Learn why Composable Customer Data Platforms (CDPs) are rapidly adopted by leading companies in their data infrastructure, how they work, and why we prefer the term “Modular CDP” to define our vision
Personalized marketing demands an in-depth understanding of your customers. Traditionally, businesses have relied on Customer Data Platforms (CDPs) to gather, store, and utilize customer data. However, the evolving complexity of customer interactions and technological advancements have exposed certain limitations in traditional CDPs, particularly their extended time to value. With the advent of the Modern Data Stack, many companies are now integrating the creation of a comprehensive Customer 360 view directly into their cloud data warehouses. That leads to a pivotal question: Is there still a need for a standalone CDP?
In this blog post, we will cover:
The operational nuances of traditional CDP
The process and benefits of building a composable CDP
The key advantages of composable CDP in today’s data and marketing landscapes
How to build a Composable CDP
Our preference for the term "Modular CDP" and its implications
This discussion aims to shed light on the evolving dynamics of customer data platforms and their impact on marketing strategies and audience engagement.
The operational nuances of traditional CDP
With recent changes to consumer privacy and increasing restrictions on third-party cookies, the ability to effectively capture and harness first party data has become more critical to survival than ever and quite frankly, is now table stakes in driving highly personalized experiences and remaining competitive.Â
Traditional Customer Data Platforms (CDPs) have long been the backbone of customer data management and activation, offering a centralized repository for various data sources (behavioral data, first-party attributes, customer insights, etc.).
These platforms primarily focus on aggregating and unifying customer data from disparate systems, thereby providing a single customer view. This unified data is then made accessible to marketing teams for various use cases and analytics.
Here is a typical functional diagram of a CDP and its main components:
Core components of a CDP
Despite their centralized nature, traditional CDPs often come with significant challenges. A primary concern is the rigidity of data storage within predefined models. This one-size-fits-all approach can be limiting, as each business has unique data needs and structures. We highlighted this issue when launching our Knowledge Store at DinMo. At DinMo, we are convinced that companies must use tools with adaptable data models that cater to their specific business and teams requirements.
Furthermore, many data teams have already centralized their customer data (in a data warehouse, a data lakehouse, or another storage solution). It creates a potential issue of data duplication when implementing a traditional CDP. This duplication not only complicates data management but can also lead to inconsistencies, with the data warehouse and the CDP potentially serving as conflicting sources of truth.
👉🏼 There is a growing preference among businesses to use a Composable CDP, given its flexibility and agility in adapting to various data sources and structures. This shift reflects a fundamental change in the CDP market, where companies no longer see the need to purchase standalone systems when they already have a robust data infrastructure and modern stack.
Traditional CDPs duplicate data, negating the concept of "Single Source of Truth"
Another critical point to consider is the high cost and complexity associated with implementing traditional CDPs. These platforms often require significant investment, both in terms of financial resources and time (with the implication of marketing and data teams). The complexity of integrating various data sources into a rigid, vendor-specific architecture further adds to this challenge, leading to a prolonged time to value. This delay can be particularly problematic in a fast-paced business environment where agility and quick adaptation to data insights are crucial for maintaining competitive advantage and customer satisfaction.
The evolution to Composable CDPs
With over 75,000 companies now using data warehouses, the approach to data collection, centralization and customer journey mapping has significantly evolved. Cloud data warehouses have become the richest sources of customer data, meticulously capturing both online and offline customer interactions. This evolution sets the stage for a new paradigm in customer data platforms and Modern Data Stack: the composable CDP.
What is a Composable CDP?
A composable CDP is a modular solution that collects, models and activates customer data from your existing data infrastructure. This customer platform leverages the comprehensive data repository of a data warehouse, which already contains a detailed map of the customer journey. Instead of operating as a separate entity, a composable CDP integrates seamlessly with this existing data infrastructure.
Oussama Ghanmi, founder and CEO @DinMo
The essence of a composable CDP lies in its ability to utilize existing data layers directly from the data warehouse. This approach eliminates the need for additional data storage or separate management systems, enabling direct activation of customer data for marketing purposes. By doing so, it substantially shortens the time to value and reduces the complexity of data management. If the distinction between Traditional and Composable CDP is still obscure for you, check our dedicated resource on the subject.
The key benefits of a Composable CDP
A Composable CDP offers a number of advantages over a traditional one:
It establishes a single source of truth for customer data, residing in the data warehouse with a solid identity resolution. This can serve all data usage purposes (Data science, analytics, audiences, activation etc.)
It moves away from rigid data models, offering flexibility in data handling and unification. It’s your business, your teams model it like they want. Composable CDPs are distinguished by their ability to integrate diverse data assets without conforming to a specific structure. This flexibility allows businesses to tackle more intricate customer data challenges effectively, ensuring that data resolution issues are minimized and audience insights are fully leveraged.
It offers an enhanced data security, improving data compliance and governance at scale. Indeed, by processing data directly from your data warehouse, Composable CDPs enhance security, ensure compliance with strictest regulations (GDPR/CCPA), provide flexibility, and drive cost efficiency. For marketers, embracing a composable CDP is a strategic move towards a more secure and efficient data management future.
It provides greater control over customer identity unification and enhanced data governance. A composable CDP is technology agnostic and integrates with any data infrastructure.
Traditional CDPs only understand clickstream data, ignoring the complexity of your business. Various offline actions, data science prediction models, and unique user attributes are overlooked and inaccessible with a packaged CDP. A number of players on the market validate the composable approach over the traditional one. This is the case, for example, of Snowflake in its annual report on the Modern Data Stack (2024).
A Composable CDP offers more flexibility to address complex use cases. Its architecture, free from traditional CDP limitations, provides greater control and specificity in building audience cohorts and powering complex marketing scenarios.
You can granularly build and define audiences thanks to behavioral data, offline interactions and Data Science metrics
You can pass over more data (first party attributes, predictive metrics, etc.) to your ads, CRM or support platforms, improving your customer knowledge and then lowering your Customer Acquisition Costs (CAC)
You can create more personalized customer experiences by orchestrating user journeys across marketing platforms and perform A/B testing in real time for various audiences
If you want to understand all the differences between Traditional and Composable CDPs, check our 2-pages guide below 👇
Comparison guide
What's the difference between Traditional and Composable CDPs?
How does a Composable CDP work?
The composable CDP can be thought of as an intermediary layer that bridges your data warehouse with your business tools. It does not store data independently but leverages the rich data environment of the data warehouse.
The core components of a Composable CDP are comparable to the one of a Traditional CDP:
Data Integration: Involves collecting customer data from different sources. This data can be collected in real time (for example, using a tool such as Segment) or in batch via ETL-type processes.
Data Centralization and Modeling: Once the data from the various sources has been integrated into a data warehouse, it needs to be reconciled and modeled using unique customer identifiers such as email or customer id. This provides a 360° view of customer interactions. This view is organized in a data model into entities centered around the customer (consumer profiles, transactions, product interactions, etc.).
Data Enrichment: The 360° view of customers available in the data warehouse can be enhanced with intelligent and predictive attributes such as churn risk, LTV (Life Time Value) or product appetence. This enables more intelligent activation of marketing programmes. This is particularly useful in B2C contexts with a large customer base and a wide range of products.
Data Activation: This crucial stage involves synchronizing data with all the business tools used to communicate with customers (CRM, Ads, support, etc.). It is the cornerstone of composable CDPs, because it ensures that the customer view is consistent across the various tools used by the company. This functionality is achieved via Reverse ETL processes.
composable-cdp
How to build a Composable CDP?
You're surely convinced about the importance of data activation. Yet, you're hesitating between buying a Reverse ETL platform or building your own API connectors between your data warehouse and operational systems.
To be clear, there's little value in your data team building and maintaining pipelines for Reverse ETL:
Manually creating API connectors can take days or weeks, even for experienced data teams.
API endpoints often can't handle real-time data transfer
Applications are constantly evolving, meaning continuous maintenance of existing connectors
Yet, turning your existing data infrastructure into a Composable CDP is really easy with DinMo. You can implement your first marketing use cases in minutes!
If you'd like to see a demo of how this can be done, go here 👇
Your Composable CDP in minutes
Book a demo with us to see how your organization can implement its own Composable CDP in minutes
Configure your workspace: Connect to your source (i.e. your data warehouse) and your destinations where you want to activate data.
(note: we only have read access, we will never store your data)
🌟 DinMo is the only European CDP with the Google Cloud BigQuery Certification, meaning that we integrate seamlessly with BigQuery for supercharged data handling.
❄️ If you chose Snowflake as a data warehouse, no worries! We also have a strategic partnership with Snowflake!
Build your data model: Choose what data you want to activate so your marketing teams can autonomously operate using the entities your data team has set in place.
Model your business and connect it to DinMo
Build your audience: Create audience in seconds thanks to our visual Segment Builder
Our SQL-free segment builder
Activate your data: Send the audiences you just built to your downstream toolsÂ
One-click activation
Why we prefer "Modular CDP" to "Composable CDP"
At DinMo, we adopt the term "Modular CDP" to better encapsulate our approach towards what is traditionally known as Composable CDP. This preference stems not just from the feature set, but more so from our overarching vision and strategy. This is where we differ radically from our competitors.
The concept of a composable CDP often revolves around a technical framework, anchored in the idea of a "Best of Breed" model. This approach typically involves assembling an array of features and platforms, each intricately designed for specific functions. However, while the technical capabilities are important, at DinMo, we prioritize the business application and its impact.
The modularity of our architecture allows you to focus on your marketing use cases rather than on "technology stacks." Many organizations need to solve use cases, but they often think too broadly about technologies. They fail to recognize that they simply need a method to utilize and activate their existing data. Implementing the right solutions can streamline campaigns and enhance organizational efficiency.
We believe that the journey to becoming data-driven should be pragmatic and ROI-focused from the outset. This means enabling clients to start small. Companies can start by focusing on immediate and tangible benefits, such as optimizing customer exclusions from acquisition campaigns. From there, the system can evolve in complexity and scope, seamlessly adding more data, functionalities, and user personas. This modular approach ensures that the tool grows in tandem with the company's maturity, continuously delivering value at each stage.
A Modular CDP with DinMo allows for a gradual expansion of capabilities to support marketing teams to activate their customer data. Begin by establishing fundamental audience strategies, then progress to incorporating conversion events. As the system matures, integrate more advanced features and use cases like scoring models and eventually, predictive analytics. This progression ensures that each step is beneficial and aligned with the current needs and capabilities of the business.
Composable CDP: A Modular Approach
Final thoughts
🚀 To understand more about how our Modular CDP aligns with your business and marketing needs and our vision of democratizing access to CDP functionalities, we invite you to explore further and contact us to get a free demo of our solution.
FAQ
Is it possible to build a Composable CDP without a data warehouse?
On paper, having a data warehouse is not compulsory; the important thing is to have a database containing all customer data to nourish all business platforms. Yet, we often recommend cloud data warehouses because they offer flexibility and scalability, enable data to be structured, and integrate perfectly with your existing data stack. Setting up a data warehouse is simplified by ETL tools and makes it possible to create a customer 360 in just a few days.
What are the most common use cases for Composable CDPs?
The use cases for composable CDPs are broadly the same as those for Reverse ETLs:
Improved ads campaigns thanks to better customer segmentation & data enrichment (conversions data, predictive metrics, conversions adjustments, etc.)
Automation and personalization to meet customers exactly where they are in their buyer's journey and then increase customer lifetime value
Better sales operations thanks to data enrichment and alerting, transforming the way sales teams interact with potential customers
Successful Customer Support activities thanks to better prioritization and always up-to-date data
Time savings for data teams, thanks to the automation and reliability of sending data to business platforms. They no longer need to build and maintain APIs or write SQL queries, they can focus on real value-added tasks.
How can I be sure that the composable CDP meets my company's needs?
The first question to ask yourself is whether or not you need a CDP.
If the first answer is yes, it depends on your existing data environment and future developments in your company. If you have already capitalised on a data cloud infrastructure and you have high marketing stakes, I can only encourage you to adopt a composable CDP.
Why choose DinMo over other Composable CDPs?
Hightouch or Census take the problem as an integration one. They address the needs of data teams (connecting platforms) more than those of the business team. It's not for nothing that their "no-code" features are exclusive to their premium plans. If you want to start small, with a few destinations and a few segments, without needing help from your data team, this is simply not possible with these tools.
Feature | DinMo | Hightouch | Census |
|---|---|---|---|
No code segment builder | ✅ For all pricing 
plans | ⚠️ For “Business” plan only | ⚠️ For “Enterprise” plan only |
Knowledge Store | ✅ Model creation 
& mapping | ⚠️ Limited functionality that 
still requires technical skills | ⚠️ Limited functionality that 
does not include mapping |
One-Click activation | ✅ Fastest audience
activation | 🚫 Go through the entire 
setup every time | 🚫 Go through the entire 
setup every time |
Data Enrichment | âś… LTV, churn, predictions and more | đźš« Data engineers needed | đźš« Data engineers needed |
Performance Analysis | ✅ AI generated reports
to assess the performance 
of the customer base | ⚠️ Only structural insights
(e.g. size, matching) | ⚠️ Only structural insights
(e.g. size, matching) |
Marketing recommendations | âś… Recommandations on new audiences, improvements,etc. | âś… AI-decisioning that helps identifying best marketing actions | đźš« No recommandations |
Data Analytics | ✅ Measuring campaigns 
performance & answering
to business questions | ⚠️ Limiting functionalities thanks to AI-decisioning | 🚫 Not available |
DinMo vs. Hightouch vs. Census
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