Connectors / Integration
Get More from Contentsquare: Combine Metrics and Raw Data for Sharper Digital Experience Analysis
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.
Contentsquare Metrics API + Contentsquare Raw Data API integration
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.
Automate & integrate Contentsquare Metrics API + Contentsquare Raw Data API
Automating Contentsquare Metrics API and Contentsquare Raw Data API business processes or integrating data is made easy with Tray.ai.
Use case
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
Use case
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
Use case
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
Use case
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
Use case
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
Use case
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
Challenges Tray.ai solves
Common obstacles when integrating Contentsquare Metrics API and Contentsquare Raw Data API — and how Tray.ai handles them.
Challenge
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.
Challenge
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.
Challenge
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.
Templates
Pre-built workflows for Contentsquare Metrics API and Contentsquare Raw Data API you can deploy in minutes.
Metric Anomaly Detection with Auto Raw Data Enrichment
Contentsquare Metrics API ContentSquare Raw Data API 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.
Nightly Contentsquare Data Warehouse ETL Pipeline
Contentsquare Metrics API ContentSquare Raw Data API 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.
A/B Test Results Package Builder
Contentsquare Metrics API ContentSquare Raw Data API 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.
Real-Time Personalization Data Feed Sync
Contentsquare Metrics API ContentSquare Raw Data API 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.
Weekly Executive Digital Experience Report Automation
Contentsquare Metrics API ContentSquare Raw Data API 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.
Page Regression Alert with Session Evidence Package
Contentsquare Metrics API ContentSquare Raw Data API 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.
How Tray.ai makes this work
Contentsquare Metrics API + Contentsquare Raw Data API runs on the full Tray.ai platform
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