Marketing teams have a love/hate relationship with data.
On the one hand, everyone knows the importance of data in decision-making. On the other hand, working with marketing data is extremely frustrating: it's scattered, unstructured, isolated in silos.
No wonder there is an enormous difficulty in extracting insights in a consistent and transversal way.
In a Gain Theory study, we saw that only 50% of professionals in the field are satisfied with the quality of the data, considering different criteria:
- breadth: does the data cover all disciplines, from always-on media to multidisciplinary campaigns (with investments in OOH, digital, influencers, and so on)?
- granularity: is the data deep enough for tactical decision-making?
- quality: are the data correct and reflect the real consequences of the teams' actions?
If we want to dream of sophisticated AI models and predictive measurement approaches (whether MMM, Incrementality, or MTA), the first step is to focus on the quality of the data that will feed these models.
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