7 May, 2025 POR Luz Castillejo Flor

The HubSpot MCP Server: New Integration, New Possibilities

HubSpot has taken a key step forward with the launch of its MCP server, now available in public beta. This new platform allows AI clients like Cursor and Claude to connect directly with HubSpot data, enabling a more robust and dynamic integration ecosystem for both B2B and B2C companies.

In this article, we explore how HubSpot’s MCP server can transform business operations from a practical and empirical perspective, analyzing its applications across sectors and its impact on large corporations.

What is HubSpot’s MCP Server?

The MCP (Multi-Client Protocol) server is a solution designed to solve one of the biggest challenges in the digital environment: efficient data integration.

Today, companies handle massive volumes of information. Without an agile system to connect that data to advanced tools, much of its strategic value is lost. That’s where the MCP server comes in—acting as a bridge between data stored in HubSpot and artificial intelligence solutions.

This protocol enables direct integration with AI clients, making it easier to automate processes, generate insights, and support faster, more accurate decision-making.

Use Cases in B2B Companies

Optimized Customer Management

In B2B environments, personalization and deep customer understanding are key. Through MCP, businesses can connect tools like Cursor with HubSpot to deeply analyze interactions and create detailed customer profiles. This allows for more precise segmentation, content strategies, and loyalty initiatives—automated and scalable.

Smart Sales Process Automation

The MCP server also powers advanced sales capabilities. By integrating AI models, businesses can predict which leads are most likely to convert, allowing teams to prioritize efforts and shorten sales cycles significantly.

Use Cases in B2C Companies

Real-Time Personalization of User Experience

In the B2C world, the MCP allows companies to connect tools like Claude to analyze customer behavior, purchases, and preferences. This results in highly personalized recommendations, more relevant campaigns, and increased customer loyalty.

Behavioral Analysis and Agile Market Response

Businesses can integrate AI to monitor user activity in real time, allowing them to adjust messaging, promotions, or products based on current trends. This reactive capability improves emotional engagement with customers and boosts marketing ROI.

Implementation in Large Corporations

Leadership and Change Strategy

For large organizations, adopting the MCP server represents a strategic shift. IT, data, and operations departments must work closely to align technical capabilities with human competencies. Internal training and change management are critical to ensure successful implementation.

Scalability for Business Growth

MCP is designed for seamless scalability. As a company grows, it can incorporate new data flows, tools, and analytical capabilities without overhauling its infrastructure. This drives continuous innovation and rapid adaptation to new challenges.

Cross-Departmental Collaboration

Finally, the MCP server promotes a more integrated work culture. With shared access to data across marketing, sales, and customer service, businesses gain a unified view that enhances customer experience and operational outcomes.

Conclusion

HubSpot’s MCP server marks a significant step toward a new era of intelligent integration and deep automation. Whether improving customer relationships in B2B or delivering hyper-personalized experiences in B2C, its applications are broad and transformative.

With the right strategic planning, MCP can become a powerful driver of efficiency, innovation, and sustainable growth for any business ready to elevate its digital maturity.
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Group your data like a pro: clustering with K-Means and BigQuery ML

Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

But what if you could automatically discover patterns and create data groups—without manual rules, endless scripts, or leaving your BigQuery analysis environment?

That’s exactly what K-Means with BigQuery ML allows you to do.

What is K-Means and why should you care?

K-Means is a clustering algorithm—a technique for grouping similar items. Imagine you have a table with thousands of URLs, users, or products. Instead of going through each one manually, K-Means can automatically find groups with common patterns: pages with similar performance, campaigns with similar outcomes, or users with shared behaviors.

And the best part? With BigQuery ML, you can apply K-Means using plain SQL—no need for Python scripts or external tools.

How does it actually work?

The process behind K-Means is surprisingly simple:

  1. You choose how many groups you want (the well-known “K”).

  2. The algorithm picks initial points called centroids.

  3. Each row in your data is assigned to the nearest centroid.

  4. The centroids are recalculated using the assigned data.

  5. This process repeats until the groups stabilize.

The result? Every row in your table is tagged with the cluster it belongs to. Now you can analyze the patterns of each group and make better-informed decisions.

How to apply it in BigQuery ML

BigQuery ML simplifies the entire process. With just a few lines of SQL, you can:

  • Train a K-Means model on your data

  • Retrieve the generated centroids

  • Classify each row with its corresponding cluster

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  • Group pages by performance (visits, conversions, revenue)

  • Detect behaviors of returning, new, or inactive users

  • Identify products often bought together or with similar buyer profiles

  • Spot keywords with unusual performance

How many clusters do I need?

Choosing the right number of clusters (“K”) is critical. Here are a few strategies:

  • Business knowledge: If you already know you have 3 customer types or 4 product categories, start there.

  • Elbow Method: Run models with different K values and watch for the point where segmentation no longer improves significantly.

  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

Real-world examples

With K-Means in BigQuery, you can answer questions like:

  • What types of users visit my site, and how do they differ?

  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

Grouping data this way not only saves time—it reveals opportunities and issues that might otherwise go unnoticed.

Conclusion

If you're handling large data sets and need to identify patterns fast, clustering with K-Means and BigQuery ML can be a game-changer. You don’t need to be a data scientist or build complex solutions from scratch. You just need to understand your business and ask the right questions—BigQuery can handle the rest.

Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 
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