MMM_TerminalStop GuessingWhich ChannelActually Works.
Bayesian marketing mix modeling that decomposes every dollar of spend into its true incremental contribution — across paid search, social, TV, podcast, and 5 more channels.
The attribution tools you're using
are lying to you.
Last-click gives Google all the credit. Platform dashboards give themselves all the credit. Traditional MMM consultancies give you a PDF in 6 months. We give you coefficients in 14 days.
| Dimension | SELECTED MMM_Terminal | Traditional MMM Consultancy | Last-Click Attribution | Platform Self-Reporting |
|---|---|---|---|---|
| Model Refresh Speed | 14 days | 4–6 months | Real-time (wrong) | Real-time (biased) |
| Channel Granularity | Sub-channel + creative | Channel-level only | Last-touch only | Own channel only |
| Holdout Accuracy | ±3% MAPE | ±12–18% MAPE | N/A (not causal) | N/A (self-reported) |
| Cross-channel Halo | ✓ Measured | ✓ Manual | ✗ Ignored | ✗ Ignored |
| Diminishing Returns | ✓ Hill function | ✓ Manual curves | ✗ | ✗ |
| Saturation Modeling | ✓ Automatic | ~ Partial | ✗ | ✗ |
| Annual Cost | From $1,200/yr | $80K–$400K | Free (misleading) | Free (walled) |
| API / Programmatic | ✓ Full REST API | ✗ PDF reports | ~ Limited | ~ Export only |
We handle the NUTS sampling, adstock priors, and saturation curves. You get the ROAS table.
Numbers that survive
a board meeting.
Every claim below is validated against geo holdout tests — real markets withheld from training, compared against actual lift. This is how we know our numbers are right.
Automated pipeline ingests spend CSVs or API feeds, runs NUTS sampling overnight, and surfaces updated coefficients by morning. No analyst babysitting required.
Every model ships with a built-in holdout validation: 20% of markets withheld during training, ROAS estimates compared against actual lift. If we miss by more than 5%, we flag it.
A mid-market DTC brand running 6 channels previously spent $180K on a consultancy for annual MMM. MMM_Terminal delivers continuous modeling for $2,400/yr with faster refresh and API access.
Adstock transformations tuned per channel type. Geometric decay for digital, Weibull for TV and OOH. Hill saturation curves fitted via gradient descent on posterior samples.
Every channel modeled
on its own physics.
Podcast has a 3-week lag. TV has halo spillover. TikTok saturates at $40K. Email has near-zero decay. We model each channel's unique adstock and saturation profile — not a one-size-fits-all curve.
Adstock: Geometric decay for digital (α fitted per channel), Weibull for broadcast. Saturation: Hill function (k, n fitted via NUTS). Priors: weakly informative half-Normal(0, 0.5). Sampling: 4 chains × 1000 draws, R̂ < 1.01 required.
Touch the terminal
before you trust it.
Pre-loaded with 2 years of synthetic DTC spend data across 6 channels. Run the model, inspect the posteriors, stress-test the ROAS curves. No signup required.
Run your first model.
No card. No call.
No consultant.
Upload a spend CSV, connect your revenue source, select your channels. The model runs overnight. By morning you have channel coefficients, ROAS estimates, and a budget reallocation recommendation.
"What's actually working?"
Now you have a number, not a narrative. Channel-level ROAS, saturation curves, and budget recommendations — derived from your actual spend data, validated against holdout markets.
Free tier · No credit card · 2 models/month included