Customer data platform

In addition, some CDPs provide additional functions such as marketing performance measurement analytics, predictive modeling, and content marketing.

Commonalities across CDPs:

• marketer-managed;
• unified, persistent, single database for customer behavioral, profile and other data, from any internal or external source;
• consistent identifier that links all of a customer's data;
• accessible by external systems and structured to support marketers' needs for campaign management, marketing analyses and business intelligence;[4]
• provide a 360-degree view of the customer;
• group customers into audience segments;[5] and
• allow users the capability to predict the optimum next move with a customer.[6]

A main advantage of a CDP is its ability to collect data from a variety of sources (both online and offline, with a variety of formats and structures) and convert that disparate data into a standardized form. Some of the data types a standard CDP should work with include:

• Customer events: Browsing activity, actions on a website or in an app, clicks on a banner, etc.
• Transactional data: Data including purchases, returns, data from a POS terminal.
• Customer attributes: Age, gender, birthday, date of first purchase, segmentation data, customer predictions
• Campaign evaluation data: Impressions, clicks, reach, engagement, etc.
• Customer-company history: data from interactions with customer service, NPS scores, data from chatbots, etc. [7]

A CDP is fundamentally different in design and function when compared with marketing automation systems, though CDPs provide some of the functionality of marketing systems and customer engagement platforms. CDP tools are designed to talk to other systems. They retain details from other systems that the engagement or automation tool does not. This is valuable for trend analysis, predictive analytics, and recommendations that can leverage historical data.[8] Marketing campaigns using predictive recommendations are 116% more effective than those that do not[9] which leads to increased ROI of a CDP.

A Data Management Platform (DMP) collects anonymous web and digital data. CDPs collect data that is tied to an identifiable individual. Users of CDP can leverage the intelligence to provide more personalized content and delivery.

A data warehouse or data lake collects data, usually from the same source and with the same structure of information. While this information can be manually synthesized, neither type of system delivers the identity resolution needed to build a consolidated single customer view. Data warehouses are often updated at scheduled intervals whereas CDPs ingest and make available data in real-time. In practice, most CDPs use the same technologies as data lakes; the difference is the CDP has built-in features to do additional processing to make the data usable, while a data lake may not.[10]

Main differences between a customer data platforms (CDP) vs. data management platforms (DMP):[6]

Although similar tools existed in the past, the term Customer Data Platform was first used in 2010. It was meant to describe a marketing software that could build a single customer view (a collection of all of a customer’s data and events into one file).

These databases were originally used to power some other type of software, such as a marketing automation suite, a personalization engine, or a campaign management tool.

The power of the database behind these systems eventually became desirable in its own right. They evolved to become full-fledged software. Simultaneously, some tag management and web analytics providers also transformed their platforms into similar solutions, creating CDPs with a different origin but the same use.

These platforms became successful, and by 2016 they had become the CDP industry. This industry experienced quick growth, due to marketers recognizing the shortcomings of alternatives like DMPs and data lakes, as well as the capabilities a CDP could offer them.[12]