Part1: Packaged vs Headless CDP: Which one is right for you?

The interest and adoption of packaged Customer Data Platforms (CDPs) spiked over the last years, but now the times are changing. Many within our industry are questioning the effectiveness, future potential and cost of packaged CDPs.

In this blog series, we will discuss the points necessary in deciding between a headless CDP and a traditional, packaged CDP. 

What is a Packaged Customer Data Platform (CDP)?

A packaged customer data platform (CDP) is a type of software solution that is designed to help businesses collect, unify, analyze, and activate customer data from various sources across their organization.

Unlike traditional customer relationship management (CRM) software that focuses on managing customer interactions and sales processes, a CDP is specifically designed to provide a unified view of the customer that can be leveraged by various departments within an organization, including marketing, sales, customer service, and product development.

A packaged CDP is a pre-built software solution designed to be deployed and configured quickly and easily, often with minimal IT involvement. These solutions typically come with a set of pre-built integrations with popular marketing and advertising platforms, as well as pre-configured data models and analytics dashboards to help businesses get up and running quickly.

Packaged CDP vendors claim their product is the solution to the problem but ignore that it will take an ecosystem to solve all customer data use cases across various tools and downstream teams.

Public cloud data (warehouse) platforms offer this ecosystem. Overlap with packaged CDPs is growing as it has become much easier to build, or more accurately, assemble, the functionality that existing CDPs are offering. 

What is a Headless Customer Data Platform (CDP)?

A headless Customer Data Platform (CDP) is an increasingly popular solution implemented without a packaged CDP tool or suite where:

• A cloud data warehouse, such as GCP BigQuery, Snowflake, AWS Redshift, acts as the data foundation. Different data sources are ingested, transformed and joined.
• Actions and insights are created on all levels, not limited to customer data. Think of product and campaign-level data.
• Activation of data in channels and platforms is implemented directly from the cloud data warehouse environment where the central view lives.
• A combination of cloud-native (data) services and specialized tools or frameworks are used.

As discussed in our article ‘The real CDP revolution is in public clouds’, we noticed the movement to the public cloud among our clients and shared our observations and experiences.

Headless CDP in more detail

A headless architecture gained popularity in the Content Management (CMS) world and is widely used these days. By decoupling the back-end (content management) and the front-end (website), the CMS now acts as a central content hub. Through an API, content and actions can be shared with all sorts of applications, such as websites, apps or even dynamically generated emails. The front-end is no longer intertwined with the content management system. The result: all content is centralized in one system, and front-end applications can be developed independently from the CMS.

The headless CDP acts as a (marketing) data, decisions and activation hub where insights and actions can be created on all kinds of levels, and these are activated / integrated with channels, tools or platforms (think of audiences, events or triggers). This is done directly from the cloud data warehouse, without the need for a stand-alone CDP tool or suite. 

However, there is no centralized user interface. The solution consists of a combination of services within the cloud environment (building blocks) and other (specialized) tools that fulfil a specific need (e.g. connectivity or decision-making).

Not limited to customer data

We are comparing the headless approach to existing stand-alone CDP solutions, which are mainly built around customer data. However, a headless data management system is not limited to customer data only. It supports interoperability with the enterprise data platform. Some examples:

• Identity management and an identity graph to link customer and organizational identifiers.
• Building a single (360°) customer view and (customer) audiences.
• Gaining insights into product performance and enriching product data / feeds.
• Creating models, such as product recommenders, engagement, conversion  scoring, CLTV,  churn prediction models or forecasting sales. These models can be used in batch and real-time. Directly in the platform, so the data doesn’t have to leave.
• Creating business rules or “triggers” (decisioning) for follow-up / Next-Best Actions.
• Sales and engagement reports based on multiple data sources. Creating notifications and alerts based on deviations within these reports.

The headless approach has a lot of overlap with the idea behind the modern data stack, also called the composable CDP. That is mainly because you can use both products from the cloud vendors and ISV SAAS solutions for every function of the platform. 

A data warehouse 'with benefits'

Many organizations recently invested in a data warehouse from one of the well-known cloud providers such as AWS, Azure or GCP. Data can be retrieved from various sources and joined together. Traditionally data warehouses were populated using periodic (daily/hourly) batches, but modern cloud data warehouses have native support for real-time data ingestion. Having live orders, delivery and clickstream data from websites and apps is more common in cloud data warehouse implementations.

Working with such a cloud data warehouse and all the different services within these cloud platforms became much easier. Services within the cloud platforms can be stacked on top of each other (building blocks), making it easier to import, transform and analyze large amounts of data. Skills that are important for working with such a data environment, such as SQL, Java or Python, are also increasingly present within organizations and their suppliers.

Reverse ETL, which means activating this data in channels and/or tools directly from the data warehouse, is what increases the overlap with existing CDPs, hence becoming an alternative.

Conclusion

While traditional stand-alone CDP solutions have been a valuable tool for businesses looking to manage customer data, the rise of headless CDP solutions is a game-changer. By decoupling the front-end user experience from the back-end data management, headless CDPs provide businesses with the flexibility, scalability, and agility they need to stay ahead of the competition.

At Crystalloids, we have architected and developed public cloud data (warehouse) ecosystems for over eight years. We call it a headless Customer Data Platform or Unified Marketing Technology Stack. When we started, the terminology "headless" was unknown within our industry. The headless approach has become mainstream, with headless CMS and more.

If you want good advice from wise and experienced solution architects on your data strategy, leave us a comment below. We also offer a CDP workshop free of charge to get you started.

Follow us for the second part of this blog series, where we’ll compare specific functionalities of packaged vs headless CDP.

ABOUT CRYSTALLOIDS

Crystalloids connects IT and business with scalable and flexible solutions. As a Premier Google Cloud Partner, Crystalloids is a specialist in end-to-end data management, including BI, data science and activation. Using transparent, agile development, Crystalloids ensures that use cases deliver immediate value and that our customers are in complete control.