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Use Case
Allocate budget across channels
Every channel has a saturation point. Spend below it and you're leaving returns on the table. Spend above it and you're burning money. Odins shows you where every channel sits on its curve — and where to move budget for the best marginal return.
Marketing intelligence that drives growth
Platform reporting tells you what each channel claims — not what it actually contributes
Google says Google works. Meta says Meta works. Your TV partner says TV works. And when you add up all the claimed conversions, the total is two or three times your actual sales.
The problem isn't that they're lying — it's that each platform only sees its own channel and uses its own attribution model. Overlapping touchpoints get counted multiple times. Organic demand gets credited to whichever platform the customer happened to click last. The result is a distorted picture that makes objective allocation impossible.
You need a single model that measures all channels simultaneously — separating genuine incremental contribution from attribution inflation.
Independent channel contribution
The model decomposes total results into the genuine incremental contribution of each channel — plus baseline (what would have happened with no marketing at all). No platform bias, no double-counting.
Saturation curves per channel
See exactly where each channel sits on its diminishing-returns curve. Is paid social still in the efficient zone? Has display already saturated? This is the foundation for every reallocation decision.
Specific reallocation recommendations
Not 'optimize your media mix' — but 'move €20K from display to paid social this month.' Each recommendation comes with an expected outcome and confidence range.
Structured test plans for uncertain channels
When the model isn't sure about a channel, it tells you how to find out — with a recommended test budget, duration, and clear success criteria. Evidence-based experimentation, not ad hoc tests.
See where every channel hits diminishing returns
Every marketing channel follows a saturation curve. The first million in TV ads reaches new audiences and drives strong returns. The fifth million mostly reaches the same people again. The tenth delivers almost nothing incremental.
The model estimates these curves for every channel using your own data. You see exactly where each channel is right now — and where the curve starts to flatten. This is the single most important insight for budget allocation: move spend from channels that are saturated to channels with headroom, and you get more results from the same total budget.
Every month: specific, ranked recommendations you can act on this week
The model retrains monthly with fresh data. Each month, you get updated recommendations: increase spend here, decrease there, test this, hold that steady. Not vague strategic guidance — specific channel-by-channel actions with expected outcomes.
Each recommendation is ranked by expected impact and reviewed by our team before it reaches you. We check the logic, consider context the model can't see, and make sure every suggestion is something we'd follow ourselves. Over time, this creates a feedback loop: you act, the model measures, and the next round of recommendations is sharper.
Branded search gets credit for demand other channels created — the model fixes that
A customer sees a TV ad, thinks about it for a week, then Googles your brand name and clicks a paid search ad. In platform attribution, paid search gets the credit. In reality, TV created the demand.
This is one of the most common distortions in marketing measurement. Branded search often has the highest reported ROAS — but much of it is just capturing demand created elsewhere. The model disentangles this by measuring all channels simultaneously, estimating what paid search drives incrementally beyond organic demand.
The result: you see the true incremental value of every channel, and you stop over-investing in channels that look good in platform reporting but aren't actually creating new demand.
Rebalancing between digital and traditional channels
The model measures digital and offline channels in the same framework. You see the true marginal return of TV vs. paid social vs. display vs. OOH — not separate reports that can't be compared. Rebalancing is based on evidence, not assumption.
Evaluating whether a new channel is worth the investment
The model designs a structured test: how much to spend, for how long, and what to measure. After the test, it measures the actual incremental impact. You make the decision with data, not a gut call.
Responding to a request to cut the budget by 15%
Run the scenario in the model. It shows the optimized allocation at the lower level, what you'd lose in total outcome, and where the biggest trade-offs are. You present options, not objections.