate prior information into your analysis. It’s built around three overlapping concepts:
The Three Pillars of Bayesian Analysis
Prior: Information from previous experiments. At the start, we use a “non-informative” prior—essentially a blank slate.
Evidence: The data from your current experiment.
Posterior: Updated information combining the prior and evidence. This is your result.
Here’s the game-changer: Bayesian A/B testing is designed for ongoing experiments. Every time you check your data, the previous results become the “prior,” and new incoming data becomes the “evidence.”
That means data peeking is built into the design. Each time you look, the analysis is valid.
Even better? Bayesian statistics let you estimate the actual gain of a winning variation—not just that it won—making Frequentist vs Bayesian methods in A/B testing very different from a decision-making perspective.
Bayesian Pros
Bayesian Cons
Peek freely: Check your data during a test without compromising accuracy. Stop losing variations early or switch to winners faster.
More computational power: Requires a sampling loop, which demands more CPU load at scale (though this doesn’t affect users).
See the gain: Know the actual improvement range, not just which version won.
Fewer false positives: The method naturally rules out many misleading results in A/B testing.
Frequentist vs Bayesian A/B Testing: The Comparison
Let’s be clear: both methods are statistically valid. But when you compare Frequentist vs Bayesian A/B testing, the practical implications are very different.
At AB Tasty, we have a clear preference for the Bayesian a/b testing approach.
Here’s why.
Gain Size Matters
With Bayesian A/B testing, you don’t just know which version won—you know by how much.
This is critical in business. When you run an A/B test, you’re deciding whether to switch from version A to version B.
That decision involves:
Implementation costs (time, resources, budget)
Associated costs (vendor licenses, maintenance)
Example: You’re testing a chatbot on your pricing page. Version B (with chatbot) outperforms version A. But implementing version B requires two weeks of developer time plus a monthly chatbot license.
You need to know if the math adds up. Bayesian statistics give you that answer by quantifying the gain from your A/B testing experiment.
Real Example from AB Tasty Reporting
Let’s look at a test measuring three variations against an original, with “CTA clicks” as the KPI.
Variation 3 wins with a 34.1% conversion rate (vs. 25% for the original).
But here’s where it gets interesting:
Median gain: +36.4%
Lowest possible gain: +2.25%
Highest possible gain: +48.40%
In 95% of cases, your gain will fall between +2.25% and +48.40%.
This granularity helps you decide whether to roll out the winner:
Both ends positive? Great sign.
Narrow interval? High confidence. Go for it.
Wide interval but low implementation cost? Probably safe to proceed.
Wide interval with high implementation cost? Wait for more data.
This is a concrete illustration of how Frequentist vs Bayesian methods in A/B testing lead to different levels of decision-making insight.
When to Trust Your Results?
At AB Tasty, we recommend waiting until you’ve hit these benchmarks:
At least 5,000 unique visitors per variation
Test runs for at least 14 days (two business cycles)
300 conversions on your main goal
These thresholds apply regardless of whether you use a Frequentist or Bayesian method, but Bayesian A/B testing gives you more interpretable outputs once you reach them.
Data Peeking: A Bayesian Advantage
Here’s a scenario: You’re running an A/B test for a major e-commerce promotion. Version B is tanking—losing you serious money.
With Bayesian A/B testing, you can stop it immediately. No need to wait until the end.
Conversely, if version B is crushing it, you can switch all traffic to the winner earlier than with Frequentist methods.
This is the logic behind our Dynamic Traffic Allocationfeature—and it wouldn’t be possible without Bayesian statistics.
Again, this is where Frequentist vs Bayesian methods in A/B testing diverge: Frequentist statistics are not naturally designed for safe continuous monitoring and dynamic allocation in the same way.
A false positive occurs when test results suggest version B improves performance—but in reality, it doesn’t. Often, version B performs the same as version A, not worse.
False positives happen with both Frequentist and Bayesian methods in A/B testing. But here’s the difference:
How Does Bayesian Testing Limit False Positives?
Because Bayesian A/B testing provides a gain interval, you’re less likely to implement a false positive in the first place.
Example: Your test shows version B wins with 95% confidence, but the median improvement is only 1%. Even if this is a false positive, you probably won’t implement it—the resources needed don’t justify such a small gain.
With Frequentist methods, you don’t see the gain interval. You might implement that false positive, wasting time and energy on changes that bring zero return.
For most campaigns, 95% confidence works just fine. But when the stakes are high—think major product launches or business-critical tests—you can dial up your confidence threshold to 97%, 98%, or even 99%.
Just know this: whether you’re using Frequentist or Bayesian methods, higher confidence means you’ll need more time and traffic to reach statistical significance. It’s a trade-off worth making when precision matters most.
While this seems like a safe bet – and it is the right choice for high-stakes campaigns – it’s not something to apply across the board.
This is because:
In order to attain this higher threshold, you’ll have to wait longer for results, therefore leaving you less time to reap the rewards of a positive outcome.
You will implicitly only get a winner with a bigger gain (which is rarer), and you will let go of smaller improvements that still could be impactful.
If you have a smaller amount of traffic on your web page, you may want to consider a different approach.
Conclusion
So which is better—Frequentist or Bayesian?
Both are sound statistical methods. But when you look at Frequentist vs Bayesian methods in A/B testing, we’ve chosen the Bayesian approach because it helps teams make better business decisions.
Here’s what you get:
Flexibility: Peek at data without compromising accuracy.
Actionable insights: Know the gain size, not just the winner.
Fewer false positives: Built-in safeguards against misleading results.
When you’re shopping for an A/B testing platform, find one that gives you results you can trust—and act on.
Want to see Bayesian A/B testing in action? AB Tasty makes it easy to set up tests, gather insights via an ROI dashboard, and determine which changes will increase your revenue.
What’s the main difference between Bayesian and Frequentist A/B testing?
When you compare Frequentist vs Bayesian methods in A/B testing, Frequentist methods test whether there’s a difference between variations using a P-Value at the end of the experiment. Bayesian methods estimate the size of the gain and let you update results continuously as new data comes in.
Can I peek at my A/B test results early?
With Bayesian A/B testing, yes. The method is designed for ongoing analysis. With Frequentist methods, peeking early creates misleading results because it effectively turns one experiment into multiple experiments.
What is a false positive in A/B testing?
A false positive occurs when test results suggest version B improves performance, but in reality it doesn’t. Bayesian methods help limit false positives by showing the gain interval, making it less likely you’ll implement a variation with minimal or no real improvement.
What confidence level should I use for my A/B tests?
95% confidence is standard for most marketing campaigns. For high-stakes A/B testing, you can increase to 97%, 98%, or 99%—but this requires more time and traffic to reach statistical significance, regardless of whether you use Frequentist or Bayesian methods.
How long should I run my A/B test?
At AB Tasty, we recommend running tests for at least 14 days (two business cycles) and collecting at least 5,000 unique visitors per variation and 300 conversions on your main goal. These benchmarks help both Frequentist and Bayesian approaches produce reliable insights.
What is Dynamic Traffic Allocation?
Dynamic Traffic Allocation is an automated feature that balances data exploration with conversion maximization in A/B testing. Once the algorithm identifies a winning variation with confidence, it automatically sends more traffic to that version—helping you maximize returns while still gathering reliable data using Bayesian methods.
The use of agentic AI for A/B testing is literally a game-changer for marketing teams, making it much easier to scale testing and experimentation programs. But with some companies making bold claims about what their AI can do, it can be hard to know just what to believe. So let’s look at how the competition’s AI really stacks up against ours.
First, let’s look at our agentic AI. Launched in November 2025, Evi is AB Tasty’s AI-powered marketing agent designed for evidence-based decision making. It transforms complex data into clear, actionable strategies for repeatable, measurable results and ensures every step you take is grounded in evidence.
But Evi isn’t just one tool. Evi is a suite of intelligent AI agents integrated throughout the entire AB Tasty platform, all optimized for specific tasks.
Agent
Function
Description
Evi Ideas
Ideation
Scans pages and generates data-backed ideas for new tests based on visual and contextual input. It uses AB Tasty’s proprietary data and UX principles.
Evi Hypothesize
Hypothesis creation
Uses a checklist of essential elements to help you create well-structured hypotheses with clear objectives. Assigns quality scores, highlights gaps and suggests edits.
Evi Content
Visual editor
Turns natural language prompts into precise on-page edits (HTML/CSS/JS) with no coding required.
Evi Analysis
Post-test analysis
Analyzes campaign data, delivers clear, actionable insights. Highlights winning variations and breaks down why they drive transactions.
Evi Feedback
Qualitative analysis
Analyses Net Promoter Score (NPS) and Customer Satisfaction (CSAT) feedback. Quickly identifies key themes and provides actionable insights from customer comments.
Evi Explore
Revenue insights
Powered by our patented metric, RevenueIQ, provides real revenue projections per visitor / per month with confidence intervals. Let’s you see what each test is worth before you launch.
Evi Formula
Catalog attributes (R&M)
Self-serve tool for creating catalog attributes using natural language prompts.
So how does Evi compare to the AI used by our main competitors?
Evi vs. Optimizely Opal
Opal is the name of Optimizely’s suite of AI tools integrated throughout their platform. It’s not a standalone product, but rather a collection of different AI agents threaded across their entire product suite, which, along with Experimentation, also includes CMS, CDP, and Commerce.
Indeed, most of Opal’s AI agents are actually focused on CMS, CDP, and Commerce rather than Experimentation. One potential drawback for customers is that many of these AI features are tied to using the entire Optimizely tech stack. Rather than talk about Opal’s features for other areas, let’s look at what AI features Opal does have specifically for Experimentation:
Feature
Description
Test ideation
Generates ideas for new experiments based on URLs and brand tone.
Variation editor
AI-assisted creation of test variations based on Google Gemini.
Campaign creation
Creates containers for both web and feature experimentation.
Variable suggestions
Suggests flag variables and variations in feature experimentation.
Chat-based data exploration
Allows conversational exploration of test data.
Results summarization
Summarizes test results and provides directional guidance.
Experiment advisor agents
These include a personalization advisor, experiment planner, and results summarizer.
Experiment scorecard
Scores experiments from the analytics interface.
Opal’s AI agents that are used specifically for testing and experimentation are very comparable to those of Evi. Both have dedicated AI agents for ideation, editing, and analysis. However, Evi also includes our proprietary RevenueIQ analysis and can leverage AB Tasty’s other AI features, EmotionsAI and Wandz for targeting and segmentation.
Some of Opal’s features also appear to be standard statistics features that have been rebranded as AI (e.g. multi-armed bandits and sequential testing).
Key differences between AB Tasty’s Evi and Optimizely Opal
Price: Evi’s AI features are included in all contracts at no additional cost. Opal is a paid add-on that costs around US$30,000 extra.
Speed: Evi’s AI editor is based on OpenAI and proven to be faster than that of Opal, based on Google Gemini.
AI Targeting: Evi can leverage our other AI features, EmotionsAI and predictive targeting (Wandz) for AI-based segmentation. Opal has nothing comparable.
Revenue Analysis: Evi Explore is based on our patented RevenueIQ metric for ROI projections. Again, Opal has no equivalent.
Experimentation focus: Evi is 100% focused on testing and experimentation. Most of Opals AI agents are designed for CMS/CDP/Commerce.
Evi vs. Kameleoon PBX
Kameleoon PBX (Prompt-Based Experimentation) is an AI-powered tool that allows users to generate A/B tests directly from natural language prompts. It is positioned as an all-in-one AI agent for test generation, fully integrated with Contentsquare.
Here is a list of PBX’s key AI features:
Feature
Description
Prompt-based test generation
Users write prompts describing what they want to test, PBX then generates the necessary code/variation.
Contentsquare integration
Turns behavioral insights from Contentsquare into A/B tests.
Automatic site analysis
Upon URL integration, 3 to 4 AI agents analyze the site’s structure (HTML/CSS/JS) giving prompts strong contextual awareness.
Figma integration
(Currently a Beta feature) Allows the uploading of mockups from Figma, reducing errors in banner creation and saving time in implementation.
Code generation
Generates HTML, CSS, and JS code for experiments.
Unlike Evi’s suite of specialized AI agents, PBX is a single generalist AI agent. This provides you with a limited amount of control and can make it hard to iterate. Kameleoon claims that by using PBX specifically, its customers can build tests faster, more accurately, and at less cost per test. But the reality is that these improvements aren’t specific to PBX, all vendors with agentic AI see similar positive impacts for their customers.
Key differences between AB Tasty’s Evi and Kameleoon PBX
Price: Evi’s AI features are included in all contracts at no additional cost. Like Opal, PBX is a paid add-on.
Usage: All AB Tasty customers can make unlimited use of Evi, while use of PBX is based around credit quotas.
Architecture: Evi is a suite of different specialized AI agents. PBX is a single generalist agent.
Speed: Evi has a prompt response time of around 30 seconds, compared to up to 3 to 4 minutes for PBX.
Advanced segmentation: Evi can leverage AB Tasty’s other AI features like EmotionsAI for advanced segmentation. However, like Opal, PBX has nothing comparable.
Structured output: Evi supports structured output (JSON, rollback, versioning), while PBX makes no mention of whether this is the case.
Production quality: Evi is fully production-ready, while some customers have reported that PBX has QA issues and webperf impact.
Your search bar is the fastest path to conversion. Yet for too long, site search solutions have been frustrating: complex, rigid, and disconnected from what e-commerce and product teams actually need to do their jobs.
Even customers using the most expensive search solutions told us they were dissatisfied. The tools were too complex, built for developers instead of business teams, and required painful manual work to sync marketing campaigns across their site.
We heard you. Loud and clear.
So we rebuilt Site Search from the ground up. The revamped AB Tasty Site Search is now available—designed to drive revenue, empower your teams, and integrate seamlessly into your workflow.
Semantic Search That Understands Intent
The biggest transformation? We’ve eliminated the endless synonym nightmare.
Traditional search engines rely on keyword matches. If a customer searches for “running shoes” but your product catalog says “athletic footwear,” you lose the sale. Fixing this meant maintaining massive synonym lists that were tedious to build and impossible to keep current.
AB Tasty Search uses a hybrid engine that blends keyword matching with semantic AI. The system understands the meaning behind queries, not just the exact words typed.
The impact:
Zero “No Results” dead ends: Semantic AI delivers relevant results even when users misspell, use natural language, or describe products contextually
No manual synonym maintenance: The system handles variations automatically
Real revenue gains: Early clients report a +11.6% increase in transactions and a +6.9% lift in add-to-cart rates
Understanding of user needs: We leverage AI to understand user queries without requiring a complex setup.
Built for Business Teams, Not Just Developers
We know the pain of being bottlenecked by engineering resources. Need to spotlight a product for a campaign? Waiting days (or weeks) for dev time kills momentum.
The new AB Tasty Search puts control where it belongs: in the hands of merchandisers and marketing teams.
We provide intuitive low-code/no-code tools that enable rapid iteration without developer dependency. This agility is delivered through two key mechanisms:
Widget-based deployment: You can deploy the search functionality quickly using a simple widget that adds an overlay when a user clicks the search bar. This widget-based deployment allows you to challenge your existing solution in days, not months, enabling faster time to value.
Intuitive merchandising controls: Merchandisers can optimize ranking, spotlight key products, and tailor results to user behavior. You can easily boost or bury products based on any catalog attribute, set query-specific redirections, and make real-time product ranking adjustments.
The result? True autonomy and agility for non-technical teams.
The Strategic Advantage: A Unified Platform for Discovery
Here’s where it gets really powerful: the revamped Search isn’t a standalone tool. It’s part of your complete optimization platform.
Search, Recommendations, and Merchandising now share the same rules, the same interface, and the same data. That marketing campaign you’re launching? Apply your merchandising logic once, and it works consistently across product discovery, search results, and recommendation widgets.
Consistency across channels: Same rules across Search, Merchandising, and Recommendations – no more manual syncing.
Personalization: Leverage your unified customer data to tailor search results by user type and behavior.
Simplified implementation, stack, and cost: One vendor, one dashboard, one connection to your catalog, and one source of truth for all your optimization activities.
This unified approach allows you to deploy product discovery as a global strategy, challenge it with unified KPIs and a dashboard, and optimize holistically.
What’s Next? Conversational Discovery
The search bar is evolving, and so is AB Tasty. We are already looking beyond traditional search to deliver a truly multi-modal experience that blends conversational, discovery, and keyword search into one seamless journey.
And with our approach to personalization, every search result can be personalized in real time based on user behavior, preferences, and context—delivering the right products to the right people at exactly the right moment. Read more about AdaptiveCX here.
Our upcoming Shopping Assistant will bring an AI-powered chat interface to your site through the same easy widget deployment. Natural language conversations will guide shoppers to the right products, increasing conversion while reducing returns.
Get Started with AB Tasty Site Search
The revamped AB Tasty Search module is generally available, ready to replace basic native search solutions (like Shopify or Salesforce) or replace your complex, developer-dependent tools.
If you are looking to eliminate “no results” experiences, empower your merchandising teams, and unify your product discovery strategy under one intelligent platform, the time to explore the new AB Tasty Search is now.
Talk to your AB Tasty representative to start testing the new Search module today. The best way to know if it works for you? Test it.
Pop-ups to personalization: why frustration is an AI-powered opportunity
Kate Feng
E-Commerce never stays still. That’s why we’ve put together a roadmap to help you understand the 2026 consumer in our e-book, The Spontaneous Shift: Consumer E-Commerce Trends for 2025. But amidst the rise of new tech, the old rules of friction still apply. Online shoppers still hate intrusive pop-ups, and they still crave connection with brands they trust.
Get your copy of the e-book “The Spontaneous Shift” now!
For the second year in a row, our research indicates that the problem of “too many pop-ups” is by far and away the number one frustration for online consumers around the world. That shows that interruption marketing treads a fine line. Pop-ups can be a great way to drive engagement on your website but overusing them is also a big reason why people leave without making a purchase.
Our research also indicates that the human desire to be recognized for who we are is stronger than ever. When we asked what makes an online shopping experience feel personal, a resounding majority said personalized offers/promotions (57%). And yet, only 10% of shoppers felt that their favorite brands “completely” understand them. While 60% said that their online shopping experience was only “moderately” personalized.
Harness the power of personalization
Taken together, what do these different pieces of information mean? What they tell us is that shoppers increasingly want relevance over noise. They don’t want more messaging; they want better messaging that’s more targeted to their needs. They want and even expect a personalized buying experience. The good news is that personalization also lets you build stronger relationships with visitors to your website.
By sending the right messages to the right people at the right time, you can turn things like pop-ups from frustrations into golden opportunities to provide value and drive engagement. And when our research also shows that 85% of shoppers are open to impulse buys, the opportunity to increase basket size through targeted personalized recommendations is massive.
Fortunately, there are now AI-powered tools available that make it easier than ever to personalize your interactions with visitors and treat them like the VIPs they are.
AdaptiveCX: say goodbye to the unknown
If you want to offer visitors to your website unique messages and personalized recommendations, you obviously need to gather some information about them. That’s much easier if you can convince visitors to create an account and provide you some basic information. But what about the up to 90% of visitors who are aren’t signed in and anonymous?
Traditional analytics can’t see visitors with no data and no history. Our AdaptiveCX predictive AI enables you to identify visitors in real-time, as they are browsing your website, even the anonymous ones. It adapts to each visitor’s behavior as it happens, ensuring they get a tailored experience and maximizing both engagement and sales.
How AdaptiveCX works in real-time
Analyzes live signals: AdaptiveCX captures every click, scroll, and pause to understand visitor intent as it happens.
Predicts intent instantly: Its predictive AI learns from these interactions to anticipate every visitor’s needs, even when they have no prior history on your website.
Delivers real-time results: Adaptive search, personalized recommendations, and tailored content are all delivered in real-time to each visitor.
Solves the anonymous visitor challenge: Transforms what were previously blind spots into conversion opportunities, no matter who is visiting.
Leading brands are now adapting every moment in real-time, and the results of our clients who are using AdaptiveCX speak for themselves:
+10% Conversion rate
2.5x Retention rate
+15% Revenue per visitor
Example AdaptiveCX use cases
Here are just a few of the ways in which you can use AdaptiveCX:
Adaptive carousels: Reorder product categories in real-time based on intent, driving deeper exploration and discovery. Users report 30-50% increased exposure to priority categories and a 40-60% increase in page views.
Adaptive experience for out-of-stock products: Instantly show personalized alternatives that match visitor intent and sharply reduce abandonment. Users report 2-3x more visits after engagement and 1.5-2x more follow-up orders.
Adaptive search: Pull visitors into search using intent-based prompts and smart widgets. Users report an increase in conversion after search of 10-15% and 10-25% more revenue per search session.
Emotions AI: see what really drives your audience
Standard personalization can help you create more relevant buying experiences for visitors. But when you understand a visitor’s emotional intent, you can take these experiences to a whole different level. Emotions drive an estimated 80% of human decisions, and the buying journey is packed with micro-moments shaped by those feelings. That’s where our Emotions AI steps in.
Built on over eight years of behavioral analytics, Emotions AI helps you uncover hidden opportunities in the user journey. It shows you not only how to improve a visitor’s experience, but also the cost of inaction. Emotions AI can identify the emotional mindset of every visitor within 30 seconds of activity on your website. This enables you to segment and target them with unmatched precision and speed.
Turn emotions into data-driven sales
While there might be a lot of different emotions running through a visitor’s mind when they come to your website, Emotions AI classifies visitors into one of ten emotional segments:
Competition: visitors strive to make the best possible choice to stay ahead and be the best.
Attention: visitors need to feel that everything is being done to satisfy them.
Safety: visitors need to feel secure and in control of their situation.
Comfort: visitors need to drift through the buying journey free from disruption and unnecessary effort.
Community: visitors feel a strong connection with the people they care about and the environment.
Immediacy: visitors need to be constantly stimulated to take action
Notoriety: visitors prefer to minimize risk by choosing options already validated by many people.
Understanding: visitors need comprehensive factual information to make decisions.
Change: visitors seek new experiences, fresh adventures, and opportunities to break from routines.
Quality: visitors need qualitative information to make decisions.
Once you know the specific segments you want to target, you can deliver personalized content to them using AB Tasty’s optimization platform and an entire library of widgets.
Real-world examples of EmotionsAI in use
Clarins launched a personalization campaign targeting the Safety and Comfort segments that displayed a pop-up presenting the benefits of a specific offer. This resulted in an 18% increase in the conversion rate of these segments.
DirectAsia wanted to increase the conversion rate of the Safety segment. When collecting visitor information, they displayed two variations of a banner to visitors from this segment, reassuring them and prompting them to move to the quote page. Variation 1 increased access to the quote page by 10.9% and variation 2 by 14.8%.
North American Insurance tested moving FAQs above the quoting area for dental insurance policies for the Immediacy segment. This resulted in an increase in application submissions of more than 140%.
Start your recommendation engines
By combining A/B testing with AI-powered insights about different segments, you can also identify opportunities to optimize personalized recommendation algorithms for these segments. These are designed to point specific visitors to suggested products beyond those they searched for or viewed.
Our AI-powered product recommendation algorithms learn from visitor behavior and transaction data. These can then be deployed across websites, personalized email campaigns, and CMS platforms like Prestashop, Shopify, or Agentforce Commerce to optimize product visibility and sales performance.
Real-world examples of personalized recommendation algorithms
Jacadi combined Emotions AI with personalized recommendations to target the Understanding segment. Visitors looking at product recommendations saw a widget that noted how much they needed to spend to qualify for free shipping. This increased revenue from these visitors by 10%, the AOV by 1.7%, and the click rate by 3%
ITM Home Equipment Group (Bricorama, Bricomarché, Bricocash) combined testing different personalized recommendations with real-time information about user behavior. This resulted in 10% more visitors using the recommendations, increased the conversion rate by 21% on the home page, and the average basket by 21% on product pages.
Conclusion
Online consumers are returning to brands that reward them and treat them like individuals. They’re willing to give you their data (and loyalty) if you give them value in return. And AI-powered tools make it easier than ever before to personalize your interactions with visitors and turn their frustration into opportunity.
AB Tasty and VWO: A New Chapter for Digital Experience Optimization
Alix de Sagazan & Rémi Aubert
What's Inside
Two years ago, we started asking ourselves a question that every ambitious founder eventually faces: What does the next chapter look like?
We explored many paths and spoke with several potential partners, but our goal never wavered: finding the right challenge that would allow us to become the undisputed global leader in digital experience optimization. Today, that mission takes a giant leap forward.
During these conversations we were super clear: doing just a commercial transaction wasn’t the goal. Finding the right partner – one that shared our vision, our values, and our commitment to customers – was what mattered.
We are incredibly excited to announce that AB Tasty and VWO are joining forces to create a global leader in digital experience optimization. Together, we are building a powerhouse with more than $100M in combined revenue, serving over 4,000 customers worldwide, and supported by a team of 800+ talented people, across 11 global offices.
The Story of “Twins Separated at Birth”
When we first began discussions with the VWO team, something felt fundamentally strong and aligned. It became obvious very quickly that we weren’t just two companies in the same market – we were like “twins separated at birth”.
Despite starting on different continents 15 years ago, our parallel histories are striking. Whether it was our early team photos in 2011, our shared milestones in 2016, or how we navigated the global challenges of 2020, our paths have consistently mirrored each other. We discovered a profound alignment across every dimension that matters: vision, ambition, culture, market approach, product, and geography.
We realized that by coming together, we weren’t just merging two companies; we were reuniting two halves of a shared dream.
Why We Are Coming Together
This combination is rooted in long-term alignment.
VWO and AB Tasty share a belief that customers benefit most from fewer, more deeply integrated platforms that bring experimentation, personalization, insights, and feature delivery closer together.
By joining forces, we are bringing together:
Complementary product capabilities
Deep experience across regions and industries
Teams that understand both scale and execution
A shared commitment to building technology that is powerful, practical, and accessible
A Leadership Team for the Long Run
The combined company will be led by the co-founders of both organizations, ensuring that the entrepreneurial spirit and customer-centric DNA of both brands remain at the core of our future.
Sparsh Gupta, current co-founder and CEO at VWO will serve as the Co-founder and CEO of the combined platform.
Rémi Aubert from AB Tasty will serve as the Co-founder and Chief Customer & Strategy Officer.
Alix de Sagazan from AB Tasty will serve as the Co-founder and Chief Revenue Officer.
Ankit Jain from VWO, will serve as Co-founder and Chief Product & Technology Officer.
All four co-founders remain significantly invested in the joint business. We are here for the long run, committed to building an enduring company that achieves our “Big Hairy Audacious Goal” of becoming one of the most respected and trusted global software leaders.
What This Means for Our Customers
To our customers: we are growing, and you are coming with us.
Business as Usual: Your day-to-day experience remains unchanged. There are no changes to your relationship managers, contracts, pricing, or SLAs.
Uncompromising Standards: All existing security, privacy, and data-hosting standards remain fully intact.
More Value Ahead: Over time, this combination brings more innovation and a deeper platform where your data lives and breathes.
A Note of Thanks
We are deeply grateful to Everstone Capital, and specifically Sandeep Singh and Avnish Mehra, for their role as long-term strategic partners who helped make this combination a reality. Their belief in our vision to build a global category leader has been instrumental.
Looking Ahead
The next chapter is about more than just being a bigger company; it’s about being the most trusted partner that empowers you to bring your vision of delightful digital experiences to life.
We are excited to begin this journey as one team with one dream.
More updates will follow as we move forward together.
Until recently, A/B testing has often been a time-consuming process for marketing teams and involved a certain amount of guesswork. But by integrating AI tools into the testing process, brands can iterate at scale, analyze data fast, and drive customer engagement like never before.
Join the AI revolution
If you think AI now seems to be everywhere, you’re probably right. It’s safe to say that AI is currently creating seismic upheaval in the marketing technology landscape. One of the chief reasons for this is AIs ability to analyze large amounts of data incredibly quickly and find patterns in that data. And that’s something that happens to make it ideal for A/B testing.
The use of AI agents for A/B testing is literally a gamechanger for marketing teams, making it much easier to scale their testing and experimentation programs. Coming up with ideas to test, designing those tests, analyzing results, and implementing changes can take hours of your time. But a lot of this can be accomplished in a matter of clicks with AI, leaving your team more time to focus on high-level tasks.
This lets you to accelerate your testing program to a scale that is literally not humanly possible. And because everything AI does is driven by hard data, it also takes the guesswork out of testing and experimentation. It gives you the confidence that everything from generating ideas to analyzing reports is based on verifiable visitor trends on your website. It can also provide you with valuable insights that a human might otherwise miss.
All of this opens up a world of enhanced personalization and continuous optimization that marketers have until recently only been able to dream about. AI agents are now set to redefine A/B testing and drive unprecedented growth for those that make them part of their testing program.
Introducing Evi, your evidence-based marketing agent
Evi is AB Tasty’s AI-powered marketing agent designed for evidence-based decision making. It transforms complex data into clear, actionable strategies for repeatable, measurable results, ensuring every step you take is grounded in evidence.
Evi helps brands scale their experimentation by facilitating fast test launching without running out of ideas. But Evi is also designed to enhance human creativity and collaboration, not to replace them. Instead, Evi empowers marketing teams, helping them move from ideas to iteration quickly and confidently.
With Evi, your team can:
Greatly accelerate the testing process, speeding up your workflow with automated code generation and content suggestions
Extract deeper insights, all driven by actual website data and feedback using built-in AI analysis
Generate and prioritize test ideas based on your objectives and recent activity patterns
But don’t just take our word for it. Over 1,000 brands are already using Evi to constantly scale their testing and experimentation programs, resulting in noticeably accelerated campaign performance. By using Evi, they’ve reported:
33% more campaigns created
53% more campaigns launched
73% faster experimentation
The pressure now on companies to integrate AI into their testing programs can mean they end up with multiple AI tools for specific tasks with varying degrees of compatibility. By using Evi across your entire testing workflow, you guarantee the same consistent level of quality, the same context, and the same great results.
Features that transform how you test, learn, and grow
Evi’s six different AI agents will help you test with confidence, learn faster, and understand your users better than ever before.
Evi Ideas
Unsure about what you should be testing next? Evi Ideas will scan pages of your website and generate ideas for new tests based on hard data that will actually impact your testing roadmap.
Evi Hypothesize
Struggling to craft a strong hypothesis for your experiments? Evi Hypothesize uses an automated checklist of essential elements to help you turn fuzzy thoughts into a sharp, well-structured hypothesis that has clear objectives.
Evi Content
Still waiting for the development team to build your experiment? Evi Content will let you turn your vision into reality in just a few clicks. No matter how good you are at coding, Evi Content will let you instantly transform concepts into actual buildable experiments.
Evi Analysis
Tired of spending hours staring at colorful charts and wondering what they all mean?Evi Analysis will analyze your campaign data and deliver clear, actionable insights. It highlights winning variations and breaks down why they drive transactions so you can feel confident in your next move.
Evi Feedback
Feel like you’re drowning in feedback but don’t know what to do with it all? Evi Feedbacktakes the heavy lifting out of Net Promoter Score (NPS) and Customer Satisfaction (CSAT) campaigns. It analyses customer responses right within your reports, quickly identifying key themes and sentiment trends.
Evi Explore
Want to know if your tests will actually drive revenue?Evi Explore, powered by our own patented metric, RevenueIQ, lets you see what each test is worth before you launch. This gives you the confidence to make faster, more profitable decisions based on real revenue projections, rather than simply relying on traditional metrics like conversion rate or average order value (AOV).
Finally, AI you can trust
And you can rest assured that Evi only uses the right data for the right task. This guarantees that your data is secure, transparent, and under your control at all times.
Proprietary intelligence: Evi is exclusively trained on AB Tasty’s proprietary data. This ensures that it delivers relevant, experiment-ready outputs.
Your inputs are yours and yours alone: Key features like Evi Ideas and Evi Content only process the prompts and screenshots that you supply, ensuring they remain private.
Secure by design: The Evi Analysis feature runs entirely on AB Tasty’s own servers. No data is sent to external services.
Evi from AB Tasty grounds every step in evidence, notices what you don’t, and never gets it wrong, transforming how you optimize the digital experience.
Understanding your customers’ paths? Not easy. Each person arrives with their own reason for visiting your site and takes their own route through your pages.
So how do you gain real insights to improve usability and spot buying trends?
Start with building a customer journey map.
In this blog, we’ll walk you through what a customer journey map is, how to build a customer journey map, which templates work best for your customer journey map, and how to put them into action. Let’s get started!
What is a customer journey map?
A customer journey map is a visual tool that shows how customers interact with your business or website—from start to finish.
It helps you spot where things aren’t working and improve the overall experience.
Think of it as a story told visually. It maps out:
What customers do
What they think
How they feel
At the heart of the map are touchpoints—specific moments where customers interact with your brand. Maybe they’re researching a product, making a purchase, waiting for delivery, or requesting a return.
Each touchpoint can be positive, neutral, or negative from the customer’s perspective. Your job? Make more of them positive.
Customer journey mapping requires a mix of hard data, customer feedback, and creative thinking. No two maps are the same—and that’s the point. Every business is different.
7 Reasons Why Use Customer Journey Maps
Customer journey mapping isn’t just a nice-to-have—it’s a strategic tool that drives real business results.
Here’s why it matters:
1. See Through Your Customers’ Eyes
Journey maps help you step into your customers’ shoes. You’ll understand their motivations, expectations, and frustrations at every stage—not just what they do, but why they do it.
That empathy translates into better decisions, smarter strategies, and experiences that actually resonate.
2. Spot and Fix Pain Points Fast
Every journey has friction. Your checkout process might be too complicated, your search function delivers the wrong results, or customers can’t find help when they need it.
Customer journey mapping reveals these bottlenecks so you can address them before they cost you customers.
3. Build Loyalty That Lasts
When customers feel understood and valued, they stick around. By removing barriers and meeting needs at every touchpoint, you strengthen the emotional connection between your brand and your audience. That connection drives repeat purchases and long-term loyalty.
In fact, a 5% increase in customer retention can lead to a 25% increase in profits.
4. Personalize at Scale
Not all customers are the same—and your experience shouldn’t treat them that way. Journey maps highlight individual preferences and behaviors, enabling you to tailor messaging, product recommendations, and support to each person.
Personalization increases purchase likelihood and makes customers feel like you actually get them.
5. Align Your Entire Team
Customer journey mapping breaks down silos. When marketing, product, sales, and support all work from the same map, everyone understands the customer’s perspective and how their work impacts the overall experience.
That shared understanding leads to better collaboration, faster problem-solving, and a more cohesive brand experience.
6. Make Smarter, Data-Driven Decisions
Journey maps aren’t just pretty visuals—they’re strategic tools backed by real data.
They guide decisions about where to invest, what to test, and which initiatives will have the biggest impact on customer satisfaction and business growth.
7. Drive Innovation and Stay Ahead
Customer needs evolve. Markets shift. New competitors emerge.
Regularly reviewing and updating your customer journey map helps you spot emerging trends, changing preferences, and new opportunities before your competitors do. It keeps your brand agile, innovative, and ready to adapt.
The Heart of Customer Journey Mapping: Buyer Personas
Buyer personas are fictional characters based on real customer data. They represent your audience in a way that’s relatable and actionable.
Most projects create between three and seven personas—and each one gets its own customer journey map. Why? Because different customers have different needs, goals, and pain points. A persona helps you walk in their shoes and design experiences that truly resonate.
Personas aren’t perfect replicas of real people. They’re broad representations that guide smarter decisions.
Who Benefits from a Customer Journey Map?
Short answer: everyone.
Customer satisfaction drives loyalty more than ever. People are more informed, more demanding, and more willing to shop around.
A well-designed customer journey map helps you:
Highlight problems customers face
Build stronger relationships with your brand
Keep customers at the center of every decision
Once your map is ready, your entire team—from marketing to product to support—can use it to stay aligned and customer-focused.
Bringing Your Whole Business Together
Customer journey mapping isn’t just for your customer-facing teams. It brings everyone together.
When you map out touchpoints, departments that don’t usually interact with customers start to see how their work affects the experience. That’s powerful.
For example:
How easy is it for someone to find return instructions on your site?
How fast do they get a response when they need help?
What happens after the purchase?
Traditional marketing often stops at checkout. But the customer journey doesn’t. Post-purchase experience matters just as much—and your map should reflect that.
How to Map the Customer Journey Visually?
A customer journey map gives you a clear picture of your customers’ experiences from their point of view.
To create one, focus on two things:
Defining customer goals – What are they trying to accomplish?
Understanding their nonlinear journey – Customers don’t move in straight lines
By mapping every interaction, you’re identifying opportunities to delight your customers and craft smarter engagement strategies.
According to Aberdeen Group, 89% of companies with multi-channel engagement strategies retained their customers—compared to just 33% of those without one.
You can build your map using:
Excel sheets
Infographics
Diagrams
Illustrations
Customer journey maps also help with:
Retargeting with an inbound mindset
Reaching new customer segments
Building a customer-first culture
All of this leads to better experiences, more conversions, and stronger revenue.
There are four main types of customer journey maps. Each highlights different behaviors and serves different goals.
1. Current State Template
Shows what customers currently do, think, and feel. Great for spotting pain points and making incremental improvements.
2. Future State Template
Focuses on what customers will do, think, and feel. Useful for planning new products, services, or experiences.
3. Day in the Life Template
Similar to the current state map, but broader. It looks at how customers behave with your brand and your competitors. Perfect for uncovering unmet needs.
4. Service Blueprint Template
Starts with a simplified current or future state map, then adds the internal processes, people, and tech behind the experience. Helps you see the full picture—front and back.
How to Create a Customer Journey Map in 7 Steps ?
Creating customer experience journey maps takes time, but the payoff is worth it. Here’s how to do it.
Step 1: Create Buyer Personas
Start with a clear objective. Who is this map for? What are you trying to learn?
Building personas is the most time-consuming part—but also the most important. You’ll need:
AB Tasty is a best-in-classexperimentation platform that helps you test variations, personalize experiences, and convert more customers—fast. With AI and automation built in, you can optimize the digital experience with confidence.
Once your map is live, review and update it regularly. Customer journeys evolve—and so should your map.
How to Collect Journey Mapping Data?
Great customer experience journey maps are built on solid data—not assumptions. You’ll need a mix of qualitative insights (the “why” behind behavior) and quantitative metrics (the “what” you can measure).
Here’s how to gather both:
1. Qualitative Data: Understanding the “Why”
Qualitative research helps you uncover motivations, emotions, and pain points that numbers alone can’t reveal.
Customer Interviews
Have real conversations with your customers. Ask about their experiences, what frustrates them, and what they love. These in-depth discussions provide rich, nuanced insights.
Surveys
Use open-ended questions to gather feedback on specific parts of the journey. Keep them short and focused to get honest, actionable responses.
User Testing
Watch how people interact with your website or product in real time. Tools like usability tests reveal where users get stuck, confused, or frustrated.
Mystery Shopping
Experience your own customer journey firsthand. Walk through every step—from discovery to purchase to support—and see what works and what doesn’t.
Support Transcripts
Review customer service conversations to identify recurring issues and common questions. These transcripts are goldmines for understanding pain points.
2. Quantitative Data: Tracking the “What”
Quantitative data gives you measurable, trackable insights that help you validate assumptions and monitor progress over time.
Website Analytics
Tools like Google Analytics show you how customers navigate your site, where they drop off, and which pages drive the most engagement.
See exactly how users interact with your pages—where they click, how far they scroll, and where they hesitate. Tools like Hotjar and Contentsquare make this easy.
Conversion Funnels
Track how customers move through key stages of the journey and identify where they abandon the process.
Customer Satisfaction Scores
Metrics like Net Promoter Score (NPS) and Customer Satisfaction (CSAT) measure loyalty and satisfaction at different touchpoints.
CRM Data
Your CRM system (like Salesforce or HubSpot) holds valuable information about customer interactions, purchase history, and behavior patterns.
Social Media Listening
Monitor what customers say about your brand on social platforms. This reveals sentiment, trends, and unfiltered feedback.
Email Campaign Metrics
Analyze open rates, click-through rates, and conversion rates to understand how customers engage with your messaging.
Support Ticket Volume
Track common issues and complaints to identify systemic problems in the customer journey.
Best Practices for Journey Map Data Collection
Combine Both Types of Data
Qualitative insights explain why customers behave a certain way. Quantitative data shows you what they’re doing. Together, they give you the full picture.
Test Your Assumptions
Don’t rely on guesses. Validate your hypotheses about customer behavior through research and real data.
Involve Stakeholders
Gather input from marketing, sales, product, customer service, and leadership. Each team has unique insights that make your map more accurate and actionable.
Keep It Current
Customer behavior changes. Markets evolve. Your journey map should too. Update it regularly to stay relevant and effective.
Customer Journey Map Examples
Customer journey maps come in all shapes and sizes. Some look like works of art. Others are simple sketches. What matters is clarity.
Here are some real-world examples of customer journey mapping in action:
1. David Jones: Simplifying Account Access
David Jones, a major Australian retailer, mapped their customer journey to understand how shoppers interacted with their account features during the buying process.
Through testing and personalization, they made it easier for customers to access their accounts, track orders, and manage preferences.
Do Experimentation Platforms Slow Down Your Site? How AB Tasty Ensures Performance First
Angelique de Taddeo
Website performance has never mattered more. Google Core Web Vitals now directly influence organic rankings, mobile conversions continue to dominate, and users expect instant experiences.
In this context, many digital teams ask the same question:
“Will an experimentation or personalization tool slow down my site?”
It’s a valid concern. After all, any third-party script has the potential to impact performance if it’s not engineered carefully.
In this article, we’ll break down what actually affects performance in an experimentation platform — and how AB Tasty has built a performance-first architecture that avoids common pitfalls.
1. The Real Reasons Experimentation Tools Can Slow Down a Website
Not all experimentation platforms behave the same. When performance issues appear, they usually come from a few well-identified causes.
1.1 Heavy, all-in-one tags
Some tools load everything upfront — all features, all experiments, for every visitor — even when most of that code is never used.
This leads to:
Slower execution in the browser
More JavaScript to download and process
Increased pressure on the main thread
Wasted network bandwidth on unused code
The result: a slower page and unnecessary work for the browser.
1.2 “Anti-flicker” scripts that block the page
To prevent visual flicker, many vendors solve flicker by hiding the page (e.g., opacity: 0) until the experiment loads.
While this may avoid a brief visual change, it comes at a cost:
The page cannot render immediately
First visual elements appear later (LCP, FCP)
It hurts SEO rankings
Users may face a noticeable “white screen,” especially on slower connections
The page looks stable — but it loads later than it should.
1.3 Limited optimization for modern websites
Modern websites are no longer simple static pages. Single-page applications, server-side rendering, and hydration flows all require precise timing.
When experimentation scripts are not adapted to these architectures:
They may re-run unnecessarily
They can interfere with rendering
They introduce delays that affect performance
2. AB Tasty’s Philosophy: Performance by Design, Not by Patch
At AB Tasty, we believe an experimentation platform should contribute to user experience — not compromise it. That’s why performance is woven directly into our architecture.
This gives CRO and technical teams full visibility and control over experimentation performance.
Conclusion: You Can Experiment Without Sacrificing Speed
A fast digital experience and an experimentation program are not mutually exclusive.
With its modular architecture, modern rendering logic, and performance-first philosophy, AB Tasty enables brands to run impactful campaigns without jeopardizing SEO or UX.
If performance is a concern for your engineering or CRO teams, we’d be happy to share:
Performance benchmarks
Technical documentation
Best practices for Core Web Vitals
Case studies from top global brands
Experiment boldly — with a platform engineered for speed.
FAQs
Does A/B testing slow down your website?
Yes, but AB Tasty minimizes it. Our tag delivers < 100ms load time, < 500ms execution, and < 10ms from cache—making us 2x faster than Kameleoon. Plus, we block releases if Core Web Vitals degrade by > 2%.
Does A/B testing affect Core Web Vitals?
It can — but AB Tasty minimizes this impact through dynamic imports, optimized rendering logic, and non-blocking execution.
Do I need anti-flicker for A/B testing?
Most of the time, no. Anti-flicker masking can degrade SEO and create a poor user experience.
Is AB Tasty fast?
Yes — benchmarks from independent sources consistently show AB Tasty among the fastest experimentation tags on the market.
