Proof the thing is actually working.
Most size chart apps can tell you a chart was opened. The question worth answering is whether it changed anything, so the headline metric here is conversion lift: the order rate among shoppers who saw a chart against those who didn’t.
Analytics
Sample dataImpressions
Click-Through Rate
Conversion Lift
Revenue Attributed
Impressions
Current PreviousWhere the numbers come from
A web pixel records the interaction
Chart impressions, opens, dismissals, time on chart, and every recommendation generated. You grant the read scopes once and activation is handled for you.
Sessions are split into saw and didn’t
Product-page visitors who triggered a chart are compared against those who never saw one, over the same window. That comparison is the whole point.
You slice it however you need
By chart, product, device, country, language, recommended size, gender, age, or fit preference, against the previous period or the same period last year.
What you get.
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Conversion lift, not just clicks
Saw-a-chart order rate minus didn’t-see order rate, derived from visitor-day counts rather than a vanity total. Some days come out negative and the dashboard shows that honestly.
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Adoption, acceptance, and override
How many shoppers reached the recommender, how many took the size it gave them, and how many picked something else. A high override rate usually means a product’s measurements need work.
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Size bracketing
Flags shoppers ordering the same item in two sizes to keep one. It is the return pattern that costs the most and the hardest to see in ordinary reporting.
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Product coverage
Which of your products actually have a chart attached, so gaps in the catalog surface before a shopper finds them.
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Who your shoppers are
Aggregate fit preference, average height, weight and age, most-recommended size, and where demand exists for sizes you don’t stock.
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Explore it before you pay
Plans without analytics can browse the real dashboard on generated sample data, so you can see the reports before deciding they are worth it.
Specifics
- Headline metric
- Conversion lift
- Dashboard metrics
- 13
- Breakdown dimensions
- 11
- Comparison
- Previous period or year
- Requires
- Web pixel + read scopes
- Available on
- Plus
Questions we get.
No. We do not have aggregate cross-merchant figures we would be comfortable quoting, and any number we invented would tell you nothing about your catalog. The dashboard is built so you can measure your own store instead of trusting ours.
The rest of it.
All featuresFit Analytics
A worklist of the products where fit is losing money, with the evidence behind each verdict.
Read moreAI size recommender
Five short questions inside the size chart popup, then one size back. Built for apparel, not footwear.
Read moreFit Notes
“Customers say this runs small. Consider sizing up.” Generated from your reviews, not written by you.
Read moreMeasure your own store.
Rather than trust an average from someone else’s catalog, turn on conversion tracking and read the lift on yours.