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A traditional data management platform (DMP) is used by digital marketers, advertisers, and publishers to store and manage audience and campaign data, often from multiple sources.
A data management platform allows organizations to create anonymous audiences so they can be activated to ad networks. DMPs work with programmatic ad solutions such as demand side platforms (DSPs) to broadcasts ads to specific audiences you want to reach, which can be identified as device IDs, look-alike targets based on your segmented audiences, or digital targets you provide. When it finds a match on the web, it serves up an offer based on the rules you’ve established and captures the results.
A customer data platform (CDP) lets organizations build and manage a single view of the customer that is fundamental to optimizing customer engagement.
It’s a new type of operational data environment that ingests an all enterprise data sources and types (first-, second-, or third-party data, structured, semi-structured, unstructured, XML, JSON, Hive, HDFS, social media, websites, CRM, transactional/operational database) to provide powerful insights into customer behaviors and preferences.
The core capabilities of a customer data platform include data ingestion, cleansing, and advanced data matching to resolving anonymous to known customer identities using deterministic, probabilistic, and heuristic matching algorithms as well as a set of automated master data management processes.
According to the CDP Institute:
“CDPs work with both anonymous and known individuals, storing ‘personally identifiable information’ such as names, postal addresses, email addresses, and phone numbers, while DMPs work almost exclusively with anonymous entities such as cookies, devices, and IP addresses. Indeed, anonymity is essential to the DMP’s role as a way to exchange information about audiences without violating personal privacy. What’s changed is that CDPs are integrating more often with advertising systems, and thus storing more DMP-type information such as cookie IDs with audience tags. Some DMPs are also storing personal identifiers, although these are carefully isolated from situations where anonymity is still important. But just adding personal identifiers doesn’t give a DMP the advanced identity matching and flexible data storage built into CDPs. So, it will be hard for most DMPs to match full CDP functionality.”
There are a number of capabilities that a customer data platform has that don’t exist in a data management platform. CDPs are able to process and store PII (personally identifiable information) data while DMPs cannot. CDPs can continuously process batch and streaming data whereas DMPs have a one- to two-day update cycle.
CDPs provide data management and advanced matching capabilities that maintain a customer golden record that persists over time. DMPs can maintain an anonymous customer record for a very short period of time.
A DMP allows you to segment data and decide how you want to market to it on an ad network, but the segmented audience groups are rudimentary. Unlike a DMP, a CDP supports building progressive customer personas (unknown to known) to hyper-personalize customer interactions across all channels.
The greatest synergies and results come from recognizing CDPs and DMPs for their differences and strengths and combining their information through a much more comprehensive approach to identity resolution. This combination of ad tech and mar tech has been often called “MadTech”. A customer data platform makes integration of first-party data easier for data management platforms to improve ad targeting, and a data management platform is a source of data that can be brought into a customer data platform to create more progressive customer profiles that will make offers and messages to customers more relevant. All of this enables organizations to drive higher revenue and lifetime customer value while lowering customer interaction cost.
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