AI Returns Prevention for a D2C Ecommerce Startup
Size and fit recommendation plus return-risk scoring that lowered return rate for a fast-growing D2C apparel brand.
Key Details
| Challenge | Return rates spiked as the catalog grew; size charts alone could not stop wrong-fit orders. |
|---|---|
| Solution | A fit recommender and checkout risk score that guides size choice and flags high-risk carts for proactive help. |
| Technologies | Python, TensorFlow, Shopify, BigQuery, Redis |
Technologies used
Python
TensorFlow
Shopify
Google Cloud
Redis
React
Client background
A D2C apparel startup scaled paid acquisition faster than its fit guidance. Return shipping ate margin, and CX spent days on “wrong size” tickets that a better PDP experience could have prevented.
Key challenges
- Static size charts ignored body-shape signals and prior purchase/return history.
- High-return SKUs were not surfaced until month-end finance reviews.
- Shopify theme experiments were hard to instrument for true fit lift.
- CX lacked a proactive playbook for carts with elevated return risk.
What we built
- Fit recommendation widget on PDP using order, return and catalog attributes.
- Checkout return-risk score for CX and post-purchase messaging experiments.
- BigQuery pipelines linking Shopify orders, returns and size events.
- Merchant dashboard for return-prone SKUs, sizes and cohorts.
Project team: 5 engineers across AI/ML, backend and product engineering — delivery over 11 weeks for a startup team that needed to ship, not slide decks.
How we delivered
01
Diagnose
Segmented returns by size, category and first-vs-repeat buyers.
02
Model
Trained fit and risk models with cold-start fallbacks for new SKUs.
03
Embed
Shipped Shopify app embeds with A/B hooks on key PDPs.
04
Operate
Weekly review of return rate, exchange rate and CX ticket mix.
Business impact
-18%Return rate
+12%Size confidence
FewerWrong-fit tickets