What is a CDP and when to use it?

INFRASTRUCTURE, TRANSFORMATION.2023.01.27

A customer data platform (CDP) is a system that collects, consolidates and manages customer data from multiple sources to create a unified view of each customer. This enables companies to better understand their customers and deliver personalized experiences across all channels.

CDPs are used to centralize customer data, enabling insights, personalized interactions and automated actions across all channels. They can be used by companies of any size, but are especially useful for companies with a large customer base and a complex data landscape.

When is a CDP useful?

CDPs can be beneficial for any company seeking to improve customer insight and drive more personalized interactions.

A CDP can be useful in the following cases:

When companies have customer data in multiple systems such as CRM, marketing automation and web analytics and need a way to consolidate and centralize this data to gain a single view of the customer.
When companies seek to improve the personalization and targeting of their marketing campaigns, for example, by using behavioral data to send targeted email campaigns or display personalized advertisements.
When companies seek to improve the customer experience on their website or mobile application, for example, by using data to personalize content or site design for different visitor segments.
When companies seek to automate customer interactions, for example, by using data to trigger automated email campaigns or push notifications based on customer behavior or interests.

How does a CDP Work?

A CDP typically includes the following components:

Data collection: takes data from a variety of sources, such as web analytics, CRM systems and email marketing platforms.
Data warehousing: stores collected data in a centralized location, often in the form of a data warehouse or data lake.
Data management: includes tools to manage and cleanse data, such as data replication, data enrichment and data governance.
Data analytics: provides tools to analyze and segment data, such as reports, dashboards and machine learning models.
Data activation: allows companies to act on customer data, such as triggering personalized email campaigns, targeted advertising and personalized web experiences.

What is Data Integration?

Data integration is the process of combining data from different sources into a single unified view. This can include data from different systems such as a CRM, marketing automation platform, web analytics and social media, as well as data from different formats such as structured data in a relational database and unstructured data in text files.

The aim of data integration is to enable insights and actions to be taken from the combined data, for example by analyzing customer behavior across different channels or automating personalized marketing campaigns.

There are several different approaches to data integration:

Extraction, Transformation and Loading (ETL): This consists of extracting the data from the source systems, transforming it to match the schema of the target system and loading it into the target system.

Data warehousing: this involves the creation of a centralized data warehouse into which data from different source systems is loaded and then queried and analyzed.

Data virtualization: involves the creation of a virtual view of the data that is accessed through a virtualization layer, rather than physically moving the data.

It is recommended to evaluate different approaches to data integration and choose the one that best suits the organization’s needs, taking into account factors such as the complexity of the data landscape, the desired level of real-time integration and the resources available for data integration and management.

In addition, it is important to consider data governance and security, to ensure that data is properly managed and protected throughout the integration process and to comply with any regulatory requirements.

Differences between a CDP, a DMP and a CRM

A customer data platform (CDP), a data management platform (DMP) and a customer relationship management (CRM) system are all tools used to manage customer data, but they have different approaches and uses.

Although the three tools can be used together, each has its own purpose.

A CDP is a system that collects, consolidates and manages customer data from multiple sources to create a unified view of each customer. A CDP typically includes functions such as data collection, data warehousing, data management, data analysis and data activation.

A DMP is a platform that collects and organizes data about users from various sources such as websites and mobile apps to create detailed profiles of each user. DMPs are primarily used for targeted advertising and personalization, providing marketers with actionable insights and audience segments.

A CRM system is designed to manage customer interactions and relationships. It is software that helps companies manage and analyze interactions and data throughout the customer lifecycle, with the aim of improving business relationships.

A CRM system can include functions such as contact management, sales force automation, marketing automation and customer service.

How to make the Most of Customer Data?

It is essential to have a team and processes in place to ensure that customer data is collected, stored, analyzed and used in a consistent and meaningful way.

There are several ways to make the most of customer data with a Customer Data Platform (CDP):

Consolidate and centralize customer data: A CDP allows customer data to be collected from a variety of sources and stored in a centralized location. This allows for a single view of the customer and better informed decisions.
Create customer segments: use the data stored in the CDP to segment the customer base and create targeted marketing campaigns, personalize the customer experience and identify high-value customers.
Personalize interactions: use customer data to personalize interactions across different channels, such as email, website, mobile app and social media. For example, data can be used to personalize the content or layout of a website for different visitor segments.
Automate customer interactions: make use of customer data to automate customer interactions, e.g. by triggering email campaigns or push notifications based on customer behavior or interests.
Measure and optimize: measure the effectiveness of marketing campaigns and optimize them to achieve better results.
Compliance: ensuring that data is being properly managed and protected throughout the process, as well as complying with any regulatory requirements.
Continuous improvement: continuously monitor and evaluate the performance of the CDP and make the necessary adjustments to improve results.
Integrate the CDP with other tools and platforms: to get a more complete picture of the customer and automate processes.

Benefits of a CDP

The use of a Customer Data Platform (CDP) has several advantages:

Unique customer insight.
Personalization of interactions across different channels. This can increase customer engagement and loyalty.
Automation of interactions based on customer behavior or interests. This can increase efficiency and save costs.
Better segmentation to create targeted campaigns, which can lead to better marketing ROI.
Better information on customer behavior, preferences and demographics, which can be used to improve customer engagement and drive business growth.
Data management for better control.
Integration with other tools and platforms, such as marketing automation, CRM and analytics tools, to get a more complete picture of the customer and automate processes.
Scalability to handle large amounts of data and grow with the business, providing flexibility.

How does a CDP Integrate with other Platforms?

Integrating a customer data platform (CDP) with other tools and platforms can provide a more complete picture of the customer and automate processes.

Here are some ways to integrate a PDC with other tools and platforms:

API integration: Most CDPs have APIs (application programming interfaces) that allow them to be integrated with other systems. Using the API, data can be passed from one system to another in a secure and controlled manner.
Data pipeline integration: A data pipeline is a set of processes that move data from one system to another. In this way, data can be extracted from different systems and loaded into the CDP.
Cloud-based integration: Some CDPs are cloud-based, allowing them to integrate with other cloud-based tools and platforms. This can be done through API or data pipeline integration.
Pre-built connectors: Some CDPs have pre-built connectors to popular tools and platforms such as CRM, marketing automation, web analytics, email marketing and social media, making the integration process easier.
Custom integration: In some cases custom integration may be necessary if the CDP does not have pre-built connectors or APIs for the tools and platforms with which it wishes to integrate.

Measuring and optimizing the performance of a CDP

Measuring and optimizing the performance of a CDP is crucial to ensure that it is delivering the best results. Here are some ways to do this:

Establish specific metrics such as increased customer engagement, improved marketing ROI or greater personalization.
Monitor the quality of data regularly to ensure that it is accurate and up to date.
Analyze customer data to gain insight into customer behavior and preferences and use that data to improve the experience and personalize interactions.
Monitor performance to ensure that you meet your objectives and that your data is accurate and up to date.
Test and optimize different strategies and tactics to see what works best and optimize the CDP accordingly.
Compare with industry benchmarks to see how you are performing relative to other companies.

How to Choose the Right CDP?

Choosing the right customer data platform (CDP) can be a complex process, as there are many different CDPs available on the market and each has its own features and capabilities.

In order to choose the right PDC it is important to consider the following factors:

Data sources: which data sources will need to be integrated with the CDP. Some CDPs have pre-defined integrations with popular tools and platforms, while others may require custom integrations.
Data governance: what the organisation's data governance and compliance requirements are. Ensure that the CDP has the necessary data security and control functions in place.
Scalability: the CDP must be able to handle large amounts of data and grow with the business.
Use cases: take into account the organisation's specific use cases, such as customer segmentation, personalization and automation. The CDP must have the necessary features and capabilities to support these use cases.
Technical capacities for data processing, storage and analysis required.
Integrations with other tools and platforms.
Ease of use: user-friendly interface.
Cost according to budget.

How to create a single customer view

Creating a single view of the customer is crucial to improving customer engagement and driving growth.

Here are some steps to create a unique customer vision:

Collect data from various sources such as web analytics, CRM systems, email marketing platforms and social media.
Store data in a centralized location.
Clean data to ensure accuracy and consistency.
Create a unique identifier for each customer such as an email address to link data from different sources.
Cross-checking and merging data from different sources
Enrich the data with additional information such as demographic data, to gain a more complete understanding of the customer.
Continuously monitor and update data to ensure that it is accurate and up to date.

*It is important to comply with data privacy regulations when collecting, storing and using personal data.

How to improve data governance and compliance

Improving data governance and compliance with a customer data platform (CDP) is crucial to ensuring data security and privacy. Here are some steps to do so:

Establish data governance policies that define how data will be collected, stored and used.
Implement data security measures such as encryption and access controls.
Monitor the quality of data to ensure that it is accurate and up to date.
Comply with data privacy regulations.
Conduct audits to ensure compliance with data governance policies and regulations.
Train employees: on data governance and compliance policies to ensure they understand their responsibilities.
Implement data retention policies to ensure that customer data is only retained for as long as necessary and then deleted or anonymized appropriately.

Contact us

If your organization is interested in implementing an expert CDP, we invite you to contact us.