AI-powered CRO platforms promise to simulate shoppers on your store, watch how these virtual customers interact with your site, then surface recommendations based on what they observe. No more agency retainers or A/B testing tools you don't know how to use. 

Just point the algorithm at your store, implement the recommendations, and watch your conversion rate climb (apparently)

Shopify has launched SimGym. Half a dozen other (albeit lesser-known) platforms offer similar promises. The underlying premise is always the same: they've seen what works across thousands of stores, and they can tell you how to optimize yours.

I understand the appeal. I've worked with enough Shopify merchants to know the exhaustion. CRO is expensive, time-consuming, requires expertise that most store owners don't have. The idea of automation solving this problem is seductive.

But the more I hear about these tools, the more convinced I am that we're optimizing for the wrong thing entirely. 

We're about to see thousands of ecommerce stores optimize themselves into identical mediocrity.

The Seduction of simulated certainty

Real conversion testing is hard. 

It requires statistical literacy many operators don't have time to develop. It demands patience: letting tests run long enough to reach significance while every fiber of your being screams to just pick the version that feels better. It costs money, either in agency fees or the opportunity cost of learning conversion principles while your inventory needs managing and your customer service queue grows and your ad account needs attention.

Then here comes a platform offering to solve all of it. Virtual shoppers testing your site while you sleep, harnessing machine learning models trained on millions of shopping sessions. 

Recommendations that arrive like prophecy: "Stores in your category see a 12% lift when they display shipping thresholds above $75."

It feels like the democratization of expertise. 

The problem is that expertise isn't what's being democratized. What's being democratized is conformity.

What simulation can't predict

My years at Smile (the largest loyalty platform for Shopify brands) gave me a particular vantage point on this.

I’ve watched thousands of loyalty programs across every imaginable vertical, including hundreds of direct competitors all in the same category, with the same customer demographics and same average order values.

The most successful loyalty programs often had almost nothing in common.

The boutique skincare brand whose reward program let customers participate in in-depth product research surveys in exchange for loyalty points — something every best practices guide would call friction — their program crushed it because their customers wanted to be part of a community of skincare obsessives who got to have skin in the game (literally). 

The outdoor gear retailer with a VIP tier structure that made zero logical sense from a gamification perspective: random point thresholds and no clear progression path. It worked perfectly because their customers were adventure seekers who valued the unexpected over the predictable.

The kids' supplement company that ignored every loyalty best practice and just gave people straightforward cashback? Their customers were busy parents buying vitamins. They didn't want to play games with multivitamins, and the repeat purchase rate showed the receipts.

Mediocrity is the killer 

Best practices are seductive because they promise to shortcut the messy, expensive work of understanding your specific customers. 

Here's what simulated CRO fundamentally misunderstands: your customer isn't a demographic cluster. 

She's not "female, 32, swimwear category, coastal urban area." She's a woman who chose your brand because your copy voice sounds like her group chat. Because your model diversity reflects bodies she actually recognizes. Because your return policy gave her confidence to order three sizes of the same suit. Because your Instagram made her laugh last Tuesday.

The invisible factors that live in the space between demographics and behavior are what make your store yours. They're also what simulation can't see.

So the platform makes recommendations based on aggregated behavior from other swimwear brands. 

You implement them. 

Your store starts looking more like theirs. 

The edges that made you different get sanded down in service of statistical significance.

When every store in a category converges on the same simulated best practices, we aren’t raising the bar.  We flatten it.

The data question nobody's asking

Here's where my skepticism gets potentially uncomfortable.

For simulated CRO to generate genuinely useful recommendations, the platform needs boatloads of data beyond basic conversion metrics. 

They need to know the exact copy on your add-to-cart button. Whether you display fast payment options. Where your trust badges sit. If customers land on product pages or collections first. Whether they're first-time visitors or returning customers. If they browse cross-category or stay focused. How your mobile experience differs from desktop. What your shipping threshold messaging says. Where your reviews display.

That's not captured in aggregate traffic data.

Which raises a question: do the platforms have this data, or don't they?

If they don't have the data

If they're working with conversion rates, traffic sources, and basic theme information, then the simulations are running on incomplete information. The recommendations might be directionally helpful (faster load times probably help), but they can't account for the specific context that makes or breaks testing. 

It's like diagnosing an illness after only checking someone's pulse.

If they do have the data

If Shopify et al have been quietly logging every single button copy and layout variations and payment displays across the entire network of their customers, then the question becomes: what else are they observing that we don't realize? How much insight into store performance and customer behavior are these platforms sitting on? And why has it taken so long for them to share this potentially critical information with merchants?

I'm not suggesting anything nefarious. Platform business models depend on merchant success. They have every incentive to help stores convert better.

 But the data question nags at me.

What good testing actually reveals

Simulated CRO might catch obvious problems. Page load issues. Broken mobile layouts. Forms that don't submit. These aren't small things either, fixing them matters.

But real conversion work is about discovering truths.
You can’t achieve true CRO just fixing bugs. 

The brands I've seen build genuine customer loyalty weren't the ones following best practices. They were the ones who stayed curious about their specific customers. 

Who resisted the seduction of best practices in favor of the harder, slower work of specific practices.

Simulated CRO offers efficiency. Testing doesn't always feel efficient. Sometimes it's messy and surprising and reveals things that resist generalization. 

The insights you actually need can't be extracted from a million other stores' data because it lives in the specific, weird, unpredictable relationship between you and the people who choose to buy from you.