You don't need a six-figure vendor — or the quarterly refresh — to know which way your budget should move. Here's how to model the mix scrappily, fast, and without fooling yourself.
New here? Start with the 2026 measurement field guide.
Scrappy MMM isn't a budget version of a vendor MMM — it's the read you run in the gaps.
Scrappy MMM is a lightweight media-mix model you build yourself to get a directional answer to "where should the next dollar go?" — fast, from weekly data, without a vendor engagement. It is not a discount replacement for a production MMM. It's the read you reach for when the official model is too slow, too coarse, or doesn't exist yet.
"Just use the MMM" misses how decisions actually arrive. Even a great vendor model — and plenty now refresh monthly — leaves gaps a scrappy read fills:
Your MMM refreshes monthly; a "should we pull $200K out of social this week?" question arrives on a Tuesday. A directional read now beats the precise read in three weeks.
You just launched Connected TV or a new partner. The vendor model has nothing to fit. A scrappy curve from early data is the only read available.
"What happens to total subs if we shift 15% from App Campaigns to Search?" You can simulate that on your own curves in minutes instead of waiting for a re-run.
Most teams can't justify $100K+/yr. Scrappy MMM is the difference between a structured directional view and pure gut.
The fastest way to discredit yourself with a scrappy model is to oversell it. Here's the honest division — and why the scrappy column is still worth running.
| Dimension | Gut / last-year ±10% | Scrappy MMM | Vendor / production MMM |
|---|---|---|---|
| Time to an answer | Instant | Hours to a day | Weeks per refresh |
| Cost | $0 | ~your time | $80K–$250K+/yr |
| Precision | None | Directional only | Estimated with uncertainty bands |
| Cross-channel halo | Ignored | Crudely, if at all | Modeled |
| Captures brand / offline | No | Partly (it's still top-down) | Yes |
| Best for | Nothing, really | Fast "which way" calls between refreshes | The quarterly budget envelope |
Every measurement choice is a trade of effort for confidence, and the trade has sharply diminishing returns. Plot it, and the question stops being "what's most rigorous?" and becomes "where's the elbow?"
Going from gut to scrappy buys you most of the available confidence. Going from scrappy to a full vendor MMM buys real rigor — but at weeks of latency and serious cost — and the last rung, MMM calibrated with incrementality, is the gold standard you reserve for the decisions that deserve it. The skill isn't always climbing higher; it's knowing which rung a given decision needs.
Four steps. None require a data-science team — though open-source tooling makes the middle two much easier.
Spend per channel per week, plus the outcome you care about (subscriptions or revenue), plus obvious controls — price changes, promos, seasonality, big launches. A year of weekly rows is a workable minimum; more is better.
Apply an adstock (carryover) so this week's spend keeps working next week, then a saturation curve so each channel bends into diminishing returns. These two transforms are what separate a real mix read from a naive correlation.
Regress the transformed spend on the outcome. Then — the step people skip — check the implied channel contributions against any incrementality test you have. If your model says social is 3× more efficient than a clean geo holdout did, trust the holdout and constrain the model.
Don't report "social ROAS = 2.31." Report "social looks past its efficient point; search and ASA still have room." Feed those response curves into an allocator and simulate the shift — directionally.
Once you have a saturation curve per channel, allocation is mechanical: shift budget until every channel's next dollar is worth the same. That's exactly what the budget allocator does — set the curves and watch the optimal split fall out.
Scrappy doesn't mean sloppy. These are the failure modes that turn a useful directional read into confident nonsense.
The triangulation framework scrappy MMM lives inside — MMM, attribution, incrementality.
Read the field guide →Feed your saturation curves in and watch the optimal split fall out.
Open the tool →Three forecast models that should disagree — and why a range beats a number.
Read it →You can run a handful of clean tests a year. How to choose them like a portfolio.
Read it →If you want a directional model stood up fast — or a sanity check on the one you have before it drives a big reallocation — let's talk.
Last updated June 2026 · Part of an in-progress series on growth measurement & budget allocation.