Return Prediction
“Which orders will be returned?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which orders will be returned?
E-commerce return rates average 20-30%, costing US retailers $816B in returned merchandise annually (NRF). Each return costs $10-20 to process (shipping, inspection, restocking, depreciation). For a retailer shipping 5M orders per year with a 25% return rate, that is $12.5-25M in annual returns processing costs alone, plus the margin loss on items that cannot be resold at full price. Fashion and apparel returns are the worst offenders at 30-40%, often driven by sizing issues, color mismatches, and impulse buying that could be mitigated with better pre-purchase guidance.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo connects orders, products, customer return history, sizing data, product reviews, and browsing behavior into a relational graph. The model predicts at the moment of purchase that Order ORD-8810 has a 68% return probability because the customer ordered two sizes (bracket buying), has returned 40% of past apparel purchases, and this product has a 4.1x higher return rate in the ordered size range. The retailer can proactively offer a virtual fitting tool, sizing recommendation, or slight discount to keep the right size and return the other before shipping both.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
ORDERS
| order_id | customer_id | total | items | shipping_method | timestamp |
|---|---|---|---|---|---|
| ORD-8810 | CU-3045 | $189.98 | 2 | Standard | 2025-09-14 |
| ORD-8811 | CU-3012 | $42.99 | 1 | Express | 2025-09-14 |
| ORD-8812 | CU-3078 | $67.50 | 3 | Standard | 2025-09-15 |
ORDER_ITEMS
| order_id | product_id | product_name | size | color | price |
|---|---|---|---|---|---|
| ORD-8810 | P-3001 | Slim Fit Blazer | M | Navy | $94.99 |
| ORD-8810 | P-3001 | Slim Fit Blazer | L | Navy | $94.99 |
| ORD-8811 | P-3020 | Running Shoes | 10 | Black | $42.99 |
CUSTOMER_RETURN_HISTORY
| customer_id | total_orders | total_returns | return_rate | common_reason |
|---|---|---|---|---|
| CU-3045 | 14 | 6 | 42.8% | Wrong Size |
| CU-3012 | 24 | 3 | 12.5% | Changed Mind |
| CU-3078 | 1 | 0 | 0% | N/A |
PRODUCT_RETURN_RATES
| product_id | overall_return_rate | size_issue_rate | size_range_variance |
|---|---|---|---|
| P-3001 | 32% | 22% | High (M-L boundary) |
| P-3020 | 8% | 3% | Low |
| P-3050 | 18% | 12% | Medium |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ORDERS.RETURNED = 'True', 0, 30, days) FOR EACH ORDERS.ORDER_ID
Prediction output
Every entity gets a score, updated continuously
| ORDER_ID | CUSTOMER | RETURN_PROB | PRIMARY_RISK | INTERVENTION |
|---|---|---|---|---|
| ORD-8810 | CU-3045 | 0.82 | Bracket Buying | Sizing Tool + Keep Discount |
| ORD-8812 | CU-3078 | 0.24 | New Customer | Standard Follow-up |
| ORD-8811 | CU-3012 | 0.09 | Low Risk | None |
Understand why
Every prediction includes feature attributions — no black boxes
Order ORD-8810 (2x Slim Fit Blazer, M & L)
Predicted: 82% return probability
Top contributing features
Bracket buying pattern (2 sizes)
M and L
32% attribution
Customer historical return rate
42.8%
26% attribution
Product size-boundary variance
High (M-L)
20% attribution
Common return reason match
Wrong Size
13% attribution
No prior purchase of this brand
First time
9% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Reduce return rates by 15-25% through proactive sizing tools and targeted interventions, saving $12-25M annually in returns processing costs for a 5M-order retailer.
Related use cases
Explore more retail & e-commerce use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
