Data ingestion is just the first step.
Once you’ve unified and normalized your marketing inputs as well as your marketing outputs, and attributed causation of those results, you need to combine the data intelligently to derive accurate data on conversion, cost of customer acquisition, and ultimately return on investment. That can happen via a unique identifier on a customer, via a deterministic attributed conversion, or via a probabilistic attributed conversion.
But it’s not just about getting simple campaign ROI.
Dimensionality is a key criterion to enabling deeper insights and optimization.
A MIP can ingest and relate data points that are different in granularity and that therefore cannot easily be matched one for one.
One simple example: aggregated marketing campaign data and customer/user-level data. Another: creative assets that are different in size, format, or even technological execution, but that share similar tone, messaging, and belong to the same campaign. Yet another: cost data connected to digital ad campaigns that is not visible in ad network reporting, such as agency fees, registration fees, or processing fees.
With dimensionality, marketers can understand campaign cross-over: prospects who received a luxury offer, for instance, who converted to an economy line. They can also get CAC and ROI per creative assets that are similar in tone and message, not just individual iterations of those assets.