Discover how a composable Customer Data Platform (CDP) unifies customer data for streamlined experiences by integrating HubSpot with Snowflake to create a scalable solution for analytics and engagement.
Many companies struggle to manage customer data spread across multiple systems, making it hard to deliver seamless customer experiences.
A Customer Data Platform (CDP) helps solve this by unifying data from different sources, enabling businesses to better understand and engage with their customers. A composable CDP takes this a step further, allowing companies to build a tailored solution using the best tools for each part of the data process.
Integrating HubSpot with Snowflake is one example of this approach, combining Snowflake's powerful data storage and analytics with HubSpot's customer engagement tools to create a flexible, scalable solution.
In this blog, we’ll explore the HubSpot and Snowflake integration and the practical steps to create a flexible, scalable data ecosystem for the future.
What is a Composable Customer Data Platform (CDP)?
A composable CDP (Customer Data Platform) offers a flexible, modular way to manage customer data by combining different, best-in-class tools that work together to deliver the required capabilities.
Unlike traditional all-in-one CDPs, which offer a fixed set of tools for data collection, processing, and activation, a composable CDP allows businesses to select and integrate specialized, best-of-breed tools for each stage of the customer data lifecycle. This includes platforms like HubSpot for CRM and Snowflake for data warehousing, creating a tailored ecosystem that suits specific needs.
How it differs from traditional CDPs
Traditional CDPs often function as prebuilt solutions with fixed features and limited customization.
A composable CDP, on the other hand:
- Decouples systems: Each component serves a specific purpose, ensuring better performance and functionality.
- Enhances scalability: You can scale individual components based on demand without overhauling the entire system.
- Prioritizes flexibility: Teams can choose and integrate tools that align with their business goals, whether for analytics, marketing automation, or sales.
The benefits of a composable approach include:
- Flexibility: Customize your tech stack to suit specific needs, such as integrating HubSpot with Snowflake for advanced data analysis.
- Scalability: Add new AI tools and expand capabilities as your business grows.
- Customization: Align data processes with organizational goals rather than conforming to rigid platform limitations.
A composable CDP allows businesses to integrate independent, best-of-breed tools into their data stack, ensuring streamlined data flow and avoiding silos. This flexibility is especially valuable for businesses that need to remain agile, quickly adapting to a fast-evolving market and leveraging emerging tools.
In today's era of AI, where new technologies are rapidly emerging, platforms like HubSpot and Snowflake are embracing a composable approach by offering more APIs and enhancing connectivity.
This allows businesses to combine these platforms with innovative, cutting-edge tools, enabling them to maintain a stable, trusted foundation while also staying flexible enough to capitalize on AI and other fast-moving trends.
The role of Snowflake in a composable CDP
Snowflake is a key component of composable customer data platforms, providing data storage and advanced analytics capabilities. Its ability to centralize and process massive datasets makes it an essential component for businesses aiming to create flexible, scalable data ecosystems.
Snowflake’s core capabilities include:
- Data aggregation and storage: Snowflake enables organizations to consolidate data from various sources, including CRM systems like HubSpot, marketing platforms, and operational databases.
- Scalable analytics: With its cloud-native architecture, Snowflake processes data efficiently, allowing businesses to perform advanced analytics at scale.
Snowflake's strength lies in its ability to act as the analytical source of truth for your organization. By centralizing data and ensuring consistency, it helps align different teams, marketing, sales, and operations, on shared insights and metrics.
- Why it matters:
- HubSpot excels as an operational CRM, managing real-time customer interactions.
- Snowflake complements HubSpot by enabling data aggregation across a variety of tools, serving as an enrichment mechanism and providing deep analytics and data modeling capabilities. This supports enterprise-level decision-making by unifying data from front-office systems and beyond, while also allowing businesses to leverage business intelligence (BI) tools for enhanced reporting and insights.
Building scalable data platforms with Snowflake
By consolidating data from a variety of sources, including CRM systems like HubSpot, Snowflake enables businesses to centralize and process vast amounts of data efficiently.
This ensures that data is aggregated across all systems, allowing businesses to scale their data operations and perform advanced analytics, supporting both day-to-day decision-making and long-term strategic goals.
- It connects seamlessly with tools like HubSpot, enabling bidirectional data flows.
- It supports advanced machine learning and AI models that can generate predictive insights, like identifying high-value customer segments or forecasting sales trends.
Potential use case of the HubSpot-Snowflake integration
Let's say a fast-growing SaaS company wanted to overcome the fragmented data silos that were preventing them from understanding customer behaviour. Integrating HubSpot with Snowflake created a centralised customer data platform that delivered actionable insights.
Data from various sources, including customer interactions, financial data, and support tickets, was integrated into Snowflake through custom integrations. This created a single source of truth, enabling advanced analytics, including machine learning models to identify churn risks. The resulting insights were then synchronized back into the CRM to enrich customer profiles with predictive churn scores.
With this integration, the customer success team could identify at-risk customers and activate automated workflows in HubSpot to deliver personalized retention campaigns. This would in turn improve customer retention and streamline collaboration across teams.
The HubSpot-Snowflake integration: bridging the gap between marketing and data
How HubSpot and Snowflake work together
The HubSpot-Snowflake integration enables organizations to achieve deeper insights by combining customer engagement data from HubSpot with broader datasets in Snowflake.
By syncing data between these platforms, businesses gain a 360-degree view of their customers and can uncover patterns that might otherwise remain hidden.
For example:
- Enhanced segmentation: While marketers typically work within their CRM and marketing tools, Snowflake’s advanced analytics can support higher-level reporting and segmentation by aggregating data from various sources. This enables the creation of highly specific customer segments based on historical interactions, purchase behavior, and even third-party data. These insights can then be used in advanced reporting tools or brought back into the CRM to enhance HubSpot’s reporting capabilities, allowing marketers to execute more targeted and effective campaigns.
- Advanced reporting: While sales and marketing teams primarily use HubSpot for day-to-day reporting, Snowflake enables more detailed, high-level analysis across the business. This includes examining trends across customer lifecycles, evaluating multi-channel attribution, and identifying high-performing campaigns. These insights can then be fed into advanced reporting tools or brought back into the CRM to enhance decision-making and strategy.
- Improved sales forecasting: Snowflake’s analytics, combined with HubSpot’s CRM data, allow sales teams to forecast revenue with greater precision. Predictive models built in Snowflake can highlight potential deal closures and help teams allocate resources effectively.
Key advantages of integrating HubSpot with Snowflake
By integrating HubSpot with Snowflake, businesses can:
- Gain deeper customer insights by combining HubSpot's operational data with Snowflake's analytical depth.
- Streamline workflows by enabling bi-directional data synchronisation between platforms to make real-time decisions.
- Improve collaboration between marketing, sales and customer success teams by providing unified, actionable data.
In essence, this integration allows enterprises to operationalize their data for smarter decision-making, ensuring marketing and sales efforts are not only aligned but also data-driven.
Key integration concepts: ETL vs. Reverse ETL
To fully understand the HubSpot-Snowflake integration, it's important to clarify the difference between ETL and Reverse ETL.
The native integration between HubSpot and Snowflake primarily focuses on extracting data from HubSpot and loading it directly into Snowflake without significant transformation. While many custom integrations include data transformation along the way, the HubSpot-Snowflake integration does not. Snowflake itself provides tools for transforming the data once it's in the system.
From there, data can be processed and either reverse-loaded back into HubSpot through reverse ETL, or simply extracted and loaded into other systems as needed, depending on the specific requirements.
What is ETL?
ETL stands for Extract, Transform, Load. It involves extracting data from a source system (e.g., HubSpot), transforming it into a usable format, and loading it into a destination, such as Snowflake. This is particularly useful for consolidating data from multiple platforms into Snowflake, where it can be analyzed holistically.
For instance, a company might use ETL to pull customer data from HubSpot, transactional data from an e-commerce platform, and support data from a helpdesk system into Snowflake. Once in Snowflake, these datasets can be analyzed to identify trends, such as which customer segments have the highest lifetime value.
What is Reverse ETL?
Reverse ETL is the process of taking data from a data warehouse like Snowflake and syncing it back into operational tools like HubSpot. This allows the insights generated in Snowflake to directly inform marketing, sales, and customer service activities.
For example, after identifying high-value leads in Snowflake, the data can be sent back to HubSpot to prioritize outreach efforts. Similarly, a customer churn risk score calculated in Snowflake could be synced to HubSpot to trigger automated retention campaigns.
By using ETL and Reverse ETL together, businesses can create a dynamic feedback loop. Data flows into Snowflake for deep analysis and then back to HubSpot for immediate action.
This ensures that marketing campaigns are informed by the latest insights, sales teams are equipped with up-to-date information, and customer experiences are always optimized.
The benefits of a flexible and scalable architecture
Integrating tools like HubSpot and Snowflake within a composable customer data platform empowers businesses to adapt, innovate, and scale. This approach brings operational agility while delivering measurable improvements in customer experience and efficiency.
- Adapting to new technologies: Composable architectures are inherently future-proof. As new technologies, such as generative AI or advanced predictive analytics, emerge, businesses can seamlessly integrate them into their existing ecosystems without overhauling their infrastructure.
- Enhanced customer experiences: With a flexible data ecosystem, businesses can unify customer data to create personalized, consistent, and timely interactions across touchpoints. Snowflake enables data unification, while HubSpot acts as the interface for customer engagement. Example: A telecom company tracks customer usage data in Snowflake and integrates it with HubSpot. When usage thresholds are reached, HubSpot triggers automated upgrade recommendations personalized to each customer’s needs.
- Scalable for growth: As businesses grow, their data needs evolve, and a composable approach allows them to scale efficiently. By adopting a composable architecture, businesses can integrate best-of-breed solutions like Snowflake as their central data warehouse, connecting it to systems like their CRM to create a unified source of truth for customer data. This flexibility enables businesses to scale their operations while leveraging specialized tools that meet specific needs. For example, a fast-growing SaaS company starting with HubSpot and Snowflake can gradually add new solutions—such as automated billing systems or advanced AI tools—ensuring data flow across all platforms as the business expands.
- The enterprise advantage: For CTOs in enterprise settings, the value lies in aligning operational tools with strategic goals. A composable architecture with HubSpot and Snowflake creates a balance between innovation and stability, ensuring that businesses are always equipped to meet the demands of a competitive market.
How to build a future proof data ecosystem
A future-proof data ecosystem is one that not only supports current business needs but is also adaptable to changes and growth.
Snowflake’s flexibility and connectability make it a key participant in a composable architecture when integrated with HubSpot, providing enterprises with the agility they need to stay competitive and respond to evolving market demands.
Adaptability for future needs
A composable approach allows organizations to connect Snowflake with best-of-breed tools, such as CRMs, without being limited by infrastructure constraints. This flexibility ensures that your data system can scale effectively, supporting future challenges and opportunities.
For example, as a business expands into new product lines or geographic markets, Snowflake can integrate data from new systems and processes with ease. As new customer data is ingested, Snowflake aggregates it, making it available for downstream systems like HubSpot.
This helps create a unified, 360-degree view of customer interactions and behaviors, while giving businesses the ability to adapt quickly to changing market demands.
Balancing operational and analytical truths
Maintaining a balance between operational data (used for day-to-day transactions) and analytical data (used for generating insights) is crucial for sustained success. In a composable architecture, Snowflake serves as the central analytical source of truth, while HubSpot manages the operational aspects, such as customer relationships and workflows.
This approach enables businesses to align both operational and analytical data across departments—sales, marketing, customer support, and beyond. For example, marketing teams can leverage advanced analytics from Snowflake to gain deeper insights into customer behaviors, and then apply these insights directly in HubSpot to personalize campaigns in real-time.
By integrating HubSpot with Snowflake within a composable architecture, businesses can create a flexible and scalable system that adapts to emerging trends, new technologies, and evolving customer expectations. This ensures a unified, forward-thinking approach to customer data management, capable of evolving alongside the business.
Implementing a HubSpot-Snowflake integration
Implementing a HubSpot-Snowflake integration can significantly enhance your data ecosystem, but it requires careful planning and execution.
Here’s a step-by-step approach to ensure a successful integration, along with guidance on overcoming potential challenges.
HubSpot Snowflake integration: a step-by-step guide
- Define business objectives and integration goals: Before beginning any integration, it's essential to define your business objectives. Are you looking to improve segmentation? Optimize customer journey analytics? Or perhaps you want to build real-time reporting for sales teams? Knowing what you want to achieve will guide your integration efforts and determine the necessary data flows between HubSpot and Snowflake.
- Evaluate your data sources and quality: Conduct a thorough review of your existing data sources. Ensure that the data coming from various systems (CRM, e-commerce, support tools, etc.) is clean, structured, and reliable. HubSpot and Snowflake are powerful tools, but their effectiveness will be limited by the quality of the data flowing into them.
- Choose the right integration method: There are two common ways to integrate HubSpot with Snowflake, depending on the level of complexity and data processing needed:
- Native Integration (Extract and Load): This approach allows you to extract data from HubSpot and load it directly into Snowflake without transformation. This is best suited for cases where minimal data processing or transformation is required.
- Custom Integration (ETL): If you require more advanced data processing, such as transforming the data before loading it into Snowflake, a custom integration using an ETL (Extract, Transform, Load) process is needed. This method gives you more control over data transformation and can better support complex data workflows as your business scales.
- Testing and quality assurance: Before rolling out the integration at scale, thoroughly test it to ensure that data flows correctly between systems, all required data points are being transferred, and HubSpot is receiving the correct insights. Running pilot tests with a small dataset can help identify issues early and minimize disruptions.
- Monitor and optimize the integration: Once the integration is live, monitor its performance closely. Are there delays in data sync? Are sales teams getting accurate reports and insights in real time? HubSpot and Snowflake both provide powerful analytics, but you may need to tweak the integration over time to ensure it remains optimized as your data needs evolve.
Overcoming potential challenges when integrating HubSpot with Snowflake
- Real-time data flow requirements: In a composable architecture, data typically flows from operational systems into the data warehouse, where it can be processed and aggregated. However, this flow is not always instantaneous; updates to the data warehouse may occur at scheduled intervals, such as every hour, daily, or even every few minutes, depending on the integration setup. While this allows for data enrichment and transformation, it may introduce some delays, which is fine for many use cases.
But when businesses require live data to be immediately available—for example, when updating a CRM like HubSpot with the latest customer interactions or sales data—point-to-point integrations are often more suitable. These direct connections between systems bypass the data warehouse, ensuring real-time synchronization of data.
To achieve this, businesses can also use tools like Reverse ETL or webhooks to push updates from Snowflake back into HubSpot or implement point-to-point connections where necessary, ensuring that marketing campaigns, customer segmentation, and sales teams always have access to the most current data without waiting for scheduled data refreshes.
- Data complexity and volume: Large companies often deal with vast amounts of data from diverse sources, which can complicate integrations. Ensuring that your data is properly organized, cleansed, and transformed before being sent to HubSpot is crucial for maintaining data quality and consistency. Proper data preparation not only helps streamline the integration process but also ensures that the data in HubSpot is accurate, relevant, and ready for analysis and decision-making.
- Managing customization needs: Enterprises often have unique data and CRM requirements that are difficult to fit into out-of-the-box solutions. Fortunately, both HubSpot and Snowflake offer strong customization options through their APIs and third-party tools.
Work closely with your development and data teams to design a solution that meets your specific needs, whether it’s custom reporting, specialized data transformations, or specific workflows that need to be automated between HubSpot and Snowflake.
To ensure long-term success, continuously assess your integration’s performance, adapt it as your business needs evolve, and use the full potential of both HubSpot and Snowflake to drive data-driven decision-making and business growth.
Empowering growth with a composable, scalable customer data platform
By integrating HubSpot with Snowflake within a composable, scalable customer data platform, businesses can achieve operational flexibility, enhance data-driven decision-making, and remain adaptable to emerging technologies.
As customer expectations and market dynamics evolve, this approach allows companies to unify their data ecosystem, drive smarter business decisions, and foster sustainable growth.
To fully realize the potential of a HubSpot-Snowflake integration, it’s crucial to partner with experts who understand the nuances of building a composable CDP.
Reach out today to learn how we can support your journey to a more agile, data-driven future.
