What are the processes of a data management platform?


A data management or DMP platform allows you to aggregate data from different sources to make them available for targeted and effective marketing strategies. The typical processes of a DMP, therefore, are concentrated in the collection, integration, processing, segmentation and conversion into outputs related to the specific type of company business.


1. Data collection

Data collection process requires data management platform to draw on three types of data from multiple sources. They range from first-party data, which include all those obtained through the company's proprietary channels (website, social pages, app, CRM, ERP and other databases) to second-party data, the result of partnerships with brands operating in areas economic connected, up to the third-party data, usually purchased on the basis of the identification criteria of the prospect to be achieved. In addition to the classic desktop and mobile channels, and therefore the tracking of web browsing, these data can also come from the detection using sensors and IoT devices in the stores.


2. Data Ingestion and integration

Due to the asymmetry of data collected and their differences, it is necessary that the data management platform can "swallow" them all and integrate them in a harmonious way. The use of data lake stores or data warehouses to bring order to the unstructured Big Data ocean. Therefore, DMP uses cloud computing and its unlimited capacity in terms of storage and computational computation. So much so that by now we tend to talk about the Cloud Data Management Platform to highlight the environment in which the platform operates, a version of which on premise would entail a level of complexity and costs that would be difficult for any company to deal with.


3. Processing with the AI

Having a huge pool of structured information is the basis for further reading that goes in depth, to discover trends, patterns and reiterations to be taken into account in view of any marketing campaign. It is therefore thanks to the cloud, that the data management platform elaboration process finds an ally to extract value from the homogeneous data. The ally in question is artificial intelligence (AI) whose algorithms can automatically detect insights within multiple and varied data sets. The AI ​​contribution is not limited to this preliminary phase but, as we will see in the output phase, generates actions that do not require human intervention.


4. Audience segmentation

In one of its most widespread applications, that of digital advertising, the data management platform allows a segmentation of the audience (age, sex, profession, preferences, geographical position, etc.) to be derived from data according to the advertisers' planned purchases. The potential of DMP segmentation process, however, goes further. For example, they allow you to identify samples of leads that you want to hit through a certain channel, of prospects to whom to offer products or services in line with their interests, of customers to be involved in a more solid engagement. The set of transactional, analytical and behavioral data thus becomes a "treasure" which, starting from in-depth knowledge of the end user, guides personalized marketing actions.


5. Output (predictive analysis and automation)

The fifth process of a Data Management Platform delivers fundamental tools to the company to support decision making strategies. First, by making multidimensional analyzes available through dashboards that not only identify past trends, but also provide future behaviors of the clusters considered. Secondly, triggering automatic mechanisms by virtue of artificial intelligence systems and machine learning algorithms fully integrated into the platform: from push notifications to the sending of personalized newsletters, from purchase proposals from an upselling perspective and cross-selling to product recommendation techniques. The range of possible variations in marketing automation activities is very wide but, from this point of view and in general for the expected outputs, data management platforms lend themselves to customizations that derive from the specific needs of companies.

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