Spectry.io

Conversion visibility is such a pain point. SMBs I talk to often don’t know what’s hurting their funnel until it’s too late. With Finsight AI, I’m tackling the same issue in finance — clarity before surprises. Excited to see how Spectry surfaces insights.

What are you using as your AI inference backend at the moment?

If you're looking for another place to launch your product, give Launch Nest a try. You can launch for free and let people discover what you're building.

You can also earn credits and get your product featured for free.

https://launch-nest-ai.base44.app

The shift from reporting to identifying opportunities is the strongest part here. The product is trying to close the gap between seeing a conversion problem and recognizing what deserves attention.

I like the shift from reporting metrics to helping teams make decisions.

The interesting challenge isn't finding more insights—it's helping teams distinguish between issues that deserve action and those that are just statistically interesting. Reducing decision fatigue may end up being a bigger competitive advantage than adding another analytics feature.

Congrats on the security upgrades! Deploying patches without interrupting the user experience is always a delicate balance. As a dev building SaaS codebases, I know how easily things can break during these updates. Did you have to run extensive regression testing for this deployment?

I still can’t believe this is real, but I actually won the lottery jackpot! It feels like a dream come true. For years, I would occasionally play without expecting much, just hoping that one day luck would finally be on my side—and it finally happened when I came across a testimony on how Dr Benjamin the great spell caster had helped alot of people win, so I reached out to him, he told me what to do and I followed his instructions, after 48 hours he gave some numbers to play, I did played the numbers as instructed.

The moment I checked my numbers and realized they all matched, my heart started racing. I had to double-check multiple times because I couldn’t believe what I was seeing. Winning the Powerball jackpot of $107,000,000,00. This has completely changed my life. It’s given me financial freedom, peace of mind, and the opportunity to support my family and invest in my future. Here are his contact information.

Whatsapp: +18588585788

Hi Niko, I really enjoyed your breakdown of heatmap patterns, especially the "Scroll Cliff" insight. Since Spectry is currently in free beta and you’re clearly an expert in conversion optimization, would you be interested in scaling the distribution of your tool to reach more growth teams and founders?

This is actually a solid breakdown—especially the way you frame heatmaps as diagnostic tools instead of just visuals. Most people definitely stop at “looks cool” and miss the insight layer.

The “ghost click” point hit hard—I’ve seen that happen a lot with card-style UI where everything looks clickable but isn’t. Feels small, but it adds up in terms of trust.

I’m curious though—have you seen which of these patterns tends to have the biggest impact when fixed? My guess would be ignored CTA or scroll cliff, but would be interesting if ghost clicks or form friction sometimes outperform those.

Also, the combo of heatmaps + session replays is underrated. On their own, heatmaps can feel a bit abstract, but pairing them with actual behavior makes it way more actionable.

I’m working on AI-driven workflows in a different space (fashion), but this idea of spotting friction from behavior patterns instead of guessing feels very transferable.

50% visit→usage on a new free tool is strong — most land under 20%. The 6% start-but-don't-finish is almost always a copy problem, not UI: the promise earlier in the flow doesn't match what the report actually delivers at the end. Worth auditing the "you're about to get X" line right before the analysis kicks off — gap between that and the output is where drop-off clusters.

Minor on your own hero: "More than analytics, a decision engine" is a category. Your post shows you iterate on specifics (246 visitors, 50% conversion, 90% single-page) — the founders in your beta list don't arrive thinking "I need a decision engine," they arrive thinking "my conversion is 2% and I don't know why." The gap between hero-phrasing and in-post phrasing is probably costing some of that 6%.

was wondering the same about the right users or random traffic do you know of any tools to know ?

Looks interesting.

Quick question — how well do you think AI tools (ChatGPT, Perplexity) can understand and describe what Spectry does from the site?

Feels like that’s becoming an underrated discovery channel.

50% usage conversion usually means the hook is strong.
The next challenge isn’t traffic, it’s proving that usage turns into qualified intent, not just curiosity.

"Great timing on this — free tools are one of the best ways to build trust before asking for anything. I built a free utility site with the same principle: give value first. How did you handle the traffic spike after launch?"

Thanks For Sharing Looks very Interesting.

Thanks for sharing you experience! Very valuable data!

This looks interesting, and I got a peculiar "critical" note; maybe you can help explain the logic behind this. Here's what I got

Impact
The hero section features an email input field and a 'Sign up' button, implying an email capture or soft lead. However, the conversion goal is 'Sign Up/Create Account'. This creates friction and confusion for Small Business visitors from Organic Social, as they might expect a full account creation process or a clearer indication of the next step after submitting an email.
Recommendation
Replace the email input and 'Sign up' button with a single, prominent 'Create Free Account' or 'Sign Up Free' button that directly leads to the account creation form. Clearly communicate the immediate action.

I fail to see how a "sign up free" button is clearer than the current "sign up" (which is also accompanied by a help text of "Start with a free account - no credit card needed.")

Still, the other comments seemed more logical and worth checking out and A/B testing. Cheers!

Nice wedge. The most interesting bit to me is not “AI CRO audit” in general, it’s that you’re compressing the first diagnosis into 60 seconds with no signup friction.

A couple things I’d test next:

1. On the results screen, show one clearly ranked “fix this first” recommendation above the full list. Most founders don’t need 12 ideas, they need confidence about the next ship.

2. Add a tiny evidence strip under the score like “based on copy clarity / CTA hierarchy / trust / offer specificity” so the output feels less black-box.

3. Create a visible diff/progress angle for repeat users. A lot of value comes after the first scan, when someone changes the page and wants to know if it actually got better.

4. If false positives are a known early failure mode, say that openly and give users an easy “this was wrong” button. That honesty probably increases trust more than pretending the model is already perfect.

If helpful, I run very cheap manual landing-page teardowns for founders who want a human second opinion after the AI pass: https://roastmysite.io/go.php?src=external_manual_ih_spectry_croaudit_apr20_usd_presell_hv

the false positive AresE flagged is the failure mode every ai audit tool hits early. a static heuristic fires on a pattern without checking what's 3 lines around it. ive seen the exact same thing in code audits. model flags a security pattern in isolation and misses that the helper right next to it handles the validation. the multi-pass context check is where most of the actual quality lives. you thinking bigger context window or a second pass as the fix?

Strong angle: “60-second audit” is clear and appealing.

But careful: promising CRO without real context (data, traffic, behavior) can feel superficial.

To increase value:

• Show real examples (before/after with impact)

• Explain what powers the analysis (heuristics, benchmarks, etc.)

• Prioritize output: not 20 tips, but 3 highest-impact changes

The real value isn’t just finding issues… it’s helping users decide what to fix first.

Love this — most CRO audits either cost $500+ or take 2 weeks, so a 60-second free version is genuinely useful for early-stage founders.

One thing I appreciate about the approach: asking the user to define the audience upfront. Most AI audit tools ignore this and give you generic "make your CTA bigger" advice that applies to nothing. The audience context is what turns it from a checklist into an actual diagnosis.

Two questions before I test it on a landing I've been staring at too long:

1. Does the tool account for landing pages in Spanish/Portuguese, or is the analysis optimized for English copy specifically? Conversion patterns differ across languages — translated CTAs that work in English often underperform in Spanish, and I haven't found a CRO tool yet that handles that well.

1. When you say "follow-up questions," how deep does that go? Can I push back on a recommendation and have it reconsider given more context, or is it one-shot analysis with clarifications?

Also curious about the underlying model — are you running a single pass with a prompt that includes CRO heuristics, or is it a multi-step pipeline where different "agents" audit different aspects (copy, layout, CTA hierarchy, trust signals)? Asked because I've been experimenting with agent pipelines for a different use case and the single-pass vs multi-step tradeoff is something I keep hitting.

I'd push back a bit - the audit time isn't the real bottleneck for most teams. The hard part is figuring out which fix to ship first when the list has 15 items on it.

60 second audits on landing pages is the format that actually gets founders to pay attention. The challenge we've seen on the Shopify side is that a static snapshot misses the moving parts. App changes, theme updates, new products pushing older code around. One scan shows you the state, but the value compounds when you can diff between scans and tell the founder what broke this week vs last week. Curious how you're thinking about returning users. Is Spectry a point-in-time tool or are you building toward continuous monitoring?

I like this. First of all, its a great way to grow your email list, I signed up right away. You give quite a bit of free advice without making someone pay to upgrade. My site, Podsplice dot com, got a 58/100. Some of the suggestions were not bad. But the top recommendation was,

"Hero Video vs. Static Image

high

If we replace the current hero background with a short, engaging video demonstrating the product's ease of use, then the Sign Up/Create Account rate will improve because small business users will quickly grasp the product's value and functionality visually."

The thing is, my site does have a video loop playing and it's not a static image. Also, I think the analysis should include uniqueness and an assessment of how it looks visually. So many new sites all look like it was obviously an AI vibe-coded site. Those would be ok, except for the fact that almost every site looks the same.

I'm curious what mark your site gets /100 if you run the test on your own site.

Great idea though, and I think a few tweaks could really improve it.

Excellent presentation 👏

The product feels polished and ready to scale.

We help digital products grow through creator partnerships and targeted campaigns 🚀

Would love to discuss opportunities.

This is actually a strong idea most founders know they have conversion issues but don’t know where to start. Curious, are you planning to go deeper into implementation fixes or just audits?

This looks really useful, especially for founders who don’t have time for manual audits.

One quick UX thought - tools like this are powerful, but a lot depends on how clearly the insights are prioritised for users. If everything is shown at once, it might become overwhelming instead of actionable.

Where did you get this idea?

This is a really interesting approach

Love the focus on speed here — most founders know something’s off in their funnel but never get around to actually diagnosing it.

Curious though — once someone identifies those ‘leaks’, how are you seeing them validate which fixes will actually move conversions vs just improving surface-level UX? That gap feels pretty real for early-stage builders.

I’ve been seeing some founders run small, high-intent experiments (fixed low entry, capped spots, strong upside) alongside tools like this to test what actually converts — surprisingly effective for turning insights into real revenue.

Feels like that layer could complement Spectry really well. Have you explored that?

idea is cool but only if the answers are actually actionable, not generic fluff… most “AI insights” tools just tell you obvious stuff like “improve performance” lol, if it can point to exact pages, real user impact, and what to fix first, then yeah that’s useful, otherwise it’s just another dashboard people open once and forget

This is a really interesting direction for analytics. Being able to “talk” to your website data feels like a natural next step.

The "ask your site how it's going" framing is clever — way more intuitive than digging through GA4 dashboards trying to find the one metric that explains why conversions dropped.

What I'd love to see: anomaly detection that pings me before I even ask. Like "your homepage LCP jumped 40% yesterday after your last deploy" — pushed to Slack or email automatically. Most site issues go unnoticed for days because nobody thinks to check until something feels off.

The Core Web Vitals breakdown is useful but the real value would be connecting performance to business outcomes. "Your LCP went from 1.8s to 2.5s and your bounce rate increased 12% during the same period" — that's the insight that makes someone actually fix it.

Are you pulling data from GA4 or using your own tracking pixel? And how are you handling the AI hallucination problem with analytics data — wrong numbers in a performance report could send someone down a rabbit hole fixing something that isn't broken.

Good luck on your journey, you're going to need it!

Cool idea. Can it go beyond insights and suggest specific fixes (like copy changes or UX tweaks), or is it more analysis-focused for now?

This is a really interesting direction, being able to just ask “how is it going?” instead of digging through dashboards is a big shift.

One thing I’m curious about:

Right now, after the AI surfaces issues (like slow homepage, poor INP, etc), do users actually act on it? Or does it still feel like “insight without a clear next step”?

Feels like there’s an opportunity to go one layer deeper, not just answering questions, but guiding users through:

– what to fix first
– why it matters in their specific context
– and what to do immediately after

Almost like turning analytics into a step-by-step flow instead of a report.

I’ve been exploring something similar from the “user action” side, where insights trigger guided next steps instead of just explanations.

Curious how you’re thinking about that part, are people mainly using it for awareness, or actually making changes from it?

This tool will amount to a watershed moment, particularly for those whose eyes are on SEO effectiveness and overall digital performance.

What makes it genuinely game-changing is the sheer scale it’s tackling: billions of websites, each with its own unique objectives, audience, and competitive context. A landing page that converts for an e-commerce store might tank for a SaaS product or a content platform — the nuances are endless. The fact that you can simply type “how is it going?” and get an intelligent, contextual breakdown of critical issues (from loading speed and UX friction to conversion leaks) is powerful. But the real differentiator will be how intelligently the AI handles the prompting layer behind the scenes.

For consistent, high-value results, the system must enforce smart guardrails: structured framing that keeps outputs relevant to the site’s specific goals, while avoiding the common pitfall of overbloated, generic advice. Equally important — and this is where many analytics tools fall short — it shouldn’t just flag problems. It needs to proactively offer clear, actionable solutions: for instance, on the landing page, “Your hero section above-the-fold is causing a R% bounce rate because of X; here’s the exact copy and layout tweak that improved similar pages by Y% in our benchmark data.”

When a tool can do both — diagnose with precision and prescribe with clarity — it stops being just another dashboard and becomes a true strategic partner. This has the potential to level the playing field for countless site owners who don’t have dedicated growth teams. Excited to see how it evolves. Great work!

Ok, interesting idea. While you mention the qualitative data, which is the main reason to find and understand the "Why", you then fix this problem just to a certain degree by using quantitative data to help you interpret behavior patterns and get qualitative data insights...Does that help you make the difference between the user's actions and his wants, needs, pains, and so on? It's hard to get a PMF this way

This makes sense, especially for people who hate tracking everything.
“Can I spend today without guilt?” is the real value here.
The challenge is communicating that fast enough.
If you turn that into a simple, relatable demo, it becomes much more compelling.
A clean 15–30 sec video showing the experience can boost conversions a lot ,if you want we can discuss and i will share some previous work

The screenshot tells the whole story.

TTFB at 1302ms means the server
is already losing the user before
a single pixel renders.

Most optimization tools show you
the symptom — LCP, FCP, INP.
Spectry is showing the cause.

Same gap exists in cart recovery.

Every tool shows you the abandonment rate.
None show you what happened
10 seconds before the tab closed.

Behavioral signal exists before the event.
Performance issue exists before the bounce.

The "what happened" tools built dashboards.
The "what to fix" tools build
diagnostic engines.

That's the right direction.

dashboards were never the bottleneck - the gap was always translating data into a next action. building around that is the right call

Completely agree — most tools tell you what happened, not what to do next. The real value is in actionable insights. How are you bridging that gap in your approach?

This hits.

Most tools tell you what happened, not what you’re losing right now.

Failed payments are a good example, founders don’t even realize revenue is slipping until months later.

The reporting vs diagnosing gap is exactly right — most dashboards tell you what happened but not why or what to do next

Same problem exists in financial dashboards for startups — founders look at revenue numbers and feel good but have no idea their cash flow is about to collapse

The businesses that make good decisions are the ones who track the right numbers not the most numbers

Data without a decision framework is just noise

this hits on something i've felt building my own product. the "dashboard illusion" is real — i spent way too much time staring at numbers that told me something was wrong but gave me zero clue what to actually change. the point about session replays is spot on. one 2-minute replay of a confused user taught me more than weeks of bounce rate analysis ever did. there's something about seeing the hesitation that just clicks differently. the framework you laid out (drop-off → replay → heatmap → errors → hypothesis) is genuinely practical. most posts like this stop at "use behavior analytics" without giving you the actual workflow. bookmarking this one. will check out spectry — the funnel-to-replay direct access sounds like exactly the missing link.

So true — I had tons of data in Google Analytics but still no clue what to fix until I started watching real user sessions. That shift from “numbers” to “behavior” is where things actually started improving.

This is the same gap I ran into — but for AI search instead of UX.

Google Analytics tells you your traffic. SEO tools tell you your rankings. But nobody was telling business owners, “ChatGPT doesn’t know you exist — and here’s exactly why.”

Different problem, same pattern — the tools that show you what’s broken are always more useful than the ones that just show you what happened.

This is interesting. How are you tracking where users drop off in the funnel?

yeah, this hits. I kept seeing the same thing — dashboards look clean, but you still don’t know what to actually change. Feels like the real value starts when you can see what the user was actually doing before dropping off

Yes! This is something I learnt while running a service based business. I am actually building a SAAS that solves the exact problem, except my dashboard is not glamorous, but simple. And it involves 3 simple steps:

1. Track raw metrics: What was your input on which platform and using which angle, what was the output and what actually converted to the result you wanted in the first place?

1. What is your conclusion?

2. What is 1 action at minimum that you can take to make this month better?

I wanna build a simpler version compared to many complex tools nowadays (targeting people who wanna start by keeping things as simple as possible, mainly for new basic service based/ local/ low ticket reselling/ teen digital product sellers) that display the analysis in the form of 10 different graphs and make you feel like a $100 million CEO but are just low ROI and take the same time to understand that you could have spent gaining 3 new clients. Do you think this will be useful (currently in my taste your own dog food stage).

That's something new. I would really check it out

This comment was deleted 5 months ago

the dashboard illusion is real. i've spent hours staring at ga4 trying to figure out why people drop off the pricing page without any luck. session replays are a game changer for this. it's one thing to see a bounce rate and another to see a user actually struggling with a broken button or confusing copy. definitely looking into spectry

Outstanding post and explanation, Spectry. I totally agree with you. Most of the time the product is not defaulty. But the website's errors and blockages stop the conversions. Mobile experience and load time are the most common problems in many websites. This decreases the duration of users stay on a website. Then the reliability of search engines get dropped

Mostly people only focus on the design of websites. But UX and SEO are two different things. If the technical structure is not proper, the product won't be visible. We have written a guide on amdigitalmarketingagency . com. It shares 7 SEO methods of website's success in 2026. It explains the importance of technical seo. 

Marketing can't work out until UX is not strong. The founders that are learning about their mistakes here, must go through our guide. In this way, their website's ranking and conversion both will be improved. 

Thanks for sharing such critical and informational insights.

Hey, really solid list — super practical!

I especially agree with #2 (too many choices) and #9 (unclear value proposition). These two quietly kill so many landing pages.

Thanks for putting this together. Saved it as a quick checklist for my own site.

Which of these 10 mistakes do you see the most often when reviewing SaaS pages?

Good list. Point 9 is the one I see overlooked the most.

Slow load times are easy to diagnose with a tool. A weak value proposition is harder, because the person who built the page usually can't see it. They know what they mean, so the vague headline still makes sense to them.

The blind spot runs deeper than most teams realize. By the time you're tweaking CTAs and trust signals, the bigger issue is often that a first-time visitor still doesn't understand what the product actually does for them.

Point 6 on trust signals is interesting too. Reviews and logos help, but specific examples tend to land better. A concrete example of what the product found or fixed is often more convincing than a five-star quote.

This looks really interesting, especially the focus on the problem Spectry is solving.

One thing I have noticed with products in this space is that the core feature usually gets attention first, but long-term growth seems to depend on how clearly the value is communicated on the first visit.

Curious how you are thinking about this:

- What has been the biggest “aha moment” users have after trying it?

- Are people immediately understanding the value, or does it take some onboarding/education?

Also, would love to know what your current biggest bottleneck is: building features or getting distribution?

Feels like with most indie products, the shift from “this works” → “people actually find it” is where things get real.

Experience the latest features we’ve implemented—now available for free for a limited time. You can access and install the update directly via the Chrome Web Store: https://chromewebstore.google.com/detail/marginix-%E2%80%93-amazon-profit/gmgajlccniogemeeocgcnagpfngomloe Try it out today and let us know what you think. I'm available if you need any assistance or have questions during your trial.

Spot on, small friction kills conversions more than anything.

Number 9 really stuck with me. For Flowly, our first landing page led with "unified productivity workspace" and feature lists. Bounce rate was brutal until we flipped it to the actual problem: "Stop switching between Todoist, Toggl, and Calendar. One app. $8/month."

The shift from explaining what we built to showing what actually changes for someone using it made a real difference.

If you ever get a moment, would be curious what you'd spot on our landing page. Always looking for the friction points we're blind to.

flowly.run

This is great for discovering new projects. What's been the most interesting build you've seen recently?

Some great point in here that I will 100% be implementing when I launch my site. Please keep up the good work :)

The value proposition point hits hard. We made the same mistake early on — explaining the "how" instead of the "what you get." Once we shifted to outcome-first language, bounce rate dropped noticeably. One thing I'd add to the list: message match between your ads and landing page. If someone clicks expecting one thing and lands on something slightly different, they're gone before they even read your CTA.

Check out our free tool in our profile that we are testing that tests for most of these plus ux compliance checks.

I appreciate the summary. It's one of those things that's "so obvious" when someone spells it out for you, yet so easily forgotten or overlooked when you're actually building. the build mobile first is an important reminder for me.

Good thoughts and nice overview - will feed this to CC and see what it comes up with 😄

Number 9 hit home. My first landing page for SheetPair (CSV/Excel comparison tool) had three paragraphs explaining the tech. Nobody cared. Replaced it with "Upload two files. See what matches and what doesn't." and a 3-step visual — upload, map columns, results. Way more people actually tried it. Plain language > clever copy every time.

The part about trust signals really stands out. It's not about having a good product. It's about how it feels when someone lands on it. Even small things can create hesitation.

I've been noticing that even when everything technically works, users still don't engage if the product feels empty or inactive. No visible activity, no sense that others are using it.

It really makes you realize that early-stage UX isn't just design or speed, it's perception.