Key takeaways
- All three vendors exist because static size charts fail; they diverge in how they replace the chart.
- Incumbents lean on cohort inference, recommending what similar shoppers kept, which is statistical company, not a measurement of you.
- Kleep matches a real body profile (30-second questionnaire or two-photo scan) to each garment's real measurements, inside a broader commerce-intelligence suite.
- In ba&sh's head-to-head A/B test, Kleep beat True Fit by 88% on add-to-cart with 20% higher tool usage.
- The 2026 question is method: inference from other people's purchases, or a direct body-to-garment match.
Three approaches to the same problem
Size recommendation has become a category, and the leading names take different routes to the same goal: telling a shopper which size to buy. Understanding the differences matters before you commit, because the method determines both the accuracy ceiling and the operational burden on your team.
This is a comparison of approach and philosophy, not a teardown. All three exist because static size charts do not work, a point we make at length in Size Charts Are Lying to Your Customers. Where they diverge is in how they replace the chart.
The data-and-cohort model
Fit Analytics and True Fit lean heavily on large purchase and return datasets, recommending a size based on what similar shoppers bought and kept. The strength is scale: years of historical behaviour to learn from, and a recommendation that improves as more of the market flows through the platform.
The limitation is that cohort matching infers your size from other people. It is statistical company, not a measurement of you against the garment in front of you. It also inherits the biases of its history: new products with no purchase record, new brands with different blocks, and shoppers whose bodies sit away from the cohort centre all get weaker answers precisely where a strong answer matters most.
Where Kleep is different
- Two ways in: a 30-second questionnaire or a two-photo body scan, so shoppers self-select the effort they want.
- Recommendations matched to each garment's real measurements, not just to cohort behaviour.
- Sizing sits inside a broader commerce-intelligence suite: try-on, search, recommendations, merchandising.
- One body profile that travels across categories and reflects the individual, not the average.
The head-to-head evidence
Comparisons usually end in claims; occasionally a brand runs the test. ba&sh A/B tested Kleep directly against True Fit on live traffic. The result: add-to-cart 88% higher with Kleep, and 20% higher usage of the tool itself, shoppers engaged more with a body-to-garment answer than with a cohort inference.
The broader production record points the same way. ETAM's A/B test showed +5% revenue and −16% size-related returns; Faguo's footwear test showed +6.7% add-to-cart and +6.8% checkout. Kleep now runs a sizing flow every 5 seconds across 300+ brands, from Givenchy and Kenzo to A.P.C. and Victoria Beckham, volume that has scaled from 17k flows in 2023 to 4.2 million in 2025.
Choosing for your store
If you only want a size widget and have deep historical data, any of the established options will function. The question is whether cohort inference alone is precise enough for your assortment, particularly if your blocks are distinctive, your catalogue turns quickly, or your customers span a wide size range.
If you want a body-specific match, a photo-scan option, and sizing that plugs into try-on and discovery as one system, that is the case for Kleep. Real measurements and a unified suite are the deciding factors.
What is the difference between Fit Analytics, True Fit and Kleep?
Fit Analytics and True Fit primarily infer size from cohort data, what similar shoppers bought and kept. Kleep builds a body profile from a 30-second questionnaire or two-photo scan and matches it against each garment's real measurements, as part of a wider commerce-intelligence suite.
Which size recommendation tool is most accurate?
Accuracy hinges on method: cohort inference weakens on new products, new brands and non-average bodies, while body-to-garment matching measures the individual directly. In ba&sh's head-to-head A/B test, Kleep outperformed True Fit by 88% on add-to-cart with 20% higher usage.
Do size recommendation tools work for shoes as well as clothing?
Yes, footwear fit is narrower in tolerance but equally chart-resistant. Faguo's footwear A/B test with Kleep measured +6.7% add-to-cart and +6.8% checkout completion.
What results should I expect from deploying AI sizing?
Production benchmarks include −11% returns at Showroomprivé, +5% revenue and −16% size-related returns at ETAM, with returns typically falling 20 to 50% on assisted purchases. Run your own A/B test, credible vendors will insist on it.
Conclusion
Fit Analytics, True Fit, and Kleep all beat the static chart. The 2026 decision comes down to method: inference from cohorts, or a match between the shopper's real body and the real garment, inside one intelligence layer. The one head-to-head test on public record, ba&sh's, suggests the match wins.
