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Data Science AnalyticsTop 10 Best Product Usage Analytics Software of 2026
Top 10 ranking of Product Usage Analytics Software for product teams, comparing Pendo, Amplitude, and Mixpanel by event tracking and reporting.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Pendo
Pendo Feedback and in-app experiences target users by behavior from its unified data model.
Built for fits when mid-size teams need analytics-driven automation with controlled admin governance..
Amplitude
Editor pickSchema management and governance around event properties tied to API-driven configuration.
Built for fits when product teams need controlled event analytics at scale..
Mixpanel
Editor pickEvent and property management through an API that supports automated schema provisioning.
Built for fits when teams need governed event analytics with API-driven automation..
Related reading
Comparison Table
This comparison table covers Product Usage Analytics platforms such as Pendo, Amplitude, Mixpanel, Heap, and Userpilot by mapping integration depth, data model design, and the automation and API surface used for event capture and routing. It also compares admin and governance controls, including RBAC, provisioning workflows, and audit log coverage. The goal is to clarify tradeoffs in schema and extensibility, configuration overhead, and API throughput under real product analytics workloads.
Pendo
product analyticsProduct usage analytics that unifies in-app behavioral events with customer context to drive segmentation, reporting, and administration via permissions and audit logging.
Pendo Feedback and in-app experiences target users by behavior from its unified data model.
Pendo’s configuration centers on mapping product events to a consistent data model that connects behavior to users and accounts. Its integration depth includes an API surface for exporting data, managing environments, and driving automation based on usage events. The schema and provisioning workflow reduce ambiguity when multiple apps or workspaces produce telemetry. Admin and governance controls include RBAC controls, configuration governance, and change traceability through audit log capabilities.
A tradeoff appears in the need to design a durable event schema early, since later renaming or regrouping can create reconciliation work. Pendo fits best when teams must coordinate feature analytics and in-app guidance across many user cohorts, like enterprise account structures. It also fits situations where automation needs API-driven inputs from usage telemetry rather than static dashboards alone.
- +Event-to-user and event-to-account data model supports reliable segmentation
- +API enables automation and data export tied to usage events
- +RBAC and workspace governance support admin-controlled access boundaries
- +In-app experiences can be targeted from measured behavior
- –Event schema upfront design is required to avoid later reconciliation
- –Multiple sources and apps increase configuration and governance overhead
Product analytics teams
Measure feature adoption by account tier
Faster release impact decisions
Customer success leaders
Trigger guidance based on user actions
Higher activation for cohorts
Show 2 more scenarios
Data platform teams
Automate enrichment from usage telemetry
Consistent analytics pipelines
Use Pendo API exports and configuration management to pipe event outcomes into downstream systems.
Enterprise admin teams
Govern cross-workspace access
Lower governance risk
Apply RBAC boundaries and track configuration changes to control who can alter schemas and targeting.
Best for: Fits when mid-size teams need analytics-driven automation with controlled admin governance.
More related reading
Amplitude
event analyticsBehavioral analytics with event schema governance, cohort and funnel automation, and API-based data ingestion plus role-based access control controls.
Schema management and governance around event properties tied to API-driven configuration.
Amplitude fits teams that must standardize event naming and properties across many apps while still exploring user journeys. Its data model centers on events, properties, cohorts, funnels, and segmentation, with schema-aware controls that reduce drift. Integration breadth includes SDK instrumentation, ingestion options, and connections to external systems for downstream reporting and activation. Admin governance includes role-based access, workspace management, and audit log visibility for configuration changes.
A tradeoff appears when teams need complex custom ETL logic inside the analytics product, since Amplitude automation focuses on configuration, event ingestion, and API-driven operations rather than deep transformation pipelines. Another tradeoff is the need to maintain consistent event and property schemas so analyses remain interpretable across teams. Amplitude works well when a product org rolls out shared instrumentation guidelines and automates validation or backfills through its API surface.
- +Event schema controls reduce naming and property drift across teams
- +Deep SDK and integration coverage supports consistent instrumentation
- +RBAC plus audit logging improves admin governance for shared workspaces
- +Automation and API enable repeatable configuration and rollouts
- –Custom transformation logic still belongs in external ETL systems
- –Schema discipline is required or cohort and funnel results degrade
Product analytics leads
Standardize event instrumentation across apps
Fewer analytics inconsistencies
Platform engineering teams
Automate schema checks via API
Repeatable instrumentation rollouts
Show 2 more scenarios
Data governance teams
Control access and track changes
Clear change accountability
RBAC limits permissions and audit logs record admin actions tied to analytics configuration.
Growth operations teams
Activate segments into external tools
Faster experiment execution
Integrations move modeled segments to downstream destinations for activation and reporting.
Best for: Fits when product teams need controlled event analytics at scale.
Mixpanel
event analyticsProduct analytics centered on event tracking schemas, funnels, retention, and experimentation, with ingestion APIs and admin controls for access and data handling.
Event and property management through an API that supports automated schema provisioning.
Mixpanel’s distinct path is configuration-driven analytics via an event taxonomy and a governed data model for identities, properties, and segment logic. Integration depth covers common data sources and destinations for syncing event definitions, backfilling attributes, and keeping identity mapping consistent across tools.
Automation and API surface support creating and managing events, cohorts, and experiments from external systems, which reduces manual setup during releases. A tradeoff appears when teams need very custom data transformations before ingestion, since Mixpanel focuses on event analytics rather than building an end-to-end transformation pipeline. For usage after major product launches, Mixpanel’s schema discipline helps prevent event drift and keeps dashboards stable across versions.
- +Event taxonomy and data model reduce schema drift
- +Automation via API supports provisioning and lifecycle workflows
- +Identity and metadata handling supports consistent cross-tool analysis
- +RBAC and audit log support admin governance needs
- –Custom pre-ingestion transformation logic stays outside Mixpanel
- –High schema governance requires upfront event design discipline
Product analytics engineering
Automate event setup on releases
Less manual event maintenance
Growth operations teams
Measure funnel progression with cohorts
More reliable attribution decisions
Show 2 more scenarios
Security and compliance admins
Control access to analytics assets
Tighter access governance
RBAC and audit log visibility support reviewable changes to roles and configurations.
Data platform teams
Sync event context to warehouses
Unified reporting datasets
Integration pathways export events and properties for warehouse-backed reporting and QA.
Best for: Fits when teams need governed event analytics with API-driven automation.
Heap
autotracking analyticsAutotracking product analytics that captures user actions into a structured event dataset and supports admin governance plus API access for automation workflows.
Automatic session replay style event capture with record-based analysis for rapid iteration.
Heap collects and analyzes product interaction events without requiring manual event tagging for every new feature. Heap’s data model centers on recorded user actions that can be queried and segmented through filters and funnels, which reduces schema churn during iteration.
Heap provides an API and automation hooks for ingesting and mapping events, exporting datasets, and driving workflows based on user behavior. Admin governance is handled through workspace controls, role-based access, and auditability of changes to tracking and settings.
- +Event capture reduces manual instrumentation workload through automatic session recording
- +API supports event and user data operations for integration and automation pipelines
- +Strong schema stability when features change because actions are already captured
- +Segmentation, funnels, and cohorts are queryable without building custom pipelines
- –Recorded-action datasets can grow quickly and require disciplined retention
- –Deep custom event definitions still require careful mapping for consistency
- –Data access patterns depend on Heap’s data model and query interfaces
- –Complex RBAC needs require testing across workspaces and admin roles
Best for: Fits when teams need high-throughput product analytics with strong API-driven automation.
Userpilot
activation analyticsProduct analytics that couples usage insights with in-app onboarding and activation flows, with segmentation controls and integration APIs for data movement.
In-app behavior campaigns driven by real-time segments from product usage events.
Userpilot collects product analytics events and identity data to measure feature adoption and funnel progress inside web and in-app experiences. It builds behavior-driven segments, then triggers in-app guides and lifecycle automations based on those user signals.
Admin features include RBAC, audit logging, and controlled workspace configuration for governance over who can edit tracking and automation logic. The integration surface combines a documented API with webhooks and event ingestion patterns that support automation and extensibility across systems.
- +Behavioral segmentation drives in-app messaging logic from product usage signals
- +Automation actions connect user states to guides, nudges, and lifecycle workflows
- +Documented API supports event ingestion, exports, and automation integrations
- +RBAC and audit logs support governance over configuration changes
- –Complex tracking schemas require careful setup across teams and products
- –High event volume can increase ingestion and configuration overhead
- –Some custom automations depend on API and workflow design effort
Best for: Fits when teams need event-driven onboarding and analytics with governed configuration and integrations.
ThoughtSpot
analytics platformAnalytics platform that combines usage-oriented product data models with governed search analytics, scheduled refresh, and API-based administration and integrations.
Spaces plus semantic layer governance with RBAC and audit logging for model and access changes.
ThoughtSpot fits teams that need analytics governed by a controlled data model and enforced access rules across discovery and consumption. It supports integration-driven analytics through connectors and platform features that map business questions to curated datasets.
Its data model centers on spaces, semantic layers, and governed fields used by search and visualization. Admin tooling focuses on RBAC and audit visibility for model, access, and deployment changes.
- +Semantic layer enforces consistent metrics and field definitions across teams
- +RBAC and space permissions support controlled access to datasets and answers
- +Audit logs track administrative actions, dataset changes, and governance events
- +Integration with enterprise data sources supports repeatable ingestion into governed models
- –Schema and semantic mapping work increases upfront model configuration effort
- –Automation depends on available API coverage for provisioning and configuration workflows
- –High governance requires disciplined space and role design to avoid access sprawl
- –Complex cross-source models can increase query planning and tuning workload
Best for: Fits when governed analytics require integration depth and RBAC plus audit-ready admin controls.
Datadog RUM
telemetry analyticsBrowser and mobile real user monitoring that instruments product usage through distributed tracing and event aggregation, with APIs for telemetry and admin governance.
RUM-to-trace correlation using shared identifiers for session-level root-cause analysis.
Datadog RUM differentiates through end-to-end integration with the Datadog observability stack, linking RUM signals to traces and metrics. It uses a structured front-end data model that maps browser sessions, page views, and user journeys into queryable fields.
Configuration and automation rely on documented instrumentation, environment-specific setup, and a governed event pipeline. Admin controls center on access scoping and audit visibility for changes to RUM-related configuration and data ingestion.
- +Tight linkage between RUM, traces, and logs for cross-surface debugging
- +Clear RUM data model with session, view, and journey fields for analysis
- +Automation via configuration and instrumentation patterns with environment scoping
- +Governance support through role-based access controls and audit visibility
- –RUM insights depend on correct front-end instrumentation coverage
- –Event volume controls can require careful configuration to manage throughput
- –Custom journey semantics demand consistent tagging across services
- –Less flexibility than full custom pipelines for highly specialized schemas
Best for: Fits when teams need governed RUM instrumentation with deep Datadog integration and queryable event models.
Dynatrace
observability analyticsFull-stack product telemetry that correlates user sessions with application performance, with data export APIs and role-based access controls.
Unified event correlation across product telemetry and service health using a shared Dynatrace data model.
Dynatrace, used for Product Usage Analytics, combines application and customer telemetry into a shared data model for product behavior analysis. It supports deep integration with observability signals via managed agents and SDKs, plus analytics-oriented views built on unified event streams.
Dynatrace emphasizes automation through APIs and configuration-driven provisioning so teams can standardize schemas and deploy instrumentation consistently. Admin controls include RBAC and audit trails that track configuration and access changes across environments.
- +Unified data model links product events with backend and infrastructure telemetry
- +Instrumentation via agents and SDKs reduces gaps between UX and system signals
- +APIs support programmatic configuration, data ingestion workflows, and automation
- +RBAC and audit logs provide governance for analytics work and access
- –Schema and event design require planning to avoid noisy or inconsistent analytics
- –API-driven setup can be complex for teams without strong platform ownership
- –Throughput tuning for high-volume events can take iteration and operational work
- –Cross-tool correlations may still depend on consistent naming and tagging
Best for: Fits when teams need governed, API-first analytics tied to real system telemetry.
Snowplow Analytics
event pipelineEvent-based product analytics pipeline that defines an event schema and supports server-side collection, sandboxing, and API-driven data integration.
Enrichment and schema validation in the pipeline lets teams apply consistent transformations before storage.
Snowplow Analytics collects and delivers event-level behavioral data through configurable tracking, with a documented API for ingestion and downstream access. Its data model centers on schemas, enrichments, and pipeline configuration that route events to destinations for analysis and operational use.
Automation and extensibility come through API-driven provisioning, event validation, and enrichment workflows that reduce manual rework. Admin and governance are supported through access controls, auditability of configuration changes, and environment separation patterns for safe iteration.
- +Schema-driven event model reduces drift across teams and pipelines
- +API-first ingestion and enrichment supports automation and repeatable deployments
- +Extensible collector and pipeline configuration supports multiple routing patterns
- +Environment separation supports safer testing without overwriting production data
- +Access controls cover workspace governance and controlled configuration changes
- –Event setup and schema management adds upfront integration work
- –High-throughput pipelines require careful tuning of buffering and destinations
- –Complex enrichment logic can increase operational debugging time
- –Governance depends on disciplined role assignment and environment hygiene
Best for: Fits when teams need schema-driven event analytics with API automation and tight governance.
Countly
product analyticsMobile and web product analytics with event schema configuration, segmentation, and API endpoints for provisioning and reporting automation.
Countly Analytics API for automated ingestion, configuration, and querying across environments.
Countly fits product and engineering teams that need high-integration usage analytics with a configurable data model and exportable event streams. It supports app and web telemetry collection, cohort and funnel analysis, and role-based access controls for operational governance.
Admin teams can automate data lifecycle tasks with API-driven ingestion and scripted configuration across environments. Countly’s extensibility centers on event schemas, plugin-like customization paths, and an integration surface for routing data to other systems.
- +API-first ingestion and analytics access supports automation at scale
- +Configurable event and schema model enables consistent cross-product reporting
- +RBAC and admin settings support controlled access to analytics and config
- +Extensible modules and endpoints support custom pipelines and integrations
- –API-driven workflows require strong schema discipline to avoid reporting drift
- –Automation complexity rises when coordinating multiple environments and tenants
- –Throughput tuning depends on deployment choices and ingestion configuration
- –Advanced governance needs manual runbooks for audit and change tracking
Best for: Fits when teams need API-led analytics integration with strict schema and admin control.
How to Choose the Right Product Usage Analytics Software
This buyer's guide covers product usage analytics tools including Pendo, Amplitude, Mixpanel, Heap, Userpilot, ThoughtSpot, Datadog RUM, Dynatrace, Snowplow Analytics, and Countly.
The guide focuses on integration depth, the event and analytics data model, automation and API surface, and admin and governance controls.
The comparison is framed around how teams instrument events, control schemas, provision configurations, and audit changes across workspaces, environments, and identities.
Product usage analytics platforms that turn in-product behavior into governed, automatable data
Product usage analytics software captures behavioral signals like sessions, views, events, and journeys, then connects those signals to identities, accounts, releases, and environments for reporting and segmentation. Tools like Pendo link events to users and accounts so teams can target in-app experiences from measured behavior.
Many products also provide an analytics governance layer through RBAC and audit logging for changes to tracking, schemas, datasets, and access. Amplitude and Mixpanel apply event schema governance tied to API-driven configuration, so cohort and funnel logic stays consistent across teams.
Evaluation criteria that map to integration depth, data model control, automation reach, and admin governance
Selection should start with the data model each tool uses to relate events to identities, accounts, and downstream analytics. Pendo’s unified event-to-user and event-to-account model supports segmentation across releases and features, while Heap’s record-based dataset model reduces schema churn.
Next, verify the automation and API surface used for provisioning, configuration, exports, and destination routing. Amplitude and Mixpanel focus on schema management via API-driven configuration, and Snowplow Analytics adds API-first ingestion plus pipeline enrichment and validation.
Event schema governance and property lifecycle control
Amplitude controls event schema governance around event properties tied to API-driven configuration, which reduces naming and property drift across teams. Mixpanel exposes event and property management through an API that supports automated schema provisioning, which helps keep funnels and retention calculations aligned.
Unified identity and account modeling for segmentation
Pendo’s event-to-user and event-to-account data model supports reliable segmentation across features and releases, which helps tie analytics back to customer context. Heap’s recorded-action dataset still supports segmentation, but teams must align mappings to keep identity semantics consistent.
API-first automation for instrumentation, provisioning, and data movement
Countly provides an Analytics API for automated ingestion, configuration, and querying across environments, which supports scripted rollout processes. Snowplow Analytics uses API-driven ingestion plus pipeline configuration for routing events to destinations with enrichment and validation applied before storage.
Admin governance with RBAC boundaries and audit logs for config changes
Pendo includes RBAC and workspace governance support for admin visibility into configuration changes, including audit logging for governance tasks. ThoughtSpot extends governance into spaces and a semantic layer with RBAC plus audit logs that track dataset changes and administrative actions.
Automation that triggers activation and in-app behavior from usage signals
Userpilot drives in-app behavior campaigns using real-time segments from product usage events, then triggers guides and lifecycle automations. Pendo also targets in-app experiences from measured behavior through its unified data model, which supports behavior-driven onboarding workflows.
Integration depth that correlates product usage with telemetry or traces
Datadog RUM links browser and mobile real user monitoring signals to traces and metrics using shared identifiers for session-level correlation. Dynatrace correlates product telemetry with application and infrastructure telemetry using a unified event correlation data model.
A decision framework for picking a governed product usage analytics tool with the right integration and automation surface
Start with the control model needed for event definitions and analytics outputs. Teams that require consistent event property naming across many squads should evaluate Amplitude or Mixpanel with schema governance tied to API-driven configuration.
Then match the tool to the operational surface available for automation and admin control. Pendo and Userpilot focus on analytics-to-in-app activation with RBAC and audit logging, while Snowplow Analytics and Countly emphasize API-driven ingestion and provisioning workflows for governed pipelines.
Select the data model that matches the segmentation and activation logic
Choose Pendo if segmentation must reliably combine events with both user and account context so in-app experiences can be targeted from behavior. Choose Heap if the priority is reducing manual event tagging through automatic session recording, then querying recorded actions with filters, funnels, and cohorts.
Lock down event schema governance early to protect funnels, retention, and cohorts
Choose Amplitude if event schema governance around event properties should be enforced through API-driven configuration, which reduces property drift across teams. Choose Mixpanel if event and property management needs to be automated through an API that supports schema provisioning.
Validate the automation and API surface used for provisioning and integrations
Choose Countly if scripted ingestion, configuration, and querying across environments needs to be driven through the Countly Analytics API. Choose Snowplow Analytics if pipeline enrichment and schema validation must happen server-side before events land in downstream destinations.
Confirm admin governance coverage for configuration edits, access, and audit trails
Choose Pendo if workspace configuration governance must include RBAC boundaries and audit visibility into configuration changes. Choose ThoughtSpot if governance must extend into spaces and a semantic layer, where audit logs track model, access, and deployment changes.
Match integration depth to the telemetry correlation requirement
Choose Datadog RUM if product usage must be correlated to traces and logs through shared session identifiers inside the Datadog observability stack. Choose Dynatrace if the required model correlates user sessions with application performance and system health using a unified event correlation data model.
Which teams should prioritize each class of product usage analytics tool
Best-fit recommendations map to the tool’s stated usage context and governance needs. The most common split is between event schema governed product analytics and pipelines that prioritize API automation and environment separation.
A second split is activation-driven product onboarding inside the product UI versus analytics tied to external telemetry and traces.
Mid-size teams building analytics-driven automation with admin governance
Pendo fits when analytics-driven automation must include controlled admin governance through RBAC and audit logging for workspace configuration. Pendo also supports behavior-based targeting via Pendo Feedback and in-app experiences tied to its unified data model.
Product teams that need controlled event analytics at scale
Amplitude fits when event schema governance must prevent property drift and protect cohort and funnel results, backed by API-based configuration and ingestion. Amplitude also provides RBAC and audit visibility for shared workspaces, which supports cross-team governance.
Teams that want API-driven schema provisioning and governed event analytics workflows
Mixpanel fits when event and property management must be automated through an API that supports schema provisioning. Mixpanel also includes RBAC and audit visibility so admin governance works across roles and data handling workflows.
Teams that prioritize high-throughput product analytics with strong API-driven automation
Heap fits when automatic session recording should reduce manual instrumentation load while keeping record-based analysis for rapid iteration. Heap also provides an API and automation hooks for event and user data operations that feed analytics and workflow pipelines.
Teams that need governed RUM or full-stack telemetry correlation with analytics
Datadog RUM fits when browser and mobile user journeys must correlate to traces and logs using shared identifiers in the Datadog stack. Dynatrace fits when unified product telemetry must correlate user sessions with application and infrastructure telemetry using a shared Dynatrace data model and API-driven configuration.
Operational pitfalls that show up when schema, governance, and automation are under-scoped
Many teams fail when event schema discipline and governance scope are treated as optional setup tasks. Amplitude, Mixpanel, and Snowplow Analytics all require upfront schema and property work to reduce drift and protect downstream cohort and funnel correctness.
Other failures come from mismanaging automation throughput and governance in high-event-volume environments. Heap’s recorded-action datasets can grow quickly and require disciplined retention, and Snowplow Analytics and Countly both need careful pipeline and ingestion tuning to prevent operational debugging overload.
Skipping event schema governance until after funnels and cohorts are already live
Amplitude and Mixpanel include schema management controls tied to API-driven configuration, so delaying schema governance typically increases naming and property drift across teams. Mixpanel also requires event design discipline to keep governed funnels and retention stable.
Underestimating how custom transformation logic shifts into external ETL
Amplitude still places custom transformation logic outside the product analytics workflow, so complex normalization often lands in external ETL systems. Mixpanel shows a similar constraint where custom pre-ingestion transformations stay outside Mixpanel.
Treating auto-captured sessions like a free pass on retention and dataset growth
Heap’s automatic session recording reduces manual tagging, but recorded-action datasets grow quickly and need disciplined retention. Governance across complex RBAC and workspaces in Heap needs testing across admin roles before production rollout.
Building governance without mapping audit and RBAC boundaries to real admin workflows
Pendo supports RBAC and audit visibility for configuration changes, but teams still need to plan workspace configuration and role assignment to avoid governance overhead. ThoughtSpot extends this into spaces and a semantic layer, so access sprawl is avoided only when space and role design is done deliberately.
Correlating usage with traces without validating instrumentation coverage
Datadog RUM depends on correct front-end instrumentation coverage, so missing identifiers or incomplete journey tagging reduces RUM-to-trace correlation quality. Dynatrace correlates across telemetry, but schema and event design planning is still required to avoid noisy analytics.
How We Selected and Ranked These Tools
We evaluated Pendo, Amplitude, Mixpanel, Heap, Userpilot, ThoughtSpot, Datadog RUM, Dynatrace, Snowplow Analytics, and Countly using the same scoring lens across features, ease of use, and value, with features weighted most heavily at forty percent. Ease of use and value each received the same secondary weight, and the overall rating used a weighted average across those categories based on the provided tool capabilities, strengths, and constraints.
Pendo stood apart in this ranking because its event-to-user and event-to-account data model directly supports segmentation across releases and features while also linking behavior to in-app experiences through Pendo Feedback, which lifted both integration-to-activation capability and governance clarity through RBAC and audit logging.
Frequently Asked Questions About Product Usage Analytics Software
How do these tools differ in event data model governance for analytics pipelines?
Which product usage analytics tools provide schema provisioning and automation through APIs?
What integration paths matter most when connecting product usage analytics to other systems?
How do Pendo and Userpilot differ for behavior-triggered in-app experiences?
Which tools are better suited for governed analytics access using RBAC and audit logs?
How is data migration handled when moving event tracking from one environment to another?
What security controls should teams verify for admin changes to tracking and configuration?
Which tools help reduce instrumentation work when new features ship frequently?
How do these platforms support extensibility for custom workflows and enrichment?
Which tool fits teams that need analytics tied to application telemetry and service health correlation?
Conclusion
After evaluating 10 data science analytics, Pendo stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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