Most returns are decided at checkout: the wrong size, a bracketed basket, a product page that oversold. Stratgik builds models that reduce ecommerce returns at that moment, then mines every return reason to fix the cause upstream.
AI return-rate reduction is a set of machine learning models that predict which orders are likely to come back and why, before and after purchase. It outputs a size recommendation per shopper, a return-risk score per order at checkout, a ranked list of products whose return reasons point to fixable page, sizing or quality issues, and flags for accounts whose return behaviour looks abusive.
Every return pays for shipping twice, a warehouse touch, and often a markdown. Generous policies win customers, but without data you cannot tell a fit problem from a product problem from a policy abuser.
US retail returns reached a projected $890 billion in 2024. That is 16.9% of annual retail sales, per NRF and Happy Returns.[1]
Almost one in five online sales is expected to be returned in 2025. NRF estimates 9% of all returns are fraudulent.[2]
Size and fit is the top reason for apparel returns. Coresight found colour second at 16% and damage at 10%.[3]
Of Gen Z shoppers say they bracket. They buy several sizes or colours intending to return some.[1]
The model learns which sizes each shopper kept, across brands and cuts, and which products run small or large based on real return notes.
Each order gets a score and a reason, so you choose the response: a nudge, a different shipping option, or nothing at all.
Free-text return reasons, reviews and CS tickets are classified into fixable causes, and account-level patterns surface wardrobing and serial returners.
Twelve to twenty-four months of orders, returns, reasons and customer IDs from your platform and returns portal. We agree the metric and a holdout group.
Return-reason mining produces the first fix list: products, sizes and pages driving avoidable returns. Some fixes need no model at all.
Size suggestions and risk-based nudges run for a share of traffic, with a control group, so the impact is measured, not assumed.
Return rate, conversion and average order value, test versus control. Early return windows are extrapolated and labelled clearly as such.
| Criterion | Tighter policy only | Off-the-shelf fit or returns app | Stratgik build + run |
|---|---|---|---|
| Speed to launch | Days | Days to weeks | 21-day measured pilot |
| Effect on conversion | Often negative; shoppers value free returns | Neutral to positive for fit widgets | Tested against a control group |
| Uses your own return reasons | No | Partly, within the app's data | Yes, across portal, reviews and tickets |
| Catches abuse without blanket bans | No | Rarely | Account-level scoring with human review |
| Best fit | Small catalogues with obvious abuse | Single-category apparel stores | Multi-brand or multi-category mid-market sellers |
| Data ownership | Yours | Shared with vendor | Models and data stay in your cloud |
Retailers cannot simply charge for returns without losing customers. The alternative is to be precise: prevent the avoidable ones and treat abuse as the exception it is.
The biggest unknown is how many of your returns are preventable. The reduction below is an assumption; the pilot measures it against a control group.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
One storefront, return-reason mining plus a live A/B test
Checkout scoring and weekly fix list for one store
Multiple storefronts, regions or brands
Cloud hosting and language-model usage for reason mining billed at cost with no markup. Taxes excluded. GBP and AED prices are indicative conversions from USD.
AI can reduce ecommerce returns meaningfully where returns are driven by size, fit, bracketing or misleading product pages, but the amount varies by category and policy. We do not quote a number in advance. The pilot runs size suggestions and checkout nudges against a control group, so you see the measured change in return rate, conversion and order value on your own traffic.
You reduce returns without hurting conversion by helping shoppers choose correctly rather than adding friction. Size suggestions and 'runs small' notes typically help conversion. Stronger measures, such as removing free returns on risky baskets, are tested on a small share of traffic first. Every change is compared to a control group, so any conversion loss is visible before rollout.
Return-risk scoring at checkout is a model that gives each order a probability of being returned, with the main reasons, in the moment before payment. Signals include basket composition, sizes chosen, the shopper's kept-order history, product return rates and discount depth. The score is written to the order so you can nudge, route or simply monitor.
We score accounts on patterns such as return ratio, worn-item returns, empty-box claims and linked addresses, then send high scores to a human review queue. Nothing is blocked automatically. NRF estimates 9% of all returns are fraudulent, so most customers who return often are legitimate; the review step protects them and your brand.
Yes. We connect to Shopify, Shopify Plus, Magento, Salesforce Commerce Cloud and BigCommerce for orders, and to returns portals such as Loop, Narvar, ReturnGO and Happy Returns for reasons and outcomes. Scores are written back as order tags or metafields, and size suggestions run as a lightweight storefront component.
A 21-day returns reduction pilot costs $12,000 for one storefront, including return-reason mining and a live A/B test. Running checkout scoring and the weekly fix list costs $2,500 a month, and multi-store Scale starts at $7,000 a month. Cloud and model usage are billed at cost. There is no percentage-of-savings fee.
A short call to confirm your platform and returns portal, then a fixed-scope pilot measured against a control group, not a vendor benchmark.