Enables raw, unsampled data export via API or integration with cloud data warehouses like BigQuery.
Offers direct access to raw data for enterprise data teams. Cloud deployments integrate with Google BigQuery, Azure, and Amazon S3 for daily data export. On-premise installations provide SQL access to the ClickHouse database. This access supports building proprietary models and merging web behavior with CRM data. Unlike competitors with premium fees, this is standard for enterprise accounts.
Exports complete, unsampled event data via native integration with Google BigQuery at no additional cost.
A native integration with Google BigQuery allows users to export raw event data. This moves analysis from the constrained UI to a data warehouse environment. Analysts can query unsampled data and build customized attribution models using SQL. This eliminates the 'black box' limitations of the dashboard. However, using the exported data requires SQL skills and incurs cloud computing costs.
Supports raw, unsampled hit-level data export via direct database access or APIs without extra fees.
Provides unrestricted access to raw hit-level data for complete data ownership. On-premise hosting allows direct SQL access to the MySQL database. Cloud-hosted versions use an API for automated dataset export. Unlike platforms with restricted exports, this access is built into the open-source architecture. Cost-effective for data science teams building custom models.
Allows basic, aggregated data export via CSV or API, but lacks raw hit-level data.
Offers basic export capabilities for external data manipulation. Users can download CSV files of aggregated metrics like pageviews or top referrers. A REST API is available for automated data extraction. However, due to strict privacy design, there is no raw hit-level data to export. User journeys are not tracked individually, so exported data is pre-aggregated.
Exports aggregated dashboard metrics via CSV or JSON API, lacking raw user-level data.
Data extraction tools are available for external visualization or dataset combination, allowing users to download CSV files of aggregated dashboard views. A JSON API is also available for programmatic metric extraction. Due to the privacy architecture, there is no raw user-level data to export, as individual users are not tracked. This means all data is pre-aggregated, which limits the depth of data science analysis possible. The focus on privacy ensures that while data is accessible, it remains secure and anonymized.
Allows raw data export in multiple formats for custom analysis, but confirm infrastructure compatibility.
Raw data export enables users to extract detailed datasets for custom analysis or integration into other business intelligence tools. Supporting various formats, it adapts to diverse analytical needs. Users should ensure compatibility with existing data infrastructure to fully utilize this functionality. This feature is ideal for businesses requiring detailed data manipulation and analysis.
Enables flexible raw data export for integration with external BI tools.
Raw data export functionality allows users to integrate analytics data into external business intelligence tools or custom data warehouses. Offering flexibility in data analysis and reporting, this feature supports various data formats for ease of use. The export process is straightforward, but users without technical expertise may require assistance to set up deep data integration workflows. This capability enhances custom data analysis, allowing businesses to manipulate and visualize data according to specific needs. Proper setup is necessary for maximizing the benefits of this feature.
Allows raw data export for deep external analysis in multiple formats.
Raw data export empowers users to extract detailed campaign data for external analysis and reporting. Important for businesses requiring deep data manipulation, users can export data in multiple formats for integration with other analytical tools. This flexibility allows tailoring analysis to specific business needs, enabling informed decision-making and strategic planning.
Supports various formats, aiding integration with other BI tools.
Capabilities for raw data export allow businesses to extract detailed datasets for deep analysis and integration with other BI tools. Supporting various export formats, it adapts to different data ecosystems. While serving most standard export needs, organizations with extensive data manipulation requirements might need additional processing tools or custom scripts. It is vital for businesses aiming to gain deeper insights and customize analytics further.
Extracts unprocessed data for deep analysis.
Businesses can extract raw, unprocessed data from their analytics environment for deep analysis or integration with other systems. This is particularly useful for organizations requiring detailed custom reports or specialized data processing techniques beyond standard analytics offerings. Flexibility and depth for data analysis are provided, though users should be aware of potential integration complexities. Appropriate data handling practices are necessary to maximize the benefits of raw-data-export.
Access to unprocessed data enables detailed analysis.
Businesses can extract and analyze unprocessed data directly from the platform, invaluable for organizations requiring detailed, granular insights. Data scientists and analysts are empowered to perform custom analyses and integrate data with other business intelligence tools. Effective use of raw data export may require specialized skills in data manipulation and integration. Strategic decisions are informed by detailed data insights.
Data extraction supports custom analytics externally.
Businesses can extract data for use outside the platform, offering flexibility for custom analytics and reporting. Exporting data to warehouses or other storage solutions supports deeper analysis and integration with business intelligence tools. This is particularly beneficial for organizations needing granular insights and custom reporting. However, setup and management may require technical expertise, especially with large data volumes.
The Data Feeds feature provides robust, automated delivery of unsampled, raw event data to enterprise data warehouses or cloud storage environments.
For organizations that need total ownership of their data for data science or deep integration with internal systems, the platform offers the Data Feeds feature. This robust mechanism exports raw, hit-level data—including all standard dimensions, custom variables, and system IDs—in daily or hourly batches directly to cloud storage solutions like Amazon S3, Azure, or Google Cloud Platform. Crucially, the exported data is completely unsampled, preserving the absolute integrity of enterprise-scale traffic. Unlike simpler tools that might only offer CSV downloads, this is a highly reliable, automated pipeline designed for big data ingestion. The main trade-off is complexity; processing and querying this immense raw data schema requires a mature data engineering team and specialized ETL infrastructure.
Aggregated dashboard data is extracted programmatically using a Stats API for custom reporting.
Automated data access is provided through a Stats API, allowing developers to query metrics, filter by timeframes, and extract aggregated data for dashboards or reports. Casual users can download CSV exports directly from the dashboard. Due to the privacy-focused architecture, there is no raw user-level data available for export. All data is pre-aggregated, which limits the ability to perform complex attribution modeling. This setup is designed to balance data accessibility with user privacy, offering a streamlined approach to data extraction.
Automatically routes raw, unsampled event streams to data warehouses like Snowflake or BigQuery.
Features a Data Pipelines add-on for centralizing behavioral data. Enables automated exports of JSON data to cloud data warehouses or storage buckets. This allows merging in-app data with financial records or training machine learning models. Unlike platforms with restricted access, this pipeline is reliable for enterprise scale.
The Data Pipelines feature allows seamless, automated export of raw, unsampled event data to external warehouses like BigQuery or S3.
For organizations needing to centralize their data architecture, the platform features native Data Pipelines (formerly known as apps or plugins). These pipelines allow teams to configure automated, continuous streams of raw, hit-level JSON data directly into major data warehouses such as Google BigQuery, Snowflake, Amazon S3, or Redshift. This enables data science teams to easily combine product behavioral data with external financial or CRM datasets. Because the platform does not artificially restrict raw data access or mandate aggressive data sampling, this feature provides total data portability and ownership, a critical requirement for enterprise data engineering teams.
The Data Destinations feature allows enterprise users to stream raw, autocaptured event data directly into external data warehouses.
Recognizing the value of its immensely rich autocaptured dataset, the platform offers "Data Destinations" for its enterprise tier. This feature provides a secure, automated pipeline to export raw, user-level behavioral data and session metadata directly into cloud data warehouses like Google BigQuery, Snowflake, or Amazon Redshift. This enables data science teams to combine qualitative UX metrics with financial data, CRM records, or use the raw behavioral events to train proprietary machine learning models. Unlike basic heatmap tools that lock data in their UI, this provides total data portability, though it requires significant data engineering resources to process the massive volume of exported JSON data.
Aggregated session and heatmap data exported via CSV or REST API, lacking direct warehouse streaming.
Data extraction options are available for external metric analysis, allowing users to download CSV files of aggregated heatmap data or session metadata. A REST API supports programmatic data extraction for dashboards. However, there are no native streaming pipelines for exporting raw data to warehouses, which limits the continuous export of massive volumes of behavioral data. This setup is suitable for users who need periodic data snapshots rather than real-time data streaming.
Natively pushes cleaned, unified B2B attribution datasets to major data warehouses.
A major competitive advantage is its approach to data ownership, explicitly encouraging data extraction. Native, automated pipelines push the cleaned, unified B2B identity graph and touchpoint data directly into cloud data warehouses like Google BigQuery, Snowflake, Amazon Redshift, and Azure Synapse. This is a significant asset for RevOps and data engineering teams. It allows them to bypass the platform's standard UI and use the perfectly mapped attribution data in custom BI tools or merge it with internal financial models. This capability enhances the flexibility and utility of the data for strategic decision-making.
Enterprise tiers offer comprehensive API access and scheduled data exports, allowing teams to pull rich behavioral data into external BI tools.
While the platform is a walled garden, it provides extensive APIs and export options for enterprise-tier users. Teams can programmatically extract their entire contact database, associated engagement history, and marketing performance metrics. For deeper analysis, they can schedule exports of processed behavioral data to external data warehouses for use in BI platforms like Looker or Tableau. This ensures that the valuable data collected within the ecosystem remains portable and accessible for proprietary long-term data analysis, satisfying the requirements of data-savvy organizations.
Enterprise users can export their behavioral and event data via extensive APIs and automated data pipelines for use in external systems.
The platform provides rich API access and data export functionality, particularly for enterprise customers. Businesses can extract raw event data, contact profiles, and engagement logs to feed into their own data warehouses for custom BI and long-term analysis. While the platform is an all-in-one marketing suite, it recognizes the need for data portability; it allows sophisticated teams to move their data into systems like Snowflake or BigQuery to build their own proprietary attribution models or custom financial reports.
Raw data export provides robust data extraction options, allowing enterprises to push user-level behavioral data into external data warehouses for deep analysis.
For data-driven enterprises, the platform offers significant data portability. It provides various options for exporting raw user-level data, including automated data pipelines that can push JSON-formatted event data into data warehouses. This allows teams to combine behavioral data from the platform with internal financial or operational datasets. This direct, granular data access is essential for data teams building their own proprietary attribution models or conducting longitudinal studies on user behavior, providing freedom from the constraints of standard platform reporting.
Lacks native streaming of raw, user-level event data to data warehouses.
While effective for visual UX diagnostics, the tool is highly restrictive regarding raw data portability. Automated pipelines or direct integrations to stream continuous, unsampled, user-level behavioral data are not provided. Export capabilities are limited to downloading CSV or Excel files with pre-aggregated metrics, such as snapshots, basic page view totals, or summarized click coordinates from specific heatmaps. This limitation means data science teams cannot extract underlying raw behavioral data to build proprietary attribution models. Additionally, combining UX metrics with external financial databases is not feasible.
Supports detailed analysis but may need infrastructure optimization for high volumes.
Enables businesses to export raw event data to preferred data warehouses or storage solutions, supporting detailed analysis and reporting. While it meets basic export needs, speed and capacity can be limited by the data warehouse's capabilities and data volume. A strategic asset for data-driven companies seeking full data control, though high-volume users might need to optimize their infrastructure for performance.
Routes raw, user-level event streams to external data warehouses via native pipelines.
Offers capabilities for exporting raw event data for enterprise organizations. Native integrations allow automated pipelines to push JSON data into cloud storage or data warehouses. This supports merging product data with financial records or CRM databases. Unlike entry-level tools with restricted exports, this is expected for enterprise tiers.
The platform does not offer raw hit-level event streaming; exports are limited to CSV files of heatmap data, survey responses, and recording metadata.
While the tool is excellent for visual, qualitative analysis within its own interface, it is highly restrictive regarding raw data extraction. Users cannot export a continuous, unsampled stream of raw hit-level events or session coordinates into external data warehouses like BigQuery, Snowflake, or Amazon S3. Export capabilities are strictly limited to downloading CSV files containing aggregated heatmap click coordinates, raw text responses from surveys, and high-level metadata regarding session recordings (e.g., duration, URL path). This means data engineering teams cannot effectively stitch the deep behavioral data captured by the tool into complex, centralized attribution models or proprietary BI dashboards.
Basic raw data export, requiring external systems for detailed analysis.
Raw data export relies on basic functionality for data extraction. Users may need APIs or external systems for detailed manipulation and analysis, as deep in-built tools are absent. Suitable for businesses needing occasional data extraction for external reporting, but may not meet needs for direct, automated data flows.
Limited raw data export functionality, requiring additional setup for detailed analysis.
Raw data export is supported but with noticeable limitations. Exporting data for external analysis may require additional configuration and lacks integration found in more deep solutions. This function is mainly beneficial for businesses with existing data warehouses or BI tools, as built-in analytics capabilities for exported data are limited. Users aiming for detailed data manipulation or integration with deep analytics platforms will likely need external workflows or programming scripts. The process is not direct and may require significant effort to manage effectively.
