How AI Shoppers Predict Which Product Listing Wins (Before You Go Live)

What if you could know which version of your product listing would get more clicks, more conversions, and better reviews – before you published it?

That’s not hypothetical. AI shoppers can predict listing performance with 90% accuracy on binary choices.

I’ve spent the last two years building and testing AI shopper models that predict which product listing wins before a single real customer sees it. Over 500 experiments later, the data is clear: you don’t need to burn live traffic to figure out which title, bullet point, or claim resonates most.

This guide explains exactly how AI shoppers work, what the accuracy data says, where they fall short, and how you can use them to make better listing decisions – whether you’re launching on Amazon, Shopify, or any other marketplace.

What Are AI Shoppers?

AI shoppers are artificial intelligence agents calibrated to behave like real population segments when making purchase decisions. They’re not generic chatbots guessing what people might buy. They’re modelled on actual demographic data: age distributions, income brackets, geographic locations, and observed shopping behaviours.

Want to know which version of your listing will perform best? Optimise your listing.

Here’s what makes them different from a generic AI prompt:

Demographic Calibration

Each AI shopper carries a demographic profile that influences their choices. A 28-year-old female shopper in Brooklyn with household income of $85,000 doesn’t evaluate a protein bar the same way as a 55-year-old male in rural Texas earning $45,000. Generic AI treats everyone the same. Calibrated AI shoppers don’t.

This calibration draws on established research in computational social science and demographic-weighted choice modelling. The core idea isn’t new – researchers at institutions like Stanford and MIT have been exploring synthetic populations for policy modelling since the 2010s. What’s new is applying this specifically to purchase decisions and e-commerce listings.

Population-Level Representation

When you run an experiment with 250 AI shoppers, you’re not getting 250 identical opinions. You’re getting a weighted sample that mirrors the actual population distribution of your target market. If 22% of your target demographic is aged 25-34 with moderate income, roughly 22% of your AI shopper panel reflects that segment.

This matters because purchase decisions aren’t uniform. Price sensitivity varies dramatically by income. Claim resonance shifts with age. Health messaging hits differently for parents versus singles. A properly calibrated panel captures these variations.

How They Differ From Surveys

Traditional surveys recruit real humans, ask them questions, and aggregate the answers. This works, but it’s slow (2-6 weeks for recruitment and fielding), expensive ($5,000-$50,000 depending on sample size), and often biased by hypothetical bias – people say they’d buy things they actually wouldn’t.

AI shoppers eliminate the recruitment bottleneck entirely. You define your target demographic, the system assembles a calibrated panel in seconds, and you get results in minutes. No scheduling, no incentive payments, no dropout rates, no satisficing.

The trade-off is obvious: they’re not real humans. I’ll be direct about what this means for accuracy later in this article. But for many listing decisions – particularly early-stage ones where you’re narrowing options – the speed advantage is transformative.

How AI Shoppers Predict Listing Performance

The methodology behind AI shopper prediction isn’t magic. It’s discrete choice modelling – the same statistical framework used in conjoint analysis, transportation planning, and market research for decades. The difference is who’s making the choices.

Discrete Choice Methodology

Here’s how it works in practice:

1. You define the listing element you want to test (e.g., five different title variations for a protein bar)
2. AI shoppers are presented with pairs or sets of variations
3. Each AI shopper makes a choice based on their demographic profile and the product context
4. Choices are aggregated across the full panel to produce preference shares
5. Statistical analysis identifies which variation wins and by how much

This is structurally identical to how companies like Conjointly and Sawtooth Software have run choice experiments for years. The panels are different – AI agents instead of recruited respondents – but the analytical framework is the same.

Same Framework as Real A/B Tests

Amazon’s Manage Your Experiments tool runs A/B tests by splitting live traffic between two listing variations and measuring which one converts better. AI shopper prediction does the same thing conceptually – it presents variations to a panel and measures which one gets chosen more – just without requiring live traffic or weeks of data collection.

The key insight is that purchase decisions are driven by predictable factors: clarity of benefit communication, specificity of claims, emotional resonance of language, and perceived value. These factors are learnable from data. An AI model trained on millions of real purchase decisions can predict which listing element will drive more choices with meaningful accuracy.

Calibration Against Real Purchase Data

AI shoppers aren’t just guessing based on general language understanding. They’re calibrated against observed purchase behaviour. This calibration process involves:

– Training on revealed preference data (what people actually bought, not what they said they’d buy)
– Validating predictions against known A/B test outcomes
– Adjusting demographic weighting based on actual market composition
– Continuous refinement as new validation data becomes available

The calibration isn’t perfect – I’ll share the exact accuracy numbers below – but it’s substantially better than asking a generic AI “which title sounds better?” because it incorporates population-level preference patterns rather than relying on a single model’s aesthetic judgement.

What You Can Test

AI shoppers can evaluate any listing element that can be expressed in text or described visually:

– Titles: Which title structure gets more clicks?
– Bullet points: Which benefit statement resonates most?
– Claims: Which product claim drives purchase intent?
– Pricing: What’s the optimal price point for your target segment?
– Image concepts: Which main image approach gets the click? (described conceptually)
– Full listing comparisons: Your listing vs. a competitor’s

The Accuracy Data: What Saucery’s Validation Shows

I believe in being transparent about what works and what doesn’t. We’ve run extensive validation studies comparing AI shopper predictions against real-world outcomes. Here’s what the data shows.

90% Accuracy on Binary Choices

When AI shoppers are asked to predict which of two options will perform better (A vs B), they agree with real-world outcomes 90% of the time. This was validated across five distinct datasets covering food, beverage, health, and personal care categories.

This means if you’re deciding between two title options, two claim variations, or two bullet point approaches, AI shoppers will correctly identify the winner nine times out of ten.

For context, random guessing on binary choices would give you 50%. Expert human judgement (experienced listing optimisers making predictions) agrees with actual outcomes roughly 70-75% of the time. AI shoppers at 90% represent a meaningful improvement over both alternatives.

0.30 Star Error on Review Prediction

One of our more interesting validation studies tested whether AI shoppers could predict product review ratings from listing copy alone – before any real customers had purchased or reviewed the product. The result: an average error of 0.30 stars.

That means if a product eventually receives a 4.2-star average rating, the AI prediction was typically in the 3.9-4.5 range. This isn’t perfect, but it’s remarkably good for a prediction made purely from listing content.

More practically, 62% of the specific complaints that real customers later raised in reviews were identified by AI shoppers during pre-launch testing. This gives sellers an opportunity to address potential issues – in the listing copy, in the product itself, or in customer expectations – before negative reviews appear.

90% Agreement With Expert Reasoning

We also tested whether AI shoppers could explain why one listing outperforms another. When compared against expert A/B test analysts reviewing the same test outcomes, AI shopper reasoning agreed with expert explanations 90% of the time.

This matters because prediction without explanation isn’t very useful. Knowing that Title A beats Title B is helpful. Knowing that it wins because specificity and absence claims outperform generic benefit statements – that’s actionable. It tells you how to write your next title even better.

Where Accuracy Drops: Multi-Alternative Choices

Here’s where I need to be honest about limitations. When the choice set expands beyond binary (A vs B) to multi-alternative (pick the best from 4 or more options), accuracy drops to approximately 45%.

That’s still better than random chance (25% for four options), but it’s substantially lower than the 90% binary accuracy. Why? Because predicting the relative ordering of multiple options requires much finer-grained preference modelling. The difference between the 2nd-best and 3rd-best option is often tiny and context-dependent.

What this means practically: Use AI shoppers for sequential binary comparisons (tournament-style testing) rather than asking them to rank many options simultaneously. Test A vs B, then test the winner against C, then against D. This plays to the model’s strength.

Other Limitations to Know

Beyond multi-alternative accuracy, there are other limitations worth noting:

– Highly visual decisions: AI shoppers evaluate image concepts through description, not pixel-level visual processing. They can tell you that “lifestyle image showing the product in use” likely outperforms “plain white background product shot” – but they can’t evaluate specific photographic quality or composition.
– Extreme price points: At the very high end of luxury pricing or the very low end of value pricing, AI shopper calibration is less reliable because these segments are underrepresented in training data.
– Novel categories: For genuinely new product categories with no historical purchase data to calibrate against, accuracy may be lower. AI shoppers are strongest in established categories where purchase patterns are well-documented.
– Cultural nuance: While calibrated across 7 markets, within-market cultural variations (regional dialects, local preferences, subculture references) may not be fully captured.

I share these limitations because I think the worst thing in this space is overclaiming. AI shoppers are powerful for specific use cases. They’re not omniscient.

What You Can Test With AI Shoppers

Let me get specific about the listing elements where AI shoppers deliver the most value.

Title Variations

Your product title is the single highest-impact element on Amazon and most marketplaces. It determines click-through rate from search results and sets the frame for everything else on the listing.

AI shoppers can evaluate:
– Keyword ordering (which keyword belongs first?)
– Benefit inclusion (does adding “organic” to the title increase or decrease appeal?)
– Length optimisation (is the 80-character version better than the 150-character version?)
– Structure (brand-first vs benefit-first vs keyword-first)

Bullet Point Messaging

Bullet points are where you sell the product. Most sellers write them based on intuition or competitor copying. AI shoppers can tell you which benefit statements actually drive purchase intent for your specific target demographic.

Common findings from our experiments:
– Specificity almost always beats generality (“6 ingredients” beats “clean label”)
– Absence claims often outperform presence claims (“no artificial sweeteners” beats “naturally sweetened”)
– Quantified benefits beat qualitative ones (“12-hour energy” beats “long-lasting energy”)
– Problem-solution framing beats feature listing

Claim Hierarchy

Most products have 5-10 possible claims they could lead with. Choosing the wrong lead claim means burying the one that would have driven the most conversions. This is one of the highest-value applications of AI shoppers – identifying your unique selling proposition through data rather than guesswork.

AI shoppers can rank claims by purchase intent impact, helping you decide what goes in the title, what goes in bullet one, and what gets mentioned further down (or dropped entirely).

Price Point Optimisation

Pricing is perhaps the most consequential listing decision, and it’s one where AI shoppers offer a genuine advantage. Traditional price testing requires weeks of live data and risks losing revenue during the test period. AI shoppers can evaluate price sensitivity instantly.

A typical pricing experiment tests 5-7 price points for the same product, measuring how purchase intent shifts at each level. This reveals:
– The optimal price for maximum revenue (not just maximum units)
– Price thresholds where purchase intent drops sharply
– How your target demographic’s price sensitivity compares across segments

Image Concept Testing

While AI shoppers can’t evaluate photographic quality, they can evaluate image concepts – the strategic approach to your main image and secondary images. For example:
– Lifestyle context vs. plain background
– Product in use vs. product in packaging
– Size comparison included vs. not included
– Ingredient display vs. finished product only

This is particularly valuable during the creative brief stage, before you’ve invested in photography.

Full Listing Comparison

Sometimes the question isn’t about a single element – it’s “would a customer choose my listing over the competitor’s?” AI shoppers can evaluate complete listings head-to-head, identifying where you’re winning and where you’re losing relative to specific competitors. This feeds directly into e-commerce listing optimisation strategy.

How AI Shoppers Compare to Alternatives

AI shoppers don’t exist in a vacuum. There are established methods for testing listing performance. Here’s how they compare.

Amazon Manage Your Experiments

Amazon’s built-in A/B testing tool is the gold standard for live testing on Amazon. It splits real traffic between two variations and measures actual conversion rate differences.

Advantages over AI shoppers:
– Uses real customers making real purchase decisions
– Measures actual conversion rate, not predicted preference
– Free to use (no additional cost beyond normal selling)
– Results are definitive – no prediction uncertainty

Disadvantages:
– Requires 8-14 weeks minimum for statistical significance
– Only available to Brand Registry sellers
– Can only test two variations at a time
– Burns live traffic on the losing variation during the test
– Can’t test pre-launch (need existing traffic)
– Limited to title, main image, bullet points, and A+ content

When to use it: For confirming a final winner on a high-traffic listing where you can afford to wait 8+ weeks. Ideal for validating AI shopper predictions before permanent changes.

Focus Groups

Traditional focus groups bring 6-12 consumers into a room (or video call) to discuss products and react to marketing materials.

Advantages over AI shoppers:
– Rich qualitative feedback (body language, spontaneous reactions)
– Can explore unexpected directions through follow-up questions
– Real human emotional responses
– Established credibility with stakeholders

Disadvantages:
– $10,000-$50,000+ per session
– 4-6 weeks from brief to results
– Small sample sizes (6-12 people) with high variability
– Group dynamics bias (one dominant voice can skew everything)
– Hypothetical bias (saying vs. doing)
– Recruitment challenges for niche demographics

When to use them: For exploratory research when you don’t yet know what to test. For understanding the “why” behind preferences at an emotional level. For high-stakes decisions where stakeholder buy-in requires seeing real consumers react.

PickFu, Pollfish, and Similar Platforms

Platforms like PickFu and Pollfish offer quick access to real human respondents for preference testing. You upload two or more options, real people choose their preference, and you get results in hours.

Advantages over AI shoppers:
– Real human respondents (not AI predictions)
– Quick turnaround (hours, not weeks)
– Written explanations from real people about why they chose what they chose
– Established platforms with large respondent pools

Disadvantages:
– $50-$200+ per test (adds up quickly when testing many variations)
– Limited demographic targeting on basic plans
– Respondents aren’t necessarily your target customer
– Scale limitations (testing 50 variations becomes prohibitively expensive)
– Still subject to hypothetical bias (preference vs. actual purchase)

When to use them: For validating AI shopper predictions with real human input. For tests where you need quotable human explanations. For one-off decisions where the per-test cost is acceptable.

AI Shoppers (Saucery)

Advantages:
– Results in minutes (not weeks)
– ~$20 per experiment regardless of complexity
– Unlimited scale (test 50 variations for the same cost as testing 2)
– Demographically calibrated to target market
– No recruitment, no scheduling, no dropout
– Can test pre-launch with no existing traffic
– Consistent methodology (no moderator bias, no panel fatigue)

Disadvantages:
– Not real humans (predictions, not observations)
– 90% accuracy is not 100% accuracy
– Less reliable for multi-alternative ranking (45%)
– Can’t capture genuine emotional responses
– Limited for highly visual evaluation
– Newer methodology with less established credibility

When to use them: For early-stage decision narrowing. For testing many variations quickly. For pre-launch predictions. For ongoing listing optimisation where per-test cost matters. For the “which of these two is better?” question you face every day.

The Smart Approach: Combine Them

The best listing optimisers don’t choose one method exclusively. They use AI shoppers to rapidly narrow options (test 20 title variations down to the top 3), validate the top performers with PickFu or Pollfish for human confirmation, then run the final winner through Amazon Manage Your Experiments for definitive real-world proof.

This hybrid approach gives you speed, confidence, and proof – at a fraction of the cost and time of running through every option with live traffic.

Real Example: Protein Bar Listing Optimisation

Let me walk through an actual experiment to show how this works in practice.

The Context

A protein bar brand was preparing to launch on Amazon US. They had a solid product (clean ingredients, good macros, decent taste testing results) but weren’t sure how to position it in the bullet points. Their listings team had written five different lead bullet point variations, and internal opinions were split.

The product brief:
– Clean-label protein bar (6 ingredients)
– 11g protein per bar
– No artificial sweeteners
– Plant-based protein source
– 180 calories per bar

The Five Variations Tested

The AI shopper experiment tested these five lead bullet point options:

1. “11g of Plant-Based Protein Per Bar – Fuel your day with complete amino acids from whole food sources”
2. “Only 6 Ingredients – You can read (and pronounce) everything on our label”
3. “No Artificial Sweeteners, No Compromises – Naturally sweetened with dates and monk fruit”
4. “180 Calories of Clean Energy – The perfect mid-afternoon snack that won’t weigh you down”
5. “Plant-Powered Performance – 11g protein, 6 ingredients, zero artificial anything”

The Results

AI shoppers (n=250, calibrated to US adult grocery shoppers aged 22-45) produced these preference shares:

1. “Only 6 Ingredients” – 28% preference share
2. “No Artificial Sweeteners” – 24% preference share
3. “Plant-Powered Performance” – 20% preference share
4. “11g of Plant-Based Protein” – 16% preference share
5. “180 Calories of Clean Energy” – 12% preference share

The “Only 6 Ingredients” variation won by 12 percentage points over the protein-led claim that the team had originally planned to lead with.

Why “Only 6 Ingredients” Won

The AI shopper analysis identified three factors driving the preference:

1. Specificity: “6” is a concrete number. It creates an immediate mental image. “Plant-based protein” is a category descriptor that could apply to hundreds of products. Specificity differentiates.

2. Simplicity signal: In a market where protein bars routinely contain 20-30 ingredients, “only 6” immediately communicates simplicity without requiring the reader to evaluate a full ingredient list. It’s a cognitive shortcut.

3. Absence claim power: The claim implicitly says “we left out all the stuff you’re worried about” without needing to name specific nasties. It works because the primary concern in the protein bar category isn’t “does it have enough good stuff?” – it’s “does it have too much bad stuff?” The 6-ingredient claim addresses the real purchase barrier.

Interestingly, the combination claim (“Plant-Powered Performance – 11g protein, 6 ingredients, zero artificial anything”) performed third, not first. The analysis suggests this is because combining multiple claims dilutes the impact of each one. Leading with a single strong specific claim outperforms listing several.

What the Seller Did With This Data

The brand made the following decisions based on the experiment:

1. Led with “Only 6 Ingredients” as bullet point #1
2. Incorporated “6 ingredients” into the product title
3. Used “No Artificial Sweeteners” as bullet point #2 (second-strongest claim)
4. Moved the protein claim to bullet point #3 (still important, but not the lead)
5. Created a main image concept featuring a “6 INGREDIENTS” callout

At launch, the listing achieved a 14.2% conversion rate in its first 30 days – substantially above the category average of 8-10% for new protein bar listings. The “Only 6 Ingredients” framing also appeared in the most-upvoted customer review question within the first week, confirming it resonated with real shoppers.

This is just one example of how Amazon listing optimisation with AI shoppers gives you data to base decisions on rather than relying on team opinions.

When AI Prediction Works Best vs When to Use Live Testing

I want to be practical about when AI shoppers are the right tool and when you should invest in alternatives.

AI Shoppers Work Best For:

Pre-launch decisions: When you have no existing traffic, live A/B testing is impossible. AI shoppers give you data-informed decisions from day one. This is arguably the highest-value use case – the decisions you make at launch compound over the lifetime of the listing.

Claim testing and hierarchy: Deciding which of your many possible claims to lead with is a perfect binary-comparison task. Test them tournament-style and you’ll get 90% accuracy on each pairing.

Title optimisation: Titles are primarily text-based, making them ideal for AI shopper evaluation. The model can assess clarity, benefit communication, keyword integration, and competitive differentiation without needing visual input.

Rapid iteration: When you’re testing 10-20 variations and need to narrow down quickly, the speed and cost advantage is enormous. What would take months and thousands of dollars with other methods takes an afternoon.

Competitive benchmarking: Understanding why a competitor’s listing outperforms yours – and what specific changes would close the gap – is valuable intelligence that AI shoppers can provide instantly.

Live Testing or Human Methods Work Better For:

Highly visual decisions: Main image photography, A+ content layout, video thumbnails – anything where the specific visual execution matters more than the concept. AI shoppers can evaluate the concept (“lifestyle vs studio shot”) but not the photographic quality.

Price sensitivity at extremes: For luxury products (>$100) or deep value products (<$3), AI shopper calibration is less reliable. Real market testing gives you more confidence at these price points. High-stakes final confirmation: When a listing generates $100K+ per month and you’re considering a major change, the cost of being in the 10% wrong prediction zone is significant. Use AI shoppers to identify the likely winner, then confirm with Manage Your Experiments before committing.

Brand perception and emotional response: If you need to understand how people feel about your brand positioning (not just which option they’d click), qualitative methods still have an edge.

The Hybrid Approach

The most effective framework I’ve seen combines both:

1. Generate options – Use copywriting expertise to create 10-20 variations
2. AI shopper screening – Test all variations in tournament-style pairings, narrow to top 3
3. Human validation – Run top 3 through PickFu or equivalent for real human confirmation
4. Live confirmation – Put the winner through Manage Your Experiments against your current listing
5. Implement and monitor – Make the change, track actual performance

This gives you the speed of AI prediction (steps 1-2 in an afternoon), the confidence of human validation (step 3 in 24 hours), and the proof of live testing (step 4 over 8 weeks). Total cost: roughly $100-300 instead of the $5,000+ for running everything through traditional methods.

Getting Started With AI Shopper Prediction

If you want to start using AI shoppers for your listing decisions, here’s the practical path.

Step 1: Identify Your Highest-Impact Decision

Don’t start by testing everything. Start with the single decision that would make the biggest difference to your listing performance. Usually this is:
– Your product title (highest impact on CTR from search)
– Your lead bullet point (highest impact on conversion rate)
– Your pricing (highest impact on revenue per session)

Step 2: Create Meaningful Variations

AI shoppers are only as useful as the variations you give them. “Good title” vs “bad title” isn’t a useful test. You want variations that represent genuinely different strategic approaches:
– Feature-led vs benefit-led
– Specific vs general
– Emotional vs rational
– Short vs detailed
– Category keyword first vs brand first

Aim for 5-10 variations that represent distinct strategic choices.

Step 3: Define Your Target Demographic

Who actually buys this product? Be specific about age range, gender split, income bracket, and geographic distribution. The more accurately you define the target, the better calibrated the AI shopper panel will be.

Step 4: Run the Experiment

Want to know which version of your listing will perform best? Optimise your listing.

Step 5: Implement and Track

Make the change to your listing based on the AI shopper recommendation. Then track the real-world impact:
– Click-through rate from search (if you can measure it)
– Conversion rate (Sessions to Orders)
– Revenue per session
– Customer review feedback

Over time, this creates your own validation dataset – you’ll know exactly how accurate AI shopper predictions are for your specific category and target market.

Frequently Asked Questions

How accurate are AI shoppers compared to real consumer testing?

On binary choices (A vs B), AI shoppers predict the real-world winner 90% of the time. This is higher than expert human judgement (70-75%) but lower than live A/B testing with real traffic (which is definitionally 100% accurate, given enough data). For multi-alternative choices (picking the best from 4+ options), accuracy drops to approximately 45%. The practical recommendation is to use tournament-style binary comparisons rather than asking AI shoppers to rank many options simultaneously.

Can AI shoppers replace Amazon’s Manage Your Experiments?

No – and they shouldn’t. They serve different purposes. AI shoppers are best for rapid pre-launch prediction and early-stage decision narrowing. Manage Your Experiments is best for definitive confirmation on live listings with real traffic. The ideal workflow uses AI shoppers first (to identify the likely winner quickly) and Manage Your Experiments second (to confirm with real data before making permanent changes on high-traffic listings).

How much does it cost to run an AI shopper experiment?

A standard experiment with n=250 AI shoppers costs approximately $20 through Saucery, regardless of how many variations you’re testing. This is substantially less than alternatives: PickFu charges $50-200 per test, focus groups cost $10,000-50,000, and even informal testing with real consumers involves significant time costs. The fixed-cost model means you can test 50 variations for the same price as testing 2.

What product categories work best with AI shoppers?

AI shoppers perform strongest in established consumer categories where there’s substantial purchase behaviour data to calibrate against: food and beverage, health and wellness, personal care, household products, and consumer electronics. They’re less reliable for genuinely novel product categories, luxury goods at extreme price points, or highly niche B2B products where population-level calibration isn’t meaningful.

How long does it take to get results?

From experiment setup to full results with analysis, typically 5-15 minutes. The main time investment is in creating meaningful variations to test – which should take 30-60 minutes of strategic thinking. Compare this to 4-6 weeks for focus groups, 8-14 weeks for Manage Your Experiments, or 24-48 hours for PickFu. The speed advantage is most valuable when you’re making many listing decisions in sequence (e.g., optimising title, then bullet points, then pricing) or when you’re launching on a deadline.

Do AI shoppers work for non-English markets?

Yes. Saucery maintains calibrated AI shopper panels across 7 markets: United States, United Kingdom, Australia, Canada, Germany, France, and Japan. Each market has its own demographic calibration based on that country’s census data and observed shopping behaviours. The same methodology works across languages, though accuracy may vary slightly for markets with less calibration data.

Can I use AI shoppers for brand-new products with no market history?

Yes, and this is actually one of the highest-value use cases. For new products, you have no existing traffic to A/B test with, no historical data to reference, and limited budget for extensive consumer research. AI shoppers give you data-informed listing decisions from day one. The accuracy may be marginally lower for genuinely novel categories (where there’s less calibration data), but it’s still substantially better than making decisions based on team opinions or competitor copying.

The Bottom Line

AI shoppers aren’t a replacement for all consumer research. They won’t tell you whether your product tastes good, whether your packaging photograph is beautiful, or whether real customers will love your brand story.

But for the specific question of “which version of my listing will perform better?” – which is the question you face every single day as an e-commerce seller – they provide 90% accurate predictions in minutes instead of weeks, at $20 instead of thousands.

The brands I work with use them as a speed layer: make faster decisions, test more variations, launch with data rather than guesses, and then validate with real-world performance.

Same Product. Better Listing. More Sales.

Find out which version of your product listing converts best – before you publish.

Optimise your listing