What is structured marketing data?

Raw marketing data is a pile of exports: each platform's API in its own schema, each agency's plan in its own spreadsheet, each currency and time zone its own. Structured marketing data is what it becomes after the pile has been mapped into one hierarchy, one daily grain and one currency, with consistent naming across sources and the sales data placed next to the spend. It is the dataset a model can be trained on and a BI tool can report from without a translation layer.

Odins keeps two layers. The structured layer is one daily dataset across every channel, mapped to the company, market, brand and product hierarchy, in one currency, offline and digital alike. It feeds every model, report and recommendation in the platform. The raw layer keeps the platform tables as detailed as each platform offers, campaign, ad group, keyword, geo or click level, with the original structures preserved for the data team's own queries.

Both can be delivered to the customer's own warehouse: BigQuery, Snowflake, Redshift or Databricks, with the BI tools the team already uses on top. The data stays the customer's property, and if they leave they take it with them.

You cannot model what you cannot compare, so the structured dataset is the foundation everything else stands on. It is also a product in its own right: some customers use Odins only for this, managed marketing data across every channel, offline included, pushed back to their own warehouse, with the modeling added later on the same foundation when the volume and the budget justify it. The integrations are set up, maintained and monitored by Odins, so an API change or an expired token never lands on the customer's team.

• Channel hierarchy: The one structure every source lands in: market, product, segment, then channel down to the spot. Digital and offline side by side.
• Offline media spend: TV, radio, out-of-home, print, influencer. No API, so it needs a pipeline, down to the individual spot.
• Marketing mix modeling (MMM): The statistical method behind everything else on this list: spend in, sales out, every channel on equal terms.
• MCP (Model Context Protocol): The open standard that lets your own AI assistant talk to Odins: run scenarios, pull data, read the model, from Claude, ChatGPT or Copilot.

Go one level deeper: Data integration. Your data: ownership and export. For data and analytics teams. Or go back to all terms in the glossary.