VWO Release Notes

When VWO introduced the visual no-code editor 16 years ago, it eliminated the primary bottleneck in A/B testing: needing a developer for basic changes.

However, as experimentation programs matured, new friction points emerged. Today, the delay in launching an experiment isn’t just writing code, it is translating design files into web elements, jumping through multi-step setup wizards, defining targeting rules across isolated UI tabs, and managing developer sprint queues for non-trivial variations.

Prompt-to-variation features are fast becoming standard across optimization tools. Wandz inside Editor is built differently. Rather than acting as a simple text-to-HTML generator, it unifies asset ingestion, visual rendering, and campaign configuration into an end-to-end operational workflow within Wingify.

Most AI editors force users to become prompt engineers – requiring long, hyper-specific instructions and endless manual parameter adjustments to get a usable output.

Wandz inside Editor eliminates prompt fatigue by using an advanced engine that automatically infers layouts, page hierarchy, and styling rules from minimal input.

What are the possibilities?

From Figma design to live experience in minutes

With Figma integration, you can go from a Figma design to a fully configured, pixel-perfect experience in under 2 minutes. Just connect your Figma account, paste in the design link, and Wandz inside Editor pulls the frame in as an attachment to your prompt – no more manually eyeballing colors, spacing, or fonts from a design file.

You can even attach a screenshot, PDF mockup, or any image alongside your prompt, and the AI will read the layout, understand the visual goal, and produce the variation in your branding.

Go from idea to live variation in seconds

What used to take hours now takes a single sentence. Just type what you want to change, and it’s done. Within seconds, a new variation is ready.

Select any part of the page and describe your vision. Wandz inside Editor interprets your intent, understands the page context, and intelligently generates the changes – whether it’s refining a single component or reimagining an entire experience.

How this changes the way you experiment:

• Speed: Wandz inside Editor removes the developer bottleneck from experimentation. A marketer’s hypothesis reaches production without waiting for an engineering sprint.
• Scalability: When each experiment takes minutes instead of hours, you can run more tests, learn faster, and compound your wins. The teams that experiment most win most.
• Everything in one place: The entire workflow – Creating variations, reviewing & refining them, configuring campaigns, and launching them – everything happens in a single session, without context-switching.

When you can build your reality as fast as you can think about it, experimentation stops being an activity and starts being a habit.

Getting Started

Wanda inside Editor is now available to all Wingify Testing customers.

Launch Wandz inside Editor: Open Editor in your workspace.

If you have any questions or feedback, please reach out to [email protected]

Original source

For feature launches, the “why” behind user interactions has always been a black box.

You get the adoption numbers, but you never really see the actual behavior behind them.

That’s the gap we have filled.

Introducing Behavior Analytics linked with Feature Experimentation.

Now, with every feature experiment, rollout, and personalization campaign, you will have integrated heatmaps and session recordings to see where a user clicks, scrolls, hesitates, or drops off.

Step into their digital shoes. Wherever they go – a website or mobile app.

Say bye to guesswork. Say hi to evidence-backed product decisions.

Why do you need it?

To investigate faster:

When a test underperforms, was it the design, the copy, a bug, or wrong placement? Figure out the hidden reason, in minutes.

To stop gambling:

A new feature launch need not be a gamble. Make it strategic with phased releases + behavior tracking to spot usability issues and friction trends before they drastically affect your core business metrics.

To check whether users are feeling special:

Once you personalize certain experiences, there’s a certain bias to think they will work. But do they? If behavior analytics is not interconnected, you might miss early signals about whether they do, or not.

To boost confidence (for real!):

PMs and developers justify roadmap decisions with visual evidence. Designers validate UX choices with real behavior. Developers spot bugs and usability issues instantly. Analysts will be able to tell the complete story behind the numbers.

How does it help you?

Understand test results deeper:

You are a PM, you tested a new checkout experience. Say, it won by 8% uplift. Understand which elements users actually engaged with, and where they hesitated, using variation-specific heatmaps.

Say, your pricing page test lost for mobile visitors. You can see the session recordings of users who dropped off without requesting a demo.

Ideate features with evidence:

As a developer, you get to see visual evidence, you can create data-backed feature stories, pitch them internally, get the buy-in from your higher-ups, and launch with a probabilistically high level of success rate.

Cut losses quickly:

As a growth marketer, you need to adjust experiments, feature rollouts, and personalization campaigns in real-time, or every visitor interaction will bleed revenue loss. Now you can visually see how visitors interact, using heatmaps and session recordings. If your new feature is performing way worse than how it should, you can instantly pause the feature release.

Find bugs faster:

As an engineer, you can use visual insights to spot where users struggle, debug feature issues faster, and validate fixes through experiments – so every release is backed by real user behavior, not guesswork.

What can you do, and where to find this?

Integrated behavioral analysis tab in campaign reports:

Every FE campaign report now has a dedicated “Behavioral Analysis” tab with heatmaps, session recordings (for website and mobile apps), pre-filtered to that campaign. No more jumping between tools and context switching.

Access from Insights:

Any heatmap, recording, or funnel in Insights can be filtered by FE campaigns, specific environments, campaign rules, or test variations. That way, you can go deep into behavioral patterns for any experiment you’ve run.

Enter from mobile app recordings & heatmaps:

Extend the same behavioral analysis to mobile app experiments, with screen taps, user flows, and sessions linked directly to your mobile SDK-powered tests.

How does it help your team?

For E-commerce & Retail:

You launched a new product recommendation engine for your mobile app. It lifted AOV, but bounce rate also rose. The recordings reveal users’ attention drifted from purchasing the product, to browsing the “You might also like” section, to check more options. You fix its position, not the algorithm. Without behavior analysis, this insight would have been hidden.

For SaaS & Software:

The trial onboarding test for your webapp is failing, but the metrics don’t explain why. Using session recordings and heatmaps, you spot a clear drop-off happening at the second step of the flow. Users took a lot of time entering their webpage URLs to configure your product, and when they entered, there were many rage clicks on the “proceed” button. With that insight, you’d know for which field you need to reduce friction in the trial flow (maybe add a regex option for URLs). That way, you catch the flaw before it drags the lead count down.

For News & Media:

Heatmaps could reveal that in the test, where you are testing a new ad placement section, the ads are getting drastically low clicks. Session recordings reveal that users are seeing a cookie consent pop-up on top of it. Rookie mistake, yes. Happens to everyone, but would you have known the reason by just looking at statistical numbers?

See the full picture of your experiments today.

Behavior Analytics in Feature Experimentation is rolling out now to all customers on Pro & Enterprise plans of both Feature Experimentation and Insights.

To get started, open an Feature Experimentation campaign report and click the new “Behavioral Analysis” tab.

Questions or feedback?

Reach out to your Customer Success Manager, or email us at [email protected].

We’re all ears.

Original source

For feature launches, the “why” behind user interactions has always been a black box.

You get the adoption numbers, but you never really see the actual behavior behind them.

That’s the gap we have filled.

Introducing Behavior Analytics linked with Feature Experimentation.

Now, with every feature experiment, rollout, and personalization campaign, you will have integrated heatmaps and session recordings to see where a user clicks, scrolls, hesitates, or drops off.

Step into their digital shoes. Wherever they go a website or mobile app.

Say bye to guesswork. Say hi to evidence-backed product decisions.

Why do you need it?

To investigate faster:

When a test underperforms, was it the design, the copy, a bug, or wrong placement? Figure out the hidden reason, in minutes.

To stop gambling:

A new feature launch need not be a gamble. Make it strategic with phased releases + behavior tracking to spot usability issues and friction trends before they drastically affect your core business metrics.

To check whether users are feeling special:

Once you personalize certain experiences, there’s a certain bias to think they will work. But do they? If behavior analytics is not interconnected, you might miss early signals about whether they do, or not.

To boost confidence (for real!):

PMs and developers justify roadmap decisions with visual evidence. Designers validate UX choices with real behavior. Developers spot bugs and usability issues instantly. Analysts will be able to tell the complete story behind the numbers.

How does it help you?

Understand test results deeper:

You are a PM, you tested a new checkout experience. Say, it won by 8% uplift. Understand which elements users actually engaged with, and where they hesitated, using variation-specific heatmaps.

Say, your pricing page test lost for mobile visitors. You can see the session recordings of users who dropped off without requesting a demo.

Ideate features with evidence:

As a developer, you get to see visual evidence, you can create data-backed feature stories, pitch them internally, get the buy-in from your higher-ups, and launch with a probabilistically high level of success rate.

Cut losses quickly:

As a growth marketer, you need to adjust experiments, feature rollouts, and personalization campaigns in real-time, or every visitor interaction will bleed revenue loss. Now you can visually see how visitors interact, using heatmaps and session recordings. If your new feature is performing way worse than how it should, you can instantly pause the feature release.

Find bugs faster:

As an engineer, you can use visual insights to spot where users struggle, debug feature issues faster, and validate fixes through experiments – so every release is backed by real user behavior, not guesswork.

What can you do, and where to find this?

Integrated behavioral analysis tab in campaign reports:

Every FE campaign report now has a dedicated “Behavioral Analysis” tab with heatmaps, session recordings (for website and mobile apps), pre-filtered to that campaign. No more jumping between tools and context switching.

Access from Insights:

Any heatmap, recording, or funnel in Insights can be filtered by FE campaigns, specific environments, campaign rules, or test variations. That way, you can go deep into behavioral patterns for any experiment you’ve run.

Enter from mobile app recordings & heatmaps:

Extend the same behavioral analysis to mobile app experiments, with screen taps, user flows, and sessions linked directly to your mobile SDK-powered tests.

How does it help your team?

For E-commerce & Retail:

You launched a new product recommendation engine for your mobile app. It lifted AOV, but bounce rate also rose. The recordings reveal users’ attention drifted from purchasing the product, to browsing the “You might also like” section, to check more options. You fix its position, not the algorithm. Without behavior analysis, this insight would have been hidden.

For SaaS & Software:

The trial onboarding test for your webapp is failing, but the metrics don’t explain why. Using session recordings and heatmaps, you spot a clear drop-off happening at the second step of the flow. Users took a lot of time entering their webpage URLs to configure your product, and when they entered, there were many rage clicks on the “proceed” button. With that insight, you’d know for which field you need to reduce friction in the trial flow (maybe add a regex option for URLs). That way, you catch the flaw before it drags the lead count down.

For News & Media:

Heatmaps could reveal that in the test, where you are testing a new ad placement section, the ads are getting drastically low clicks. Session recordings reveal that users are seeing a cookie consent pop-up on top of it. Rookie mistake, yes. Happens to everyone, but would you have known the reason by just looking at statistical numbers?

See the full picture of your experiments today.

Behavior Analytics in Feature Experimentation is rolling out now to all customers on Pro & Enterprise plans of both Feature Experimentation and Insights.

To get started, open an Feature Experimentation campaign report and click the new “Behavioral Analysis” tab.

Questions or feedback?

Reach out to your Customer Success Manager, or email us at [email protected].

We’re all ears.

Original source

Hours, days, weeks – How long does it practically take to gather insights from user interactions, brainstorm about what would solve their friction, and execute that strategy?

That’s the question we asked ourselves, and one of us thought –

Why not just a few moments?

Sounded ambitious to us too, until we actually built it.

Introducing Wingify AI Chat.

Now you can just talk to Wingify AI about everything you know about your users.
Every interaction, friction, feedback – everything!
Ask for a quick recap of what you’ve optimized, check the status of your projects, summarize what your users have been doing lately, get ideas on what to optimize next, launch experiments, or quite honestly, get anything done that feels like grunt work to you.

It’s like having a personal CRO assistant. One who has read every report, watched every user interaction, and knows what works & what doesn’t.

How exactly do we help you optimize experiences faster?

• Compress hours of analysis: Get summarized, filtered answers about how users interact. Whether you want to see how users from a specific country behaved in a test, or investigate the reasons behind why users bounced off without buying, just ask away!
  Let Wingify AI do the insight hunting, while you focus on strategy.

• Eliminate human errors: We can only analyze so much at once. But Wingify AI can scan millions of signals across user behavior & test results, to identify hidden optimization opportunities you’d never spot manually. Nothing slips through.

• Turn insights into action instantly: The gap between “knowing what’s wrong” with your user experience and “fixing it” just vanished. Find a friction point and launch a test to validate your idea, all in the same conversation. No clicking through menus, no switching tabs.

• Make the optimization process more efficient: Whether you’re a solo operator or leading a team, Wingify AI multiplies your capacity. Junior team members can pull nuanced insights that would have needed years of experience to spot, and operate at a much deeper level. Experienced seniors can get snippet summaries, and oversee the program efficiently.

What are the possibilities?

Wingify AI can help you:

• Catch user friction faster:
  Example: “Where are users dropping off most frequently in our checkout funnel, and what’s causing it?”
  The AI looks across your funnel data, session recordings, and heatmaps, spots the exact step where people leave, and connects it to what they were doing right before they left.

  Try it yourself – Interactive demo

• Get detailed report summaries:
  Example: “Summarize all the campaigns I’ve run on my pricing page, and tell me what has worked, and what has caused drop-offs.”
  The AI goes through every test you’ve run on that page, compares the results, understands the patterns, and tells you what lifted conversions, what caused dropoffs, and what you can learn from both.

  Try it yourself – Interactive demo

• Get practical optimization recommendations:
  Example: “Give me the top 3 high-impact changes to reduce friction on our product pages, based on both heatmaps and recordings.”
  The AI watches how users surfed your pages, spots the moments of hesitation or confusion, and suggests the fixes based on what has worked for you in the past, not generic tips.

  Try it yourself – Interactive demo

• Get stakeholder-ready reports:
  Example: “Create a monthly summary of our tests, and evolved changes in user behavior patterns. I need to share it with my leadership.”
  The AI gathers your test results of the month, combines them with how user behavior changed incrementally, and structures it all into a clean summary that you can share with your team lead. No more drafting your “work summary” every month.

  Try it yourself – Interactive demo

Most AI tools give answers. We show you the full picture.

Reference specific tests and personalization campaigns: Type # followed by a test or a personalization campaign’s name to instantly pull in context. Ask “Why did #homepage-redesign-test underperform?” and the AI will analyze that specific experiment.

Call specific AI agents in your prompts: Type @recordings to focus on session data. Want heatmap analysis? Use @heatmaps.
You control exactly which data the AI draws from.
You can create these custom AI agents specific to your use cases. Use available tools like heatmaps, session recordings, campaign analysis, web search, UX analysis, ROI analysis, and many more!

Attach files and images to give deeper context for AI: Upload screenshots, competitor pages, or design mockups. The AI can analyze visual content alongside your data, so that it stays context-rich and gives highly relevant responses.

Find patterns and chat with the data from third-party platforms: Connect to GA4, Mixpanel, Amplitude, Notion, Sentry, or Semrush, and prompt what you want to know about your users. Discover hidden patterns that would otherwise go unnoticed.

How does it help your business?

For E-commerce & Retail:

Prompt:
“Compare the checkout behavior of users who converted during Black Friday versus those who abandoned. What friction points were unique to the high-traffic period?”
The AI can find the what, why, and where to ensure you know how your Black Friday sale performed.

For SaaS & Software:

Prompt:
“Which user segments saw the biggest drop in trial conversion this quarter, and what changed in their product experience?”
The AI can discover hidden user cohorts that showed confusion in your test variations, study their behavioral patterns, detect the unseen reason behind this drop-off, and suggest how it can be fixed, for higher trial conversions.

For News & Media:

Prompt:
“For which cohorts did increasing paywall limit work, instead of discounting the subscription price?”
The AI can reveal behavior patterns of specific users, so that you align your editorial and product teams on a data-backed optimization strategy for higher retention.

Get smarter at optimizing today.

Wingify AI is currently available for early access.To request early access, please reach out to your Customer Success Manager, or drop us a note at [email protected].

We’ve put together a playbook for you to get started. Click here to start exploring!

Original source

Hours, days, weeks – How long does it practically take to gather insights from user interactions, brainstorm about what would solve their friction, and execute that strategy?

That’s the question we asked ourselves, and one of us thought –

Why not just a few moments?

Sounded ambitious to us too, until we actually built it.

Introducing VWO AI Chat.

Now you can just talk to VWO AI about everything you know about your users.

Every interaction, friction, feedback – everything!

Ask for a quick recap of what you’ve optimized, check the status of your projects, summarize what your users have been doing lately, get ideas on what to optimize next, launch experiments, or quite honestly, get anything done that feels like grunt work to you.

It’s like having a personal CRO assistant. One who has read every report, watched every user interaction, and knows what works & what doesn’t.

How exactly do we help you optimize experiences faster?

Compress hours of analysis: Get summarized, filtered answers about how users interact. Whether you want to see how users from a specific country behaved in a test, or investigate the reasons behind why users bounced off without buying, just ask away!

Let VWO AI do the insight hunting, while you focus on strategy.

Eliminate human errors: We can only analyze so much at once. But VWO AI can scan millions of signals across user behavior & test results, to identify hidden optimization opportunities you’d never spot manually. Nothing slips through.

Turn insights into action instantly: The gap between “knowing what’s wrong” with your user experience and “fixing it” just vanished. Find a friction point and launch a test to validate your idea, all in the same conversation. No clicking through menus, no switching tabs.

Make the optimization process more efficient: Whether you’re a solo operator or leading a team, VWO AI multiplies your capacity. Junior team members can pull nuanced insights that would have needed years of experience to spot, and operate at a much deeper level. Experienced seniors can get snippet summaries, and oversee the program efficiently.

What are the possibilities?

VWO AI can help you:

• Catch user friction faster:

  Example: “Where are users dropping off most frequently in our checkout funnel, and what’s causing it?”

  The AI looks across your funnel data, session recordings, and heatmaps, spots the exact step where people leave, and connects it to what they were doing right before they left.

  Try it yourself – Interactive demo

• Get detailed report summaries:

  Example: “Summarize all the campaigns I’ve run on my pricing page, and tell me what has worked, and what has caused drop-offs.”

  The AI goes through every test you’ve run on that page, compares the results, understands the patterns, and tells you what lifted conversions, what caused dropoffs, and what you can learn from both.

  Try it yourself – Interactive demo

• Get practical optimization recommendations:

  Example: “Give me the top 3 high-impact changes to reduce friction on our product pages, based on both heatmaps and recordings.”

  The AI watches how users surfed your pages, spots the moments of hesitation or confusion, and suggests the fixes based on what has worked for you in the past, not generic tips.

  Try it yourself – Interactive demo

• Get stakeholder-ready reports:

  Example: “Create a monthly summary of our tests, and evolved changes in user behavior patterns. I need to share it with my leadership.”

  The AI gathers your test results of the month, combines them with how user behavior changed incrementally, and structures it all into a clean summary that you can share with your team lead. No more drafting your “work summary” every month.

  Try it yourself – Interactive demo

Most AI tools give answers. We show you the full picture.

Reference specific tests and personalization campaigns: Type # followed by a test or a personalization campaign’s name to instantly pull in context. Ask “Why did #homepage-redesign-test underperform?” and the AI will analyze that specific experiment.

Call specific AI agents in your prompts: Type @recordings to focus on session data. Want heatmap analysis? Use @heatmaps.

You control exactly which data the AI draws from.

You can create these custom AI agents specific to your use cases. Use available tools like heatmaps, session recordings, campaign analysis, web search, UX analysis, ROI analysis, and many more!

Attach files and images to give deeper context for AI: Upload screenshots, competitor pages, or design mockups. The AI can analyze visual content alongside your data, so that it stays context-rich and gives highly relevant responses.

Find patterns and chat with the data from third-party platforms: Connect to GA4, Mixpanel, Amplitude, Notion, Sentry, or Semrush, and prompt what you want to know about your users. Discover hidden patterns that would otherwise go unnoticed.

How does it help your business?

For E-commerce & Retail:

Prompt:

“Compare the checkout behavior of users who converted during Black Friday versus those who abandoned. What friction points were unique to the high-traffic period?”

The AI can find the what, why, and where to ensure you know how your Black Friday sale performed.

For SaaS & Software:

Prompt:

“Which user segments saw the biggest drop in trial conversion this quarter, and what changed in their product experience?”

The AI can discover hidden user cohorts that showed confusion in your test variations, study their behavioral patterns, detect the unseen reason behind this drop-off, and suggest how it can be fixed, for higher trial conversions.

For News & Media:

Prompt:

“For which cohorts did increasing paywall limit work, instead of discounting the subscription price?”

The AI can reveal behavior patterns of specific users, so that you align your editorial and product teams on a data-backed optimization strategy for higher retention.

Get smarter at optimizing today.

VWO AI is currently available for early access.To request early access, please reach out to your Customer Success Manager, or drop us a note at [email protected].

We’ve put together a playbook for you to get started.

Click here to start exploring!

Original source

Your checkout team ships a winner.

The individual test reports are glowing green, but when the quarterly review hits, you see little to no impact on your revenue.

Why? Because your “winning” updates might be silently colliding, and there’s no way to know if they are.

You are left wondering:

“Is our product roadmap ‘actually’ delivering the impact?”

Enter Holdouts

Now you can exclude a small, intentional subset of your traffic from a series of new feature rollouts, experiments, and personalization campaigns, over weeks, months, or quarters.

This subset acts as a pure, stable baseline and represents exactly what user behavior would look like if you didn’t deploy any product changes.

How to use Holdouts?

Setting up a Holdout in Wingify is seamless and fits right into your existing workflow. By associating a holdout with your feature flags, it universally works across all your feature rollouts, personalizations, and experiments.

We offer two types of Holdouts:

1. Global Holdouts: Automatically exclude your Holdout users from all new feature flags across your product to measure your entire roadmap’s impact.

2. Selective Holdouts: Manually associate specific feature flags to your Holdouts to measure the combined impact of a specific theme or initiative.

Once active, you get a definitive, dashboard-level view comparing total visitors, conversions, and primary metric values between the Holdouts and users bucketed in your campaigns overall.

How does it help your industry?

• For E-commerce & Retail:

  Imagine you launch guest checkout, revamp your recommendation algorithm, introduce an enhanced search experience, revamp the product listing page, and evolve the search experience, all in a single quarter. Individual A/B tests for each, can’t prove the cumulative bottom-line impact. By keeping a set of users that are isolated from these releases, you get scientific proof about these releases, whether they are even increasing your conversions, AOV, or revenue.

• For SaaS & Software:

  Move the conversation from “Did a specific onboarding change win?” to “Did our Q3 product roadmap actually increase user retention and Customer Lifetime Value?” You finally have the data to prove your strategy works.

• For News & Media:

  If you launch a new personalization algorithm for the news feed, a metered paywall, and start testing a new ad platform, make a few UI/UX changes, and revamp article writing style, in a single quarter. Such individual A/B tests can’t prove cumulative impact on engagement & revenue. By having the Holdout users as your original baseline, you get a clear indication of the impact of your launches.

Why is this important to you?

Justify your product investments and illustrate ROI:

Solve your leadership’s biggest struggle: “Tangibly understanding the ROI of product development.” Deliver a data-backed analysis to prove exactly how your development efforts are impacting key business goals.

Proactively detect anomalies and course-correct:

Get an early warning if your recent series of improvements is actually causing unintended harm and trending negatively. This allows your team to intervene and course-correct before long-term damage occurs.

Set a culture of true accountability:

Use Holdouts to bring transparency and align your teams towards one common vision, by tying feature launches directly to business health. You shift your team’s mindset from simply shipping features to actually driving business outcomes.

Start visualizing your true roadmap ROI today!

Holdouts are currently available for the Enterprise Plan (Early access) of Wingify Feature Experimentation. Please reach out to your Customer Success Manager to get access.

To know more, head over to our Knowledge Base.

If you have any questions or feedback, we are all ears at [email protected].

Original source

Your checkout team ships a winner. Your search team launches a hit.
The individual test reports are glowing green, but when the quarterly review hits, you see little to no impact on your revenue.

Why? Because your “winning” updates might be silently colliding, and there’s no way to know if they are.
You are left wondering:
“Is our product roadmap ‘actually’ delivering the impact?”

Enter Holdouts.

Now you can exclude a small, intentional subset of your traffic from a series of new feature rollouts, experiments, and personalization campaigns, over weeks, months, or quarters.
This subset acts as a pure, stable baseline and represents exactly what user behavior would look like if you didn’t deploy any product changes.

How to use Holdouts?

Setting up a Holdout in VWO is seamless and fits right into your existing workflow. By associating a holdout with your feature flags, it universally works across all your feature rollouts, personalizations, and experiments.

We offer two types of Holdouts:

1. Global Holdouts: Automatically exclude your Holdout users from all new feature flags across your product to measure your entire roadmap’s impact.

2. Selective Holdouts: Manually associate specific feature flags to your Holdouts to measure the combined impact of a specific theme or initiative.

Once active, you get a definitive, dashboard-level view comparing total visitors, conversions, and primary metric values between the Holdouts and users bucketed in your campaigns overall.

How does it help your industry?

• For E-commerce & Retail:

  Imagine you launch guest checkout, revamp your recommendation algorithm, introduce an enhanced search experience, revamp the product listing page, and evolve the search experience, all in a single quarter. Individual A/B tests for each, can’t prove the cumulative bottom-line impact. By keeping a set of users that are isolated from these releases, you get scientific proof about these releases, whether they are even increasing your conversions, AOV, or revenue.

• For SaaS & Software:

  Move the conversation from “Did a specific onboarding change win?” to “Did our Q3 product roadmap actually increase user retention and Customer Lifetime Value?” You finally have the data to prove your strategy works.

• For News & Media:

  If you launch a new personalization algorithm for the news feed, a metered paywall, and start testing a new ad platform, make a few UI/UX changes, and revamp article writing style, in a single quarter. Such individual A/B tests can’t prove cumulative impact on engagement & revenue. By having the Holdout users as your original baseline, you get a clear indication of the impact of your launches.

Why is this important to you?

Justify your product investments and illustrate ROI:
Solve your leadership’s biggest struggle: “Tangibly understanding the ROI of product development.” Deliver a data-backed analysis to prove exactly how your development efforts are impacting key business goals.

Proactively detect anomalies and course-correct:
Get an early warning if your recent series of improvements is actually causing unintended harm and trending negatively. This allows your team to intervene and course-correct before long-term damage occurs.

Set a culture of true accountability:
Use Holdouts to bring transparency and align your teams towards one common vision, by tying feature launches directly to business health. You shift your team’s mindset from simply shipping features to actually driving business outcomes.

Start visualizing your true roadmap ROI today!

Holdouts are currently available for the Enterprise Plan (Early access) of VWO Feature Experimentation. Please reach out to your Customer Success Manager to get access.

To know more, head over to our Knowledge Base.

If you have any questions or feedback, we are all ears at [email protected].

Original source

We’re excited to announce Wingify Pulse, a new Voice of Customer product that helps you capture contextual feedback at the right time, from your users and automatically turn it into actionable insights.

What Wingify Pulse does?

Capture feedback in the moment, across channels

Capture feedback at the point of experience and not days later via email. Pulse lets you deploy surveys inside your web app, mobile app, or via shareable links, triggered by the exact behavior or moment you care about.

Run continuous feedback programs without spamming users

Build continuous feedback loops such as NPS tracking, onboarding checks or feature adoption surveys, that run automatically without over-surveying users.

Pulse’s fatigue controls let you set frequency limits, “only once” rules, and cross-survey coordination to protect user experience and data quality.

From thousands of responses to clear patterns in minutes

With Wingify’s Copilot, teams no longer have to spend hours reading through thousands of open-text responses. Pulse automatically categorizes feedback based on theme and sentiment, and provides user patterns in minutes, not hours.

Get user buy-in before you spend time on developing features

Run unmoderated concept tests to get feedback on designs or prototypes from your users, before spending time on engineering effort. This helps reduce the risk of building features users don’t want.

Close the loop from insight to experiment in one platform

By integrating with the Wingify platform, teams can setup a closed-loop workflow

1. Observe and spot the friction in user experience.
2. Ask by triggering Pulse at that moment.
3. Hypothesize based on feedback themes and form ideas.
4. Test the ideas via experiments run on VWO.
5. Validate the results by sending a post-test survey.
6. Launch the variation with confidence.

Here’s what’s coming for Wingify Pulse subscribers:

• Uncover hidden themes automatically with AI-powered topic extraction and categorization across all your responses.
• Get instant clarity on what users are saying via AI-generated summaries that synthesize feedback into actionable themes.
• Track metrics that matter with custom dashboards that let you visualize and monitor feedback trends your way.
• Connect feedback directly to action through integration with Wingify Insights, Testing, and more.
• View user feedback in one place by consolidating support tickets, app reviews, and other feedback sources into a single view.

Get Started

Explore Wingify Pulse- You’ll see the new Pulse section in your left navigation in the product tab.

If you want a walkthrough tailored to your needs, book a personalized demo anytime. You could also read this KB article that will give you a deeper understanding of Pulse.

Original source

We’re excited to announce VWO Pulse, a new Voice of Customer product that helps you capture contextual feedback at the right time, from your users and automatically turn it into actionable insights.

What VWO Pulse does?

Capture feedback in the moment, across channels

Capture feedback at the point of experience and not days later via email. Pulse lets you deploy surveys inside your web app, mobile app, or via shareable links, triggered by the exact behavior or moment you care about.

Run continuous feedback programs without spamming users

Build continuous feedback loops such as NPS tracking, onboarding checks or feature adoption surveys, that run automatically without over-surveying users.

Pulse’s fatigue controls let you set frequency limits, “only once” rules, and cross-survey coordination to protect user experience and data quality.

From thousands of responses to clear patterns in minutes

With VWO’s Copilot, teams no longer have to spend hours reading through thousands of open-text responses. Pulse automatically categorizes feedback based on theme and sentiment, and provides user patterns in minutes, not hours.

Get user buy-in before you spend time on developing features

Run unmoderated concept tests to get feedback on designs or prototypes from your users, before spending time on engineering effort. This helps reduce the risk of building features users don’t want.

Close the loop from insight to experiment in one platform

By integrating with the VWO platform, teams can setup a closed-loop workflow

1. Observe and spot the friction in user experience.
2. Ask by triggering Pulse at that moment.
3. Hypothesize based on feedback themes and form ideas.
4. Test the ideas via experiments run on VWO.
5. Validate the results by sending a post-test survey.
6. Launch the variation with confidence.

Here’s what’s coming for VWO Pulse subscribers:

• Uncover hidden themes automatically with AI-powered topic extraction and categorization across all your responses.
• Get instant clarity on what users are saying via AI-generated summaries that synthesize feedback into actionable themes.
• Track metrics that matter with custom dashboards that let you visualize and monitor feedback trends your way.
• Connect feedback directly to action through integration with VWO Insights, Testing, and more.
• View user feedback in one place by consolidating support tickets, app reviews, and other feedback sources into a single view.

Get Started

Explore VWO Pulse- You’ll see the new Pulse section in your left navigation in the product tab.

If you want a walkthrough tailored to your needs, book a personalized demo anytime. You could also read this KB article that will give you a deeper understanding of Pulse.

Original source

Note

Wingify recently launched a full Go SDK for teams building Golang applications, enabling low-latency performance and native capabilities without relying on the Wingify Gateway Service.

Shipping backend changes has never been about writing code alone. It is about managing risk, coordinating teams, and making sure performance never drops, especially when the code runs in business-critical paths.

For teams building in Go, those constraints are even tighter.

The Wingify Go SDK enables experimentation and feature control directly within your Go application, without introducing latency, complexity, or operational overhead. It allows teams to ship more frequently while staying safe, observable, and in control.

Release management that fits real engineering teams

One of the biggest shifts we see in mature engineering organizations is the move away from tying every change to a deployment. Teams want to decouple engineering effort from product and configuration changes, so they can release continuously without putting production at risk.

With the Go SDK, feature flags and experiments become part of the runtime, not the release event. Backend teams can roll out new logic gradually, pause changes instantly if something goes wrong, or run controlled experiments on high-impact systems like pricing, search, recommendations, or subscriptions. This makes release management calmer, without slowing teams down.

Easy to integrate, easy to live with

The Go SDK is lightweight and is designed to drop into existing services without retooling.

Integration is straightforward, and once in place, the SDK stays out of the way. Teams can monitor, manage, and evolve experiments using REST APIs, IDE integrations, and clear, example-driven documentation. Whether you are running a monolith, a microservice, or an event-driven system, the Go SDK adapts to your environment rather than the other way around.

Execution support that works with your stack

Modern backend systems rarely exist in isolation. They connect to frontends, mobile apps, data pipelines, and third-party services.

The Go SDK supports OpenFeature and integrates cleanly with existing delivery pipelines. This ensures targeting logic and feature behavior remain consistent across platforms, while letting teams adopt experimentation incrementally. Wingify augments what you already have; it does not replace it.

Governance without slowing development

Speed and governance do not have to be opposites.

With Wingify Feature Experimentation, teams can define role-based access, approval flows, and environment-specific permissions. Production remains tightly controlled, while development and staging environments can be opened up for faster collaboration using Open Access. This allows developers to move quickly where it is safe, while leadership retains confidence where it matters most.

Performance first, always

For backend teams, performance is non-negotiable.

The Go SDK evaluates feature flags entirely in memory, using local data. There are no network calls in the request path, which means decisions happen in sub-millisecond time. Even in high-throughput services, experimentation adds no measurable latency and introduces no runtime dependency on external systems.

This makes the Go SDK safe for critical flows, from checkout and pricing to authentication and personalization.

Keeping experimentation clean over time

As teams experiment more, feature flags can pile up. Without discipline, this leads to dead code and long-term technical debt.

Wingify includes specialized tools and workflow integrations that help teams identify stale or unused flags, clean them up confidently, and prevent flag sprawl from becoming a problem. Experimentation stays structured and intentional, even as it scales.

In addition to flag hygiene features built into feature workflows, Wingify also offers a Tech Debt Client that helps teams take a proactive stance on managing technical clutter. This client analyzes your codebase and checks how Wingify feature flags are being used, generating actionable recommendations that guide teams toward removing unused or redundant flags. By surfacing where flags aren’t referenced or are no longer serving a purpose, the Tech Debt Client reduces uncertainty around clean-up decisions and helps teams maintain a lean, intentional experimentation layer as their codebase and test portfolio scale.

Complementing cleanup tooling, the Wingify MCP (Model Context Protocol) Server bridges your development environment with Wingify’s feature experimentation system so developers can manage flags directly where they code, without context-switching.

It supports AI-powered IDE integrations, letting teams create, list, update, delete, and toggle feature flags in familiar tools like Cursor or VS Code, and ensures consistent control across environments. This server makes flag operations more fluid and accessible, improving workflows and reducing overhead while giving dev teams tighter command over feature release logic and lifecycle.

Developer experience

A recurring theme in modern development is context switching, or more accurately, the desire to avoid it.

Wingify’s developer experience is designed so that feature experimentation lives exactly where developers already spend their time.

Feature flags inside VS Code

With the Wingify VS Code extension, developers can view, toggle, and manage feature flags without leaving their editor. Flags can be enabled or disabled instantly, environments can be switched from the status bar, and SDK code snippets can be inserted automatically.

Instead of treating feature management as a separate task, it becomes part of the coding workflow.

MCP Server and AI-native workflows

As AI becomes a daily development companion, feature management needs to integrate with it naturally.

Wingify’s MCP Server brings feature experimentation directly into AI-powered IDEs like Cursor and Copilot Workspace. From within the IDE, AI agents can create flags on the fly, wrap code blocks, configure experiments, suggest exposure rules, and even help monitor rollout impact.

Developers can also bootstrap SDK integrations instantly. With a single command, MCP Server configures Cursor rules, generates language-specific SDK examples, and ensures best-practice implementations from the start. Installation is intentionally simple, requiring nothing more than an npx command and Wingify credentials.

Over time, MCP Server also helps teams stay clean by scanning codebases for stale or unused flags, reducing technical debt before it accumulates.

One SDK, every stack

The Go SDK is part of a broader, full-stack experimentation ecosystem. Wingify supports Go alongside Ruby, NodeJS, Java, .NET, Python, and more, and works seamlessly with code generated by tools like Cursor, Windsurf, Antigravity, or Replit.

No matter where your logic runs, experimentation behaves consistently.

Why this matters

Together, the Go SDK, MCP Server, and Wingify Feature Experimentation enable teams to treat experimentation as an engineering capability, not a risky add-on.

Teams ship faster without losing control, experiment deeply without hurting performance, and scale optimization without creating chaos. Feature flags become clean, intentional, and easy to manage, even in complex systems.

For Go teams building high-performance, business-critical systems, this is experimentation done right.

Get started

Get started with Wingify Feature Experimentation and start shipping features with more confidence, cleaner workflows, and faster learning from day one.

Original source

Note: VWO recently launched a full Go SDK for teams building Golang applications, enabling low-latency performance and native capabilities without relying on the VWO Gateway Service.

Shipping backend changes has never been about writing code alone. It is about managing risk, coordinating teams, and making sure performance never drops, especially when the code runs in business-critical paths.

For teams building in Go, those constraints are even tighter.

The VWO Go SDK enables experimentation and feature control directly within your Go application, without introducing latency, complexity, or operational overhead. It allows teams to ship more frequently while staying safe, observable, and in control.

Release management that fits real engineering teams

One of the biggest shifts we see in mature engineering organizations is the move away from tying every change to a deployment. Teams want to decouple engineering effort from product and configuration changes, so they can release continuously without putting production at risk.

With the Go SDK, feature flags and experiments become part of the runtime, not the release event. Backend teams can roll out new logic gradually, pause changes instantly if something goes wrong, or run controlled experiments on high-impact systems like pricing, search, recommendations, or subscriptions. This makes release management calmer, without slowing teams down.

Easy to integrate, easy to live with

The Go SDK is lightweight and is designed to drop into existing services without retooling.

Integration is straightforward, and once in place, the SDK stays out of the way. Teams can monitor, manage, and evolve experiments using REST APIs, IDE integrations, and clear, example-driven documentation. Whether you are running a monolith, a microservice, or an event-driven system, the Go SDK adapts to your environment rather than the other way around.

Execution support that works with your stack

Modern backend systems rarely exist in isolation. They connect to frontends, mobile apps, data pipelines, and third-party services.

The Go SDK supports OpenFeature and integrates cleanly with existing delivery pipelines. This ensures targeting logic and feature behavior remain consistent across platforms, while letting teams adopt experimentation incrementally. VWO augments what you already have; it does not replace it.

Governance without slowing development

Speed and governance do not have to be opposites.

With VWO Feature Experimentation, teams can define role-based access, approval flows, and environment-specific permissions. Production remains tightly controlled, while development and staging environments can be opened up for faster collaboration using Open Access. This allows developers to move quickly where it is safe, while leadership retains confidence where it matters most.

Performance first, always

For backend teams, performance is non-negotiable.

The Go SDK evaluates feature flags entirely in memory, using local data. There are no network calls in the request path, which means decisions happen in sub-millisecond time. Even in high-throughput services, experimentation adds no measurable latency and introduces no runtime dependency on external systems.

This makes the Go SDK safe for critical flows, from checkout and pricing to authentication and personalization.

Keeping experimentation clean over time

As teams experiment more, feature flags can pile up. Without discipline, this leads to dead code and long-term technical debt.

VWO includes specialized tools and workflow integrations that help teams identify stale or unused flags, clean them up confidently, and prevent flag sprawl from becoming a problem. Experimentation stays structured and intentional, even as it scales.

In addition to flag hygiene features built into feature workflows, VWO also offers a Tech Debt Client that helps teams take a proactive stance on managing technical clutter. This client analyzes your codebase and checks how VWO feature flags are being used, generating actionable recommendations that guide teams toward removing unused or redundant flags. By surfacing where flags aren’t referenced or are no longer serving a purpose, the Tech Debt Client reduces uncertainty around clean-up decisions and helps teams maintain a lean, intentional experimentation layer as their codebase and test portfolio scale.

Complementing cleanup tooling, the VWO MCP (Model Context Protocol) Server bridges your development environment with VWO’s feature experimentation system so developers can manage flags directly where they code, without context-switching.

It supports AI-powered IDE integrations, letting teams create, list, update, delete, and toggle feature flags in familiar tools like Cursor or VS Code, and ensures consistent control across environments. This server makes flag operations more fluid and accessible, improving workflows and reducing overhead while giving dev teams tighter command over feature release logic and lifecycle.

Developer experience

A recurring theme in modern development is context switching, or more accurately, the desire to avoid it.

VWO’s developer experience is designed so that feature experimentation lives exactly where developers already spend their time.

Feature flags inside VS Code

With the VWO VS Code extension, developers can view, toggle, and manage feature flags without leaving their editor. Flags can be enabled or disabled instantly, environments can be switched from the status bar, and SDK code snippets can be inserted automatically.

Instead of treating feature management as a separate task, it becomes part of the coding workflow.

MCP Server and AI-native workflows

As AI becomes a daily development companion, feature management needs to integrate with it naturally.

VWO’s MCP Server brings feature experimentation directly into AI-powered IDEs like Cursor and Copilot Workspace. From within the IDE, AI agents can create flags on the fly, wrap code blocks, configure experiments, suggest exposure rules, and even help monitor rollout impact.

Developers can also bootstrap SDK integrations instantly. With a single command, MCP Server configures Cursor rules, generates language-specific SDK examples, and ensures best-practice implementations from the start. Installation is intentionally simple, requiring nothing more than an npx command and VWO credentials.

Over time, MCP Server also helps teams stay clean by scanning codebases for stale or unused flags, reducing technical debt before it accumulates.

One SDK, every stack

The Go SDK is part of a broader, full-stack experimentation ecosystem. VWO supports Go alongside Ruby, NodeJS, Java, .NET, Python, and more, and works seamlessly with code generated by tools like Cursor, Windsurf, Antigravity, or Replit.

No matter where your logic runs, experimentation behaves consistently.

Why this matters

Together, the Go SDK, MCP Server, and VWO Feature Experimentation enable teams to treat experimentation as an engineering capability, not a risky add-on.

Teams ship faster without losing control, experiment deeply without hurting performance, and scale optimization without creating chaos. Feature flags become clean, intentional, and easy to manage, even in complex systems.

For Go teams building high-performance, business-critical systems, this is experimentation done right.

Get started

Get started with VWO Feature Experimentation and start shipping features with more confidence, cleaner workflows, and faster learning from day one.

Original source

We are excited to announce User Aliasing, a new feature designed to eliminate the headache of managing temporary user IDs and ensure a consistent experience for all your users.

Previously, when a user hadn’t logged in, our FE SDKs required you to generate a random temporary ID for them to ensure a consistent experience. This meant you had to persist that random ID even after the user logged in and a genuine ID was available, creating an unnecessary burden on your development team.

The solution: User Aliasing

With User Aliasing, we remove this friction. Your team can now:

1. Use a random temporary ID (the aliasId) for anonymous users before they log in.
2. Once the user logs in and their actual userId is available, simply call our new method to map the two IDs together.

This simple mapping ensures that no matter which ID is used after login, the temporary one or the actual one, the user will get the exact same experiment variation decision as before.

Benefits

• Consistent Experience: Ensure a seamless and uninterrupted experience for your users as they transition from anonymous to logged-in states.
• Accurate Reporting: Wingify reports will recognize the temporary ID and the permanent ID as the same user, leading to cleaner, more reliable data and metrics.
• Reduced Development Effort: No need for complex logic to manage and persist temporary IDs across sessions.

Crucial requirement- Gateway service

The Gateway Service is required to use User Aliasing. The Aliasing API (like setAlias()) depends on Gateway endpoint logic to reconcile multiple user IDs reliably.

How it works

To enable this feature, simply pass the isAliasingEnabled flag during SDK initialization:

vwoClient = await init({
 accountId: '123456',
 sdkKey: '32-alpha-numeric-sdk-key',
 gatewayService: {
 url: 'http://your-custom-gateway-url',
 },
 // Required to use Aliasing
 isAliasingEnabled: true,
});

Once the user has logged in, and their actual userId is available, call the setAlias() method to do the mapping.

vwoClient.setAlias(userContext, 'aliasId');

That’s it! User Aliasing takes care of the rest, ensuring your users are consistently bucketed and accurately reported.

Get started with user aliasing today

User Aliasing is now available in the latest Mobile and server-side SDKs. Upgrade your SDK to the latest version if you haven’t already, and start delivering consistent experiment experiences across your mobile app. Also, feel free to book a demo to understand this feature from our product experts.

Original source

The solution: User Aliasing

We are excited to announce User Aliasing, a new feature designed to eliminate the headache of managing temporary user IDs and ensure a consistent experience for all your users.
Previously, when a user hadn’t logged in, our FE SDKs required you to generate a random temporary ID for them to ensure a consistent experience. This meant you had to persist that random ID even after the user logged in and a genuine ID was available, creating an unnecessary burden on your development team.

The solution: User Aliasing
With User Aliasing, we remove this friction. Your team can now:

• Use a random temporary ID (the aliasId) for anonymous users before they log in.
• Once the user logs in and their actual userId is available, simply call our new method to map the two IDs together.
  This simple mapping ensures that no matter which ID is used after login, the temporary one or the actual one, the user will get the exact same experiment variation decision as before.

Benefits

• Consistent Experience: Ensure a seamless and uninterrupted experience for your users as they transition from anonymous to logged-in states.
• Accurate Reporting: VWO reports will recognize the temporary ID and the permanent ID as the same user, leading to cleaner, more reliable data and metrics.
• Reduced Development Effort: No need for complex logic to manage and persist temporary IDs across sessions.

Crucial requirement- Gateway service

The Gateway Service is required to use User Aliasing. The Aliasing API (like setAlias()) depends on Gateway endpoint logic to reconcile multiple user IDs reliably.

How it works

To enable this feature, simply pass the isAliasingEnabled flag during SDK initialization:

vwoClient = await init({
  accountId: '123456',
  sdkKey: '32-alpha-numeric-sdk-key',
  gatewayService: {
    url: 'http://your-custom-gateway-url',
  },
  // Required to use Aliasing
  isAliasingEnabled: true,
});

Once the user has logged in, and their actual userId is available, call the setAlias() method to do the mapping.

vwoClient.setAlias(userContext, 'aliasId');

That’s it! User Aliasing takes care of the rest, ensuring your users are consistently bucketed and accurately reported.

Get started with user aliasing today

User Aliasing is now available in the latest Mobile and server-side SDKs. Upgrade your SDK to the latest version if you haven’t already, and start delivering consistent experiment experiences across your mobile app. Also, feel free to book a demo to understand this feature from our product experts.

Original source

What is Open Access?

A team at a large enterprise spent half a day trying to adjust a simple rule in their staging environment. Not because the change was risky, but because the person who needed to make it didn’t have the right permissions.

Three messages, two tickets, and one Slack reminder later, the update finally went through. The test that should have started in the morning began only by the evening.

Nothing was technically difficult; the process was.

This everyday slowdown is exactly what Open Access solves.

Open Access provides product and engineering teams room to move without compromising control. It replaces rigid bottlenecks and risky shortcuts with a clear operating model where speed and safety work together, not against each other.

The outcome is simple. Teams move faster, and leaders stay confident.

Open Access is an environment-level setting inside Wingify Feature Experimentation that removes unnecessary permissions for low-risk spaces.

When an environment is marked as Open Access, any user on the platform can create changes and start rules there. No approvals or dependency loops.

Teams get frictionless collaboration in dev and staging, while production remains fully governed with existing permissions intact.

In practice, it enables teams to create, manage, and launch experiments without relying on central teams for every step, and without compromising production.

You can think of it as self-service experimentation with built-in guardrails.

How it works

Open Access is built on three practical layers.

Environment-by-environment flexibility

You choose which environments should be open and which should stay restricted. Development and staging often benefit from being open, while production stays protected.

Autonomy where it’s safe

Designers, PMs, engineers, analysts, and growth teams move independently in non-critical environments, making experimentation cycles faster with zero overhead.

Governance where it matters

Open Access doesn’t relax controls in production or compliance-sensitive environments. Your highest-risk areas remain fully permission-bound.

Who it is for

Open Access is built for organizations where:

• Multiple teams collaborate across several environments
• Access requests create bottlenecks
• Experimentation needs to move faster
• Production and compliance environments must stay secure

It is designed for teams that feel caught between speed and safety:

• Product leaders who want faster learning cycles
• Platform engineers who are responsible for stability
• Security and compliance teams that need visibility
• Growth teams running frequent experiments

What teams gain

With Open Access, organizations shift from permission-heavy workflows to clean, efficient collaboration. They gain:

• Faster build-test-iterate cycles
• Immediate self-service for safe environments
• Fewer tickets and fewer blockers
• A clear separation between collaborative spaces and controlled production
• A calmer release rhythm

It’s a practical way to accelerate experimentation without compromising structure. Teams using Open Access move away from fragile release processes and towards a cleaner, calmer way of working. Instead of choosing between velocity and safety, teams get both.

Get started

Open Access is now available inside Wingify Feature Experimentation.

You can enable it from your environment settings and begin configuring permissions and approval flows.

If you would like to see how this works in your setup, your Wingify account team can walk you through it.

Original source

Wingify Feature Experimentation already enables you to run powerful rollout, testing, and personalization campaigns using advanced segmentation. Along with that, our reporting (post-segmentation) capabilities help you slice and dice experiment results to uncover deeper insights into feature performance. Now, we’re taking segmentation to the next level with major upgrades that bring more power, more flexibility, and significantly less engineering dependency.

1. Introducing built-in targeting attributes for client-side SDKs

We’ve added the following new attributes that are now automatically captured by our client-side SDKs and displayed in the segmentation UI:

Attribute name Mobile/Web support App version Mobile Browser version Web OS version Mobile + Web Device model Mobile Locale Mobile

Earlier, these commonly used attributes had to be passed manually as custom variables, creating unnecessary engineering dependency and bloated SDK logic for standard use cases. With this update, these attributes are automatically captured by the SDK. No need to pass them manually.

2. Observed values against these new attributes are auto-inferred and can be used in reports (post-segmentation)

Values against the above listed attributes (i.e. App Version, Browser Version, OS Name/Version, etc) are now automatically inferred by the SDK and made available for analysis in your experiment reports. For example, suppose you want to segment your experiment report by App version. In that case, you open the ‘custom segment’ module and simply select ‘App Version’ from the attribute list. The SDK automatically shows the values detected from your users (like 1.0.0, 1.0.1, 1.0.2, etc.). You select a value, apply an operator, and filter the report. That’s it.

No engineering dependency to pass this information separately like it used to be earlier.

This gives you a powerful capability to slice and dice your results to answer critical questions like:

• Are users on version A converting better than version B?
• Is a specific device manufacturer causing unexpected performance issues in my experiment?
• Does my experiment’s impact differ significantly across different geographies

3. Reuse targeting context in post-segmentation analysis

Previously, context attributes could only be used in campaign targeting, i.e., before a user becomes part of the campaign (pre-segmentation). If you wanted to use those same attributes later to filter a campaign report, they had to be passed again separately, which meant extra work for engineers. Now, that changes.

With this enhancement, the same targeting context is now also available in reports without any additional setup. For example, if you target users in your campaign based on Age (say Age > 20), you can later filter your campaign report for individual age or ranges (say Age between 25 and 30) to understand the specific impact for each group.

Try these powerful segmentation features today

While we believe the above tutorial will be helpful, we are also leaving you with this and this KB document that will give you a deeper understanding of these features.

Want a walkthrough tailored to your needs? Book a personalized demo anytime. Or just write to us at [email protected]. We’d love to hear from you.

Original source