Marketing Mix Modeling without the consulting bill
See which channels actually pay you back. A marketing mix model looks back at 2 to 3 years of your history, separates what each channel did from seasonality, prices and promotions, and lets you explore scenarios. Data ingest is automated and professional services tunes the model with you every month.
The model reads spend by channel next to the controls it separates out:
Modeled returns flatten at higher spend, so each extra dollar adds less:
Results are conditional on the control set and the time split. An experiment is the way to test a channel directly.
Good question. This preview is an illustrative example. Book a demo and we will walk through it on your own channels.
What a black-box export does not give you
A human in the loop
Improvado professional services tunes the model with you every month, and the modeling team is on the board call. It is not a black-box export.
What-ifs in seconds
Scenario re-optimization runs at request time, not next quarter. Move a budget and explore the scenario.
Modeling starts in week one
Data ingest is automated from the connectors you already use, so there is no long data-engineering project before the first model runs.
How it works
1. Ingest in week one
Spend and outcomes flow in automatically from your platforms, so modeling starts in the first week instead of after an engineering project.
2. Model the past
2 to 3 years of history with seasonality built in, across TV, social, search, email, podcasts and billboards, plus weather, prices and promos.
3. Tune every month
Professional services reviews and tunes the model monthly, with the modeling team on the board call.
4. Explore scenarios
Ask a what-if and explore a re-optimized scenario at request time. Every scenario depends on the model's assumptions.
What MMM cannot tell you
Assumptions carry the result
Identification rests on assumptions: the controls must close every back-door path from spend to outcome. Read every result as conditional on the control set and the time split.
Hidden confounders stay hidden
Unmeasured confounding cannot be repaired by any estimator. Only variation that moves spend for reasons unrelated to demand, such as an experiment, can test it.
A good fit is not proof
A model can pass cross-validation and still describe the calendar. Cross-validation fit is not causal proof.
Privacy-first by design
No cookies, no PII, no tracking pixels
The model does not use cookies, personal data or tracking pixels.
SOC 2 Type II, examined by BARR Advisory
The environment that prepares your data carries a SOC 2 Type II attestation.
U.S. or EU servers, pilot first
Your data can be hosted on U.S. or EU servers, and you start with a pilot.
“We are at the point where we can use MMM to start making optimizations, even if we're marketing, not data people.”
See which channels actually pay you back
Start with a pilot. Bring the channels you spend on and the outcome you measure.