Product leaders use CData Connect Cloud with Google BigQuery to identify aha moments and user habits, transforming months of PLG analysis into minutes with AI-powered vibe querying.
The fourth episode of our Vibe Querying series takes us into the world of product-led growth, where we explore how product leaders can leverage CData Connect Cloud with Google BigQuery to gain actionable insights from their user telemetry data. Joining us for this episode is Mike Albritton, SVP of Cloud at CData Software, who demonstrates how he uses MCP to transform months of PLG analysis into minutes of conversational data exploration.
Watch Now: Vibe Querying with MCP - Episode #4
Introducing MCP, CData MCP Servers, and Vibe Querying
Model Context Protocol (MCP) is a standard protocol designed to connect AI models to external data sources, tools, and workflows securely and efficiently. MCP enables AI models to access real-time data and interact naturally with that information through conversational AI or agent-driven actions.
CData MCP Servers, released in beta a few weeks ago, connect CData's connector library of 350+ data sources to your favorite AI clients like Claude or Gemini. This combination gives AI models access to the work data they need to be genuinely useful in business contexts. In this episode, we're showcasing a preview of the remote MCP server capability through our Connect Cloud product.
This brings us to the concept of "vibe querying" - much like vibe coding, it's a conversational approach to data exploration. You no longer need intimate knowledge of data schemas or prebuilt pipelines. You simply ask questions in natural language, and the MCP server provides live access to data while the AI uses its knowledge to provide answers.
Today's focus: Product led-growth intelligence
For today's episode, we're tackling a challenge many product teams face: identifying aha moments and habit moments for product-led growth (PLG) initiatives. Specifically, Mike's team is working on PLG strategies for CData's Connect Cloud product.
Mike's team at CData has been working with consultants for months, involving cross-functional teams from marketing, product, engineering, and sales—all meeting weekly to build a better PLG experience. The focus? Finding those critical moments when users first experience success (the "aha moment") and when they start creating habits around tool usage (the "habit moment").
As Mike explains: "We've been working with consultants and we've actually spent months and months of lots of weekly meetings with a cross-functional team... trying to answer these questions, what is the aha moment, what is the habit moment, is actually really hard."
The setup: Connect Cloud meets Claude
Instead of downloading and installing a local MCP server, Mike demonstrates CData Connect Cloud's remote MCP server capability. Here's what makes this powerful:
Connect Cloud acts like a database in the cloud, but uses CData's library of 350+ connectors to access live data from any system you have set up. For this demo, Mike connects to Google BigQuery containing product telemetry data.
Key setup features:
- Easy connection setup: Choose from 350+ connectors with simple credential configuration
- AI-optimized metadata: Provide context about custom tables and columns to help AI understand your specific data structure
- Secure authentication: Connect using your existing credentials and access controls
Query 1: Finding aha and habit moments for product-led growth
The question
Mike provided Claude with context about his product-led growth initiative and asked:
"Find what the aha and habit moments are for our trial users, using data from the last month. I want to understand when users first experience success with the tool and when they start creating habits around usage."
What Claude did
Claude automatically executed multiple sophisticated queries to analyze the telemetry patterns:
- Data structure analysis: First examined the available tables and columns to understand the telemetry schema
- User activity patterns: Analyzed query volumes, execution times, success rates, and error codes across all trial users
- Behavioral segmentation: Grouped users by engagement levels and success patterns without being explicitly instructed
- Persona analysis: Cross-referenced telemetry data with survey information to identify which departments and roles were most successful
- Temporal analysis: Examined the timing of user actions to identify critical success windows
The impact
Claude's analysis revealed precise, actionable insights that had taken Mike's team months to discover manually:
Primary aha moment identified:
- Users who successfully retrieve data from their first connection within 12 hours of signup
- This represents the first experience of core product value
Habit moment definition:
- Users performing 2-3 queries across multiple connections over 2+ weeks
- Indicates when users begin creating repeatable workflows
Bonus insights (unprompted):
- User segmentation showing Finance, IT, and Marketing as the most successful departments
- Connector performance analysis identifying which data sources drove highest success rates
- Client tool preferences revealing optimal connection methods
As Mike noted: "It took us months and months to find what we believe to be the aha and habit moments. Some of the aha and habit moments that it recognized are even better than what we came up with."
Query 2: Detailed product improvement strategies
The question
Building on the initial analysis, Mike asked:
"Can you get more detailed analysis of what we can do to improve the aha and habit moments for users?"
What Claude did
Rather than running additional queries, Claude leveraged the comprehensive data it had already gathered from Query 1 to provide strategic recommendations. This demonstrated the AI's ability to:
- Synthesize existing insights into actionable strategies
- Prioritize recommendations based on data patterns
- Provide specific implementation guidance for product teams
- Connect user behavior patterns to product design opportunities
The impact
Claude delivered immediate, implementable strategies organized by impact area:
Immediate actions:
- Smart connection wizard with curated experiences for specific personas
- Prebuilt query templates to eliminate blank-slate syndrome
- Data volume validation to ensure meaningful first data retrieval
- Department-specific onboarding optimization targeting Finance, IT, and Marketing users
Habit formation strategies:
- Progressive engagement ladder with clear goals for day 1, 3, and 7
- Email notification campaigns to maintain engagement momentum
- Error rate recovery systems to help users overcome initial failures
Advanced product features:
- Multisource capabilities highlighting the ability to combine data across different systems
- Real-time user guidance based on telemetry patterns
The speed of this analysis was transformational. What typically required additional weeks of consultant meetings and dashboard building was delivered instantly, allowing the team to move directly to implementation planning.
Query 3: Email campaign development
The question
Mike wanted to operationalize the insights immediately and asked:
"Can you help us draft some email campaigns based on these findings to help users through the funnel?"
What Claude did
Claude seamlessly transitioned from data analysis to content creation, using the full context of the telemetry insights to craft:
- Persona-specific email sequences tailored to Finance, IT, and Marketing users
- Behavioral trigger campaigns based on the identified aha and habit moments
- Technical content including sample queries users could try immediately
- Value proposition messaging highlighting multisource capabilities
The impact
Claude generated ready-to-implement email campaign content that was:
Data-Driven: Every message was informed by actual user behavior patterns from the telemetry analysis Actionable: Included specific sample queries and next steps for users Personalized: Tailored messaging for different departments and use cases Technical: Provided placeholders for dynamic content based on user progress
Example elements included:
- Welcome sequences with department-specific sample queries
- "Multisource magic" campaigns demonstrating cross-system data integration
- Recovery emails for users who encountered errors during initial setup
This demonstrated how MCP enables AI to move fluidly from analytical insights to practical marketing implementation, all informed by real business intelligence. The content was immediately usable, saving additional weeks of campaign development time.
The business impact: Speed and accuracy
The results speak for themselves. As Mike reflected: "We meet weekly. We talk about this stuff very frequently. And so, you know, being able to come to a solution within minutes is certainly very helpful for us and can save us a lot of time."
Marie added: "What Claude produced at the end of this is pretty awesome, and kind of can help any team, especially a product team trying to achieve this, move a lot faster on these questions and get a great direction to start working towards."
Technical excellence: Query optimization in action
One of the standout features demonstrated was Claude's query building capability. Throughout the session, users could see exactly what SQL queries Claude was constructing, providing transparency and learning opportunities. This showcases the unique power of CData MCP Servers' SQL-based approach to enterprise AI.
Unlike experimental MCP servers that rely on custom API integrations, CData MCP Servers create a standardized SQL layer across all 350+ data sources. This means:
- AI speaks the language it already knows: LLMs are trained extensively on SQL, making them naturally proficient at generating complex queries without requiring custom training for each data source
- Universal query capabilities: The same SQL knowledge works across any CData-connected system - from Salesforce to BigQuery to NetSuite
- Enterprise-grade performance: Features like query pushdown, parallel execution, and bulk operations ensure fast responses even from large datasets
- Comprehensive data access: Every endpoint, field, and custom object becomes accessible through standardized SQL operations
The session demonstrated these capabilities in action:
- Intelligent query construction based on natural language requests
- Performance optimization through automated query pushdown to the source system
- Contextual understanding of business metrics and relationships across complex telemetry data
- Multi-table analysis combining user activity, survey data, and behavioral patterns seamlessly
This SQL-first approach solves the "N×M integration problem" that typically plagues enterprise AI implementations. Instead of building custom integrations for each AI tool and data source combination, CData MCP Servers provide a single, standardized interface that any SQL-capable AI can immediately leverage.
What this means for product teams
This episode showcases how AI-powered data analysis can transform product management:
- Speed: Compress months of analysis into minutes
- Depth: Uncover insights you might not have thought to look for
- Actionability: Get specific, implementable recommendations
- Collaboration: Share findings immediately across teams
- Iteration: Test new hypotheses quickly as market conditions change
Getting started with your own PLG analysis
If you want to unlock similar insights for your product-led growth initiatives:
- For Connect Cloud customers: Use the open-source MCP server available on GitHub at github.com/cdatasoftware
- For new users: Download free beta MCP servers from cdata.com/solutions/mcp
- Join the community: Connect with other practitioners in the CData community and Vibe Querying subreddit
From analysis paralysis to PLG insights in minutes
This episode demonstrates that we're moving beyond dashboards and into conversational intelligence. When AI can access, analyze, and act on your business data through natural language, the bottleneck isn't technology—it's imagination.
Product teams can now ask the questions they've always wanted to ask but never had time to investigate. They can test hypotheses instantly, validate assumptions with data, and turn insights into action faster than ever before.
The age of vibe querying for product intelligence has arrived. Are you ready to start the conversation with your data?
Join us for future episodes of Vibe Querying with MCP, where we continue exploring how natural language AI transforms business intelligence. Until next time, stay curious and keep vibing with your data.
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