E-commerce
Online experience, size recommendation.
What really happens at this stage
Online garment retail depends on a digital presentation layer that must substitute for touch and fit trial. Product photography, 360-degree spins, fabric close-ups and short video clips are used to convey drape, stretch and surface texture that a shopper cannot otherwise assess. Copywriting states fibre content, care instructions and true-to-size guidance, and is checked against the same technical pack used for production so that claims on the listing match what actually ships. Colour management between the studio camera, the retouching monitor and the customer's device is never perfect, so merchandising teams accept a tolerance and rely on written shade description alongside the image rather than the image alone.
Size recommendation engines combine the brand's own grading rules with data gathered from the shopper, such as height, weight, preferred fit of a reference garment, or body measurements entered manually or via a virtual fitting tool. These engines are only as good as the base size chart and the return reason data feeding them, so they need periodic recalibration against actual fit complaints. Checkout, delivery promise and return policy are presented clearly because they materially affect purchase decisions on apparel, where fit uncertainty is the leading cause of hesitation. Post-purchase communication, including delivery tracking and easy access to size exchange, is treated as part of the buying experience rather than an afterthought.
How it is done
- 1Build a shared technical pack
Align product description, fibre content, care label text and size chart with the same document used by production QA, not a separately written marketing version.
- 2Standardise photography setup
Use consistent lighting, camera distance and calibrated colour targets (for example an X-Rite or similar reference card) in every shoot to hold colour and scale variance within an agreed visual tolerance.
- 3Publish a measured size chart
Provide body and garment measurements in cm and inches for each size, taken flat from production samples, not only generic S/M/L labels.
- 4Deploy or tune a size recommendation tool
Feed the tool with actual return-for-fit data at least quarterly so recommended sizes track real customer experience rather than only the original grading assumption.
- 5Set delivery and return expectations upfront
State estimated delivery windows, return period and any local consumer-law-driven rights clearly on the product page before checkout.
- 6Monitor post-launch listing accuracy
Sample-check live listings against shipped garments monthly, flagging any colour, fabric or fit description drift for correction.
Key metrics (indicative targets)
| Metric | Working target | Why it matters |
|---|---|---|
| Fit-related return rate | Indicative working target below 15-20% of units sold | High fit returns signal a mismatch between size chart, recommendation tool and actual garment grading. |
| Product page conversion rate | Track against category baseline, brand-specific | Reflects whether imagery, copy and size guidance are giving shoppers enough confidence to purchase. |
| Size recommendation accuracy | Indicative 85%+ agreement with kept-order sizing | Confirms the recommendation logic is genuinely reducing guesswork rather than adding noise. |
| Listing-to-shipment accuracy | 100% match on fibre content and care label text | Mismatches between listing claims and shipped product create compliance and trust exposure. |
| Customer service tickets per 1,000 orders | Indicative downward trend quarter over quarter | High ticket volume on sizing or fabric questions indicates gaps in the online information provided. |
Targets are indicative working ranges, not standard or legal limits.
Control points to check and sign off
- Sign-off that live listing text matches the approved technical pack before publishing.
- Colour proof approval comparing studio image against a physical swatch under standard light.
- Size chart version control so the chart shown online matches the current production grading.
- Periodic audit of size recommendation tool outputs against known-good reference orders.
- Review of return reason codes tagged as size or fit to feed back into content and grading.
Common pitfalls and their consequences
- Publishing marketing copy that was never checked against the technical pack, leading to fibre or care claims that do not match the shipped garment.
- Using a single generic size chart across all styles despite different block shapes, causing inconsistent fit and elevated returns.
- Skipping colour calibration in photography, so customers receive garments that look noticeably different from the listing image.
- Ignoring return reason data when tuning the size recommendation engine, leaving it stuck on outdated assumptions.
- Overstating delivery speed or stock availability, which drives cancellations and damages repeat purchase trust.
Main activities
- In-store and online sell-through
- Returns, repair, resale and refurbishment
- Take-back and material recovery
Quality risks
- Wrong-size returns
Sustainability risks
- Return logistics
AI opportunities
- AI size guidance
Official sources
Learn the skills used at this stage
Free GarmentEd lessons with worked calculations, checklists and practice questions for the work described above.
Also relevant: Omnichannel Inventory and Fulfilment.