-1.png?width=1132&height=292&name=Asset%201@2x%20(12)-1.png "Odins.ai Logo")
For Marketing Managers
Stop guessing which channels to push — and which to pull back
You manage the day-to-day spend, but the data lives in 15 different platforms and none of them agree. Odins gives you one view: what's working, what's saturated, where to move budget, and what to test next. Updated monthly, specific enough to act on.
Your job is to make every euro count — but the data is in 15 places
You're pulling numbers from Google Ads, Meta, TikTok, programmatic platforms, your CRM, and a handful of spreadsheets. Each platform tells a different story. Attribution is inconsistent. And when your CMO asks 'should we increase paid social or TV?' — you're stitching together a best guess, not presenting a clear answer.
Odins connects all your data sources into a single statistical model. One consistent view of what's driving results, how each channel is performing relative to its potential, and where the next best euro should go.
Monthly recommendations
Each month, you get specific actions: increase this channel by X, decrease that one by Y, hold this one steady. Not vague strategy — concrete numbers you can implement in your platforms the same week.
Saturation curves
See where each channel sits on its diminishing-returns curve. Is paid search still in the efficient zone? Has display already saturated? This is the insight that turns budget allocation from guesswork into science.
Structured test plans
For channels where the model is uncertain, you get structured test designs: how much to spend, for how long, and what to measure. No more winging it with ad hoc experiments that don't produce clear answers.
One data view
All your channels, all your data, one model. No more reconciling conflicting numbers from different platforms. The model handles the complexity so you can focus on decisions, not data wrangling.
Monthly recommendations, specific enough to act on
The model doesn't just tell you 'optimize your media mix.' It tells you: move €20K from display to paid social this month. Hold TV steady. Increase branded search by 15%. And here's what we expect that to deliver.
Each recommendation comes with an expected outcome and a confidence range. You know exactly what to do, what to expect, and how sure the model is. If something doesn't perform as expected, the next month's model picks it up and adjusts.
Structured tests for the channels you're not sure about
There's always a channel you're curious about but can't justify a big bet on. TikTok? Podcasts? A new programmatic partner? The model helps you design a test that will actually produce a clear answer.
Instead of throwing budget at it and hoping for the best, you get: recommended test budget, minimum duration, expected range of results, and clear success criteria. After the test runs, the model measures the actual impact and tells you whether to scale, continue testing, or stop.
Your CMO asks where to cut if the budget drops 15%
Run the scenario. The model shows the optimized allocation at the lower level, what you'd lose, and where the biggest trade-offs are. You have the answer in minutes, not days of spreadsheet work.
Platform ROAS looks great but sales are flat
The model separates genuine incremental impact from attribution inflation. You see what's really driving results vs. what's just taking credit — so you can stop funding channels that look good on paper but don't move the needle.
You need to justify a new channel investment
Present the test design, expected outcomes, and success criteria — all from the model. It's a structured business case, not a hunch. And after the test, the model measures the actual impact objectively.
Trusted by marketers across finance, retail, and consumer brands
