A little less than 10 years ago, Data Management Platforms (DMPs) facilitated the implementation of data-driven strategies for most major advertisers. But because they were largely designed around the use of third-party data (cookies), the current regulatory and technological context is making DMPs obsolete.
To make a difference today, companies need to enrich their data assets and gain the trust of their audiences. To meet this challenge, they must equip themselves with tools dedicated to the collection and processing of first-party data in compliance with regulations, as well as reinforce their audience segmentation capabilities. Customer Data Platforms (CDPs) have become the preferred solution for advertisers and content providers.
Technical and regulatory issues: a death knell for outdated DMPs
Safari, Firefox and soon Chrome… The world’s three leading browsers have removed or will soon remove the use of third-party cookies. Their demise is scheduled for 2023. As for mobile phones, the use of advertising IDs has become increasingly complex on the iPhone since the launch of iOS 14.5 in opt-out by default.
This global trend is significantly impacting the targeting and measurement capabilities of advertising campaigns. Many advertisers are or have been heavily using retargeting and last-generation DMPs based on segments populated with third-party data. This is why alternatives to cookies are flourishing: unique IDs, SSO or contextual targeting such as Topics recently proposed by Google to replace its FLoC.
While they all need to be tested and combined to meet the cookieless challenge, the key is knowing how to exploit first-party data to its full potential. This is why CDPs, or next-generation DMPs, are becoming a must-have for advertisers and content providers.
CDPs: interconnected tool ecosystems for first-party data
CDPs are useful for collecting, storing, processing, enriching and activating first-party data, as well as for measuring the performance of actions taken. These platforms allow data to be used across all channels, whether for media, direct marketing or site personalization.
The term covers two types of technological ecosystems. On the one hand, advertisers can equip themselves with complete tools. On the other hand, CDPs can be best-of-breed, i.e., designed by interconnecting the best tools on the market according to the type of functionalities provided by different vendors. Regardless of the technological solution chosen, there is no single tool that fully integrates all the features an advertiser can expect. To perform all these functions (collection, storage, activation, measurement), a CDP must necessarily interconnect different tools.
Whichever option is selected, CDPs are based on two crucial concepts. The first is their ability to send data to various media activation, site or application management, or direct marketing tools (email / call center / WhatsApp, etc.). The first prerequisite for selecting a CDP is therefore good connectivity. Customer Data Platforms must also be suitable for use by different departments that may lack technical proficiency.
The primary objective of these solutions is to provide good visualization of consolidated data, create advanced segmentation, cross-reference measurement data, etc. Therefore, the user experience of these tools and their interface must be adapted to all types of users.
Audience engines: stand out from the competition
Audience segmentation is a good example of CDP functionality that requires the addition of company-specific algorithms. To make the most of first-party data in digital marketing campaigns, it’s important to be able to build segments that are relevant, exclusive and easily activated. This is the role of audience engines.
They enable the combination of data using algorithms to meet different needs (scoring, clustering or look alike, A/B tests). A/B tests can be proposed either directly by the platform used or developed by and for the advertiser. Off-the-shelf algorithms are interesting, especially for setting up initial POCs (Proofs Of Concept), but these can be used by all competitors. Personalized and self-learning algorithms, on the other hand, based on advanced Machine Learning technologies, can capture all the specificities of proprietary data and continuously improve the results.
In today’s rapidly evolving data environment, a CDP is an essential foundation for exploiting first-party data to its maximum potential. It centralizes all proprietary data and easily activates it. Audience engine and performance measurement solutions then add more value to it. They optimize marketing investments, save time for teams, and provide actionable insights for future digital advertising campaigns. The result? Real competitive advantages in an increasingly complex data landscape.
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