Building a Customer Data Platform (CDP) from scratch is a massive undertaking—one that many companies underestimate. It involves not just the technical aspect of collecting and storing data, but also the intricate challenges of profile identification, merging, orchestration, and distributed processing. At Tracardi, we know firsthand how challenging this journey can be, because we’ve done it multiple times. This article will explain why Tracardi is the ideal starting point for companies looking to build their own CDP, saving them years of trial and error.
The Challenges of Building a CDP from Scratch
Creating a robust and effective CDP requires navigating a labyrinth of technical and organizational challenges. Let’s be honest—building a full-featured CDP is not something that can be accomplished in a few months. In fact, it’s often a four-year process involving many unforeseen discoveries and hurdles. Here’s a closer look at some of the challenges that make building a CDP so demanding:
1. Profile Identification and Resolution
Identifying users accurately across various touchpoints is no easy task. You need to aggregate data from multiple channels, each potentially representing the same user with different identifiers. Creating an engine capable of accurate profile identification and matching these disparate data points is one of the hardest challenges, which requires deep domain expertise.
The complexity arises from the diversity of identifiers that can exist for the same person. For example, the same user might interact via multiple devices, email addresses, and social profiles, each of which generates its own identifier. Matching these without duplicating profiles is extremely challenging and can lead to a fragmented view of your customers if done incorrectly.
Moreover, some devices and channels allow data storage, while others do not. This inconsistency makes accurate profile identification even harder, as some interactions may leave little to no trace, leading to gaps in the customer profile.
2. Data Integration and Consistency
Data integration involves connecting multiple, often disparate, data sources and ensuring they work together in a coherent way. It is not just about bringing data in but also ensuring that the quality, consistency, and accuracy of that data remain intact. This means building pipelines for real-time data ingestion and creating a unified view, which involves heavy data transformation and normalization processes.
3. Profile Merging and Field-Level Conflict Resolution
When multiple data sources contribute to a single profile, merging those profiles requires conflict resolution at the field level. This means deciding which source of information should be trusted for each data point. Without a solid strategy and technical architecture to support it, profile merging can result in inaccurate or incomplete views of your customers.
Field-level conflict resolution is one of the more nuanced challenges that requires a deep understanding of data confidence scores and priority rules. Not every data point carries equal value, and building a rules-based or machine learning-based system that can intelligently decide which value to retain can become incredibly challenging.
4. Early vs. Late Profile Binding
An often-overlooked challenge is deciding whether to bind a user’s profile early—when they first interact with your system—or later, once more data has been collected. This decision has a significant impact on data accuracy, personalization, and processing speed. Getting it wrong can result in degraded performance and poor customer experiences.
Early binding has the advantage of quickly associating user data, which helps in real-time personalization. However, it risks associating incorrect identifiers if all relevant information is not yet available. Late binding, on the other hand, provides more accuracy but can lead to slower decision-making processes and delayed customer responses.
5. Orchestration of Customer Journeys
CDPs are supposed to provide the capability to act on data, but orchestrating customer journeys effectively requires more than just having the data—it requires an engine that can trigger actions based on real-time customer behaviors. Building this type of orchestration from scratch involves complex rule definitions, triggering mechanisms, and action flows.
Effective customer journey orchestration means the CDP should be able to detect events, interpret user behaviors, and decide the best next actions in real-time. This involves building systems that are always listening to incoming data and can trigger the appropriate workflows at the right time. Coding these complex orchestration mechanisms from scratch can take years, and testing them thoroughly is an equally daunting task.
6. Scalable Data Processing and Distributed Computing of Profiles
As your customer data grows, a CDP must scale appropriately. Building a system that efficiently handles distributed data processing, ensures data integrity, and performs well under load is extremely challenging. Distributed computing requires expertise in both data engineering and cloud infrastructure to ensure profiles can be computed in real-time.
Distributed computing is especially crucial for companies dealing with millions of customer profiles and billions of data points. The volume of data alone moves the development to a different level when you need to consume billions of events and merge them into single profiles. This requires building an architecture that can handle enormous amounts of data in real-time without compromising accuracy or speed.
Another major challenge is building real-time segmentation capabilities. To deliver personalized experiences, the system must be able to create and update segments on the fly based on customer interactions. This real-time segmentation is computationally expensive and requires a well-thought-out distributed architecture to support it effectively.
7. Data Privacy and Compliance
Data privacy and regulatory compliance are critical components of any CDP. Ensuring that customer data is collected, stored, and used in accordance with GDPR, CCPA, and other privacy regulations requires a great deal of care and rigorous auditing capabilities.
Building privacy-first capabilities involves not only ensuring secure data storage but also providing features for consent management, data encryption, and user access control. Building these compliance features from scratch requires expertise in both legal and technical domains to ensure customer trust and avoid hefty penalties.
8. Real-Time Capabilities
To remain competitive, companies need their CDPs to provide insights and trigger actions in real time. Achieving real-time data processing, streaming, and decision-making is a monumental task that requires a scalable architecture capable of low-latency processing. Building this from scratch is not only expensive but also requires specialized skills in event-driven architectures and streaming technologies.
9. Integration with Existing Systems
Most organizations already have multiple tools for CRM, marketing, sales, and analytics. Building a CDP from scratch means also building integration capabilities for all of these tools. Ensuring smooth interoperability, maintaining data consistency across systems, and handling different API structures can be extremely time-consuming and prone to errors.
Why Tracardi is Different
Tracardi is an open-source CDP that is built to solve these very challenges—efficiently and effectively. Instead of spending years tackling each issue from the ground up, Tracardi offers a robust foundation upon which you can build and customize your own solution.
1. Open-Source and API-First
Tracardi is open-source, meaning you get full visibility into how the system works, and you can adapt it as you see fit. With its API-first approach, you can easily integrate it with your existing stack, allowing for seamless data flow and extension to other applications. This makes Tracardi a highly flexible and adaptable solution that can evolve as your business grows.
2. Hexagonal Architecture with Plugin-Based Flexibility
Tracardi features a hexagonal architecture that emphasizes modularity. The plugin-based system means you can add or modify features without disturbing the core system. This architecture is designed to allow for easy customization, enabling companies to meet their specific requirements without the risks of modifying a monolithic system.
3. No-Code Automation
Tracardi’s no-code automation capabilities mean that even non-technical team members can create workflows and automate data processes. This feature significantly reduces the barrier to entry for operationalizing customer data and allows teams across departments to collaborate on building effective customer experiences.
4. Pre-Solved Complexities
Tracardi is designed to address all the major challenges that make building a CDP from scratch so daunting:
- Profile Identification and Merging: Tracardi comes with built-in mechanisms for identifying profiles and merging them effectively.
- Field-Level Conflict Resolution: The system is already equipped to handle field-level conflicts, making it easier to maintain consistent and accurate profiles.
- Early or Late Profile Binding: Tracardi gives you the flexibility to decide whether you want early or late binding, depending on your use case.
- Customer Journey Orchestration: The platform has built-in orchestration capabilities, allowing you to trigger actions based on real-time events.
- Distributed Processing: Tracardi is designed for distributed computing, making it scalable as your customer base and data volume grow.
- Privacy and Compliance Tools: Tracardi includes features to help you stay compliant with data privacy regulations, including consent management and secure data handling.
Four Iterations and Counting
The current version of Tracardi is the result of four complete rebuilds from scratch, each iteration improving upon the lessons learned from the last. We know exactly how challenging it is to build a CDP, which is why we’ve done the hard work for you. Our experiences have led us to create a platform that’s both powerful and adaptable, meeting the needs of businesses large and small.
Conclusion
Building a Customer Data Platform from scratch is a lengthy, complex journey with many pitfalls. Tracardi offers a ready-made solution that solves the most challenging aspects of CDP development while giving you the freedom to adapt and innovate. With Tracardi, you can avoid spending years building and iterating, and instead start leveraging customer insights right away.
If you’re ready to get started, check out our open-source version today and see how it can fit your needs. We’ve been on this journey for years, and we’d love for you to benefit from our experience.