For fashion & online shops

The size advisor that cuts returns

Three questions instead of a size chart: the interactive size advisor recommends the right size based on body measurements and fit preference — right on the product page. Fewer wrong buys, fewer return costs.

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How does the recommendation work?

10-20 € cost per avoided return (shipping, refurbishment, value loss)

The returns problem in numbers

30-50% return rate in fashion

Main reason: wrong size. Many customers order two sizes — one is guaranteed to come back. Every return costs you 10 to 20 euros in shipping, refurbishment and value loss.

Nobody reads the size chart

The PDF chart with chest width in centimetres assumes the customer fetches a tape measure. Almost nobody does — they guess, and a wrong guess means a return.

Fit finders only for the Zalando league

Fit-finder SaaS like Fit Analytics only pays off at enterprise volume. Small and mid-size shops are stuck with the static chart — and carry the return costs that eat their margin.

How Questee fixes this

  1. 1

    Build the advisor with your size logic

    Customise the "size advisor" template: your cuts, your size runs. Three to four questions — height, weight or the size of a familiar brand, fit preference (slim, regular, oversized).

  2. 2

    Recommendation via calculation engine

    The answers run through your stored calculation logic and produce a concrete recommendation: "Based on your details M fits — if you prefer loose, take L." No guessing, no chart.

  3. 3

    Embed on the product page

    Place the advisor as a popup button "Which size fits me?" next to the size selector — via embed snippet, no shop rebuild. Works with Shopify, WooCommerce and any shop system that allows HTML.

What sets the advisor apart from the PDF

Calculation engine

Real calculation logic instead of a static chart — size recommendation combined from multiple inputs.

Conditional logic

Different questions for tops than for trousers or shoes — one advisor, multiple product categories.

Embed without shop rebuild

Popup, iframe or inline embed on the product page — one snippet, no developer project.

Mobile-first

One question per screen, thumb-friendly — where most fashion purchases happen.

Response analytics

You see which sizes get recommended and where customers drop off — data for assortment and cuts.

GDPR without footnotes

Body measurements are personal data. Hosted in Germany, no US transfer, DPA included.

A fit finder at shop budget

Free to try (3 forms). Pro for live operation (unlimited forms, 10,000 responses/month, your branding) — one avoided return per month pays the subscription.

Free

3 forms, 250 responses/month

Pro

Unlimited, 10,000 responses/month, AI included

View all plans

Answers from shop practice

How accurate is the size recommendation?
As accurate as your logic — and you know it best. You define the calculation rules based on your cuts and experience ("runs small" feeds straight in). Unlike black-box fit finders you can sharpen the rules any time, e.g. when return data shows a model fits differently.
Do I need a developer for the integration?
No — you copy an embed snippet into the product description or your shop template. For Shopify and WooCommerce an HTML block suffices. Alternatively just link the advisor as a button next to the size selector.
Aren't body measurements sensitive data?
Height and weight are personal data — not a special category under Art. 9 GDPR, but sensitive enough for care. The advisor works entirely without name or e-mail (usable anonymously), data sits on German servers, and you can set short retention periods. It belongs cleanly in your privacy policy — we provide the DPA.
Does this work for shoes or bras?
Yes — the logic is freely definable. For shoes ask foot length or current size plus brand comparison, for bras underbust and bust measurements. With conditional logic you cover several categories in one advisor or build one per category.
Can I ask for the e-mail address at the end?
Yes, optionally — e.g. "receive your recommendation by mail" with newsletter opt-in. The size advisor doubles as a lead magnet. Important: the field must be voluntary and the opt-in GDPR-compliant and separate — the template shows it done right.
How is this different from Fit Analytics & co.?
Enterprise fit finders run machine learning on millions of purchase records — at enterprise prices and requiring matching volume. Questee gives you the same customer experience (guided questions, concrete recommendation) with your own size logic, at €9/month. For shops below the Zalando league this is the realistic route.
How do I measure whether the advisor really cuts returns?
Compare the return rate of orders with and without advisor usage: the advisor analytics show usage and recommendations, your shop system shows the returns. The benchmark alone helps: at 10-20 euros per return, the advisor only needs to prevent a handful of wrong buys per month to clearly pay off.

Three questions, the right size, fewer returns

Start template, define your size logic, embed on the product page. Free trial, no contract.

Try template now