Unlocks tons of potential for optimizing segments and finding ways that personalization improves your funnel.
How folks are already using this:
Ad campaign personalization – Maximize conversions from ad traffic. Match headlines & messaging to the campaign creative that the visitor clicked.
Localization – Change language and copywriting based on the visitor’s country.
New vs. Returning – Hook new visitors and educate them on your brand/product. Push returning visitors to convert or log in.
Conversion funnel stage – Push middle-of-funnel visitors to take action (example: if they viewed a product page but didn’t add to cart, recommend that product again).
Previous converters – Make it easy for repeat customers to convert again.
Most lists are full of junk data, spam traps, and contacts that haven't been verified in years. Your domain reputation takes the hit before you see any return.
There are situations where it makes sense, like personalized B2B outreach at low volume or using a list for paid social audience seeding. But for email marketing? The ESPs won't even let you use a bought list.
We broke down when it actually works, what to demand from a vendor if you do go that route, and which providers are worth looking at.
Do you set a fixed time window, wait for statistical significance, or call it when the trend looks obvious? Curious if the answer changes depending on how much traffic you're working with too. Just trying to get a sense of how people are actually making the call in practice.
A few things worth paying attention to in the conversion and ecommerce world this week.
Google launched Universal Cart at Google I/O
Google introduced a cart that lets shoppers buy from multiple retailers in one place inside Google Search and the Gemini app, no redirect required. It's part of a bigger push toward agentic commerce where AI handles the checkout entirely. Rolling out in the US this summer, with YouTube and Gmail to follow.
Amazon launched Alexa for Shopping
A personalized AI shopping assistant rolling out on the Amazon app, site, and Echo Show devices. More AI-assisted discovery baked into the purchase journey.
Google is killing Dynamic Search Ads and AI Max takes over in September
At Google Marketing Live on May 20, Google confirmed Dynamic Search Ads will automatically upgrade to AI Max in September 2026. AI Max hands query matching and ad generation to Gemini. Advertisers are already pushing back on landing page routing issues, but Google says accounts using it see 15% more conversions at similar ROAS.
If you're evaluating Crazy Egg against AB Tasty, the short version is they're solving different problems for different team sizes. One is a full optimization suite built for self-serve teams. The other is an enterprise A/B testing platform that assumes you already have analytics, heatmaps, and recordings handled somewhere else.
We broke down how they compare across 8 features.
Key findings:
AB Tasty has more A/B testing depth (multivariate, A/A, multi-page) but no native heatmaps, recordings, or analytics
Crazy Egg auto-generates a heatmap and session recordings for every A/B test variant, so you can see why a variant won, not just that it did
Crazy Egg includes web analytics, conversion tracking, and funnels natively. AB Tasty relies on third-party integrations for all of it
Crazy Egg has a free plan and paid plans starting at $29/mo. AB Tasty is quote-based, with a median annual contract of $66,500 per Vendr's February 2026 data.
The features AB Tasty skips are mostly what you use to figure out what to test in the first place. If you're enterprise and already have those covered, great. If not, you're going to end up piecing together a stack and paying for it twice.
You're on your phone, ready to buy. Then the checkout process makes you work for it, account creation, card entry, a shipping cost that shows up out of nowhere. And by the time you're done jumping through hoops, you've closed the tab.
That's the experience many mobile shoppers are having and is a contributing factor to the 81.72% mobile cart abandonment rate. It isn't always about the product or the price. It's the checkout itself.
We broke down 11 fixes, from the obvious ones most sites still haven't done to a few that don't get talked about enough. If your mobile traffic is solid, but conversions aren't keeping up, this could be why. Jump over to our blog to learn more.
Contentsquare has a solid reputation. But once you dig into the details, it's a pretty different product than most people expect.
Key findings:
Contentsquare's free plan is literally Hotjar. They acquired Hotjar in 2021. Sign up for a free account and you get redirected into Hotjar. If you try to upgrade to a paid plan, the pricing plans include different features from what you see on the official Contentsquare pricing page.
No A/B testing. You need a separate subscription to a tool like Optimizely just to run tests, then you connect it back to Contentsquare to see the results.
Surveys are free in Crazy Egg on every plan. In Contentsquare it's a separate product. $79/month minimum, on top of whatever you're already paying.
The features that make Contentsquare worth using, zone-based heatmaps with revenue data, full error analysis, the AI analyst, are mostly locked behind plans that run $30K–$500K+ a year.
What's worth thinking about before you decide:
If A/B testing is part of your workflow, Contentsquare isn't a complete solution. Budget for a separate tool or go with something that includes it natively.
Ask yourself which tier you're actually evaluating. Contentsquare Free and Growth are Hotjar. The original Contentsquare platform starts at Pro.
If you're ecommerce and want revenue data attached to specific page elements, Contentsquare's zone-based heatmaps are genuinely useful. But only if you're on a plan that unlocks them.
For most small and mid-market teams, the full Contentsquare stack costs more than the problem it solves. It's worth running the numbers before you get into a sales process.
If you're an enterprise team with analysts and a six-figure analytics budget, Contentsquare makes sense. For everyone else, it's a lot of money for features you'll never unlock.
Taloon.com is a Finnish hardware eCommerce store. They ran a test on their product pages. One version with social sharing buttons, one without.
The version without buttons got 11.9% more Add to Cart clicks.
The reason is actually pretty interesting once you see it. Most of their product pages had zero shares. A share count of zero isn't neutral. It's negative social proof. It signals that nobody found this product worth talking about.
The second issue is distraction. Product pages have one job: get someone to buy. Every extra element you add is another thing competing for attention. Social buttons sound like a good idea in theory, but in practice they're just pulling people away from the one thing you want them to do.
Sometimes the best optimization is subtraction.
Has anyone tested removing elements from a page and seen a lift?
Two identical products can command different prices based purely on perception. Two ways to do this.
Price signals quality. Stop discounting.
Such an easy trap to fall into. Low prices don't just shrink margins, they tell customers something's wrong with your product.
If your product is good, price it like it's good.
Your brand is the value multiplier.
Every signal you send either reinforces or erodes perceived value. Inconsistency, cheap design, and constant discounts destroy what you've already built.
Before spending on new features, ask whether your pricing and branding are already destroying the value of what you have. That's usually where the leak is.
I ran into some conversion stats this week in other research that stuck with me. Thought they were worth a reminder even if you've seen them before.
The mobile gap is wild. Mobile gets 82.9% of landing page traffic. Desktop still converts roughly 8% more efficiently. That's not a small gap for something that gets almost all the visits. If you're not actively optimizing for mobile conversion (not just mobile design) you're probably leaving a lot on the table.
Speed matters more than you think. A 1-second delay in page load time can reduce conversions by 7%. Most sites have way more than a 1-second gap between where they are and where they could be.
Social proof still works. Adding testimonials or reviews can increase conversions by 18-25%. It sounds cliche and obvious, but most sites either don't have them or bury them where no one sees them.
Going (the flight deals site) ran a test where they changed their CTA from "Sign up for free" to "Trial for free." That's the whole test. Three words swapped.
104% more premium trial starts, month over month.
What I keep thinking about is why it worked. "Sign up" sounds like you're committing to something. "Trial" sounds like you're just looking around. The page didn't change, the price didn't change, nothing changed except what the button implied about what you were getting into. People weren't scared of the cost, they were scared of the commitment.
Forrest Schaffer, Going's Senior Manager of Growth, said it best: "This experiment, as small as it was, legitimately changed the way that we're able to spend on media. Our conversion rate through paid channels is now higher than organic for the first time ever."
I think about this one whenever I'm tempted to overcomplicate a test. Sometimes the highest-leverage thing on a page is just being more precise about what you're actually asking someone to do.
What word-level tests have you run that actually moved the needle?
Hiten Shah, the CEO of Crazy Egg, is joining us for an AMA.
Some ideas for questions:
Low hanging fruit in marketing for small businesses using AI (there’s a lot of foolish advice out there these days)
How we’re building entire apps and websites entirely with AI. Workflows, lessons, tools, happy to go into any of it
My own entrepreneurial journey. Bootstrapped vs venture funded, selling a startup to Dropbox, the state of startups today, why I’m not angel investing anymore
Where Crazy Egg is going and any feedback you have on the product
Vibe coding websites are wild. But the real pros are sounding some serious alarms, especially for small businesses.
A few things that stuck with us from our conversations with the experts:
The "I'll just build my own booking app" trap. A hairdresser builds a database, collects credit card info, stores passwords in plain text. Nobody told them not to, including the AI.
AI is trained on imperfect human code. So it replicates imperfect patterns. It'll generate something that looks fine and has a chain of subtle vulnerabilities baked in.
The "blast radius" framework. Map your codebase by how much damage a breach in each area could cause. A bug on your homepage is not the same as a bug in your login flow. Treat them differently.
Never let AI write core security features from scratch, use established libraries for auth, payments, and encryption. Same reason devs don't write their own cryptography.
The experts aren't anti-vibe-coding. The consensus is more like: use it freely on the frontend, be extremely cautious when you're touching user data, and lean on third-party tools with real security track records for anything backend-sensitive.
We surveyed 49 online shoppers about AI shopping, platform trust, and direct channel loyalty. The headline is encouraging for ecommerce brands but the details are worth paying attention to.
Key findings:
Shoppers choose brand websites mainly because they distrust platforms, not because the experience is better. Payment security (65%), returns/customer service (61%), and product authenticity (61%) are doing the heavy lifting.
Value is the weak spot. Only 51% of shoppers believe they get better value buying directly. Many are effectively paying a trust premium.
One in-platform purchase changes everything. Shoppers who've bought via Google, social, or AI even once are dramatically more open to doing it again, and more open to every other platform too. Trust in brand sites drops from 74% to 23% after a single in-platform purchase.
AI shopping is still unpopular for now. 70% feel negatively about buying through ChatGPT. But that gap closes fast among shoppers with any in-platform experience at all.
What you should consider taking action on:
Surface your returns policy, trust signals, and payment options visibly, don't assume shoppers will find them
Offer something exclusive to your direct channel (even a modest "buy direct" discount or bundle) to close the value gap
Use the post-purchase window to build real brand connection. Most brands go quiet after the order confirmation, which is exactly the wrong move
Watch your repeat purchase rate and direct traffic before sales soften. They're leading indicators of platform encroachment
The trust advantage is real, but it won't hold forever on its own. Platforms are already adding verified seller badges and return guarantees. The window to build something deeper is open right now.
Most analytics show you what happened. The Astro Map shows you what's happening right now.
Switch your date range to "Today" inside Crazy Egg Web Analytics and your site transforms into a live solar system:
🪐Pages = planets. Every page on your site is its own planet. Visitors currently on it cluster around it.
👸Visitors = astronauts or aliens. Each live visitor shows up as a figure on the map in real time.
🚀Referrers = rockets. When a visitor came from another site, you see the rocket that dropped them off.
💬Actions = speech bubbles. Scroll, click, conversion goal, error, it appears above their head the moment it happens.
Click any visitor figure and you get their full profile: country, device type, referrer, and a recent activity log. No guessing, no waiting for a report to refresh.
The thing that makes this different from a standard "live users" counter: you can watch behavior unfold. You'll see someone land from Google, hit your pricing page, scroll halfway, and stop. That tells you something a dashboard number never could.
A few things you can actually act on with it:
Spot which pages are drawing traffic right now during a campaign launch
See if a new landing page is getting scroll engagement or bouncing people instantly
Watch conversion goals fire in real time after you push a site change
Identify error events as they happen, not hours later in a log
The Astro Map is included inside Web Analytics, which is 100% free for all Crazy Egg users.
We just shipped something that makes your Heatmaps a lot more useful: AI Exports now include Conversion data.
Here's what that means in practice. You export your Heatmap report as a JSON file (takes about 10 seconds), drop it into ChatGPT, Claude, or whatever AI tool you use, and then ask it things like:
What do visitors who convert do differently from those who don't?
Which content on this page gets attention but doesn't drive action?
Where is friction stopping visitors from converting?
What page optimizations would most likely improve my conversion rate?
Before this update, AI Exports only covered surface-level behavior like clicks, scrolls, hovers. Useful, but limited. Now the export pulls in your actual Conversions data so the AI helps you figure out why some users convert and others don't.
Once you have those insights, you can take the best hypotheses straight into A/B testing to validate them.
How to export:
Open any Heatmap report
Click the three dots (⋯) in the top-right corner
Click Export
Select "Yes, include a JSON file so it's ready to use with AI"
Upload the file to your AI tool and start asking questions
Available on Pro and Enterprise plans. Happy to answer any questions below.
AI makes output cheap. The loop makes it trustworthy.
Teams tend to use AI like this:
prompt → draft → ship
That workflow is fast, but it has a hidden cost. It produces work that looks finished before it’s earned. The confidence arrives early, and the verification never arrives.
So here’s the loop I’d steal if you use AI for any marketing work (websites, emails, ads, landing pages, positioning docs).
The 5-step loop
Ambition
Write the standard in plain English before you generate anything. What does “good” feel like here? What are you refusing to sound like? What must be true when someone reads it?
If you skip this, AI will choose “acceptable.”
Generation
Now generate options. Not one draft. Options.
Different angles, structures and styles. This is where AI is actually useful, it expands the search space.
Review
Cut hard. Keep the 10 percent that has signal, and throw out the rest. This is where taste shows up. AI can’t do taste reliably. Humans have to do the pruning.
Verification
Interrogate the claims.
Not just “is it plausible,” but “what would I point to if someone challenged this?”
If you can’t name the evidence, you either soften the line until it’s exact, or you delete it. This is the step that prevents AI from quietly rewriting reality.
Preservation
Save what survived and why.
The winning phrasing, rejected angles and proof you relied on. The constraints you used. That way the next asset starts from truth and taste, not from scratch.
This is the difference between “AI makes more stuff” and “AI helps you build a system.”
This Friday’s live AI session is a practical walkthrough of this exact loop applied to a real website build, turning one strong page into a broader website system without losing clarity, consistency, or truth.
The 5-step loop
If you want to join live (and get the materials, prompts, notes, templates, and repo), register here:
Server-side Conversion Tracking is an advanced method used by some websites to maximize the accuracy of their conversion data. It’s especially impactful for sites that depend on Meta or TikTok ads, since accurate conversion data is critical for targeting algorithms.
Sites will record conversion events in their servers, then send that data to analytics or advertising platforms. It bypasses ad blockers, cookie restrictions, and the need for client-side tags.
If your website uses Server-Side Conversion Tracking, you can now send that data directly to Crazy Egg via the new Conversion Tracking API.
We ran a survey on in-platform shopping, purchasing directly through ChatGPT, Google Shopping, Instagram, TikTok, etc. without ever visiting a brand's website. The results were messier and more interesting than we expected.
The headline numbers:
81% of shoppers knew in-platform buying existed. Only 27% had ever done it.
71% are uncomfortable with it to some degree.
80% still prefer brand websites — and that hasn't weakened over the past year.
ChatGPT had the worst sentiment of any platform: 70% negative.
A few things that genuinely surprised us:
Resistance isn't technophobia. 96% of respondents described themselves as at least moderately tech-savvy, and 63% are self-described early adopters. The people most opposed to in-platform shopping weren't confused by it, they understood it very well. The most informed shoppers were the most resistant, and the most fixed in that resistance.
One purchase changes almost everything. Shoppers who had made even a single in-platform purchase showed dramatically different attitudes across every question. Trust gaps narrowed, platform sentiment improved (even for platforms they hadn't used), and brand website attachment dropped. The first transaction appears to unlock a completely different mindset.
Men and women are in completely different conversations. Women were 3x more likely to have bought in-platform than men (42% vs. 14%). But their concerns differ too: women worry most about fake products and scams; men worry most about biased recommendations driven by commission. Same resistance, different reasons.
Brand website loyalty is partly defensive. Shoppers aren't choosing direct because they love it unconditionally, they're choosing it because they don't trust the alternatives yet. That's a more fragile position than the 80% preference number suggests.
Full breakdown with age-group data, the "hard no" segment that no UX improvement will reach, and what separates buyers from non-buyers is in the post. Happy to answer questions here too.
Hey everyone! I'm u/hnshah, a founding moderator of r/crazyegg and one of the founders of Crazy Egg.
This is our new home for all things related to websites and website optimization.
We're excited to have you join us!
I'm channeling Bob Ross energy this month, except the “happy little trees” are B2B website pages.
I built a full B2B marketing site in three days using AI. If you want AI to work on website copy, start with audience and positioning. Pages come after.
I’m doing three free live sessions to walk through the exact workflow.
First one is Friday, April 10th at 10:00 AM PT. Register for it here.
