5 ways to query Contentsquare data with Claude

Contentsquare gives product managers, analysts, and researchers comprehensive data on what users are doing on your site or app. 

But with so much information at your fingertips—not to mention competing priorities and urgent asks—it can be difficult to find the time to truly dig in and get the answers you need.

Contentsquare’s MCP Connector for Anthropic Claude bridges the gap between experience insights and your AI assistant. Instead of requiring you to navigate dashboards, build segments, and switch between platforms, the MCP connector brings Contentsquare data into Claude, where you can query it directly. 

Together, this lets you streamline workflows, perform complex analyses faster, and prioritize next steps based on real business needs—without even changing tabs.

This article gives you five powerful use cases to get started today.

Key insights

• When deep analysis is as simple as asking Claude a question, everyone, from marketing to product design to customer support, is empowered to get curious, own the problem, and make real customer experience (CX) improvements—no matter how big or small. 

• Claude can combine experience data from Contentsquare with data from other MCP-connected tools—like revenue data from your ERP platform, customer transaction data from your CRM, and tickets from your support platform—to perform multi-system analysis and uncover connections between business areas, faster. 

• The MCP Connector is easy to set up, simple to use, and available on any Contentsquare plan (even Free!), so there are no barriers to access.

How does the Contentsquare MCP Connector work?

Model Context Protocol (MCP) is a universal standard that lets AI applications connect to external systems—like tools, files, and databases—instantly and without complex integrations.

For example, Contentsquare’s MCP server allows you to access and analyze your Contentsquare data directly using AI tools like Anthropic’s Claude, OpenAI’s ChatGPT, and Microsoft Copilot. 

With the connector, users can ask Claude questions about Contentsquare data in plain language, like

• Why were conversions down this week compared to last week?

• What are the most expensive friction points on our website?

• How do the journeys of users who convert compare to those of users who don’t?

Claude uses the MCP tool to fetch up-to-date data, then uses that data to diagnose issues, recommend next steps in order of priority, and write reports.

Setting up the Contentsquare MCP Connector for Claude is simple. A one-time connection directly from Claude settings brings Contentsquare data into Claude.ai, Claude Desktop, and Claude Mobile, enabling teams to use it from anywhere—no technical training or analytics expertise required.

This improves access to data, helping teams 

• Save time: ask questions directly from Claude, eliminating the need to switch between tools and tabs, run reports, or set up analyses manually

• Remove bottlenecks: reduce reliance on analyst resources or time-consuming training and get expert-level insights in real time

• Prioritize efficiently: quickly identify your most urgent issues, make data-driven decisions, and take immediate action

❓ What’s the difference between the MCP Connector and Sense Analyst, Contentsquare’s built-in AI agent?

• Contentsquare’s MCP Connector brings Contentsquare data into external AI assistants, allowing you to query it from within those tools without opening Contentsquare

• Sense Analyst is accessed through the Contentsquare platform. It can perform complex multi-step analyses, query experience data, and surface insights—all within the platform.

Both are capable of a deep analysis of your experience data, identifying risks and opportunities, and recommending next steps. The key difference lies in where the analysis happens and how users access it.

5 ways to query Contentsquare data with Claude 

With Claude connected to Contentsquare, your behavioral data becomes easier to explore, understand, and act on. Instead of navigating multiple reports or building complex queries, you can ask questions in plain language and quickly uncover the insights behind your customers’ digital experiences. Here are five practical ways to use Claude to query Contentsquare data and turn behavioral signals into smarter decisions.

1. Analyze behavior

Ask Claude any question about user behavior and get immediate, contextualized answers. 

For example, ask:

“What are the top three sources of frustration on our checkout page right now?” 

Claude queries Contentsquare data and pulls information like

• Page-level frustration and rage click metrics

• Scroll depth and engagement zones

• Conversion metrics from each step of your funnel

• Error rate and session impact data

Then, Claude uses that data to provide

• A ranked list of frustration sources, prioritized by impact

• A clear explanation of what each frustration signal means and why it matters

• The projected revenue impact of each friction point, calculated from session data

Ask follow-up questions in the same conversation to learn more, or jump straight to action.

What this looks like in real life

A product manager is in a stakeholder meeting when someone asks, “What’s the biggest UX issue in our checkout flow right now?” 

Instead of circling back after the meeting when she’s had a chance to pull the data, she asks Claude, which then reveals that the primary CTA button is being rage-clicked by a large number of users, likely because it appears unresponsive after the initial click.

She then asks Claude whether the issue is worse on mobile, and it confirms that it is, because the loading state is longer on slower connections. Without leaving the meeting room, stakeholders assign the fix to developers, who add it to the next sprint.

Before, this analysis would have been an action item for after the meeting. Now, a question became a decision in just a few minutes. 

2. Automate reporting

Replace time-consuming manual work with Claude-generated reports on a weekly or monthly basis.

Use a prompt like: 

“Generate this week’s UX performance report. Focus on checkout and onboarding. Flag any negative changes compared to last week and explain what’s driving them.”

Claude queries Contentsquare for your most critical metrics, like

• Weekly metric changes across key pages

• Top friction points by revenue impact

• Funnel conversion trends by device and segment

• Error rate changes and JavaScript errors 

Claude formats the returned data into a structured report with an executive summary, key findings, and any anomalies. Each finding is explained, with a root cause hypothesis and a suggested owner. This lets teams quickly spot issues they might otherwise miss, and reduces the repetitive, manual work of pulling data, which can easily introduce errors.

Using additional integrations, users can easily share the report via Slack, Notion, or email, ready for standups, leadership reviews, or to keep stakeholders aligned.

What this looks like in real life

Every week before her regular sync with leadership, the head of product used to spend an hour pulling charts in Contentsquare, copying numbers into Notion, and writing a summary of the key changes.

With the MCP Connector, she replaced that workflow with a single saved prompt. Claude queries the Contentsquare data to pull the week’s key metrics, identify significant changes, and summarize the most important takeaways, formatting it into a structured report on Notion—freeing up an hour a week for high-impact work.

This week’s report flagged that the rage click rate on the promo code field had increased since last week. Claude cross-referenced this with Contentsquare’s error data and acquisition analysis to reveal that a recent campaign had driven unusually high traffic to that field, but that the promo codes hadn’t been correctly loaded into the system. The head of product shared this insight with leadership, got buy-in from engineering, and prioritized a fix before it further impacted the campaign.

3. Diagnose funnel drop-offs

See where your funnel breaks and why. Use Claude to query Contentsquare’s step-by-step conversion data to

• Discover the highest-impact drop-off points

• Diagnose likely causes using behavioral signals

• Prioritize fixes based on revenue impact

Give Claude a prompt like:

“Analyze our checkout funnel for the last 30 days. Which steps are losing the most users, and what's the most likely cause based on behavioral data?”

Claude draws on the Funnel Analysis capability to identify

• Conversion rates at each step of the funnel

• Drop-off volume and percentage per funnel step

• Time spent on each step and hesitation signals

• Device and segment breakdowns per step

This allows you to pinpoint which steps are damaging conversions and why, share the findings with stakeholders, and make crucial improvements, like copy or UX fixes, to recover revenue.

What this looks like in real life

Conversions in the checkout funnel were lower than expected, but a full analysis would require a dedicated analytics sprint: pull the data, build the segments, write up the findings, and schedule a readout—and that was just to diagnose, not fix, the problem.

The team lead opens the Claude app on their phone during their commute and asks it directly why the funnel was underperforming. Claude pulls the funnel data from Contentsquare and finds that the biggest issue is in the payment entry step, particularly on mobile. It then cross-references this with error data and hesitation signals and discovers a spike in rage clicks on the card number field on iOS, consistent with a formatting mask that was stripping valid inputs. The fix is a two-line code change.

The team lead quickly shares the finding with the engineering team right from their phone, and they prioritize the fix before the commute ends.

4. Add behavioral context to tests

Use Claude to interpret A/B tests and understand why one variant won and the other lost—you get conversion, engagement, and experience analysis by device and audience segment. It also summarizes key patterns and limitations, and suggests follow-up questions.

Use a prompt like:

“We have two experiment cohorts configured in Contentsquare. Compare their conversion and engagement metrics by device and audience segment. Highlight the largest behavioral differences, identify any experience signals that may explain them, and flag what we still need to validate in our experimentation platform.”

Claude interprets the Contentsquare data and drafts a recommendation.

What this looks like in real life

The growth team was struggling to turn their A/B test results into clear next steps. Variant B was winning on mobile conversion, but on desktop the results were roughly even, leaving the team unsure whether to ship the change, wait for more data, or segment the rollout.

A product lead asks Claude to analyze the Contentsquare results. It finds that Variant B performed better on mobile, and on desktop overall, the two variants were at parity—but for users over 45, Variant A performed better, which had been pulling down Variant B’s average.

Claude recommends shipping Variant B globally, with a desktop override for the identified segment and post-launch monitoring. It drafts a recommendation with supporting evidence and caveats, which the product lead shares with stakeholders to inform decision-making.

5. Identify and prioritize frustration

Ask Claude to analyze Contentsquare frustration metrics on important pages and flows, such as product pages, account areas, and checkout. Use the results to identify pages or segments with elevated frustration and prioritize which issues to investigate first.

Use a prompt like:

“Analyze the frustration signals available for our checkout and account pages. Rank the pages or page groups with the highest frustration, compare the associated conversion metrics, and explain which issues we should investigate first. Flag any limitations in the available data.”

Claude summarizes the returned Contentsquare data, explains what the signals may indicate, compares affected segments, and drafts an investigation plan.

What this looks like in real life

The support team was receiving a high volume of complaints about the checkout process, so a product designer asks Claude to review frustration signals for the checkout flow.

Claude identifies pages with elevated frustration and compares those patterns with conversion metrics. It suggests investigating a particular checkout step in which users enter a promo code, which the team does using Contentsquare. 

After validating the issue, the product designer collaborates with the engineering team to get a fix into production. They then monitor frustration and conversion metrics to measure the impact.

Enrich your AI assistant with experience intelligence data

As teams increasingly operate in AI environments, organizations must bring critical data—like experience intelligence insights—securely into those workflows. 

Contentsquare’s MCP connector for Claude gives every team member access to rich behavioral and experience data, removing traditional blockers to data usage, like reliance on analyst resources, technical training, or manual setup. Now, you can get the answer to any CX question in minutes, so you can improve experiences, increase conversions, and drive long-term growth.

FAQs about Contentsquare’s MCP Connector for Anthropic Claude