Do we even know what we are referring to these days when we say "marketing database"?
The evolutions and revolutions in marketing have been and remain relentless. What was once the marketing database has evolved as marketers have adopted customer data platforms (CDPs) and new ecosystems of activation platforms.
Perhaps the rise of the CDP brought a sense that marketers were finally mastering first-party data—drawn from their enterprise systems and operational data lakes—and at last making strides in managing customer interactions.
But much still remains to be done. Changes are affecting the very definition of marketing and the way brands operate. Increasing privacy concerns are being driven by both consumer sentiment and well-intentioned regulators. The ability to deliver personalized customer experiences is challenged consistently as consumers grow accustomed to more personalized interactions. Companies are moving from managing campaigns to managing journeys. The death of the third-party cookie has made some marketers' older tricks obsolete.
Marketers have quickly adopted CDPs and are slowly scaling them, one use case at a time. The traditional focus on CDPs drawing from first-party data has limited their functionality in prospecting, collecting zero-party data, and integrating second- and third-party data. Finding more relevant ways to target prospects will be critical as third-party cookies disappear, forcing a focus not only on first-party audiences but also on the ability to acquire new customers.
Many CDPs function poorly (or not at all) in data cleansing. The CDP doesn't enrich data with new dimensions outside of the ones it collects directly, limiting the amount of insights and analytics that can be garnered through it.
The marketing database as we've known it is dead, in a sense. But what takes its place has to be much bigger and do vastly different things. CDPs and, in fact, the marketing data and tool environment as a whole are not keeping up.
As the role of marketing continues to expand, IT departments are struggling to keep up and are not solving for all the needs of marketing. For example:
- Most enterprise data platforms (EDPs) focus on financial, transactional, and operational data but do not create the segments and personas necessary to fuel effective marketing; however, CDPs are not built to house all the data needed for marketing. First-party data is not enough; a tool needs to include connections to second- and third-party data—for example, the ability to use third-party data to create look-alike audiences.
- Typical EDP use cases are focused on the enterprise data warehouse's needs, and the CDP is unable to manage all the data processing necessary to move from lake constructs to consumption data objects. Churning large amounts of historical and transactional-level data can create the right segments/personas for the CDP to utilize in real time. But those segments can get lost between the enterprise data lake and the CDP, as neither is focused on that capability.
- CDPs are built to operationalize data, so they focus only on recent customer profiles and rarely on historical events, which limits views needed for examining full marketing performance insights and identifying trends over time.
- Most CDPs do not solve use cases for integration with adtech systems, and they also struggle to create complex outbound extracts. Many large established companies still rely on strong performing and direct mail campaigns, which require complex outbound-extract capabilities.
- Most CDPs are not focused on managing the corresponding metadata needed to efficiently drive quality marketing insights and analytics. Such data is critical to demonstrating channel value and measuring corresponding KPIs.
The shortcomings common to most CDPs limit a marketer's ability to effectively respond to changes to customers and in the marketplace. New capabilities are needed.
Beyond the Traditional Marketing Database: The Audience Layer
For most (even advanced) organizations, the CDP will struggle to support more than a few simple use cases, being unable to scale because of a lack of data management capabilities. Thus, we introduce the audience data layer.