You're launching a new product. Traditional forecasting models need historical data, but your new SKU has zero or near-zero sales records. So what do you forecast?
ARIMA, ETS, Prophet, LightGBM — all need history.
"Use last year's number" — there's no last year.
"Guess from gut" — works at small scale, falls apart across hundreds of launches.
Analog blend — borrow from similar SKUs that DO have history.
Attribute regression — learn how attributes drive volume across the catalog.
Seasonality libraries — apply attribute-matched 52-week shapes.
All three are built into the workbench. Pick by what data you have — and you can combine them.
Find similar SKUs (by attribute or by AI judgment), then blend their historical forecasts weighted by similarity.
Output: 13-week forecast curve.
Train Ridge + Random Forest on every historical SKU's attributes → first-period volume. Predict launch volume from your new SKU's attributes directly.
Output: first-period units + 80% CI + implied life total.
Match attributes to a 52-week profile in the seasonality library, multiply by an analog-derived BSR. Returns a fully-shaped weekly forecast.
Output: weekly forecast × seasonal shape.
In the SKU Forecaster workbench, scroll to the purple "🆕 New Product Mode" panel and click Enable. Attribute selectors populate from your active dataset's columns.
Set product characteristics:
More attributes = more accurate analog matches. You can leave some blank — the system widens the search until it finds candidates.
A toggle above the "Find Similar SKUs" button lets you pick how analogs get chosen.
Claude reads the catalog and picks analogs based on semantic similarity. Slower, but catches "T-shirt as an analog for Polo" cases. One-line reason per pick.
Deterministic exact-attribute scoring: +2 per exact match, +1.5 for "similar" numerics. Repeatable, no LLM round-trip. Each match is explicit in the results.
If you have 20+ historical SKUs to train on, the Attribute-Based Launch Model fits a Ridge + Random Forest ensemble on (attributes → first-period sales) and predicts directly for your new SKU.
Trained on all 42 historical SKUs · Ridge + RF ensemble
Feature importance: subcategory 50% · price 29% · category 11% · color 10%
Click "Auto-Fill Sell-Through" in the modal to push implied life into the Sell-Through panel and turn this point estimate into a weekly schedule.
Most teams use Attribute Launch to set the initial buy quantity, and Analog Blend or Seasonal Component to schedule weekly receipts.
Category, subcategory, color, price — whatever the catalog tracks. More attributes = better matches.
Get a defensible first-period number + 80% CI. Use this as your initial buy or shelf-set quantity.
Get a 13-week forecast curve. Compare against the Attribute Launch life total — if they agree within ~30%, you have a defensible plan. If they disagree, that's a signal to investigate.
Click Auto-Fill Sell-Through in the Attribute Launch modal to distribute the implied life total across weeks using a configurable decay curve.
Cold-start forecasting is inherently uncertain. Use the right method for the right situation.
• You have 20+ historical SKUs in similar categories
• Attributes are well-populated (≥3 set)
• Analog Blend & Attribute Launch agree within ~30%
• The Attribute Launch model R² > 0.5
• Methods disagree by >50% (orchestrator flags these)
• Fewer than 5 analogs found
• Confidence reported as "low" or "very_low"
• The new SKU represents a category your catalog hasn't seen