What is a CDP?
A customer data platform (CDP) unifies customer data into persistent, person-level profiles that make data available to downstream marketing systems. Their goal is to create a “360-degree” view of the customer including profile information, demographics, purchase history, and engagement data that can be leveraged for ongoing marketing efforts. CDPs are typically integrated software modules within broader marketing platforms, enabling marketers to standardize customer identities and orchestrate data flows across web, email, online ads, SMS, and social marketing channels.
What is a composable CDP and how is it different from a traditional CDP?
A composable CDP, or go-to-market data lake, has evolved in response to challenges that traditional CDPs faced and has several key differences:
- Decoupled architecture: Operates independently upstream from the marketing activation platform allowing greater flexibility and design to accommodate a broader range of use cases.
- Cloud-native infrastructure: Rather than being a commercial software product with a dedicated user interface; a composable CDP is built on a modern cloud data platform and functions more like infrastructure – allowing specialized tools and services to be layered on top.
- Design flexibility: Emphasizes design flexibility, cost efficiency, and simple scalability, making it easy to start small and expand linearly as successful use cases are built.
What are the limitations of traditional CDPs?
Traditional CDPs are optimized for speed and simplicity, and therefore make several important trade-offs that a buyer should consider:
- Data often needs to be cleansed and transformed before ingestion.
- The vendor controls the database layer, so your data model must adapt to pre-defined schemas. This makes it difficult to support custom business data entities.
- Advanced analytics use cases often require exporting data to a separate data warehouse, increasing complexity and cost.
- Pricing is typically based on the number of customer profiles managed, which can drive up costs for use cases that require larger data sets.
- Features and functionality are tied to vendor roadmaps, which may slow your ability to quickly pivot on new marketing strategies or channels.
Do composable CDPs provide more flexibility?
Yes, composable CDPs offer more flexibility in several ways:
- Data models can be designed to reflect your organization’s business logic, rather than adapting to a vendor’s predefined schemas.
- Supports a more modular approach to implement best-of-breed tools for ETL (extract-transform-load), identity resolution, audience building, reporting, and advanced analytics and machine learning.
- Can be provisioned quickly and cost-effectively, starting with a small use case and minimal upfront platform costs.
- Greater control over the costs as you scale by only paying for the compute resources based on actual usage.
- AI and machine learning capabilities can be built directly within the same data environment.
How do you know if a traditional CDP or a composable CDP is a better fit for the organization?
Composable CDPs are especially valuable for organizations with complex data needs, diverse marketing channels, and a need to go beyond channel-level activation and reporting. If your marketing relies primarily on direct-to-consumer online selling with well-structured first-party website data, email, and digital advertising, then a traditional CDP may be sufficient.
However, a composable CDP is often a better choice when:
- You need to measure and activate across difficult-to-track channels such as TV, radio, billboard, direct mail, earned media, organic social, and other offline sources.
- Your marketing campaigns use custom data sources that require significant cleansing and transformation. This may include spreadsheets, product data, finance data, or customer service-related data.
- You require advanced techniques for customer acquisition and retention, segmentation, or predictive analytics.
- Your business sells through multiple distribution channels (direct, resellers, distributors, etc.).
Where do composable CDPs fit into the marketing tech stack?
Composable CDPs sit upstream from customer experience platforms and marketing automation platforms that deliver content via web, email, and ad networks. They serve as the single source of truth for analyzing, modeling, and curating customer or account lists, which are pushed downstream to activation systems.
Digital engagement and purchase data generated by activation systems is fed back into the composable CDP for reporting and analysis. This closed-loop architecture enables a continuous improvement of marketing strategies.
Do composable CDPs integrate with other tools in the marketing tech stack?
Yes, major modern marketing platforms support robust integration with composable CDPs through connectors, APIs, and native data sharing features. Additionally, third-party tools like Fivetran and Rivery are available that can make the job of interconnecting data and marketing systems easier through well-structured and controlled schemas.
What are the leading composable CDP data platforms?
Leading composable CDP infrastructure includes Databricks, Snowflake, AWS Redshift, and Azure Synapse. Each of these offer scalable storage and compute, advanced analytics capabilities, and enterprise class security features.
Organizations should carefully consider platforms that have already been adopted within their corporate IT ecosystem to reduce onboarding time and leverage existing security and compliance frameworks.
How long do they take to implement?
The timeline for implementing a composable CDP varies based on the organization’s data maturity, existing vendor relationships, and complexity of the initial use case. For companies with a well-defined use case and accessible data, a solution can be deployed within three months. This initial phase includes aligning the stakeholders, integrating data sources, building data pipelines, and creating consumable data output. However, common roadblocks such as security and compliance approvals, vendor onboarding, and sourcing data can extend the timeline. Organizations that encounter these hurdles may require 3-6 months to complete implementation.
What are the cost considerations for a composable CDP vs traditional CDP?
There are 2 key costs to consider:
- Resources
Costs to staff a traditional vs. composable are similar. A composable CDP is operated by a data engineer who can build tables and views, data pipelines, and output. A traditional CDP requires a marketing operations role to be trained on the platform to ingest data and build audiences. CDPs promise marketers self-service capabilities, but non-technical marketing users often hit roadblocks when dealing with unclean data. - Licensing
Traditional CDPs have high upfront software investments and typically require long-term agreements and implementation fees. There are also ongoing fees based on the number of customer or account profiles managed within the system.Composable CDPs have low upfront platform fees, no required commitments, and the flexibility to scale as workloads are increased. The main cost with a composable CDP is compute time for processing. Data storage is another cost but is relatively inexpensive.
What AI and Machine Learning use cases are enabled by a composable data stack?
Big data advanced analytics
A composable CDP enables the processing of very large data sets up to billions of rows in a cost-efficient way. This is useful for analyzing digital activities like advertising impressions, web activity, and other digital engagements to develop customer attribution models, trend analysis, and behavioral analytics.
Rich data tools
Composable CDPs have support for first-class data science toolkits including runtime support for Python and R, and Data Science Notebooks. These can be used to develop and train various types of machine learning models.
Sentiment analysis
Composable CDPs have LLM functions built into the data layer. This enables processing of unstructured data such as text and documents for use cases like classification and sentiment analysis.
What KPIs should be established to evaluate CDP success?
The best way to prove ROI with a Composable CDP is by building a series of marketing effectiveness metrics that provide visibility into the effect that marketing dollars have on sales.
- Customer lifetime value: The total revenue expected from a customer over the duration of the relationship with your company.
- Customer acquisition cost: The average cost incurred to acquire a new customer.
- Net retention rate: The percentage of recurring revenue retained from existing customers over a defined period of time.
- Channel ROI: Return on investment for a particular marketing channel, showing how much revenue is generated for each dollar spent.
- Incremental lift: Additional sales or conversions directly attributable to specific marketing activities, above what would have occurred without it.
These measures empower growth leaders to make better strategic decisions, justify budget allocations, and prove the impact that marketing efforts have toward broader business goals.
Ready to talk about building a composable CDP? Reach out!
