# Contentsquare Metrics API + Contentsquare Raw Data API integration > Connect Contentsquare's aggregated Metrics API with its granular Raw Data API to build end-to-end analytics pipelines, enrich reporting, and make better UX decisions. **Canonical page:** https://tray.ai/connectors/contentsquare-metrics-api-contentsquare-raw-data-api-integrations/ **Contentsquare Metrics API connector:** https://tray.ai/connectors/contentsquare-metrics-api-integrations/ **Contentsquare Metrics API documentation:** https://tray.ai/documentation/connectors/service/contentsquare-metrics-api **Contentsquare Raw Data API connector:** https://tray.ai/connectors/contentsquare-raw-data-api-integrations/ **Contentsquare Raw Data API documentation:** https://tray.ai/documentation/connectors/service/contentsquare-raw-data-api ## Overview Contentsquare's Metrics API and Raw Data API are two complementary parts of the same digital experience intelligence platform — one delivers high-level aggregated performance indicators, the other exposes granular, session-level behavioral data. When you connect them on tray.ai, teams can correlate macro trends with individual user journeys, moving from 'what is happening' to 'exactly why it's happening' without switching tools or manually stitching exports. This pairing matters most for data engineering, analytics, and product teams who need an automated, unified view of digital experience data flowing into warehouses, dashboards, or downstream AI models. Relying on either API alone leaves real gaps in your analytics story. The Metrics API gives you fast, pre-aggregated KPIs — bounce rates, engagement scores, conversion rates, zone-level interaction data — but without session-level context you can't explain anomalies or segment behavior deeply. The Raw Data API provides that granular session and event detail, but querying it alone without the high-level metrics framework means drowning in data with no efficient way to prioritize analysis. By automating workflows that bridge both APIs on tray.ai, organizations can trigger raw data pulls based on metric thresholds, enrich aggregated reports with session-level evidence, feed unified datasets into data warehouses like Snowflake or BigQuery, and power AI-driven personalization or A/B test analysis — without manual data wrangling or fragile custom scripts. ## Use cases ### Metric-Triggered Raw Data Deep Dives When the Metrics API surfaces a significant drop in conversion rate or engagement score for a specific page or zone, automatically trigger a targeted Raw Data API pull to extract the individual session replays and event sequences behind the anomaly. This removes the manual step of cross-referencing dashboards and exporting session files. Teams get anomaly context delivered directly to their analytics environment within minutes of detection. - Cut mean time to insight for UX anomalies from hours to minutes - Automatically scope raw data queries to only the affected segments, reducing API costs and processing time - Deliver pre-contextualized session data to analysts without manual intervention ### Unified Digital Experience Data Warehouse Ingestion Build a fully automated ETL pipeline that periodically pulls aggregated metrics across pages, devices, and segments from the Metrics API, then enriches each record with corresponding raw session and event data from the Raw Data API before loading everything into a centralized data warehouse. This creates a single source of truth for digital experience data that BI tools like Tableau, Looker, or Power BI can query directly. No more siloed exports or inconsistent metric definitions across teams. - Eliminate manual CSV exports and fragile spreadsheet-based reporting - Keep metric definitions consistent across BI tools and data consumers - Enable self-serve analytics on unified Contentsquare data for the entire organization ### A/B Test Performance Validation and Session Evidence Collection When an A/B or multivariate test concludes, automatically fetch variant-level performance metrics from the Metrics API and pull the corresponding raw user session data from the Raw Data API to validate statistical results with behavioral evidence. Winning variants get backed by qualitative session-level patterns, not just numbers. Results and supporting session samples can be automatically pushed to a Slack channel, Confluence page, or product management tool. - Speed up experiment review cycles by delivering combined quantitative and qualitative evidence automatically - Reduce the risk of shipping false-positive test winners by cross-referencing session behavior - Create a self-documenting audit trail of test results with session evidence attached ### Personalization Engine Data Feed Power real-time or near-real-time personalization platforms by combining zone-level engagement metrics from the Metrics API with individual user behavioral signals extracted from the Raw Data API. The integrated workflow maps high-performing content zones identified in aggregate metrics to the specific user cohorts and session patterns most associated with those engagements. This enriched dataset feeds directly into personalization engines, CDPs, or recommendation systems for more accurate targeting. - Improve personalization accuracy by combining aggregate intent signals with session-level behavioral data - Automate the data pipeline between Contentsquare and personalization or CDP platforms - Reduce engineering overhead for maintaining custom data connectors between systems ### Page Performance Regression Monitoring and Alerting Schedule recurring Metrics API polls to track core UX KPIs — scroll rate, click rate, hesitation rate, exposure rate — across priority pages. When a metric crosses a defined threshold, automatically pull a batch of raw session records from the Raw Data API that show the regression in action, then route the packaged alert to the right team via email, Slack, or PagerDuty. The result is a fully automated regression detection and triage system. - Catch UX regressions immediately after deployments rather than days later in weekly reviews - Deliver actionable, session-backed alerts instead of raw metric numbers with no context - Cut triage time for UX and engineering teams by pre-packaging evidence with each alert ### Customer Journey Segmentation and Cohort Analysis Use the Metrics API to identify high-value or underperforming user segments based on aggregated journey metrics, then automatically extract the full raw session event streams for those cohorts from the Raw Data API for deeper journey mapping and funnel analysis. The resulting enriched cohort datasets can be loaded into analytics platforms, customer journey mapping tools, or machine learning pipelines. This workflow replaces manual segment-by-segment data extraction that can take analysts days to complete. - Automate cohort data extraction that previously required multiple manual API calls and data joins - Give ML and data science teams ready-to-use, pre-segmented behavioral datasets - Improve funnel analysis fidelity by combining metric-level segment definitions with event-level session detail ### Executive and Stakeholder Reporting Automation Automatically compile weekly or monthly digital experience performance reports by pulling high-level KPI summaries from the Metrics API, enriching findings with illustrative session-level data points from the Raw Data API, and formatting the results into structured reports delivered to stakeholders via email, Google Slides, or a BI dashboard refresh. This eliminates the hours analysts spend manually compiling reports from multiple Contentsquare views. - Save analyst time by fully automating multi-source report compilation and distribution - Ensure stakeholder reports always reflect the latest data without manual refresh steps - Back executive summaries with specific session-level evidence that makes abstract KPIs tangible ## Templates ### Metric Anomaly Detection with Auto Raw Data Enrichment Polls the Contentsquare Metrics API on a scheduled basis to detect KPI anomalies across defined pages or segments. When an anomaly threshold is crossed, automatically queries the Raw Data API for session records matching the affected time window and segment, then packages and routes the enriched alert to Slack or email. Connectors used: Contentsquare Metrics API, ContentSquare Raw Data API ### Nightly Contentsquare Data Warehouse ETL Pipeline Runs a nightly ETL workflow that extracts aggregated metrics from the Metrics API across all tracked pages and segments, then fetches corresponding raw session data from the Raw Data API for the same period, joins and transforms the datasets, and loads the unified records into a data warehouse such as Snowflake, BigQuery, or Redshift. Connectors used: Contentsquare Metrics API, ContentSquare Raw Data API ### A/B Test Results Package Builder Automatically fetches variant-level performance metrics from the Metrics API when a test concludes, retrieves supporting raw session samples from the Raw Data API for each variant, and compiles a structured test results package delivered to a Confluence page, Google Doc, or project management tool. Connectors used: Contentsquare Metrics API, ContentSquare Raw Data API ### Real-Time Personalization Data Feed Sync Combines zone-level engagement metrics from the Metrics API with raw user behavioral event data from the Raw Data API to generate enriched user intent signals, then pushes the processed dataset to a CDP, personalization platform, or recommendation engine on a defined schedule. Connectors used: Contentsquare Metrics API, ContentSquare Raw Data API ### Weekly Executive Digital Experience Report Automation Generates a weekly digital experience performance report by combining KPI summaries from the Metrics API with curated session insights from the Raw Data API, then distributing the formatted report to stakeholders via email or BI dashboard update. Connectors used: Contentsquare Metrics API, ContentSquare Raw Data API ### Page Regression Alert with Session Evidence Package Monitors core page performance metrics via the Metrics API after each deployment and, on detecting a regression, automatically pulls raw session data from the Raw Data API to package contextual evidence and routes a prioritized alert to engineering and UX teams. Connectors used: Contentsquare Metrics API, ContentSquare Raw Data API ## Challenges Tray.ai solves ### Coordinating Pagination and Rate Limits Across Two APIs Simultaneously Both the Contentsquare Metrics API and Raw Data API have their own rate limits, pagination schemes, and quota constraints. When workflows call both APIs in sequence or in parallel — especially for large date ranges or high-traffic sites — it's easy to hit limits on one API while the other is still processing. The result: incomplete data pulls, failed jobs, and inconsistent datasets that need manual remediation. **How Tray.ai helps:** tray.ai's workflow engine natively handles pagination loops, retry logic with exponential backoff, and conditional branching to manage rate limit responses (HTTP 429) from each API independently. Workflows can be configured to queue and throttle requests to each API according to their specific limits, so you get complete, consistent data extraction without writing custom engineering to manage it. ### Joining and Reconciling Different Data Schemas The Metrics API returns pre-aggregated, structured metric objects keyed by page, segment, and time period, while the Raw Data API returns flat event and session records with their own field taxonomy. Joining these two datasets meaningfully — for example, correlating a zone's click rate metric with the raw click events behind it — requires careful schema mapping and transformation logic that's error-prone when done manually in scripts or spreadsheets. **How Tray.ai helps:** tray.ai's visual data mapper and JSONPath transformation tools let teams define reusable field mapping logic that reconciles the two schemas without writing custom code. Transformation steps can normalize keys, compute derived fields, and produce a unified output schema ready for loading into a data warehouse or downstream tool, with the mapping logic version-controlled and auditable. ### Managing Large Raw Data Volumes Without Overloading Downstream Systems The Raw Data API can return very large volumes of session and event records, especially when pulling data for high-traffic pages over extended time windows. Piping the full raw data output directly into downstream systems like databases, Slack, or email can overwhelm those targets, cause timeouts, or exceed payload limits — dropping data or breaking workflows entirely. **How Tray.ai helps:** tray.ai workflows can include intermediate buffering, batching, and filtering steps that process Raw Data API responses in manageable chunks before passing them downstream. Conditional logic can sample, summarize, or prioritize records based on Metrics API findings, so only the most relevant raw data reaches each downstream system in appropriately sized payloads. ### Keeping Scheduled Pipelines Resilient to API Downtime or Schema Changes Production ETL pipelines that depend on both Contentsquare APIs are doubly exposed to disruption. If either API goes down, returns unexpected fields, or changes its response schema, the entire pipeline can fail silently or push corrupted output into data warehouses and reports before anyone notices. **How Tray.ai helps:** tray.ai has built-in error handling, alerting, and workflow monitoring that detects failures at each individual API call step and notifies the responsible team immediately. You can add schema validation steps to catch unexpected response structures before they propagate downstream, and failed workflow runs can be replayed from the point of failure once the API issue is resolved — without re-running steps that already succeeded. ### Securing and Governing Access to Sensitive Session-Level Data Raw session data from the Contentsquare Raw Data API can contain sensitive behavioral information about individual users. When automated pipelines move this data into warehouses, BI tools, or third-party platforms, organizations need to manage credentials securely, handle data in compliance with GDPR and other privacy regulations, and govern who can access session-level data across those workflows. **How Tray.ai helps:** tray.ai stores all API credentials in an encrypted secrets vault with role-based access controls, so Contentsquare API keys are never exposed in workflow configurations or logs. Workflows can apply anonymization, field filtering, or aggregation steps to raw session data before it leaves the integration layer, supporting privacy-by-design principles and reducing the compliance risk of automated data pipelines. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/