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T-Soft powers a large share of Turkey's mid-market fashion stores, but out of the box it has no native way to tell a shopper "this will fit you." That gap shows up later as size-related returns, refund costs, and shoppers who simply abandon the product page because they aren't sure which size to pick. Adding a size recommender to T-Soft is a scoped, well-defined integration project — not a platform rebuild — if you know which endpoints to touch.
Why T-Soft merchants need t-soft size recommendation
T-Soft stores tend to run lean catalogs with frequent restocks, and many sell apparel across multiple supplier brands with inconsistent size labeling. That inconsistency is the root cause of most size-related returns: a shopper who normally wears a size M from one brand orders M from a T-Soft store and gets a garment cut for a different body. Fit intelligence software solves this by mapping the shopper's body measurements (or previous purchase history) to each product's actual dimensions, rather than relying on a generic size chart.
Turkish e-commerce data backs up how material this problem is: sizing and fit are consistently the top-cited reason for apparel returns in market reports, and mid-market platforms like T-Soft carry a disproportionate share of that volume because they serve smaller brands without in-house fit teams. Addressing it directly improves both conversion and margin.
The T-Soft REST/token auth model
Before writing any recommender integration, you need to understand how T-Soft handles API access, because it differs from OAuth-style flows common on other platforms.
Token-based session auth
T-Soft's REST layer (commonly exposed as rest1 in merchant panels) uses a token-auth pattern rather than persistent API keys:
- You authenticate once against the login endpoint with store credentials to receive a session token.
- That token is attached to subsequent requests (typically as a header or query parameter, depending on the T-Soft version).
- The token expires — in most T-Soft deployments this is roughly a 24-hour window — so your integration needs a refresh routine, not a one-time setup.
This matters for a size recommender because your product-sync job and your live recommendation calls both depend on a valid token. If you build the integration assuming a static key, it will silently start failing once the token lapses.
Practical implementation pattern
The cleanest approach is a small middleware service that:
- Owns the token lifecycle (fetches, caches, and refreshes it before expiry)
- Pulls product and category data from T-Soft's
getProducts/getOrders-style endpoints - Normalizes T-Soft's product fields (dimensions, categories, variant/size labels) into the schema your size-recommendation engine expects
- Exposes a lightweight endpoint or widget that the storefront theme calls to render the "Find Your Size" prompt
Mapping T-Soft product data to a fit engine
T-Soft product records vary in how consistently they store fit-relevant fields (garment measurements, fabric type, fit category). Before going live, audit a sample of SKUs across your top categories.
| T-Soft field | Fit-engine use | Typical gap to fix |
|---|---|---|
| Variant/size label | Base size input | Inconsistent labeling across supplier brands |
| Category | Garment-type detection (top, bottom, dress) | Sometimes too generic, needs sub-mapping |
| Custom attributes | Fabric stretch, fit style (slim/regular/oversized) | Often missing, needs manual tagging pass |
| Product images | Optional visual fit confirmation | Not always high-res or consistent angle |
Where custom attributes are missing, a one-time tagging pass on your best-selling SKUs (fit style, stretch, true-to-size notes) delivers most of the accuracy gain — you don't need every SKU tagged before launch.
Handling multi-brand size charts
Because T-Soft stores commonly aggregate multiple supplier brands, a single "size M" label can map to different physical dimensions depending on the brand. Your integration should key size charts by brand-plus-category rather than by size label alone, and fall back to a generic chart only when brand-specific data is unavailable.
Storefront placement and checkout impact
Once the backend mapping is in place, where you surface the recommendation matters as much as its accuracy.
Product page placement
Put the size prompt directly next to the size selector, not buried in a tab. Shoppers who are asked to choose a size before they've resolved fit uncertainty are the ones most likely to guess, order two sizes, or abandon the page.
Cart and pre-checkout confirmation
A brief fit confirmation at cart level ("Your recommended size: M, based on your saved measurements") reduces last-minute size swaps and gives your support team fewer pre-purchase sizing questions to answer manually.
If you're also running T-Soft alongside other Turkish platforms, the setup differs meaningfully — teams comparing how to add a size recommender to Ticimax will find Ticimax's plugin architecture is more prescriptive, while T-Soft's REST/token model gives you more control but requires more custom wiring.
Measuring the impact after launch
Track these metrics for at least one full return-cycle (typically 30-45 days for apparel) before judging results:
- Size-related return rate, segmented by category
- Size-recommender engagement rate (percentage of product-page visitors who interact with the prompt)
- Conversion rate for shoppers who used the recommender versus those who didn't
- Average order value change, since confident sizing often reduces the "order two sizes, return one" pattern
Retail-wide, sizing and fit remain the dominant driver of apparel returns, a pattern documented across multiple industry analyses including NRF's consumer returns research. T-Soft merchants who instrument fit data properly typically see the clearest wins in categories with the most size variance — dresses, denim, and outerwear.
Choosing the right approach for your store
Not every T-Soft store needs the same depth of integration. A boutique running under 200 SKUs can often launch with a lighter, manually-tagged fit engine, while a multi-brand marketplace-style store needs the full brand-aware size-chart mapping described above. If you're still evaluating vendors and approaches, our guide on how to choose a size recommendation solution walks through the tradeoffs between rule-based size charts, measurement-based fit engines, and hybrid approaches — all relevant whether you're on T-Soft, Ticimax, or another stack.
Frequently Asked Questions
Does T-Soft have a native size recommendation feature?
No, T-Soft does not ship a built-in fit-intelligence or size-recommendation module. Merchants need to integrate a third-party engine through T-Soft's REST API, using the token-auth flow to pull product data and push recommendations into the storefront theme.
How long does a T-Soft size recommender integration typically take?
For a store with clean product data, a working integration can go live in one to two weeks, most of which is spent auditing and tagging fit-relevant product attributes. Stores aggregating many supplier brands with inconsistent sizing take longer because the size-chart mapping work is more extensive.
Will a size recommender slow down my T-Soft storefront?
Not if implemented correctly. The recommendation widget should call a lightweight, cached endpoint rather than hitting T-Soft's REST API directly on every page load, since the token-auth session and full product sync should run as a background job, not inline with the shopper's page request.
Can I run a size recommender only on select categories?
Yes, and it's often the recommended rollout path. Launching first on your highest-return categories (typically dresses, denim, and fitted tops) lets you validate accuracy and measure return-rate impact before expanding store-wide.