Stores aggregated, anonymized data indefinitely, ensuring unlimited historical access.
Despite adhering to strict privacy measures, long-term data accessibility is maintained. Anonymized and aggregated data storage aligns with GDPR principles, allowing indefinite retention on active accounts. Users can analyze year-over-year trends without data purging concerns. This contrasts with platforms that delete historical data after short periods unless premium plans are purchased.
Administrators can configure how long user-level and event-level data is stored before it is automatically deleted from the platform's servers.
The platform enforces strict, configurable data retention limits for all user-level and event-level data (data associated with cookies or user IDs). For standard, free properties, administrators can choose to retain this granular data for either 2 or 14 months before it is permanently purged; premium enterprise users have extended options up to 50 months. It is important to note that this retention limit only applies to granular data used in Explorations and custom funnels; standard, aggregated reporting metrics remain unaffected and accessible indefinitely. This mechanism is crucial for minimizing legal risk and adhering to data minimization principles under privacy laws. Organizations needing to retain raw user journeys for longer historical analysis must actively export their data to a warehouse.
Retention varies by contract, with shorter periods for recordings and longer for metrics.
Session playbacks require significant storage, leading to contract-based retention limits. Typically, recordings are kept for 30 to 90 days to comply with data policies. Aggregated data, like funnel metrics, can be retained longer. Organizations needing long-term archives must export videos before retention ends, balancing storage needs with analytical capabilities.
Enterprise contracts govern customizable data retention periods, typically ranging from 25 to 37 months, though extended retention is available for historical analysis.
Data retention policies are deeply customizable but heavily tied to the specific enterprise contract negotiated with the vendor. By default, most standard implementations retain detailed, hit-level data for a baseline period (often 25 to 37 months), allowing for robust year-over-year reporting and historical deep dives. Unlike simpler platforms that strictly purge granular data after a few months to save server costs, this platform allows organizations to negotiate significantly longer retention periods if their business compliance or long-term analytical models require it. However, storing immense volumes of enterprise data for extended periods inevitably impacts the licensing cost. Organizations managing strict data minimization policies must work closely with their account managers to configure the system to automatically purge data to meet specific legal requirements.
Organizations have absolute control over their data retention policies, with the ability to store granular historical data indefinitely if legally permissible.
Because the platform can be hosted on proprietary infrastructure, data retention limits are dictated entirely by the organization's own server capacity and local legal requirements, rather than vendor-imposed restrictions. Administrators can easily configure automated scripts to purge old, granular log data after a specific timeframe (e.g., 6 months) to comply with data minimization laws, while preserving aggregated report data indefinitely. Conversely, if an organization requires deep historical analysis and has the legal basis to do so, they can retain unsampled, user-level data for years without incurring the premium storage fees typical of SaaS analytics vendors. This level of infrastructural control is a primary reason enterprise and government sectors choose this platform.
Enterprise clients have extensive control over data retention policies, with options to retain raw data for 25 months or longer, depending on the contract.
The platform offers highly flexible data retention policies tailored to enterprise compliance requirements. For premium cloud accounts, the standard retention period for raw, unaggregated data is typically 14 to 25 months, allowing for robust year-over-year reporting. However, clients can negotiate custom contracts to retain this hit-level data indefinitely if required for long-term historical modeling. Importantly, the platform allows administrators to configure automated data purging rules to comply with strict data minimization policies, ensuring that user-level identifiers are deleted after a set period while aggregated reporting totals are preserved indefinitely.
Allows indefinite retention of anonymized, aggregated data without historical limits.
By focusing on anonymized, aggregated data, this approach avoids privacy law conflicts. No arbitrary data retention limits are imposed, enabling users to access complete historical data indefinitely. This supports accurate long-term trend analysis, unlike enterprise platforms that restrict access to save storage costs or encourage premium subscriptions.
Indefinitely retains anonymized traffic data, free from historical reporting limits.
With no personal data collection, this method bypasses strict data minimization rules. As a result, no arbitrary retention limits are applied, allowing indefinite access to historical traffic data. Users can perform year-over-year comparisons without fear of data purging, unlike other platforms that delete data to save server space.
Offers flexible data retention with cloud plans and limitless storage for self-hosted instances.
Data retention limits depend heavily on the chosen deployment method. For the managed Cloud version, standard retention for granular event data ranges from 1 year to 7 years depending on the specific premium plan, ensuring deep historical analysis for long-term product trends. However, organizations that choose to self-host the open-source version on their own infrastructure have absolute control over their data retention; they can retain unsampled, user-level data indefinitely, limited only by their own server capacity. The platform also includes administrative tools to automatically purge old data if an organization needs to enforce strict data minimization rules for legal compliance.
Session recordings are retained for 30 days, while heatmap data is stored for 13 months at no cost.
Retention limits are fixed and strictly enforced due to the platform's free nature, with no premium tiers available. Session recordings and behavioral video playbacks are kept for 30 days before automatic deletion. Aggregated data, such as dashboard metrics and heatmaps, are stored for 13 months, enabling basic year-over-year comparisons. Organizations needing permanent archives must export video files within the 30-day window. This setup provides a balance between data availability and cost-free access.
Retention is tier-based, ranging from 1 to 12 months for session recordings.
Due to the storage demands of session replays, retention limits depend on the subscription tier. Entry-level plans retain recordings for 1 to 3 months, while higher tiers extend this to 12 months. Data is purged once limits are reached to comply with minimization policies. Organizations must download recordings before expiration to maintain archives.
Aggregated metrics are retained indefinitely, supporting long-term trend analysis.
Balancing historical depth with performance, the platform retains aggregated metrics indefinitely. Granular engagement logs may be archived based on volume, but metrics like email clicks and traffic are stored long-term. This enables businesses to conduct extensive trend analysis. Administrative controls allow for data lifecycle management, ensuring compliance while maintaining necessary historical records. This approach supports strategic reporting over many years.
Provides extensive data retention for trend analysis, but manage storage to avoid excessive costs.
Extensive data retention ensures ongoing access to historical data, necessary for longitudinal studies and trend analysis. Supporting significant data storage, businesses must manage storage plans to prevent excessive accumulation. This management is important to avoid performance impacts or additional costs. The feature is valuable for businesses focused on long-term data analysis.
Supports long-term analysis with configurable settings for historical data.
Detailed data retention policies allow businesses to maintain historical data over extended periods. Supporting long-term analysis and trend identification, it is important for strategic decision-making. Users can configure retention settings to suit specific needs, ensuring critical data preservation while minimizing storage costs. Although flexible, businesses with high data volumes or specific legal requirements may need additional storage solutions. It effectively supports strategic data management.
Aligns data storage with policies, optimizing costs and minimizing risks.
Defines data storage duration to align with policies and regulations. Important for managing data lifecycle and ensuring compliance. Optimizes storage costs and minimizes data exposure risks. Users must balance retention periods with business needs. Avoids premature data deletion that could impact analytics insights.
Supports customizable storage durations for compliance and cost management.
Defining storage duration is important for compliance with governance policies. This flexibility aids in efficiently managing the data lifecycle. Customizable retention periods help balance data utility with privacy concerns, potentially reducing storage costs and privacy risks. Efficiency relies on a well-defined data strategy and a detailed understanding of regulatory requirements. Organizations must ensure proper policies are in place to maximize these benefits.
Customizable retention periods support compliance and cost management.
Retention periods are defined to comply with privacy regulations and manage storage costs. Customization allows data to be retained only as long as necessary, based on business needs. While offering flexibility, complex policies may require additional processes. Full compliance might necessitate external tools. Organizations should evaluate their specific requirements to effectively implement these retention strategies.
Standard enterprise contracts allow for five years of data retention, providing robust historical depth for long-term product analysis.
Because product lifecycles often span years, the platform offers very generous data retention policies. By default, standard enterprise contracts retain granular, user-level event data for up to five years, a significantly longer period than the 14-month limits frequently imposed by marketing-focused web analytics tools. This extended retention allows analysts to perform deep historical queries, run multi-year retention analyses, and evaluate the long-term impact of major product updates. Organizations with strict data minimization requirements can configure the system or request manual purges, but the platform fundamentally supports extensive historical data availability for deep behavioral modeling.
Data retention is capped at 365 days for all paid plans to comply with data minimization.
A strict 365-day retention limit applies to session recordings, heatmaps, and survey responses, regardless of the premium tier. Free accounts face even shorter limits, with surveys retained for 365 days and recordings for 30-180 days based on legacy plans. After this period, data is permanently purged to align with GDPR principles. This policy prevents indefinite stockpiling of sensitive data. Organizations needing longer archives must consider alternative tools.
Standard retention is capped at 30 days for recordings, limiting historical analysis.
High-fidelity session recordings require extensive storage, leading to strict 30-day retention limits on standard plans. Granular session data, heatmaps, and form analytics are purged after this period. Enterprise plans may extend retention slightly, but long-term archiving is unsupported. This aligns with GDPR principles but limits year-over-year comparisons. Analysts must act quickly on insights, as the platform does not support prolonged data storage.
Customer and event data are generally retained indefinitely, supporting deep modeling.
Indefinite retention of contact and event history provides a rich data foundation for e-commerce growth. This enables analysts to access extensive records of customer journeys, enhancing predictive models. Multi-year analysis of purchase patterns and lifecycle shifts is possible, aiding in trend identification and future behavior prediction. This setup supports long-term marketing optimization and customer value enhancement. The platform's retention policy is a key advantage for detailed data analysis.
Subscription limits retention, typically up to one year for data storage.
To manage storage costs, retention limits are enforced, with standard plans holding data for up to a year. Entry-level plans may have shorter periods. Once limits are reached, data is purged. Organizations needing long-term archives must export data before expiration, balancing storage management with historical UX comparisons.
Offers data retention for historical analysis, though the retention period may be limited.
Data retention capabilities allow businesses to store and access historical data for analysis and reporting purposes. Although the retention period may be limited, this feature supports the retention of key data points over time, necessary for trend analysis and historical comparison. Users seeking long-term data storage may need to explore additional data archiving solutions. This limitation requires businesses to plan their data storage strategies carefully. Despite its constraints, the feature provides valuable insights into past performance.
Data retention ranges from six to twenty-four months, possibly needing external solutions for long-term storage.
Data retention capabilities vary according to pricing tier, ranging from six to twenty-four months. While sufficient for short to medium-term analysis, long-term storage may require external solutions. The policy ensures access to historical data for performance analysis and trend identification within the defined period but may not fully support extensive historical data needs.
Supports trend analysis, with longer periods in deep plans.
Data retention policies allow businesses to store session data for analysis over a specified period. This feature is valuable for tracking long-term trends and assessing the effectiveness of changes made over time. The standard retention period is one month, but extended retention options are available in higher-tier plans. While the basic offering may be sufficient for short-term analyses, businesses seeking to conduct deeper longitudinal studies may need to opt for plans with longer retention periods. This flexibility supports both immediate and strategic data analysis needs.
Helps manage data lifecycle, supporting basic compliance needs.
Provides basic settings to manage data storage duration. Aids compliance with privacy regulations like GDPR. Offers control over data lifecycle management, ensuring data is not retained longer than necessary. Users might need additional tools for complex retention policies. Not as detailed as some enterprise-level solutions.
Supports compliance by automating data lifecycle management.
Defines data storage duration before automatic purging. Vital for compliance with data protection regulations. Ensures outdated data is automatically deleted. Fundamental retention management is provided. Complex policies might need additional configuration or integration with other tools.
Generally retains granular, user-level behavioral data indefinitely for enterprise accounts.
Product analytics often requires tracking the lifecycle of a user over several years, especially for SaaS and B2B products. Unlike marketing analytics tools that aggressively purge granular data after 14 months, standard enterprise contracts on this platform typically allow for indefinite retention of raw event data. Analysts can directly run multi-year retention charts or retroactively build complex funnels spanning back to the product's inception. Organizations requiring strict data minimization for legal compliance can manually configure the system or request account-level purges, but by default, the platform encourages long-term historical data storage.
Historical touchpoint and CRM data are generally retained indefinitely for active customers.
B2B sales cycles often span many months, making short-term data purging unsuitable for accurate attribution. The platform retains digital touchpoints, CRM activities, and ad impressions indefinitely for active accounts, supporting long-term analysis. This allows for accurate connection of early interactions to later sales outcomes. Organizations needing data minimization can request purges, but the platform is designed for deep historical analysis. This setup supports detailed B2B sales cycle tracking.
Limited data retention support, necessitating external solutions for extensive archival needs.
Data retention capabilities are basic, offering limited native support for storing and managing historical data over time. Users have some control over data retention settings, but the feature may not provide the archival and retrieval options that enterprises require for long-term strategic analyses. Businesses seeking extensive data retention and archiving might need to integrate with external storage solutions or data warehouses to fully meet their needs. The current setup may not suffice for organizations with significant data retention requirements.
Limited data retention, requiring external solutions for long-term storage.
Data retention capabilities are constrained, with limits on storage duration and access. Businesses may need to export data periodically to external storage solutions for historical records. Adequate for short-term analytics, but may not suffice for enterprises needing extended data histories for trend analysis. Supplementary storage strategies are advisable for detailed data availability.
