New Product Launch Prediction
“Which customers will buy this new product with zero purchase history?”
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A real-world example
Which customers will buy this new product with zero purchase history?
Retailers launch 25,000-50,000 new SKUs annually, but 70-80% fail to meet sales targets in the first 90 days (Nielsen). Traditional demand models require 8-12 weeks of sales history before making accurate predictions, leaving the critical launch window unoptimized. Overstocking a failed product wastes $50-200K per SKU in inventory carrying and markdown costs. Understocking a hit product forfeits $200-500K in lost revenue during the peak-demand window. The cold-start problem costs large retailers $500M-$1B annually in misallocated launch inventory.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo does not need sales history for the new product because it learns from the relational graph connecting product attributes, similar products, customer preferences, and market signals. When a new organic protein bar (P-6001) launches, Kumo's graph neural network recognizes its attributes (organic, high-protein, $3.49 price point) and connects them to customers who buy similar products. Customer CU-3012 has bought 6 organic snack products in the past 90 days and lives near a store where health-food trends are strong. The model predicts first-week demand at each store without any prior sales data.
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
NEW_PRODUCT
| product_id | name | category | attributes | price | launch_date |
|---|---|---|---|---|---|
| P-6001 | Peak Organic Protein Bar | Snacks | Organic, High-Protein, Gluten-Free | $3.49 | 2025-10-01 |
SIMILAR_PRODUCTS
| product_id | name | category | weekly_units_avg | customer_overlap |
|---|---|---|---|---|
| P-5801 | RX Bar Protein | Snacks | 420 | High |
| P-5802 | Kind Protein Bar | Snacks | 380 | High |
| P-5803 | Clif Organic Bar | Snacks | 310 | Medium |
CUSTOMER_PREFERENCES
| customer_id | organic_affinity | protein_purchases_90d | snack_spend_90d |
|---|---|---|---|
| CU-3012 | High | 12 | $84.50 |
| CU-3045 | Medium | 4 | $32.00 |
| CU-3078 | Low | 0 | $8.50 |
STORE_TRENDS
| store_id | health_food_index | organic_growth_yoy | similar_product_velocity |
|---|---|---|---|
| S-14 | 8.4 | +22% | High |
| S-22 | 6.1 | +12% | Medium |
| S-37 | 4.2 | +5% | Low |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ORDERS.PRODUCT_ID = 'P-6001', 0, 7, days) FOR EACH CUSTOMERS.CUSTOMER_ID WHERE CUSTOMER_PREFERENCES.ORGANIC_AFFINITY IN ('High', 'Medium')
Prediction output
Every entity gets a score, updated continuously
| STORE_ID | PREDICTED_WEEK1_UNITS | TARGET_CUSTOMERS | STOCK_REC | CONFIDENCE |
|---|---|---|---|---|
| S-14 | 285 | 1,420 | 350 | High |
| S-22 | 140 | 680 | 180 | Medium |
| S-37 | 55 | 210 | 75 | Medium |
Understand why
Every prediction includes feature attributions — no black boxes
New Product P-6001 (Peak Organic Protein Bar) at Store S-14
Predicted: 285 units predicted in first week
Top contributing features
Similar product velocity at this store
High
28% attribution
Customer base organic affinity
62% High/Med
25% attribution
Attribute similarity to top sellers
92% match
21% attribution
Store health-food trend index
8.4/10
15% attribution
Price point within target range
$3.49 (sweet spot)
11% 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: Accurately forecast first-week demand for new products with zero sales history, reducing launch inventory misallocation by 40-60% and recovering $500M-$1B in industry-wide launch losses.
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.
