Product Mix

Product Mix Modeling

Attribute revenue accurately to product features
and find your winning combination.

Upload CSV

Data secure. We value your privacy.

Features contribution

Feature contribution

12.8%

21.5%

Optimal product mixes

1. Import Your Data

Paste from a spreadsheet or upload a CSV. Include a date column, a revenue column, feature launch or rollout columns, and optionally control variables like pipeline, demand, or marketing context.

📁 Drag & drop a CSV file here, or click to browse

2. Assign Variable Roles

Map each column. Features can be binary launch flags or rollout indicators. Revenue and controls should be continuous.

🧬 Prior Configuration

Set your belief about how long features typically take to pay off. This acts as a Bayesian prior via data augmentation.

Payoff Center 6 months

Prior Strength 1%

Strength = percentage of synthetic rows relative to your real data. Higher = more weight on the prior.

Building ARDL matrix...

🧪 Product Mix Modeling

Feature Long-Run Multipliers

💡 Insights

🔍 Diagnostics (click to expand)

💡 Note: PMM estimates the Long-Run Multiplier (LRM) — the total cumulative revenue impact of a feature over its lifecycle. The Bayesian prior centers this at your configured payoff horizon. For causal identification, lag modeling, and seasonality controls, explore our consulting services.

Feature Ranking by Long-Run Multiplier

Feature Maturity Curve

How revenue impact distributes over lag periods — shows how quickly features pay off.