Global CDP revenues topped $700 million last year, and by 2023, will hit $2,950 million. Clearly, confidence in the potential of CDPs to boost returns is high, as shown by their peak position on Gartner’s hype cycle. And with the power to streamline data and create the coveted single customer view, this isn’t surprising.
But there is an obstacle marketers must tackle before these benefits can be realised: misperception about CDPs and what separates them from another popular insight coordination tool, the data management platform (DMP).
So, what’s a CDP?
Gartner defines CDPs as platforms that “enable marketers to stitch together an array of data sources and orchestrate that data for activation across numerous execution systems”. Essentially, their core aim is producing a consolidated and useable store of customer insight; and they achieve this by gathering, merging and augmenting data from diverse tech, channels, and screens. The unified hub can be leveraged to obtain a complete view of individuals and used across companies in real time.
How are DMPs different?
DMPs differ from CDPs as they focus primarily on website behaviour. Though there are some similarities — DMPs also collate and organise data — the information they capture relates specifically to site interactions, and is harnessed for audience segment creation. Generally, segments then are either distributed to tools that enable programmatic advertising, such as demand-side platforms (DSPs), or used to drive analysis.
A sizeable gap between functions
These attributes matter and are the reasons CDPs and DMPs are suited to different purposes. Marketers must carefully assess the features of each platform to ensure they have the right tool for the task at hand. Let’s start with DMPs.
The ultimate goal of DMPs is generating sales leads, typically by increasing incoming site traffic using top of the funnel targeting. They are frequently seen as tech that purely covers a particular aspect of digital marketing. Additionally, the tendency to regularly connect with DSPs means they use a common insight format – third-party data – and while this allows for faster communication it also restricts DMP scope to snippets of customer activity. When combined with limited identification efficacy, this means DMPs can’t provide a comprehensive understanding of individuals. After all, the data gathered is anonymous and platforms can only store third-party identifiers, which are largely probabilistic and need to be paired before ingestion.
CDPs, however, have a broader range of uses. Advanced orchestration abilities equip them to link all sources — first, second, and third-party — and produce a unified, enriched, and actionable data pool. Plus, they have in-built capacity to resolve identity for certain customers across channels, match and retain any identifiers associated with individuals, and usually rely on accurate deterministic data.
As a result, CDPs produce a centralised data resource that can be used to create unique profiles that drive meaningful experiences and results, as well as giving marketers the means to keep persistent track of multi-channel journeys. That’s not to mention streamlining organisational efficiency by allowing teams to work from a single source of insight and ensuring communications are seamless, not siloed.
The bottom line
When it comes to building the 360-degree view marketers need to accurately tailor messages, CDPs undoubtedly have the edge over DMPs. Their all-encompassing, real-time data proficiency makes them ideal for increasing relevance and return on spend.
But DMPs still have a role to play. As well as being one of the cross-channel sources CDPs draw on to achieve full customer understanding, they can also utilise the collective insight store to increase their own precision. For example, data extracted from CDPs can improve the quality of lookalike audiences and provide tighter audience segments; meaning programmatic ads have a better chance of securing customer interest and inspiring them to take action.
Smart marketing isn’t necessarily about only using one tool; the key is learning the advantages of each platform as it emerges and when to deploy it for maximum effect.