The Battle Between Packaged and Composable CDPs

What’s up everyone, today we’re taking a deep dive into customer data and the stack that enables marketers to activate it. We’ll be introducing you to packaged customer data platforms and the more flexible options of composable customer data stacks and getting different perspectives on which option is best.

I’ve used both options at different companies and have had the pleasure of partnering with really smart data engineers and up and coming data tools and I’m excited to dive in.

Here’s today’s main takeaway: The debate between packaged and composable CDPs boils down to a trade-off between out-of-the-box functionality and tailored flexibility, with industry opinions divided on what offers greater long-term value. Key factors to consider are company needs and data team size. But if you do decide to explore the composable route, consider tools that focus on seamless integration and adaptability rather than those who claim to replace existing CDPs.

The 8 Core Components of Packaged CDPs: What the Experts Say
Okay first things first, let’s get some definitions out of the way. Let’s start with the more common packaged CDPs.

A Customer Data Platform (CDP) is software that consolidates customer data from various sources and makes it accessible for other systems. The end goal is being able to personalize customer interactions at scale.

I’ve become a big fan of Arpit Choudhury of Data Beats, he articulates the components of a packaged CDP better than anywhere I’ve seen in his post Composable CDP vs. Packaged CDP: An Unbiased Guide Explaining the Two Solutions In Detail.

8 packaged CDP components:

1. CDI (Customer Data Infrastructure): This is where you collect first party data directly from your customers, usually through your website and apps.
2. ETL (Data Ingestion): Stands for Extract, Transform, Load. This is about pulling data from different tools you use and integrating it into your Data Warehouse (DWH).
3. Data Storage/Warehousing: This is where the collected data resides. It’s a centralized repository.
4. Identity Resolution: This is how you connect the dots between various interactions a customer has with your brand across platforms and devices.
5. Audience Segmentation: Usually comes with a drag-and-drop user interface for easily sorting your audience into different buckets based on behavior, demographics, or other factors.
6. Reverse ETL: This is about taking the data from your Data Warehouse and pushing it out to other tools you use.
7. Data Quality: This refers to ensuring the data you collect and use is valid, accurate, consistent, up-to-date, and complete.
8. Data Governance and Privacy Compliance: Ensures you’re in line with legal requirements, such as user consent for data collection or HIPAA compliance for healthcare data.

So in summary: Collect first party data and important data from other tools into a central database, id resolution, quality and compliance, finally having a segmentation engine and pushing that data to other tools.

I asked recent guests if they agreed with these 8 components.

Collection, Source of Truth and Segmentation
Boris Jabes is the Co-Founder & CEO at Census – a reverse ETL tool that allows marketers to activate customer data from their data warehouse.

When asked about his definition of a packaged CDP, Boris elaborated on the role these platforms have carved for themselves in marketing tech stacks. To him, packaged CDPs are specialized tools crafted for marketers, originally in B2C settings. Their primary utility boils down to three main functions: data collection, serving as a reliable data source specifically for the marketing team, and data segmentation for targeted actions.

The ability to gather data from various customer touchpoints, such as websites and apps, is crucial. These platforms act as the single source of truth for that data, ensuring that marketing teams can trust what they’re seeing. Finally, they provide the capability to dissect this data into meaningful segments that can be fed into other marketing tools, whether that’s advertising platforms or email marketing solutions.

Though Boris mentioned the term “DMP,” it’s essential to differentiate it from a CDP. Data Management Platforms (DMPs) have historically been tied to advertising and don’t provide that rich, long-term profile a CDP can offer. The latter offers a more holistic view, allowing businesses to target their audience not just based on advertising metrics but on a more comprehensive understanding of consumer behavior.

Key Takeaway: Packaged CDPs are functional units that collect, validate, and segment data for marketing utility. If you’re considering implementing an all-in-one CDP, look for these three core features: comprehensive data collection, a single source of truth for that data, and robust segmentation capabilities.

Adding Predictive Modeling to Packaged CDPs
Tamara Gruzbarg is the VP Customer Strategy at ActionIQ – an enterprise Customer Data Platform.

When asked about her stance on 8 components of a packaged CDP, Tamara generally concurred but added nuance to each element. Starting with data collection and ending with data activation, she emphasized the critical nature of these components. Tamara also advocated for the necessity of drag-and-drop UI for audience segmentation, which paves the way for data democratization and self-service.

Going beyond mere segmentation, Tamara revealed that her platform offers insights dashboards. These aren’t just Business Intelligence (BI) tools; they help marketers understand segment overlaps and key performance indicators, which further empower them to design more efficient campaigns. Her approach involves offering two types of audience segmentations: rule-driven and machine learning (ML) driven. The latter is a distinct component that allows clients to construct audiences based on predictive models, and it’s an option that has gained traction especially among mid-market businesses.

Tamara also touched upon a salient point regarding large enterprises. Even these giants can benefit from predictive tools when dealing with new data sets they hadn’t previously accessed. Collaboration with their in-house data science teams ensures the quality and reliability of this predictive modeling.

Key Takeaway: A well-designed CDP should not just offer data collection and segmentation but also facilitate data activation and provide actionable insights. Whether you’re a large enterprise or a mid-sized business, the predictive modeling feature in some modern CDPs offers a fast track to gain valuable insights into your audience. Keep an eye out for these extended functionalities when evaluating a CDP for your business.

The Importance of Data Quality and Governance
Michael Katz is the CEO and co-founder at mParticle, the leading packaged Customer Data Platform.

When asked about his agreement with the often-cited eight components of a packaged Customer Data Platform (CDP), Michael did more than just nod in approval. He concurred that these elements are, at a minimum, the pillars of first-generation CDPs. Yet, he warned that very few platforms are strong across all these functionalities, giving his own platform as an exception for its comprehensiveness. According to Michael, a robust CDP is not just a collection of features but an integrated system where the entire value is greater than its individual parts.

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