What is synthetic A/B testing?

Synthetic A/B testing runs proposed site variants against modeled buyer cohorts before any live traffic sees them. Squoosh models shoppers from your behavioral data, runs every variant through a paired test, and ranks which version is most likely to lift conversion — so you decide before customers are exposed.

How accurate is synthetic A/B testing?

Squoosh matched the live winner in 81% of completed A/B tests in our blind benchmark across e-commerce, SaaS, and fintech flows. We picked a winner before seeing the live result, then compared. Full evaluation report available on request.

How is this different from running a live A/B test?

A live A/B test exposes real customers to every variant and waits for statistical power, which usually takes weeks and burns conversions on losing variants. Synthetic testing decides before live traffic is committed — under an hour from hypothesis to ranked answer, with zero live visitors exposed.

Do you store personal data from my customers?

No. Squoosh models buyer cohorts from aggregated behavioral signals — device, intent, category affinity, funnel position. No cookies, no PII, no waiting on real traffic. The shoppers running through your variants are simulated, not tracked humans.

What integrations are supported?

Shopify (orders, products, sessions), Google Analytics 4, Fullstory, Segment, Contentsquare for inputs. Vercel, Cloudflare, LaunchDarkly, GitHub, and arbitrary webhooks for outputs. Optimizely / VWO / Adobe Target / AB Tasty for live confirmation handoff.

A/B test answers before live traffic

Know what will convert before you ship.

Squoosh tests proposed site changes before launch, ranks the variants most likely to lift conversion, and shows where buyers would drop off.

First Shopify-approved app for synthetic A/B testing

Trusted by teams turning site ideas into conversion decisions.

How it works

From hypothesis to ranked variants,
in under an hour.

Bring a hypothesis. Squoosh tests each option, ranks the likely winner, and shows what to fix before live traffic sees it.

01Model your shoppersModel the buyer segments that matter using behavioral data, funnel signals, and your site context — without cookies or PII.

Maya

Robert

Priya

Dana

Elena

Jordan

Sana

Tomás

Anika

Kenji

Leah

Carlos

0shopper journeys ready

02Test each variantEvery shopper tries every variant in a paired test. Same brains, different pages — so the delta you see is the design, not the crowd.

Current Site

Buy Now

Now testing

Robert“compare brands”

0 / 1,8200%

conversions

2.42%

conversion rate

Urgency

Get it today

Now testing

Priya“price sensitive”

0 / 2,1400%

conversions

3.55%

conversion rate

Reviews

Join 40K+

Now testing

Dana“read reviews”

0 / 1,6400%

conversions

2.71%

conversion rate

03Rank & decideSee the likely lift, the confidence behind it, and the reason shoppers chose or dropped. Ship the strongest version or keep iterating before customers see it.

Conversion Rate

+0.00%

Revenue / Visitor

+0.00%

Bounce Rate

-0.00%

81%

matched live A/B winners

<1 hr

from hypothesis to decision

250M+

behavioral signals grounding shoppers

live visitors exposed

The benchmark

We checked our work against real A/B test winners.

We ran Squoosh blind against completed live A/B tests with known winners across e-commerce, SaaS, and fintech flows — checkout, product pages, pricing, onboarding, and landing pages. Then we compared Squoosh's predicted winner to the live result.

✓Completed tests only — known live winners, not synthetic-only benchmarks.
✓Blind prediction — Squoosh picked a winner before seeing the live result.
✓Controlled comparison — each variant was evaluated under the same modeled buyer conditions.
✓Pre-traffic decision — use the result to decide what deserves live traffic.

Prediction accuracy

Squoosh matched the live winner in 81% of completed A/B tests.

How we comparewinner accuracy

Squoosh81%

Gemini 2.5 Pro56%

GPT-553%

Gemini 2.5 Flash51%

Winner accuracy in a live confirmation test.

Full evaluation report available on request.

From a customer using Squoosh weekly

“Nothing short of revolutionary.”

Charlotte Welean

Head of CRO, Credo Agency

Integrations

Purpose-built on your data, not a generic persona.

Squoosh uses your analytics, order history, session behavior, and segment data to model how your actual shoppers move through the funnel — where they hesitate, what they trust, and why they drop.

Pull data from

Shopify

order history · cart composition

Google Analytics 4

funnel paths · drop-off events

Fullstory

hesitation patterns · rage clicks

Segment

segment definitions · user traits

Contentsquare

scroll depth · zone engagement

Your synthetic shopper

Priya Shah

Modeled buyer segment Returning mobile buyer · modeled from 4,318 similar shopper sessions

#SH-04318

Behavior signals

Avg. cart $42 · 2.3 itemsShopify

Drops at shipping stepGA4

Hovers on promo code fieldFullstory

Returning mobile buyerSegment

Scrolls past hero, skims PDPContentsquare

Send the winner to your launch workflow

Squoosh shows which variant deserves live traffic. Push it to production, route it into your A/B tool, or hand it to your team for review.

Deploy direct to

VercelCloudflareLaunchDarklyGitHubWebhook

Or send to your experimentation stack for live confirmation

OptimizelyVWOAdobe TargetAB Tasty

Decide before a single customer drops off.

Bring a site change. We'll test it live in 30 minutes.

See the benchmark

A/B test answers before live traffic

Know what will convert before you ship.

Squoosh tests proposed site changes before launch, ranks the variants most likely to lift conversion, and shows where buyers would drop off.

First Shopify-approved app for synthetic A/B testing

Trusted by teams turning site ideas into conversion decisions.

How it works

From hypothesis to ranked variants,
in under an hour.

Bring a hypothesis. Squoosh tests each option, ranks the likely winner, and shows what to fix before live traffic sees it.

01Model your shoppersModel the buyer segments that matter using behavioral data, funnel signals, and your site context — without cookies or PII.

Maya

Robert

Priya

Dana

Elena

Jordan

Sana

Tomás

Anika

Kenji

Leah

Carlos

0shopper journeys ready

02Test each variantEvery shopper tries every variant in a paired test. Same brains, different pages — so the delta you see is the design, not the crowd.

Current Site

Buy Now

Now testing

Robert“compare brands”

0 / 1,8200%

conversions

2.42%

conversion rate

Urgency

Get it today

Now testing

Priya“price sensitive”

0 / 2,1400%

conversions

3.55%

conversion rate

Reviews

Join 40K+

Now testing

Dana“read reviews”

0 / 1,6400%

conversions

2.71%

conversion rate

03Rank & decideSee the likely lift, the confidence behind it, and the reason shoppers chose or dropped. Ship the strongest version or keep iterating before customers see it.

Conversion Rate

+0.00%

Revenue / Visitor

+0.00%

Bounce Rate

-0.00%

81%

matched live A/B winners

<1 hr

from hypothesis to decision

250M+

behavioral signals grounding shoppers

live visitors exposed

The benchmark

We checked our work against real A/B test winners.

✓Completed tests only — known live winners, not synthetic-only benchmarks.
✓Blind prediction — Squoosh picked a winner before seeing the live result.
✓Controlled comparison — each variant was evaluated under the same modeled buyer conditions.
✓Pre-traffic decision — use the result to decide what deserves live traffic.

Prediction accuracy

Squoosh matched the live winner in 81% of completed A/B tests.

How we comparewinner accuracy

Squoosh81%

Gemini 2.5 Pro56%

GPT-553%

Gemini 2.5 Flash51%

Winner accuracy in a live confirmation test.

Full evaluation report available on request.

From a customer using Squoosh weekly

“Nothing short of revolutionary.”

Charlotte Welean

Head of CRO, Credo Agency

Integrations

Purpose-built on your data, not a generic persona.

Squoosh uses your analytics, order history, session behavior, and segment data to model how your actual shoppers move through the funnel — where they hesitate, what they trust, and why they drop.

Pull data from

Shopify

order history · cart composition

Google Analytics 4

funnel paths · drop-off events

Fullstory

hesitation patterns · rage clicks

Segment

segment definitions · user traits

Contentsquare

scroll depth · zone engagement

Your synthetic shopper

Priya Shah

Modeled buyer segment Returning mobile buyer · modeled from 4,318 similar shopper sessions

#SH-04318

Behavior signals

Avg. cart $42 · 2.3 itemsShopify

Drops at shipping stepGA4

Hovers on promo code fieldFullstory

Returning mobile buyerSegment

Scrolls past hero, skims PDPContentsquare

Send the winner to your launch workflow

Squoosh shows which variant deserves live traffic. Push it to production, route it into your A/B tool, or hand it to your team for review.

Deploy direct to

VercelCloudflareLaunchDarklyGitHubWebhook

Or send to your experimentation stack for live confirmation

OptimizelyVWOAdobe TargetAB Tasty

Decide before a single customer drops off.

Bring a site change. We'll test it live in 30 minutes.

See the benchmark

Know what will convert before you ship.

Trusted by teams turning site ideas into conversion decisions.

From hypothesis to ranked variants,in under an hour.

We checked our work against real A/B test winners.

Purpose-built on your data, not a generic persona.

Decide before a single customer drops off.

Know what will convert before you ship.

Trusted by teams turning site ideas into conversion decisions.

From hypothesis to ranked variants,in under an hour.

We checked our work against real A/B test winners.

Purpose-built on your data, not a generic persona.

Decide before a single customer drops off.

From hypothesis to ranked variants,
in under an hour.

From hypothesis to ranked variants,
in under an hour.