New Collection Launch Recs
“For each customer, which items from the new collection will they purchase in the next 30 days?”
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A real-world example
For each customer, which items from the new collection will they purchase in the next 30 days?
New products have zero interaction history — collaborative filtering fails completely. Fashion retailers launch 200-500 new items per season and have no data on who will buy them. Traditional approaches default to showing new items to everyone or using simple attribute matching. First-week sell-through on new collections averages 8-12% when it should be 25-35%. For a fashion retailer doing $2B annually, improving new-collection sell-through by 10 points is worth $40-60M per season.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo solves the cold-start problem by using the relational graph — similar products, same brand, category affinity, style attributes, and cross-customer purchase patterns — to recommend items with zero interaction data. The model learns that customers who bought last season's linen collection from the same brand, in similar colorways, at similar price points, are the best targets for the new Spring 2025 line. No interaction history required.
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
CUSTOMERS
| customer_id | name | segment | signup_date |
|---|---|---|---|
| C001 | Sarah Chen | premium | 2023-06-15 |
| C002 | James Wilson | standard | 2024-02-10 |
| C003 | Aisha Patel | premium | 2022-08-30 |
PURCHASES
| purchase_id | customer_id | product_id | amount | timestamp |
|---|---|---|---|---|
| PUR201 | C001 | P301 | 189.00 | 2024-09-15 |
| PUR202 | C001 | P305 | 145.00 | 2024-10-02 |
| PUR203 | C003 | P302 | 210.00 | 2024-09-28 |
PRODUCTS
| product_id | name | category | collection | price |
|---|---|---|---|---|
| P301 | Linen Blazer (Fall 2024) | Outerwear | Fall 2024 | 189.00 |
| P601 | Linen Shirt (Spring 2025) | Tops | Spring 2025 | 129.00 |
| P602 | Wide Leg Trouser (Spring 2025) | Bottoms | Spring 2025 | 165.00 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT( PURCHASES.PRODUCT_ID WHERE PRODUCTS.COLLECTION = "Spring 2025", 0, 30, days ) FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| C001 | P601 | 0.87 | 2025-03-12 |
| C001 | P602 | 0.79 | 2025-03-12 |
| C003 | P601 | 0.82 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C001 (Sarah Chen, premium segment)
Predicted: Will purchase P601 (Linen Shirt, Spring 2025) — score 0.87
Top contributing features
Same-brand linen purchases
2 linen items from same brand in Fall 2024
34% attribution
Category affinity (Tops)
35% of purchases are Tops
24% attribution
Price range match
$129 within typical spend range ($95-$210)
19% attribution
Graph neighbors (linen buyers)
68% of similar customers targeted
15% attribution
Seasonal purchase pattern
Buys new collections within 2 weeks of launch
8% 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: 10-15 point improvement in first-week sell-through for new collections. For fashion retailers, this translates to $40-60M in incremental seasonal revenue and drastically reduced markdowns.
Related use cases
Explore more personalization 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.
