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4 types of customer data platforms

Data consolidation CDPs simply collect and unify customer data. Other CDP types, such as automated analytics and automated actions CDPs, go a step further and analyze data.

Data-backed decision-making, or business intelligence, is both a science and an art.

This technology-driven process involves the collection and analysis of data to help organizations make well-informed business decisions. A subset of business intelligence software, customer data platforms (CDPs) are packaged systems that collect customer information from a number of data sources.

Although CDPs come in different types and vary in their capabilities, they all aim to help organizations reach the following goals:

  • Centralize, organize and protect customer data of all types.
  • Create customer behavior and customer journey profiles that users can update and revise in near real time.
  • Offer a better understanding of existing and prospective customers.
  • Improve customer-focused operational efficiencies.
  • Boost marketing efforts through personalized and automated campaigns.

Types of CDPs

Generally, CDPs come in four distinct types. These include data consolidation and unification, identity and engagement, analytics and insights, and automated actions.

1. Data consolidation and unification CDPs

Data consolidation and unification CDPs -- the most basic type -- simply aggregate and unify customer data. As organizations collect data from different sources, such as third-party websites, mobile apps, social media and CRM platforms, these CDPs use APIs to collect information. After collection, the platform sorts, categorizes and unifies the data in a central repository.

2. Identity and engagement CDPs

Identity and engagement CDPs also consolidate and unify customer data, but they go a step further. These CDPs use AI to ensure data is accurate and has no duplicates, which helps create profiles that accurately represent customer behaviors and customer journeys. An analytics team can then analyze and act upon these customer profiles to spot common behaviors or aspects of a customer journey it can improve upon.

A chart that lists the four types of customer data platforms.
Simple CDPs collect and aggregate data, whereas more complex platforms can create customer profiles, analyze data and launch marketing campaigns.

3. Automated analytics CDPs

Organizations with automated analytics CDPs don't need teams of analysts to review customer data and manually create actionable goals. Instead, these platforms use AI to automate the analysis process after they collect data and build customer profiles. Although automated analysis can take time to calibrate within organizations and requires continuous fine-tuning to provide accurate results, it often analyzes large data sets and identifies business opportunities more efficiently than human analysts.

4. Automated actions CDPs

Technologically advanced organizations that want to automate business processes can implement automated actions CDPs to automate the marketing lifecycle. These CDPs automate data collection, identity categorization, analysis and data-driven actions.

For instance, these CDPs can use AI insights to automatically formulate and release digital marketing campaigns across multiple customer-facing touchpoints, such as websites, social media, email and digital signage. They can then analyze the targeted campaign's success or failure to learn which marketing strategies worked and which did not. In this sense, the automated action process within a CDP can fine-tune itself.

How to choose the right CDP type

Each type of CDP -- from data consolidation and unification to automated actions -- builds upon the capabilities of the last. Therefore, organizations without a CDP should focus on data consolidation and unification first.

Once they implement a CDP that perfects this process, business and IT leaders can then determine if and to what degree they can realistically take advantage of a CDP's AI-backed analysis processes, based on their marketing goals, technical prowess and expected results. In many cases, achieving fully automated actions is not possible or even fiscally sound from an ROI perspective.

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