Introducing Customer Data Platform

Objective

After completing this lesson, you will be able to define customer data platform and discuss how it is different than other data stores.

What is a Customer Data Platform, or CDP?

In this lesson, you will be introduced to Customer Data Platforms. You will learn about how these platforms create a comprehensive view of each customer and how they restructure data for use by other systems.

A customer data platform is a packaged software that creates a persistent, unified customer database, which is accessible to other systems.

Capabilities of CDP Packaged Software

A CDP is packaged software, usually bought, and controlled by business users, most often in marketing. This distinguishes it from a data warehouse or data lake which is usually custom-built by corporate or external IT specialists. The packaged nature of the system makes it easier to deploy and change according to your needs.

Persistent, Unified Customer Database

A CDP creates a comprehensive view of each customer by capturing data from multiple systems, linking information related to the same customer, and storing the information to track behavior over time. The CDP contains personal identifiers used to target marketing messages, and track individual-level marketing results. CDPs work primarily with data gathered by a company’s own systems about identified individuals. They may also include data from external sources and about anonymous individuals.

Accessible to Other Systems

Data stored in the CDP can be used by other systems for analysis and to manage customer interactions. The CDP restructures the data, adds calculated values such as trends and model scores, and shares the results in formats that other systems can accept. Access methods typically include APIs, database queries, and file extracts.

Customer Data Platform vs. Delivery Platforms

In the video, you will learn that a Customer Data Platform (CDP) is distinct from delivery platforms, such as email marketing and social media management systems, as the CDP focuses on creating a unified customer database and interacts with delivery systems to send messages and collect engagement data for improved marketing execution.

CDP vs. Other Data Stores

Table showing how CDP fully supports Unified Customer Data, Persistence, Package System, Real-time capability, and Open Access. Data Management platforms poorly support Unified Customer Data and Persistence and fully support the rest. A CRM poorly supports Real-time Capability, moderately supports Unified Customer Data and Open Access, and fully supports the rest. A Data Warehouse poorly supports Packaged System and Real-Time Capability and fully supports the rest. Finally, a custom solution fully supports Persistence, moderately supports Real-time capability, and poorly supports the rest.
CDP
The SAP Customer Data Platform is different from other data stores, such as data management platforms, CRMs, data warehouses, and custom solutions.
DMP
A data management platform, or DMP, is designed to target and retarget anonymous users for advertising purposes. In fact, it focuses more on anonymous segments and categories than individual customers. Much of the information it collects typically expires after 90 days. By contrast, a CDP created a database of your identified customers that you can use for more than just advertising. It creates a persistent customer profile, stores the data, and keeps a history. Then it provides a single record that combines everything you know about that customer.
CRM

A customer relationship management system, or CRM, includes the operational details businesses need to understand customers and prospects during the sales process.

They’re built to engage with customers based on historical and general customer data to create a persistent customer profile. However, they are not build to ingest huge volumes of data from other sources. By contrast, a CDP can connect all types and sources of customer data, whether internal or external, structured or unstructured, as well as batch or streaming.

Data Warehouse

A data warehouse, also known as an enterprise data warehouse, is a system used for reporting and data analysis. Data warehouses are central repositories of data used to create analytical reports. Two approaches are used to build a data warehouse: Extract, Transform, Load (ETL) and Extract, Load, Transform (E-LT).

A CDP’s packaged natures allows it to be more flexible as needs change. It also has more real capabilities and functionality that go far beyond mere reporting and analysis.

Custom Solution
Many IT projects try to achieve their own custom CDP solutions. However, they can represent large investments, involving a significant amount of time and risk. Compare with CDPs, which aren’t just databases, but also standardize and package ‘hidden’ features. These features contain prebuild marketing databases and packaged tools to facilitate database creation and operation.

Summary

In this lesson, you've explored the fundamentals of Customer Data Platforms (CDPs), understanding their significant role in fostering a unified, persistent database tailored to enhance customer engagement and marketing effectiveness. Unlike other data stores such as data management platforms (DMPs), customer relationship management systems (CRMs), or data warehouses, CDPs offer a comprehensive view by capturing and integrating both internal and external data about identified individuals. They allow businesses to create enduring customer profiles that aid in crafting personalized marketing strategies while being readily accessible by other systems through APIs and data extracts for further analysis and interaction management.

Moreover, CDPs stand out with their packaged software nature, making them business-user-friendly, especially for marketing purposes, and distinct from custom IT solutions that often require extensive development resources and risk. They interact seamlessly with delivery platforms to refine marketing execution by gathering engagement data and facilitating refined customer profiling. This enables businesses to not only understand but also anticipate customer needs effectively, leveraging individual-level insights over mere segment-based approaches, thus transforming data into actionable strategies and fostering a cohesive, personalized customer experience.