Do I need technical expertise to use MCP?

Setting up MCP requires a one-time installation handled by your IT or data team—a simple 7-step process that takes under an hour. Once connected, no technical expertise is needed. Any team member can query Contentsquare in plain English directly from their AI tool, with zero training and no platform login required.

What is MCP and how does it complement Data Connect?

MCP (Model Context Protocol) is Contentsquare's AI-native integration that lets any team member query Contentsquare insights in natural language directly from ChatGPT, Claude, or Microsoft Copilot. While Data Connect brings raw behavioral data into your warehouse for deep technical analysis, MCP delivers instant, conversational insights to every team—no SQL, no dashboard required. Together, they cover the full spectrum: technical depth for data teams, and instant accessibility for everyone else.

What can I do once my data is in the warehouse?

Once connected, analyzing data directly in your warehouse or building dashboards in BI tools like Looker or Tableau requires data analyst skills and SQL knowledge. The use cases are limitless: power predictive AI and machine learning models, fuel marketing automation campaigns, run A/B testing and personalization, build executive BI dashboards, detect fraud, or predict churn. Combine Contentsquare data with your CRM, VoC, or support data for a complete picture of your customers.

Do I need engineering resources to set up Data Connect?

No. Data Connect is designed to be self-serve—no APIs to configure, no ETL layers to build, no developers required. Setup takes just a few steps, and data syncs automatically every day from that point forward.

Do I need engineering resources to set up Data Connect?

No. Data Connect is designed to be self-serve—no APIs to configure, no ETL layers to build, no developers required. Setup takes just a few steps, and data syncs automatically every day from that point forward.

What can I do once my data is in the warehouse?

Once connected, analyzing data directly in your warehouse or building dashboards in BI tools like Looker or Tableau requires data analyst skills and SQL knowledge. The use cases are limitless: power predictive AI and machine learning models, fuel marketing automation campaigns, run A/B testing and personalization, build executive BI dashboards, detect fraud, or predict churn. Combine Contentsquare data with your CRM, voice of customer, or support data for a complete picture of your customers.

What is MCP and how does it complement Data Connect?

MCP (Model Context Protocol) is Contentsquare's AI-native integration that lets any team member query Contentsquare insights in natural language directly from ChatGPT, Claude, or Microsoft Copilot. While Data Connect brings raw behavioral data into your warehouse for deep technical analysis, MCP delivers instant, conversational insights to every team—no SQL, no dashboard required. Together, they cover the full spectrum: technical depth for data teams, and instant accessibility for everyone else.

Do I need technical expertise to use MCP?

Setting up MCP requires a one-time installation handled by your IT or data team—a simple 7-step process that takes under an hour. Once connected, no technical expertise is needed. Any team member can query Contentsquare in plain English directly from their AI tool, with zero training and no platform login required.

Data Connect automatically syncs your Contentsquare experience data to your warehouse, including users, sessions, pageviews, transactions, custom variables, frustration signals, errors, performance metrics, and more. The data arrives in a clean, structured schema that’s ready to query and combine with the rest of your business data.

Clean experience data, straight to your data warehouse

Name: We were up and running in no time, collecting JavaScript, API and custom…
Rating: 5
Author: Ruben Canosa

Connect once, sync continuously, and make your experience data available wherever your teams need it. No custom ETL or pipeline to maintain.

Watch a quick product tour

See it in action

Make your experience data instantly available everywhere

Clean pipeline, engineered for data teams

Sync behavioral, performance, and error data directly to your warehouse—clean, structured, and ready to use.

Ready to query

Combine structured experience data with CRM, marketing, and transactional data. No transformations required.

Minimal maintenance

Automatically sync new data—even when goals or events change. No custom ETL or manual exports.

Built to connect

Integrate natively with Snowflake, BigQuery, Databricks, Redshift, and Amazon S3. Governed, versioned, and GDPR-compliant.

Key features of Data Connect

All your data, right where you need it

Watch a quick tour

Set up once and stream all Contentsquare data directly to your warehouse. No complex transformations.

Common use cases

Turn experience data into action

Power smarter ML models
Feed behavior data, frustration scores, and engagement signals directly into your AI workflows. Get structured data, clean and ready to train on.
Optimize product placement
Identify top-performing products and common checkout combos. Then surface them in high visibility zones to increase add-to-cart rates.
AI-powered marketing personalization
Measure customer preferences and behaviors. Build AI-driven scoring models to activate the right audience at the right stage of engagement.
Increase form completion
Combine frustration signals, funnel analysis, and lead data to pinpoint drop-off, reduce time to purchase, and lower cost per acquisition (CPA)

We were up and running in no time, collecting JavaScript, API and custom errors, with a dashboard in Looker to share insights across teams. What really stood out was how quickly we were able to go from setup to actionable analysis. It's been a major win for our team.

Ruben Canosa

Head of Data and Transformation, Iberostar

Discover more

Bring insights into your AI tools

Connect Contentsquare to the AI agents your teams already use. Ask questions and get answers without switching tools.

Discover MCP

See what customers actually experienced

Replay key moments across any journey and quickly spot friction, errors, and behavior patterns with AI-powered summaries.

Explore Session Replay

Find the journeys that matter most

See how customers move through your site or app, where they drop off, and which paths lead to conversion and return visits.

Explore Journeys

Run smarter experiments

Let AI find friction and recommend what to test next. Launch experiments and see why variants win—all in one platform.

See what's new

[visual] Monitor and optimize customer conversations with Contentsquare Conversation

Turn conversations into action

Understand why customers reach out, uncover friction and sentiment shifts, and see where human and AI interactions can improve the experience.

Explore Conversation Intelligence

Frequently asked questions

Got another question?

No. Data Connect is designed to be self-serve—no APIs to configure, no ETL layers to build, no developers required. Setup takes just a few steps, and data syncs automatically every day from that point forward.
Once connected, analyzing data directly in your warehouse or building dashboards in BI tools like Looker or Tableau requires data analyst skills and SQL knowledge. The use cases are limitless: power predictive AI and machine learning models, fuel marketing automation campaigns, run A/B testing and personalization, build executive BI dashboards, detect fraud, or predict churn. Combine Contentsquare data with your CRM, voice of customer, or support data for a complete picture of your customers.
MCP (Model Context Protocol) is Contentsquare's AI-native integration that lets any team member query Contentsquare insights in natural language directly from ChatGPT, Claude, or Microsoft Copilot. While Data Connect brings raw behavioral data into your warehouse for deep technical analysis, MCP delivers instant, conversational insights to every team—no SQL, no dashboard required. Together, they cover the full spectrum: technical depth for data teams, and instant accessibility for everyone else.