Omniconvert

Omniconvert is the straightforward solution for eCommerce experimentation 

Our suite:

Explore 
- real-time web personalization (based on weather, geolocation, cookie and behavior data, etc)
- A/B testing using an advanced WYSIWYG editor
- triggered mobile and desktop interactions (exit-intent technology, on-load, on-scroll or on-click)
- surveys with branching logic that you can easily build in order to get strong insights about specific segments of visitors (new, returning, sleepy, lost, VIP, etc)

Pulse
- NPS monitoring
- real-time alerts based on NPS + RFM segment
- advanced NLP analysis of customer feedback

Reveal: 
- Advanced customer segmentation 
- Customer analytics - CLV, retention, cohorts, etc
- Audience Builder - target customers based on their NPS, RFM, product, category, time since the last order, city, etc

Orgo.space

Investor

Omniconvert CVO Academy

There's a shift going on in the entire retail industry: from products to customers and from acquisition to retention. 
To be able to make the shift, companies have to understand the customer behavior and the jobs that their products are being hired for by their customers. 

Amazon makes a great job on this aspect: they manage to keep 9 out of 10 customers coming back and buy again. 
Customer lifetime value should be the north star metric for any eCommerce company willing to thrive.

Together with 8 great minds in the digital eCommerce and marketing arena, I am starting an online academy to help companies understand and improve customer lifetime value. 
It is a serious endeavor - for the first course, we condensed all our knowledge about customer lifetime value into >100 lessons.

In a world where 3rd party cookies are becoming irrelevant, becoming an expert in a field that’s just shaping up will be an unfair advantage for you and your company. 

Working in an email marketing, CRO, or PPC agency? This is going to give you huge leverage: you will be able to actually know what’s going on with your eCommerce customers better than they do. 

Leader of a retailer willing to nail digital transformation? Don’t neglect this: the main reason that you’re in the digital transformation is to become customer-centric and adapt to the new market conditions. 
How can you do that, if your team has outdated knowledge?

Founder & Course Instructor

Pintilie Broker de Asigurare

Business development
Marketing strategy
Innovation

Marketing Manager

Co-founder

Etcetera

Owner

Sover Optica Shops

Sales Trainer

BNet Computers

Co-owner

Sover Optica

Key Account Manager

Delta RFI

Weekly radio-show producer, just for fun: getting in touch with thousands of ears might be delightful:)

Radio-Show Producer

Founder at Omniconvert: I help retailers increase their Customer Lifetime Value and Conversion Rate with our advanced solutions

I help eCommerce brands and retailers unlock growth by leveraging CRO & CLV! 

I’m the founder of Omniconvert, where we empower eCommerce players like Patagonia, Shiseido or Tempur make progress through experimentation & AB testing 

After 18 years in eCommerce, here are three significant achievements that brought me here:

➔ My first eCommerce company started with just $700 and went on to become the largest online car insurance player in Romania, thanks to the principles of Conversion Rate Optimization (CRO) and Customer Lifetime Value (CLV).

➔ After realizing the game-changing potential of Conversion Rate Optimization I launched OmniConvert, a growth experimentation suite for eCommerce companies, where >1K eCommerce companies ran >60K experiments 

➔ My team helped >800 eCommerce companies like Patagonia, Sephora, and Max Mara run experiments to improve their conversions and CLV

I’ve finally put all my experience in one place and wrote a best-selling book: The CLV Revolution - a practical guide with actionable insights to leverage the power of customer research, CX, and experimentation to improve the Customer Lifetime Value in eCommerce. 

Currently, we're developing the first AI-powered CRO audit in collaboration with 100 leading experts from the CRO community, with the goal of establishing the global CRO standard for eCommerce

✉️ Let’s connect! DM me to chat about AI in CRO, CX, CLV, or experimentation, or reach me at valentin [@] omniconvert [.] com

15 years ago, I was an eCommerce founder with a tiny budget and a big problem. 
I kept guessing. 
New tools, new hacks, more guessing. 
Money burned, momentum stalled, customers misunderstood.
Then I flipped the script. 

I treated growth like a lab: small tests, fast cycles, pissed-off developers, and ruthless honesty with the data. 
Quali + Quanti started to tell a story. Conversions climbed. CLV followed. 
No massive budgets, no heavy dev teams, just disciplined experimentation and clear decisions. 

The company that I co-founded with $700 was making 14M in turnover, and 12 months of ruthless experimentation led to 60% in conversion rate. 
After that, Omniconvert was born. 
A platform built to help eCommerce teams learn faster than they spend. 

Since then, thousands of users have run more than 70kA/B tests and keep rating us highly for one simple reason:
we are small enough to care.

We are not a vanity-metric company. 
We are a results company. 
We strip out the noise, help you focus on customer behavior, and turn experiments into compounding gains.
Now we are launching Omniconvert 3.0. 

It is the next leap in AI experimentation, engineered to make advanced CRO, fast, and accessible to any store, any team size, any budget.

If you are done guessing, or simply want to see what we're all about, join me for the 3.0 launch:

https://lnkd.in/dqCP7Bts

Valentin Radu

omniconvert

marketizator

cvo-academy-by-omniconvert

JSESSIONID

LinkedIn Login, Sign in | LinkedIn

li_at

Talks about #clv, #cro, #cvo, #nps, and #ecommerce

Omniconvert: we help companies grow with AI / AB testing

This is a very dangerous lie: 
"Good work speaks for itself."

It doesn’t.
I spent years building in silence. 
I thought if I just kept my head down and grinded, the market would notice. 
But here is the hard truth: If you don’t use your voice, the market files you in the wrong folder: "Ignore"
And when the moment comes, they won’t think of you.

My journey wasn't a straight line to the boardroom.
Event 1: Humble beginnings in Bucharest ->
Event 2: 2h in a butcher shop when I was 15 -> 
Learned that life can get real ->
Event 3: Traded time for money without creating leverage
Event 4: Scraped together $700 to launch my first eCommerce business. Learned by doing. I stumbled. I got back up.

But the real unlock didn't come from better code or more ads. 
It came when I stopped guessing and started listening. 
I took customer insights and turned them into experiments.

For years, I hid behind the label "Builder, not Speaker." 
That label kept me small. 
So, I rewrote the pact with myself.
I decided to share the scars. 
To teach what I know. 
To challenge the status quo gently but firmly.

Now, my folder in the market is clear:
I help companies stop guessing using our tech + services. 
I turn data and customer behavior into experiments that lifts 3 things:
1. Conversion Rate
2. Customer Lifetime Value (CLV)
3. NPS

That’s it. No "growth hacking." 
No shiny tactics.
CLV is the compass.
Experiments are the engine.
Customer Truth is the fuel.
I poured this methodology into my book, The CLV Revolution, and I built it into our software.
Today, there are 2 years since I launched my book. 
One of the hardest, yet best projects of my life so far.

The takeaway?
Your voice is not vanity. It is the index tag in people’s minds. 
If you don’t write it, they will write it for you, and they’ll probably get it wrong.
I’m choosing to write mine.
So when you are ready to turn data into experiments that actually move the needle? 
You know exactly where to find me.

Happy Birthday, Romania!

May your next experiments have solid hypotheses!

That’s my current state: 
Happy as we’re officially launching Omniconvert Explore 3.0

Join me next Tuesday to see how we’re aiming to deliver massive value to the ecommerce and Shopify ecosystem!

 https://lnkd.in/dV-FT_Wm

I’ve been building for 20 years. 
I thought more features meant more growth. 
It didn’t.
9 years ago, we raised capital. 
I hired fast. 
I fired (too) slow. Twenty people. Painful. Necessary. 
That’s how we cracked churn and built a real operating cadence. 
Systems over vanity metrics. 
Clarity over chaos.

For SaaS, the market has now changed and is louder + more crowded than ever before.
LinkedIn wants 50 dollars for a single click. 

Come see me at Tekpon AI Summit. 

I will share how...

we stopped wasting money
I dodged burnout 
I did channel math. 
and why PMF without CMF is a mirage. 
to shape offers people can't ignore
to tune the message until it punches through.
To combine product engineering with GTM engineering

What worked. What hurt. 
What actually scales now. No fluff.

You’ll get the moves, the scorecards, the templates. 

Walk out ready to stop building more and start growing smarter in 2026.

Happy to join heavy hitters like Sara Maldon, Oren Greenberg, Frank Sondors, Mike Korba, and many more! 
Tx for making this happen, Cristian Dina & Alexandru Stan!

Retail is about to be disrupted. 
Experimentation is about to be disrupted.
Massively.
As pretty much anything else on this planet.
Figure this:
We are moving from disconnected human silos to a unified, Agentic AI ecosystem.

The manager's brain is the bottleneck. 
The data flood:
- competitor ad spend
- real-time sentiment
- pricing
- competitor ads & offers
- stock availability
Is too vast for manual analysis. 
Old-school "If-This-Then-That" logic is dead; it’s too slow and rigid for shrinking margins.

The Solution: An autonomous, self-learning engine.

Input (Total Recall): 
Ingests everything: from Shopify customer data to unstructured reviews to competitor stock levels.
Process (The Brain):
 AI Agents don’t just analyze; they hypothesize. They predict impact, rank experiments, and decide the next move.
Action (The Hands): 
The system auto-adjusts levers, swapping creative, changing pricing, or triggering SMS, without waiting for approval.
The Mandate: 
Experiment everywhere. The goal isn't to be "right" once, but to test constantly at machine speed.

The Shift: 
The E-commerce leader is no longer a "doer" pulling levers, but a Supervisor auditing the machine’s logic.

See you there!

I am 45.
I’ve started to appreciate the discipline after 3 long decades in which I wrestled to gain consistency and perseverance. 

Now I know they’re a huge part of the game.

In your personal life and in your professional life, as well.
Systems beat sporadic inspiration. 

The consistency creates compounding.

While perseverance to do things that you hate turns taste into output.

And here’s one more: the feedback loops > random epiphanies.

Creativity isn’t a lightning strike; it’s following the context you create and the process you run.

We picked the most incredibly important aspects for our users and then we have executed.

That’s how meaningful work ships.

See you at our launch event for Omniconvert 3.0 this Tuesday:

https://lnkd.in/dV-FT_Wm

I got into the SaaS with a lot of stupid ideas in my head.
And made a ton of mistakes.

That’s why, I am trying to spare fellow entrepreneurs from making the same ones - tomorrow at Tekpon AI 

For the first 3 years, I thought I was a genius for building features. If churn was high? Code a new feature. If leads were low? Code a new feature.

I was treating every business problem like a nail, and "Product Engineering" was my only hammer. 

The result? 
I built a spaghetti-code monster and walked straight into the Red Ocean (see image #2). 
High supply. 
Incredibly high CAC. 
Low close rates.

It cost me $1M and a painful round of layoffs to learn the difference between Product Engineering and GTM Engineering.

In my upcoming presentation, I’m sharing the unfiltered playbook. No fluff, just the scars and the system.

We will cover:
1. The $1M Mistake: Why hiring more sales reps before you nail a system is suicide. 

2. PMF vs. CMF: Why Product-Market Fit means nothing without Channel-Market Fit. 

3. How to nail GTM these days - out step by step process

Come for the scars. Leave with a system.

Who’s ready to get out of the Red Ocean? 👇

I've been an idiot. 
Many times. 
Here's one story of how reality humbled me.
And how I re-learned the price you pay when you ignore the cognitive load.

Last year, I had a "brilliant" idea.
My team pushed back, but I am the CEO. I should know better, right?
So, we invested a lot in a big pricing experiment:

Let's build a "quiz-like" pricing page at Omniconvert. 
The idea was a wizard asking two simple questions before revealing our pricing. 
We asked what customers were after (Conversion Rate, NPS, CLV) and what best defined their company (eCommerce, Agency, other).

My hypothesis?
If we confirm to our users that we know what's their pain, more of them will want our solution.
The outcome?
A "statsig demonstrated disaster." 
We had to let it run for 2 months - the pain of experimenting on the bottom of the funnel. 

The cognitive load for users was simply too high. It was a clear failure.

This experience solidified my belief in the "Keep It Simple, Stupid" principle. 

In A/B testing, adding more steps often adds friction. 

You have to test if the trade-off to push more complexity pays off.

Every A/B test essentially does three things: 
1. it modifies what's there (less, more, differently), 
2. removes elements, or 
3. adds new ones. 

Too often, we focus heavily on adding more stuff to the user journey.

However, I've found that removing what's already there is often the most underrated and impactful action. 

Sometimes, the best optimization is removing unnecessary complexity. 
It’s about outcomes, not the features.

So, now we have a beautiful straightforward pricing page. 

KISS: what things can you remove from your 
- product 
- process 
- website 
- life 
... that can deliver surprising positive results?

CRO isn’t broken, the incentives are. 
We’ve turned rigorous experimentation into a stage show that rewards loud wins, cherry‑picked stats, and “lift” that never hits the P&L.

I wrote about the CRO disinformation circus: why vanity testing thrives, how weak hypotheses and bad math poison decisions, and what it takes to rebuild trust with customer research, proper stats, and a CLV compass. 

If the goal isn’t profitable growth, it’s theater.

If you’re done with “+40% CVR” posts that don’t move margin, this is for you. 
I break down the traps, the incentives behind them, and a practical path back to evidence and outcomes.

Read the whole guide here : https://bit.ly/CRO-circus

I'm Valentin. I write (almost) daily about growing eCommerce companies through data, experimentation, and deep customer insights.
If you want to scale smarter, click that bell button on my profile.

The CRO Disinformation Guide

In 2010, I was that frustrated eCommerce marketer: 

Staring at Google Analytics at 2 a.m., trying to figure out why our CAC was climbing and our conversion was stuck. 

Every change I asked for took weeks. 
The devs were annoyed. I was losing sleep. 
So I hacked a way to change the site without waiting. 
That hack became the ancestor of Omniconvert.
Fast-forward. 

We went up against the big, well-funded players. 
They had billboards; we had scars and customer calls. 

When Optimizely raised a monster round, our CPCs exploded overnight. 

I remember seeing bids jump from around a dollar to north of $40, feeling like we’d been priced out of our own category. 

That day forced a decision: we can’t win on noise, so we’ll win on outcomes.
We doubled down on three things:

1. Evidence over ego: every feature earns its place through real experiments and real revenue impact.

2. CLV over vanity: optimize journeys that compound, not just pages that spike.

3. Partner over vendor: fast, human support, and hands-on onboarding so teams actually ship tests.

That’s why today, real users share that Omniconvert Explore is the tool they enjoy using more than the well-funded alternatives. 
Not because we’re louder, but because it’s easier to get to a clean hypothesis, target the right segment, launch without code, and learn fast. 

And when they get stuck, a human shows up.

I started with $20K and a pain I couldn’t ignore. 

The product you see today is just that pain, polished by 70k+ experiments and thousands of conversations with people like you who need momentum, not marketing. 

If you’re in the eCommerce arena, we built this for you.

98% of the Shopify traffic leaves without buying.
And you're spending more on ads every month to replace them.

Here's the problem: most Shopify stores are fighting a single battle.

They're obsessing over traffic and ad spend while ignoring the 5 leverage points that actually move the needle on revenue.

After helping 5,000+ ecommerce stores and scaling my own to $40M before exiting, I've identified the exact patterns that separate 1% converters from 5% converters.

It's not about working harder, ofc! 
It's about working smarter.

In my latest video, I break down the 5 Shopify optimization strategies that can generate 15%+ revenue lift in 2026 without discounting:

→ How to eliminate wasted ad spend by "scaring away" non-customers 
→ The survey strategy that reveals exactly why 98% don't buy 
→ Why the best customers hold the key to scaling (RFM analysis) 
→ How (and why) can you generate hundreds of creatives in hours 
→ The one page that's bleeding conversions (and how to A/B test it)

If you're making 500+ orders per month and your margins are getting squeezed by rising ad costs, this is for you.

Watch the full breakdown here 
👇
https://lnkd.in/dipkYpHK

P.S. There's a $4K giveaway for the ones that are patient enough to watch the whole 7 minutes

Losing customers is one of the most painful things for e-commerce leaders.
But how can you stop it? 

I have +20 years in eCommerce and I worked directly with companies like Avon, Sephora, Orange, Decathlon, Ikea, Patagonia, and many more. 

I wrote a best-selling book about nailing CLV and retention in eCommerce. 

The backbone of my understanding about eCommerce customer behavior is in this framework.

The condensed 10-step framework to prevent customer churn in eCommerce.

Hint: It's not about bombarding customers with discounts. 

In fact, that's often the wrong approach. 
This is addressing the main reasons 80% of your customers are leaving and show you how to fix them.

In this guide, you will learn:

> How to identify your "High-value Loyalists" vs. "Low-Value at-risk" customers.

How to use RFM segmentation and NPS (Net Promoter Score) to find your top 5 churn reasons.

The 3 pillars of customer retention: 

1. Your Product
2. Your Customer Experience (CX)
3. Your Marketing

See it here:

https://lnkd.in/dftbhReu

FREE RESOURCE ALERT: Get the complete Post-Purchase eCommerce Flow to - just comment anything to get access to it

Stop scaling loss. 
Your Shopify store is stuck at 1.5% conversion because you’re making three mistakes.

Everyone’s talking about traffic, but UX is a real bottleneck. 
If your Shopify store won't convert, you're just scaling your losses. 

Inside, I'm breaking down the 3 reasons you're stuck at 1.5% and giving you the 4-part system we use to fix it on real stores. 

Spoiler: It starts with asking "why not?"

Watch now to fix your conversion rate and go from optimizing for sessions to optimizing for shoppers. 

https://lnkd.in/dxwAwHFQ

youtube.com

Why Your Shopify Store Converts at 1.5% (and Shouldn’t)

Brian Massey

Black Friday = Pretty charts on Linkedin

Here’s why you shouldn’t trust inflated “+X% conversion” claims:

1. No baseline, no truth. 
+47% vs what? 
From 0.8% to 1.2% on 2k sessions is noise, not a business shift.

2. Cherry-picked windows. 
Two great days in a 14-day buying cycle can look like a “winner,” then regress.

3. Wrong denominator. 
“Conversion” on clicks, add-to-carts, or micro-signups isn’t revenue per visitor or net margin.

4. Survivorship bias. 
You see wins; you don’t see the 9 losing tests that were never posted.

5. Misattribution. 
New LP gets credit while price cuts, email pushes, or seasonality did the heavy lifting.

6. Vanity vs actionable. 
Followers, views, CTRs feel good; repeat rate, AOV, RPV, and CLV pay the bills.

7. Hard to reproduce “hacks.” 
If the uplift can’t be replicated on new traffic or cohorts, it’s a lucky strike.

What to demand before believing any uplift:
Baseline and sample size, split, runtime, statistical confidence, and the buying cycle length.

Primary KPIs: revenue per visitor, checkout completion, refund rate, and net margin after returns.

Cohort view: first product bought, device, channel. 
Did it lift second purchase rate?

Reproducibility: rerun on a fresh audience and show stability across weeks. 

Operator’s rule: if it doesn’t move revenue per visitor, repeat rate, or margin, it’s theater. 
Test, measure, then talk.

I'm Valentin. I write (almost) daily about growing eCommerce companies through data, experimentation, and deep customer insights.
If you want to scale smarter, click that bell button on my profile.

Discounts are a tax on being lazy.

But worse than that, most discounts are unvalidated experiments.
Too many companies treat pricing like a guessing game. 

They slap a "20% OFF" sticker on a product without calculating the break-even point or the long-term damage to the brand. They panic, slash prices, and they hope volume fixes the margin leak.
It rarely does.

I learned this the hard way back when I ran an online car insurance business.

The biggest player in the market entered our category. 
We discovered the segment of truck drivers: a tricky, high-intent, extremely lucrative, but incredibly price-sensitive segment. 
We quickly realized that if you use discounts as a blunt instrument, you bleed money.

So, we started using discounts as a scalpel.

Here is exactly how we did it:

We used real-time rate calculations and competitive syncing. 
Every hour, we pulled fresh competitor benchmarks and recalculated the break-even point. 
We used the "Uneven Nudge." 
Before showing the price, we had a loader in which we articulated our points of difference + testimonials.
We didn't matched the blanket 15% off like our competitors. 

We surfaced a tiny, specific incentive at checkout: 7% for 10 minutes.

And we only did it when the math said the contribution margin was still safe:
- higher discounts for larger AOVs ; lower for lower AOVs

We managed to offer discounts from 7 to 18% (for larger AOVs) 

The result?
We hit the sweet spot. 
We protected our contribution margin because our baseline price was smart. But we also spiked conversion because that odd, contextual discount tipped the fence-sitters without dragging the whole customer base into a "wait-for-a-deal" mindset.

Whenever we experimented with wider, rounder discounts (like a standard 10% or 15%), our margins eroded and customers started waiting for the next sale.

The lesson is burned in:
Stop guessing. 
If you don't know your exact break-even on a discounted unit, you aren't selling; you're gambling.
Get weird with the numbers. 
A 10% discount feels arbitrary. 
A 7% discount feels like you sharpened your pencil, did the math, and cut the price to the absolute bone to help them out.
Fix the story. 
Use precision to close the deal, but use storytelling to make the discount unnecessary in the first place.
Discounts shouldn't be a tax. 
They should be a calculated ongoing experiment.

There are the 4 phases of Agentic eCommerce:

Phase 1: Assisted ops with behavior‑linked creatives & websites

What happens: 
AI generates copy, images, and video variants from product feeds and on‑site behavior signals (views, recency, cart status). 
Creatives adjust headlines/CTAs to match intent and stage, then test automatically. 

Threats: 
Creative sameness and weak hypotheses; privacy/compliance gaps when tying creatives to individual behavior; data noise that misguides automation. 

Phase 2: Predictive personalization using geo‑sentiment

What happens: 
Models blend first‑party behavior with geo‑located sentiment from public reviews to adapt offers, social proof, and messaging by city/region. 
Positive local sentiment boosts urgency; negative themes trigger objection‑handling creatives. Location rules respect consent. 

Threats: 
bias from dirty or unrepresentative sentiment data; overfitting to short‑term CVR at the expense of CLV; consent and transparency risks with location data. 

Phase 3: Autonomous experimentation + price/creative coupling

What happens: 
Systems auto‑spin creatives and page layouts, then coordinate them with competitor price feeds and margin floors. If a rival drops price, AI shifts from discounting to value‑led creatives or bundles, staying within guardrails to protect contribution margin. 

Threats: 
black‑box decisions you can’t justify; price wars from naive competitor‑matching; test pollution and false positives without governance. 
Set minimum margin floors, document logic. 

Phase 4: Journey orchestration for CLV

What happens: 
AI aligns acquisition, onsite UX, pricing, and retention. Creatives adapt in real time by cohort, inventory, and geo performance; pricing varies by rules while offers stay transparent. Success optimized to contribution margin and CLV, not just CVR. 

Threats: 
stack brittleness and vendor lock‑in; misaligned incentives between teams; ethics and trust if pricing or nudges feel unfair. Keep human oversight, clear price logic, and CLV guardrails. 

How managers win:
Anchor to CLV and margin floors; prohibit reckless price‑matching. 

Clean first‑party data and segment by behavior; use consented geo only. 
Feed creatives with real behavior and local sentiment, then test for lift, not volume. 

Standardize experimentation quality and document decision logic to keep humans in control

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