CRM, CDP, DMP and much more: the marketing technology market is in continuous ferment.
Faced with post-modern consumers, increasingly informed, aware, hyperconnected, but also disenchanted and elusive, brands must really know their customers in depth and oversee every phase of their customer journey.
Data and information thus become strategically relevant to orchestrate customers’ and prospects’ decision-making process. However, the right technological contribution is needed to actually make the data useful and usable in a data-driven marketing perspective.
The martech sector offers numerous solutions designed to meet the most diverse needs concerning data collection, organization and activation. It is for this reason that companies tend to often forget that integration can be a real added value for our strategy.
In today’s article, we chose to focus on CDPs (Customer Data Platform) and DMPs (Data Management Platform), trying to capture the added value of their integration.
Customer Data Platform and Data Management Platform usage and functionality
Customer Data Platform usage and features
We have had several occasions in our blog to elaborate on features and functionality of a Customer Data Platform. But, let’s recall them briefly.
Although there is no comprehensive definition to date, we can define the CDP or Customer Data Platform as a platform that collects and unifies user and customer data from multiple channels and sources, normalizing them to the level of a single customer view. So through a Customer Data Platform one has the ability to create complete, accurate and updated user profiles in real-time through the integration of behavioral data, demographic data, transactional data, i.e., mostly first-party-data.
CDP enables data collection, data normalization but not only that: in fact, many cdp marketing platforms have customer analytics services integrated with AI and machine learning for even more advancedprofiling and predictive marketing activities. From data collection to data activation to customer journey orchestration: state-of-the-art CDPs integrate marketing automation capabilities, marketing personalization and omnichannel campaign management.
Data Management Platform: features and functionality
DMPs or Data Management Platforms arise mostly to support media buying activities, allowing data and information to be collected to optimize ADS campaigns. DMPs mostly manage data from digital channels, behavioral data, and audienceinformation, and enable the creation of audiences of users with similar interests and characteristics that can be used for more targeted ADS activities and campaigns. Once the data has been collected, first-party, but mostly second- and third-party, DMPs organize it to build a profile of each individual user, but in a completely anonymous way. Having acquired the information, DMPs then share it with digital advertising platforms and internal marketing channels so that these systems know which ads to run. Finally, DMPs in turn collect information on ad performance to optimize campaigns even more.
Data Management Platform: areas of use
Used in advertising, but not only: as can be easily guessed,effective data management such as that provided by a DMP can really make a difference even for purely “corporate” use. When combined, after being collected by these tools, first, second and third-party data have incredible potential for identifying the best prospects interested in the company’s products and services.
CDP and DMP compared: objectives, data collection, and profiling methods.
Simplifying, we could say that DMPs are mainly used to meet needs and objectives related to the top end of the funnel. Unlike CDPs, which offer an all-inclusive view of specific users with deterministic insgiht, DMPsare shown to be particularly effective for activities related to audience creation and amplificationthrough probabilistic insights. Indeed, their strength lies in their ability to process and relate massive amounts of data.
CDP as opposed to DMPs in addition to collection and normalization encompasses many other features: in fact, many cdp marketing platforms have customer analytics services integrated with AI and machine learning for even more advancedprofiling and predictive marketing activities.
From data collection to data activation to customer journey orchestration: state-of-the-art CDPs integrate marketing automation capabilities, marketing personalization and omnichannel campaign management.
In addition, a CDP allows profiling of anonymous users.
CDP and DMP the benefits of integrating them
Customer Data Platform and Data Management Platform, when combined, enable, in effect, a true data-driven marketing approach. The analytical capabilities and identity resolution of a Customer Data Platform that enable the creation of comprehensive profiles and unified overviews of each individual user can, for example, be used by DMP to boost campaign performance through the ability to create new, more highly profiled audiences, while also ensuring the personalization of automated messages.
Blendee MOS beyond the union between a cdp and a dmp
Blendee ‘s marketing operating system moves in precisely this direction: it combines the functionality and potential of a modern Customer Data Platform with that of a high-performance Data Management Platform.
The result? A platform that collects and normalizes data from multiple sources at the single customer view level, allowing the creation of complete and accurate user profiles, updated in real-time. Data collection, data normalization, but not only: the features offered by a DMP enables the expansion of the known starting audience, thanks to anonymous users. Moreover, the platform’s AI engine makes it possible to derive information about anonymous users based on their behavior. It is thus possible to go further and build predictive models that identify the correlations between user characteristics and target actions. From data collection, to data activation, to orchestration of the entire customer journey, for a truly omnichannel strategy, thanks to integrated marketing automation and marketing personalization activities.
The data, even if collected, is irrelevant on its own: it is, in fact, a simple component of a much larger and more complex mechanism. Data acquires value only if made accessible and usable: it is precisely at this stage that it can become a real gold mine for companies – but the right technology stack is needed.
