We setup tracking, data pipelines, and measure your media. Updated weekly.
No engineering team required. We will setup your data infrastructure for you.
How the engagement works
Two phases. The first ends with a data foundation you own outright. The second runs the model on that foundation and is always on.
We stand up the warehouse, the database and the pipelines, then land your spend and outcome data in one structure a model can actually use. It runs in your account. You own it.
Your model refreshes weekly and informs you while campaigns are still in flight. No more waiting three months for results.
The foundation is useful whether or not you ever run a media mix model on it. It is the same layer your reporting, dashboards and finance reconciliation should have been built on, which is why we hand it over rather than hold it.
Inside phase two
Each stage feeds the next, and the last one restarts the first. That closed cycle is the product, not the deck at the end of it.
Spend, impressions, sales and outcome data pull automatically from ad platforms, sales systems and tracking. No quarterly data request. No analyst assembling a spreadsheet.
A Bayesian hierarchical model estimates carryover, saturation and regional structure together on the current window, without an analyst kicking off the run.
Which channels carry incremental lift, where returns are saturating, and how much of revenue is base versus media-driven.
Reallocation recommendations with credible intervals, written up and sent. Weekly, monthly, or whenever the model finds something worth acting on.
New data and the decisions you executed feed the next fit. Priors carry forward, so every cycle sharpens the estimate instead of starting over.
Why cadence changes the answer
A model that retrains on its own is the mechanism. What you get out of it is the ability to move budget while the campaign is still running.
A quarterly read tells you what to do differently next quarter, by which point the money is already spent. A weekly refit catches a saturating channel or a fatiguing creative in week two, while the flight is still live.
Shifting two million from linear to CTV runs against the current response curves and comes back immediately, with a credible interval around it. No scoping call, no two-week turnaround on a question that takes a second to ask.
One quarterly correction captures one decision. Weekly correction captures fifty-two, each starting from a better position than the last. The gap between the two widens every cycle.
If spend never varied enough, or two channels always moved together, the model reports that instead of returning a confident number built on nothing. Knowing a question is unanswerable is worth more than a precise answer to it.
| Cadence | Traditional engagement | Running continuously |
|---|---|---|
| Model refresh | Quarterly | Weekly |
| Time to an answer | Two to four weeks, scoped and staffed | Seconds, against the current model |
| What prompts a look | The reporting calendar | The model, when something actually moves |
| Cost of one more refresh | Another engagement | Compute |
| Budget you can still influence | Next quarter's plan | The flight running right now |
Weekly reallocation applies to the flexible part of a plan: digital, programmatic, social and search. Upfront and long-lead commitments move on their own calendar, and the model treats those as constraints rather than pretending they are free.
What it does
Three parts of one system. An estimate nobody has tested is an opinion, and an estimate nobody acts on is a report.
Offline and online channels measured together, at the level budgets are actually set.
Controlled tests that establish ground truth, and hold the model to it.
The decision layer, where an estimate becomes a budget you can execute.
What you learn
One model, fit once, answers all of these. They are different questions asked of the same posterior rather than separate studies you commission one at a time.
Which channels drive incremental sales, separated from the ones that merely appear near a conversion. Offline and online judged on the same footing.
Where the next dollar earns the most and where a channel has stopped returning. The reallocation that buys more outcome at the same total spend.
Whether you are buying more people or the same people more often, and which of the two your response curves say the plan actually needs.
Which messages and creative carry lift, so the message is judged on what it moved rather than on recall scores or internal preference.
How much of revenue is base versus advertising driven, and whether that base is growing on its own or being held up by spend.
The share of sales that would persist if media stopped. Brand shows up as the base the model separates from incremental lift, tracked over time.
Method
We build on the open-source estimators the industry has converged on rather than a proprietary black box. You can audit what the model is doing, and you are never locked into a method you cannot inspect.
The estimator is public and well-tested. Nobody should pay six figures for a regression, and the ones behind a paywall are not better than the ones that are not. What we charge for is the pipeline feeding it and the operation keeping it correct.
Priors encode what you already know about your categories and channels, so estimates stay sensible when a channel has thin data. That is the failure mode that breaks automated modeling.
Multilevel structure recognizes that response differs by DMA. Regions borrow strength from each other instead of being modeled in isolation or averaged into one national curve.
Adstock and diminishing-returns curves capture effects that persist past the flight, so campaigns are credited across their real decay rather than the week they ran.
A model that reallocates budget on its own has to know what it doesn't know. Posterior intervals gate which recommendations ship automatically and which get escalated to a human.
Event instrumentation, conversion tracking, signal loss and consent handling are audited before the first fit and re-checked before every refit after it. A model that retrains unattended inherits whatever the pipeline hands it, so the pipeline is held to the same standard as the model. When the data cannot support a causal claim, that is the finding we report.
Track record
Four engagements across four categories, at national scale, on agency-supplied spend data. Budget efficiency was the objective in each: given a fixed plan, find the reallocation that buys more outcome for the same money.
The methods do not change at a tenth of the spend. What kept them out of reach was never the statistics, it was the cost of the people running them.
A broad channel set spanning store and ecommerce outcomes, where last-click credited the wrong end of the funnel and the reallocation question spanned both.
Demand is local and uneven, so a single national curve hides more than it shows. Allocation was set market by market, with regions informing each other rather than modeled alone.
Brand and performance running together over a considered purchase cycle, where effects persist well past the flight and crediting them to the week they ran understates the upper funnel.
Sustained pressure across a mature category, where the practical question is less which channel works than where the next dollar stops earning its return.
Results
Who this is for
Between five and fifty million in annual media, a real channel mix, and no analytics team standing by. Every measurement platform on the market asks you to arrive with clean, structured, well-governed data. Almost nobody has that. Building it is phase one, not a prerequisite we assume you already met.
Get started
A paid two-week look at your spend and outcome data. You get a written finding on what your data can answer today, what it cannot, and exactly what it would take to close the gap. It stands on its own, and it is how every engagement here begins.
Or email eric@mediamixscience.com directly.